Understanding Pathogen Behaviour Virulence, Stress Response and Resistance

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Understanding pathogen behaviour Virulence, stress response and resistance Edited by Mansel Griffiths

CRC Press Boca Raton Boston New York Washington, DC

Cambridge England

Copyright © 2005 by Taylor & Francis

Published by Woodhead Publishing Limited, Abington Hall, Abington, Cambridge CB1 6AH, England www.woodheadpublishing.com Published in North America by CRC Press LLC, 6000 Broken Sound Parkway, NW, Suite 300, Boca Raton, FL 33487, USA First published 2005, Woodhead Publishing Ltd and CRC Press LLC © 2005, Woodhead Publishing Ltd The authors have asserted their moral rights. This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. Reasonable efforts have been made to publish reliable data and information, but the authors and the publishers cannot assume responsibility for the validity of all materials. Neither the authors nor the publishers, nor anyone else associated with the publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from the publishers. The consent of Woodhead Publishing and CRC Press does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing or CRC Press for such copying. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Library of Congress Cataloging in Publication Data A catalog record for this book is available from the Library of Congress. Woodhead Publishing ISBN-13: 978-1-85573-953-6 (book) Woodhead Publishing ISBN-10: 1-85573-953-4 (book) Woodhead Publishing ISBN-13: 978-1-84569-022-9 (e-book) Woodhead Publishing ISBN-10: 1-84569-022-2 (e-book) CRC Press ISBN-10: 0-8493-3426-8 CRC Press order number: WP3426 The publishers’ policy is to use permanent paper from mills that operate a sustainable forestry policy, and which has been manufactured from pulp which is processed using acid-free and elementary chlorine-free practices. Furthermore, the publishers ensure that the text paper and cover board used have met acceptable environmental accreditation standards. Typeset by Ann Buchan (Typesetters), Middx, England Printed by TJ International, Padstow, Cornwall, England


Contents

Contributor contact details

xiii

Introduction

xvii

Part I

Understanding virulence, stress response and resistance mechanisms

1 Understanding the behaviour of pathogenic cells: proteome and metabolome analyses S. Vaidyanathan and R. Goodacre, The University of Manchester, UK 1.1 Introduction 1.2 Rationale behind analysing proteomes and metabolomes 1.3 Strategies for proteome analyses 1.4 Metabolome analyses 1.5 Proteomic and metabolomic fingerprinting and footprinting . . . . 1.6 Bioinformatics and in silico approaches 1.7 Applications in understanding pathogen behaviour 1.8 Future trends 1.9 References 2 Mechanistic modelling of pathogen stress response Y. he Marc and J. Baranyi, Institute of Food Research, UK and T Ross, University of Tasmania, Australia 2.1 Introduction 2.2 Mathematical modelling of microbial population kinetics 2.3 Primary models 2.4 Secondary modelling: growth rate modelling 2.5 Secondary modelling: lag time models

1

3 3 4 6 21 26 29 32 39 40 53

53 54 54 59 63

vi

Contents 2.6 2.7 2.8 2.9

Validation and prediction of bacterial growth Modelling the effects of stress Conclusions References

66 68 73 73

3 The development of pathogenicity C. Gyles, University ofGuelph, Canada 3.1 Introduction 3.2 Pathogenicity and virulence 3.3 Genetic exchange and the development of pathogenicity 3.4 The food processing environment and the evolution of pathogenicity 3.5 Predicting and controlling pathogenicity 3.6 Future trends 3.7 Sources of further information and advice 3.8 References

78

4 Host-pathogen interactions A. Roberts and M. Wiedmann, Cornell University, USA 4.1 Introduction 4.2 Host defense mechanisms and pathogen survival strategies . . . . 4.3 Pathogenic cell adhesion and invasion 4.4 Factors affecting virulence expression 4.5 Blocking infection 4.6 Future trends 4.7 Sources of further information and advice 4.8 References

78 79 80 91 92 92 93 94 99 99 100 103 105 107 109 Ill Ill

5 Factors affecting stress response 115 C. Dodd, The University of Nottingham, UK 5.1 Introduction: heterogeneity in cellular response to stress 115 5.2 Cellular stage of growth and bacterial resistance 117 5.3 Response regulators 119 5.4 The role of free radicals in stress responses 122 5.5 Summary: consequences of stress response and resistance . . . . 124 5.6 Sources of further information and advice 124 5.7 References 124 6 Cross-protective effects of bacterial stress L. Rodriguez-Romo and A. Yousef, Ohio State University, USA 6.1 Introduction 6.2 Microbial stress adaptation and cross-protection 6.3 Types of microbial stress adaptation 6.4 Regulation of cross-protective responses 6.5 Stresses that induce cross-protection

128 128 129 132 132 134

Contents 6.6 6.7 6.8 6.9 6.10

Detecting and quantifying cross-protection Anticipating cross-protection Preventing cross-protection Future trends References

7 Sublethal injury, pathogen virulence and adaptation D. Nyachuba and C. Donnelly, University of Vermont, USA 7.1 Introduction: defining sublethal injury 7.2 Processing conditions producing sublethal injury 7.3 Impact of sublethal injury on recovery of foodborne pathogens 7.4 Consequences or potential consequences of sublethal injury on food safety 7.5 Injury and virulence 7.6 Designing preservation processes to prevent sublethal injury or maximize injury 7.7 Future trends 7.8 References 8 Detecting sublethally damaged cells M. Adams, University of Surrey, UK 8.1 Introduction 8.2 Resuscitation of injured cells 8.3 Detecting injury 8.4 Future trends 8.5 References Part II

Virulence and stress response mechanisms of particular pathogens

9 Salmonella: virulence, stress response and resistance J. Maurer and M. Lee, University of Georgia, USA 9.1 Introduction 9.2 Understanding the molecular basis of Salmonella virulence . . . 9.3 The genetic regulation of Salmonella's growth, survival and virulence 9.4 Salmonella's resistance to particular types of stress 9.5 Acknowledgements 9.6 References 10 Escherichia coli: virulence, stress response and resistance P. McClure, Unilever, UK 10.1 Introduction 10.2 Virulence mechanisms and pathotypes

vii 139 142 143 145 145 152 152 156 158 184 185 190 190 191 199 199 200 203 208 208

213 215 215 216 222 227 228 228 240 240 242

viii

Contents 10.3 10.4 10.5 10.6 10.7 10.8

Factors affecting virulence Types of stress affecting pathogenic strains and response mechanisms Summary: improving risk assessment and control in food Future trends Sources of further information and advice References

11 Campylobacter: stress response and resistance S. Park, University of Surrey, UK 11.1 Introduction 11.2 Campylobacters in the food supply 11.3 Stress responses in food and the environment 11.4 The pathogenesis of Campylobacter infection 11.5 Future trends 11.6 Sources of further information and advice 11.7 References 12 Bacillus cereus: factors affecting virulence C. Nguyen-the and V. Broussolle, Institut National de la Recherche Agronomique, France 12.1 Introduction 12.2 Taxonomy of Bacillus cereus 12.3 Virulence factors of Bacillus cereus 12.4 The spores of Bacillus cereus 12.5 Ecology and epidemiology of Bacillus cereus 12.6 Future trends 12.7 References 13 Staphylococcus aureus as a food pathogen: the staphylococcal enterotoxins and stress response systems J. Gustafson, New Mexico State University, USA and B. Wilkinson, Illinois State University, USA 13.1 Introduction 13.2 Staphylococcal enterotoxins 13.3 Growth of Staphylococcus aureus in the food e n v i r o n m e n t . . . . 13.4 Food processing and preservation: what microbes encounter . . 13.5 The response of Staphylococcus aureus to particular types of stress 13.6 Prevention of staphylococcal food poisoning 13.7 Future trends 13.8 Acknowledgements 13.9 References

251 260 265 268 270 270 279 279 282 285 295 297 299 299 309

309 310 311 316 317 323 324

331

331 333 342 343 343 347 348 349 350

Contents 14 Vibrio species: pathogenesis and stress response F. Reen and E. Boyd, University College Cork, Ireland 14.1 Introduction 14.2 Quorum-sensing in Vibrio species 14.3 Biofilm formation and surface adhesion 14.4 Stress response mechanisms 14.5 Risk assessment in food 14.6 Future trends 14.7 Sources of further information and advice 14.8 Acknowledgements 14.9 References Part III

ix 358 358 361 369 369 373 377 378 379 379

Pathogen resistance and adaptation to particular stresses . .. 389

15 Understanding pathogen survival and resistance in the food chain S. Brul and J. Wells, University of Amsterdam, The Netherlands and J. Ueckert, Unilever, The Netherlands 15.1 Introduction 15.2 Stresses encountered in animal hosts 15.3 Food preservation strategies 15.4 Microbial stress responses to food preservation regimes 15.5 Genomics-based detection in the food chain 15.6 Future trends 15.7 Sources of further information and advice 15.8 Acknowledgements 15.9 References 16 Pathogen resistance and adaptation to heat stress V. Juneja, United States Department of Agriculture, USA and J. Novak, American Air Liquide, USA 16.1 Introduction 16.2 Predicting pathogen resistance 16.3 Factors influencing the development of resistance 16.4 Targets of heat damage 16.5 Strategies to counter pathogen resistance 16.6 Future trends 16.7 Sources of further information and advice 16.8 References 17 Pathogen resistance and adaptation to emerging technologies G. Gould, University of Leeds, UK 17.1 Introduction 17.2 Ionising irradiation 17.3 High-pressure processing

391

391 393 399 404 411 412 413 413 414 422

422 423 424 429 432 435 436 436 442 442 443 446

x

Contents 17.4 17.5 17.6 17.7

High-voltage pulsed electric fields Conclusions Sources of further information and advice References

18 Pathogen resistance and adaptation to natural antimicrobials . . . . P. Davidson, T. Taylor and L. Santiago, University of Tennessee, USA 18.1 Introduction 18.2 Types of natural antimicrobials by source 18.3 Potential resistance responses by pathogens to natural antimicrobials 18.4 Factors influencing development of resistance 18.5 Predicting pathogen resistance 18.6 Strategies for overcoming resistance 18.7 Sources of further information and advice 18.8 References 19 Pathogen resistance and adaptation to disinfectants and sanitisers A. van Asselt and M. te Giffel, NIZO food research, The Netherlands 19.1 Introduction: type of disinfection 19.2 Types of disinfectant and their mode of action 19.3 Strategies for optimisation of cleaning and disinfection 19.4 Types of pathogen response to disinfectants 19.5 Predicting microbial resistance 19.6 Future trends 19.7 Sources of further information and advice 19.8 References 20 Pathogen resistance and adaptation to low temperature J. Sutherland, London Metropolitan University, UK 20.1 Introduction 20.2 Effect of temperature on microbial growth 20.3 Effect of low temperature on the structure, physiology and metabolism of bacterial cells 20.4 Pathogenicity in relation to low temperature 20.5 Conclusions 20.6 Future trends 20.7 Sources of further information and advice 20.8 References

450 453 454 454 460

460 460 466 467 472 473 474 474

484

484 485 492 497 499 501 502 503 507 507 508 513 517 522 522 523 523

Contents Part IV

Appendix

21 Clostridium botulinum M.W. Peck, Institute of Food Research, UK 21.1 Introduction 21.2 Taxonomy and properties of the Clostridium botulinum group . 21.3 Characterisation and types of botulism 21.4 Epidemiology of foodborne botulism 21.5 Incidence of Clostridium botulinum in the environment and in foods 21.6 Factors influencing growth, survival and neurotoxin formation. 21.7 Conclusion and future trends 21.8 Acknowledgement 21.9 References

xi 529 531 531 532 533 535 539 539 542 542 543

22 Quorum-sensing and virulence in foodborne pathogens M. Griffiths, University of Guelph, Canada 22.1 Introduction 22.2 Gram-negative bacteria 22.3 The definition of a signal molecule 22.4 Quorum-sensing in Gram-negative foodborne pathogens 22.5 Other Gram-negative bacteria of significance in food 22.6 Gram-positive bacteria 22.7 Alternative quorum-sensing systems 22.8 Quorum-sensing and host cells 22.9 Strategies to interfere with quorum-sensing 22.10 Quorum-sensing and food microbiology 22.11 References

549 549 550 558 558 576 576 583 584 584 585 586

Index

599

Contributor contact details (* = main point of contact)

Chapter 1

Chapter 3

Dr Seetharaman Vaidyanathan* and Dr Royston Goodacre School of Chemistry The University of Manchester PO Box 88 Sackville Street Manchester M60 1QD UK email: [email protected]. uk email: [email protected]

Professor Carlton L. Gyles Ontario Veterinary College University of Guelph Guelph Ontario Canada N1G 2WI

Chapter 2 Dr Yvan Le Marc and Dr József Baranyi* Institute of Food Research Norwich Research Park Colney Norwich NR4 7UA UK email: [email protected] email: [email protected] Dr Tom Ross School of Agricultural Science University of Tasmania Australia email: [email protected]


email: [email protected]

Chapter 4 Dr Angela Roberts and Dr Martin Wiedmann* Department of Food Science Cornell University Ithaca NY 14853 USA email: [email protected]

Chapter 5 Dr Christine Dodd School of Biosciences University of Nottingham Sutton Bonington Campus Loughborough LE12 5RD UK email: [email protected]

Chapter 6

Chapter 9

Dr Luis A. Rodriguez-Romo and Professor Ahmed E. Yousef* Department of Food Science and Technology Ohio State University 2015 Fyffe Road Columbus OH 43210 USA

Dr John J. Maurer* and Dr Margie D. Lee Department of Avian Medicine College of Veterinary Medicine The University of Georgia Athens GA 30602 USA email: [email protected]


Chapter 10 Chapter 7 David Nyachuba* and Catherine Donnelly Department of Nutrition and Food Sciences Carrigan Building Room 209 University of Vermont Burlington Vermont 05405 USA email: [email protected] email: [email protected]

Dr Peter McClure Unilever R&D Colworth Colworth House Sharnbrook Bedford MK44 1LQ UK email: [email protected]

Chapter 11 Dr Simon P. Park School of Biomedical and Molecular Sciences University of Surrey Guildford GU2 7XH email: [email protected]

Chapter 8

Chapter 12

Professor Martin R. Adams School of Biomedical and Molecular Sciences University of Surrey Guildford GU2 7XH UK

Dr C. Nguyen-the* and Dr V. Broussolle Institut National de la Recherche Agronomique Domaine Saint-Paul Site Agroparc 84914 Avignon Cedex 9 France




Chapter 13 Professor John E. Gustafson* Department of Biology New Mexico State University Las Cruces NM 88003-8001 USA email: [email protected] Dr Brian J. Wilkinson Department of Biological Sciences Illinois State University Normal IL 61790-4120 USA email: [email protected]

Chapter 14 Dr F.J. Reen and Dr E.F. Boyd* Department of Microbiology University College Cork National University of Ireland Cork Ireland email: [email protected]

Chapter 15 Professor Stanley Brul* Chair Molecular Biology & Microbial Food Safety Integrated Microbiology Amsterdam (IMA) Swammerdam Institute for Life Sciences University of Amsterdam Nieuwe Achtergracht 166 1018 WV Amsterdam The Netherlands email: [email protected]


Professor Dr Stanley Brul and Dr Joerg Ueckert Department of Microbiological Control Food Research Center Unilever Olivier van Noortlaan 120 3133 AT Vlaardingen The Netherlands Professor Jerry Wells Chair of Cellular of Microbiology Swammerdam Institute for Life Sciences University of Amsterdam Nieuwe Achtergracht 166 1018 WV Amsterdam The Netherlands Also at TNO Quality of Life Zeist The Netherlands email: [email protected]

Chapter 16 Dr Vijay K. Juneja* United States Department of Agriculture Agricultural Research Service Microbial Food Safety & Research Unit Eastern Regional Research Center 600 East Mermaid Lane Wyndmoor, PA 19038 USA email: [email protected] Dr John S. Novak Research Scientist American Air Liquide 5230 S East Avenue Countryside, IL 60525 USA email: [email protected]

Chapter 17

Chapter 20

Professor Grahame W. Gould 17 Dove Road Bedford MK41 7AA UK

Dr Jane P. Sutherland Food Microbiology Unit Department of Health and Human Sciences London Metropolitan University 166–220 Holloway Road London N7 8DB UK


Chapter 18 Professor P.M. Davidson*, Dr T.M. Taylor and Dr L. Santiago Department of Food Science and Technology University of Tennessee 2605 River Drive Knoxville, TN 37996-4591 USA email: [email protected]

Chapter 19 A.J. van Asselt and Dr M.C. te Giffel* NIZO food research Kernhemseweg 2 6170 BA Ede The Netherlands email: [email protected] email: [email protected]


Chapter 21 Professor Michael W. Peck Food Safety and Computational Microbiology Group Institute of Food Research Norwich Research Park Colney Norwich NR4 7UA UK email: [email protected]

Chapter 22 Professor Mansel W. Griffiths Department of Food Science and Canadian Research Institute for Food Safety University of Guelph Ontario Canadia N1G 2W1 email: [email protected]


Introduction M. Griffiths, University of Guelph, Canada

In these days of avian influenza, BSE (bovine spongiform encephalopathy) and other infections that affect the animals we eat and spark consumer concerns about the safety and security of our food supply, it is comforting to know that research is being targeted to better understand the behaviour of the common pathogens that contaminate our food and water. Data suggest that 1 in 4 of the US population suffers from a foodborne illness each year (Mead et al. 1999) and that, worldwide, there have been several thousand cases of illness, such as the 1994 outbreak of salmonellosis in the USA, caused by contaminated ice cream that affected about 224 000 people, and the Staphylococcus aureus outbreak related to milk in Japan that resulted in more than 14 000 cases of illness. Closer to my home, an outbreak of illness caused by Escherichia coli O157:H7 related to drinking water at Walkerton, a small town in Ontario, Canada, caused seven deaths, 2300 cases of illness and a loss of $64.5 million, as well as the criminal prosecution of the manager of the water treatment facility. In developing countries the WHO reports that there are 2.1 million deaths each year related to diarrheal illness, with the majority of these being children. In industrialized countries clinical illness related to foodborne pathogens may exist in 30 % of the population and 20 deaths/million population are attributed to foodborne illness. There are also reports indicating that there are long-term implications of contracting foodborne illness. A recent study by Kåre Mølbak and colleagues at the Statens Serum Institute in Copenhagen (Helms et al., 2003) reports that deaths from foodborne pathogens, such as Salmonella, kill more people than was previously believed. Just one year after patients had contracted foodborne illnesses, they found the relative mortality was 3.1 times higher in patients who had contracted yersiniosis, salmonellosis or campylobacteriosis than in controls. Thus, food and water safety is of paramount importance, particularly as several factors are contributing to heighten food safety concerns. These include changes in agricultural and processing practices, changing demographics and the adaptive capacity of microbial populations. Thus, to make a significant impact on food safety we must address the entire food chain.


What happens on the farm cannot be divorced from subsequent food-handling procedures. At the processing level, governments and industry are working together to implement preventative management systems, and there has also been interest in using novel intervention strategies to improve the quality of food. These include techniques such as irradiation, which is gaining consumer acceptance in the USA. In the retail and food service sector, inspection systems based on HACCP (hazard analysis and critical control point) principles have been adopted, and there is increased awareness of safety issues. The ‘mad-cow disease’ phenomenon in the UK and other countries has highlighted the need for better trace-back procedures across all sectors of the food industry. Several consultation exercises have been conducted to prioritize food safety research and many of these have recognized the importance of research on microbial adaptation. In a report to the American Academy of Microbiology (Doores, 1999), it was stated that: A better understanding of the genetics, physiology, and virulence of foodborne pathogens, as well as how microbes, humans, and animals interact, has provided an intellectual and technological framework upon which new pathogen control programs and disease prevention strategies are being built. I fully endorse this statement and echo the statement that, to help reduce contamination at all points in the food chain, we need a better understanding of how microorganisms interact with their environment, as well as the processes that allow microorganisms to survive stress, whether it is starvation induced by a nutrientlimited environment such as water, or acid- and temperature-induced stress in foods. It is becoming much easier to accumulate this information through the use of new technologies linked to proteomics, genomics and metabolomics. The American Academy of Microbiology report also states that: Continued research in microbial ecology is essential for improving our understanding of … foodborne disease. Traditional thinking in this area has focused on the ecology of individual microbes. The study of how microbes interact with each other, and with the environment, and with animals and plants at various stages … of the food chain has been largely neglected. Again, the development of molecular techniques is allowing us to gain new insights into the ecology of foods and food production and processing environments. This book brings together some of the latest thinking on how microorganisms survive in stressful environments, how they adapt to these environments and how this affects virulence. The initial chapters of the book indicate how new technologies can help us understand the behaviour of pathogens at the molecular level and how we can use this knowledge to predict how pathogens will survive stress. The book then goes on to discuss evolutionary aspects of pathogenicity and how an understanding of this can be used to predict the emergence of novel pathogens. A chapter is also


devoted to the interactions of pathogens with their hosts and how this knowledge may be used to combat infection. We now realize that subjecting an organism to a sublethal stress can make it more resistant to subsequent stress: a phenomenon called cross-protection. The concept of ‘what doesn’t kill you makes you stronger’ is explored in the book from various angles. These include how sublethally damaged cells can be detected right through to their implications for the food industry. Part II is devoted to reviews of individual bacteria including Salmonella, E. coli, Campylobacter, Bacillus cereus, Staph. aureus and Vibrio spp. The mechanisms of pathogenicity and stress responses of each organism are laid out, as well as their practical consequences. Part III concentrates on the significance of stress tolerance and adaptation to the food industry and includes chapters on thermal resistance; resistance to novel processing technologies such as irradiation, high pressure and pulsed electric fields; resistance and adaptation to natural antimicrobial compounds including bacteriocins; and finally, a review of adaptive responses to disinfectants and sanitizers. The contributors to this book are internationally renowned experts in their field. Their contributions to this book will be an invaluable resource for food safety professionals, academics and students.

Acknowledgements On a personal note, I would like to thank all of the contributors and the staff at Woodhead Publishing who have made this book a reality. I would also like to thank my wife, Susan, and family; Megan, Darren, Bethan and Eric, for their support; and especially my grandson, Rhys, for never ceasing to amaze me.

References DOORES, S. (1999) Food safety: Current status and future needs. A report from the American Academy of Microbiology. http://www.asm.org/ASM/files/CCPAGECONTENT/ docfilename/0000003763/Foodsafetyreport[1].pdf. 4 March 2005. HELMS, M., VASTRUP, P., GERNER-SCMIDT, P. AND MØLBAK, K. (2003) Short and long term mortality associated with foodborne bacterial gastrointestinal infections: registry based study. British Medical Journal 326, 357–360. MEAD, P.S., SLUTSKER, L., DIETZ, V., MCCAIG, L.F., BRESEE, J.S., SHAPIRO, C., GRIFFIN, P.M. AND TAUXE, R.V. (1999) Food related illness and death in the United States. Emerging Infectious Diseases 5, 607–625.


1

Part I Understanding virulence, stress responce and resistance mechanisms

2

3

1 Understanding the behaviour of pathogenic cells: proteome and metabolome analyses S. Vaidyanathan and R. Goodacre, The University of Manchester, UK

1.1

Introduction

Post-genome science and technology are defined by the need to characterize function at the level of the genes, transcripts, proteins and metabolites to explain cellular processes. The understanding of biological systems is increasingly being driven by a paradigm shift in emphasis from the traditional divisive biochemical approaches that concentrate on local cellular processes, one at a time, to global approaches of analysing cellular compositions in parallel and in its entirety, with a view to obtain a more ‘holistic’ picture. Genomic sequencing initiatives have resulted in the sequencing of over 250 organisms, which include several pathogens (http://www.genomesonline.org). However, the functions of many (typically ~40 %) open reading frames (ORFs) within the sequenced genomes are still unknown. In this regard, analysis at the level of the functional units, i.e. transcripts (transcriptome), proteins (proteome) and metabolites (metabolome) is increasingly becoming relevant. In particular, post-transcriptional regulation of cellular activities necessitates analysis at the level of the proteome and the metabolome to better understand cellular processes. Strategies for the simultaneous high-throughput measurement of several analytes at the level of the proteome and metabolome are progressively being developed, and these form the subject-matter of this chapter. In some cases, techniques that enable differences to be delineated between


4

Fig. 1.1

The traditional deductive cycle of knowledge and the hypothesis generation cycle via holism.

different biological systems or even between different states of a system may be useful in understanding cellular processes, even when a complete knowledge of the genetic make-up of the system is not available. In this regard, fingerprinting approaches are discussed that provide proteomic and metabolomic ‘snapshots’ and offer potential for rapid assessment of biological systems at the functional level. Although deductive reasoning dictates much of scientific inference, the practice of ‘holistic’ science and the inundation of data as a result necessitate a greater participation of inductive reasoning in the cycle of knowledge (Oldroyd, 1986) (Fig. 1.1). In this regard, computational sciences have much to offer, resulting in the birth of data-driven sciences such as ‘bioinformatics’, the application of which to proteome and metabolome analyses will also be discussed. Finally, the contribution of proteome and metabolome analyses to the understanding of pathogen behaviour is discussed, with relevant examples. The topical nature of the subject and its wide scope makes it difficult for a comprehensive coverage. Instead an attempt is made at capturing the general themes and trends to help keep the readers abreast of the developments.

1.2 Rationale behind analysing proteomes and metabolomes The first complete genomic sequencing of a free-living organism was that of a human pathogen, Haemophilus influenzae, in 1995 (Fleischmann et al., 1995). Ever since, the genomic sequence of several pathogens have been revealed, including foodborne pathogens, with Campylobacter jejuni being the first food pathogen genome to be sequenced in 2000 (Parkhill et al., 2000). The availability of genetic information has enabled comparative genomic assessments that have contributed to the understanding of pathogen behaviour (Alm et al., 1999).


5

Fig. 1.2

Schematic of ‘omic’ expression, showing some prominent accompanying events.

However, genomic information alone is not sufficient for understanding biological processes. With the sequencing of the human genome, it is now known that we as humans have only three times as many genes as a nematode (Caenorhabditis elegans), with respect to the number of genes carried in our respective genetic make-up, and that the genetic make-up of diverse species are remarkably similar. Indeed genome sequencing projects alone have shown that we have 50 % genes in common with the fruit fly, 85 % with our canine friends and 99 % with chimpanzees; not forgetting that there is a significant portion shared with prokaryotes. This suggests that more than the genetic make-up, the contextual combination of gene products confers complexity and diversity to the functional genome. Consequently, in addition to cataloguing genomes and their function, it is necessary to generate an understanding of which gene products are expressed and how they come together to constitute a functional unit that responds to the different stimuli, be it of growth or environmentally induced. There is therefore a greater need to analyse at the level of the transcriptomes, proteomes and metabolomes (Fig. 1.2). Transcriptomic analysis results in monitoring gene expression. Nucleic acid arrays produced by the robotic deposition of polymerase chain reaction (PCR) products, plasmids or oligonucleotides onto a glass slide or in situ synthesis of oligonucleotides using photolithography have been used in hybridization experiments to monitor gene expression. Array-based approaches, especially those that probe tens of thousands of genes, are useful in that they enable the development of a ‘holistic’ and unbiased view, rather than a targeted view of cellular response, without a priori knowledge of which genes or mechanisms are important. These


6 and other tools, such as sequential analysis of gene expression (SAGE), can be used for monitoring morphological and physiological/phenotypical differences and can be indicative of cellular response to environmental stimuli and perturbations. However, mRNA is only an intermediate in translating the genetic information to cellular response and function, the business end of which is enacted by proteins and metabolites. Changes in the temporal expression and accumulation patterns of these latter entities would therefore throw more light on the phenotypic responses of the cell (Fig. 1.2). Several observations support the argument for monitoring proteomes and metabolomes that will be useful in understanding pathogen behaviour and cell function in general, some of which are listed below: • The existence of an ORF does not necessarily imply the existence of a functional gene. • It is now recognized that the relationship between genes and gene products (i.e. proteins) is not necessarily linear. A given gene can express more than one protein. In fact, in eukaryotes, six to eight proteins are expressed per gene. • Expression profiles at the protein level may throw more light on function than those at the transcript level, as mRNA levels do not necessarily correlate with protein levels (Gygi et al., 1999). • Cellular activities are mediated by complex networks of interactions in response to physiological signals, and the nature of the response is dependent on the cell type and states. These aspects cannot be accounted for by investigating at the genomic or transcriptomic levels alone, as genomes and transcriptomes are fairly conserved between cell states and types, within a system. • Several biochemical events occur post-transcriptionally that define the response of cells to stimuli, for instance, alternative splicing, post-translational modifications, regulation of enzyme activities and distribution of metabolites between cellular compartments, all of which necessitate analysis at the level of the proteome and the metabolome. • At a molecular level, cellular function is closely associated with activities of the proteins and metabolites. They are instrumental in translating the genetic information to phenotypic function and thus offer an ideal platform for characterizing cellular activities.

1.3

Strategies for proteome analyses

Proteins are integral structural and functional components of cells and, in turn, of organisms. The ‘proteome’ has been defined as the entire protein complement of a cell, tissue or organ, and was originally conceptualized in the mid-1990s (Kahn, 1995; Wasinger et al., 1995; Wilkins et al., 1996). The large-scale identification and characterization of several proteins in parallel has given rise to the field of proteomics (James, 1997). Proteome analyses, as originally conceived, involved the assessment of the proteome in terms of the quality and quantity of the expressed proteins. In recent years, it has been extended to include characterization of the


7 structural, functional and contextual aspects, such as post-translational modifications, protein–protein interactions and subcellular localizations. In a general sense, proteome analysis translates to mapping the cellular proteins in a spatial and temporal manner. Unlike the genome, where the information content is conserved for a given organism, with the proteome the information content is dynamic, depending on the cell type, physiological state and functional context. The nature of a protein can vary, depending on post-transcriptional (splice variants) and post-translational (phosphorylation, glycosylation, acetylation, etc.) modifications. In addition, very often the functional entities in the cells are multiprotein complexes, whose concerted action effects cellular processes. This would involve proteins to interact with other proteins and (macro) molecular types to effect a cellular action. In a comprehensive sense, the objective in proteomics would therefore be to define the identities, quantities, structure and function of proteins, and characterize the differences in these in a cellular context. Several good reviews on different aspects of proteomics exist (e.g. Ferguson and Smith, 2003; Zhu et al., 2003, and an excellent compilation of insights in Nature vol. 422, March 2003). In a broad sense, three proteomic streams emerge: 1. expression proteomics, which involves assessing the identity and quantity of the expressed proteins; 2. functional proteomics, which involves characterizing the nature of the expressed proteins, in terms of its function or activity in the cell, leading to the study of post-translational modifications, protein biochemical activities, protein–protein interactions and subcellular localizations; and 3. structural proteomics, which involves elucidating the structural principles that underlie protein function and cellular activities, in turn (Fig. 1.3).

1.3.1 Expression proteomics The analytical challenge in expression proteomics is to be able to detect proteins in a complex mixture of analytes in the cellular milieu, to identify the detected proteins and, where possible, to quantify the identified proteins in maximum numbers with a high efficiency. However, even the ability to compare information on proteomic expression can give valuable qualitative (or semi-quantitative) information with respect to differential expression of proteins between the compared samples. Three major analytical biochemical strategies (Fig. 1.3) have evolved in expression proteomics: (a) two-dimensional sodium dodecyl polyacrylamide gel electrophoresis (2D-SDS PAGE)-based separation followed by mass spectrometric (MS) identification of separated proteins, (b) (multidimensional) liquid chromatography (LC) or capillary electrophoresis (CE)-based separation of proteins/digested peptides, followed by MS-based identification, and (c) analytical microarray technology. There is a fourth strategy that is based on genetically engineering the cell.


8

Cell extract Sample pre-fractionation/clean-up Protein fractions (Proteome) Multiple 2D-GE p/

Global protein digestion

Mr

Peptides

CE

LC

Protein digestion

Tandem LC

Multiplexed spot excision

Microarray-based approaches

High-throughput detection

Peptides

MALDI-MS

b) (b)

Database search

(nano-) ESMS (/MS)

(nano-) ESMS (/MS)

Database search

Fig. 1.3 Streams in proteomics (a) and a schematic overview of prominent strategies in expression proteomics (b). (MS, mass spectrometry; LC, liquid chromatography; CE, capillary electrophoresis; NMR, nuclear magnetic resonance; GE, gel electrophoresis; ES, electrospray; MALDI, matrix-assisted laser desorption ionization.)

Separations based on 2D-SDS PAGE 2D-SDS PAGE is the most widely used expression proteomics tool. It consists of charge (pI)-based separation in the first dimension using isoelectric focusing (IEF) and size (Mr)-based separation in the second dimension. The separations are usually carried out in slab gels. The gels can be used at an analytical scale to look for prominent signals, or at a micro-preparative scale for specific identifications


9 and further characterization of the separated proteins. The advent of immobilized pH gradient strips (IPG) has improved resolution and reproducibility of analysis when analysing mixtures containing milligram quantities of proteins (Gorg et al., 2000). In-gel sample loading and the development of sensitive protein stains, such as ammoniacal silver, radioactive stains and fluorescent dye tags, have contributed to improvements in the technique over the years. For a comprehensive assessment of expressed proteomes, the analytical technique must be capable of separating and resolving the different expressed proteins, ideally with minimal sample preparation. The application of 2D-SDS PAGE in this regard is compounded by the dynamic range in the amount of expressed proteins (which may vary, for instance, from as little as 100 copies/cell to 2 000 000 copies/ cell in yeast, leading to variations in protein concentrations of up to 10 000-fold (Futcher et al., 1999), and it would not come as a surprise if even larger variations exist). Often the capabilities of 2D-SDS PAGE can, at best, enable the detection of proteins that differ in concentration by about two to three orders of magnitude. In addition, the chemical diversity of expressed proteins with respect to a wide range in molecular weights, isoelectric points and differences in solubility pose considerable challenges in devising generic analytical strategies. As a consequence, a full complement of the proteome is seldom seen; proteins of low abundance often go undetected; membrane-bound proteins escape analysis because of their low solubilities during the IEF separation; post-translational modifications could result in multiple spots for a protein, and despite two dimensions of separation, each spot may contain more than one protein, owing to limits in resolution. Some of these challenges are being addressed, and in some cases solutions that minimize the influences are appearing. The use of very narrow range IPGs can improve protein resolution at extreme pH values or with low copy numbers. The introduction of new reagents for protein solubilization in conjunction with the use of organic solvents has provided a noteworthy increase in the total number of proteins isolated on 2D gels and a better separation of hydrophobic proteins (Rabilloud, 1999). The use of pre-electrophoresis subfractionation can be useful in simplifying the complexity of the protein mixtures and for concentrating low copy number proteins. The use of specific strategies such as the addition of glycerol and isopropanol to the IEF medium (Hoving et al., 2002), the use of IPGs in the alkaline range, and subfractionation of basic proteins (Bae et al., 2003) from the total proteomic pool can enable characterization of highly basic proteins that would otherwise go undetected by standard procedures. Prefractionation strategies (Pitarch et al., 2003) such as microscale solution IEF (Zuo and Speicher, 2002), carrier ampholyte-free solution IEF (Shang et al., 2003), and the application of immobilized heparin chromatography (Shefcheck et al., 2003) can improve the chances of observing less abundant proteins, generally improve resolution and increase the dynamic range of measurements. The use of multiphasic buffer systems has been shown to be useful in analysing proteins over a wide mass range of 3–300 kDa (Tastet et al., 2003). To analyse high molecular mass proteins agarose-gel IEF as the first dimension has been proposed and found to be effective


10 in significantly improving 2D-GE (gel electrophoresis) separation of proteins larger than 150 kDa and up to 500 kDa (Oh-Ishi and Maeda, 2002). Greater flexibility of image acquisition with multilabel imagers (Lopez et al., 2003) for high-sensitivity multiple fluorescence detection, and the application of computational methods, such as fuzzy alignment of features (Kaczmarek et al., 2003) for matching gel patterns should improve the scope and applicability of 2D-GE in proteomics. LC/CE and liquid-based separations The limitations of using 2D-PAGE have prompted the application of highefficiency capillary separation techniques such as capillary liquid chromatography (CapLC) or capillary electrophoresis (CE). CapLC separation efficiencies have dramatically increased over recent years and efficiencies greater than 105 plates/ column are now achievable with capillary columns packed with 1.5 µm particles (Romijn et al., 2003). Improvements in column manufacture and operating pressure have also contributed to the developments. CE includes both dynamicstate electrophoresis, such as capillary zone electrophoresis (CZE), and static-state electrophoresis, such as capillary isoelectric focusing (CIEF) and capillary isotachophoresis (CITP), the former two preferred for analytical separations and the latter usually for preconcentrations (Shen and Smith, 2002). Two major strategies have evolved in proteomic LC/CE separations – separation of intact proteins from complex mixtures, followed by subsequent analysis of separated proteins, the ‘top-down’ strategy, or global digestion of proteins using specific proteases, separation of peptides and subsequent analysis of the separated peptides, the ‘bottom-up’ strategy. LC separations can be used for separating intact proteins or digested peptides. They can be applied prior to 2D-GE as a clean-up or prefractionation operation (e.g. Champion et al., 2003), or for further separation of co-migrating proteins from 2D-GE resolved protein spots. Giorgini et al. (2003) described an in-gel IEF LC separation technique in which IEF separated proteins are digested in-gel followed by the LC separation of peptides. As opposed to gelbased IEF, liquid-based IEF methods have also been used in combination with SDS PAGE to increase analyte throughput (Hoffmann et al., 2001). Cytosolic proteins from human cancer cell lines were separated into 96 fractions using freeflow electrophoresis, and each fraction was then subjected to SDS PAGE, and subsequently analysed by reverse-phase high-performance liquid chromatography mass spectroscopy (RP-HPLC-MS). Fractionation of intact protein complexes was reported. Coupling of continuous-tube gel electrophoresis with RP-HPLC (Rose and Opiteck, 1994) and continuous elution-gel electrophoresis using a novel acid-labile surfactant instead of SDS and RP-HPLC (Meng et al., 2002) has also been reported for fractionation of proteins. Alternatively, LC separations can be used as gel-free systems in different multiple configurations (Link, 2002; Wang and Hanash, 2003). They can be based on several principles, such as polarity, ion exchange, size exclusion and affinity. Increased peak capacities (number of individual components resolved) and load capacities (amount of material that can be run maintaining good chromatographic


11 resolution) can be realized by combining different principles of LC separation, i.e. orthogonal LC. Opiteck et al. (1997, 1998) combined size exclusion chromatography (SEC) with RP-HPLC to separate proteins from Escherichia coli lysates based on size and hydrophobicity. Orthogonal separation strategies have also been described for the separation of peptides. The combination of strong cation exchange (SCX) with RP-HPLC has been demonstrated to be useful in separating over 3000 peptides from human haemofiltrates (Raida et al., 1999). The same combination in a single biphasic column (Link et al., 1999) results in a more efficient separation and has been developed as multidimensional protein identification technology (MudPIT), as demonstrated in the analysis of the yeast proteome (Washburn et al., 2001). The application of such a strategy has been shown to be useful in characterizing large proteomes including that of Caenorhabditis elegans (Mawuenyega et al., 2003), where 1616 proteins were identified, including 110 secreted/targeted proteins and 242 transmembrane proteins. Among the approximately 5400 peptides assigned in this study, many peptides with post-translational modifications, such as N-terminal acetylation and phosphorylation, were reportedly detected. Similarly, the use of a non-specific protease, proteinase K, in conjunction with MudPIT enabled identification of membrane protein topology and of post-translational modifications in the membrane proteins (Wu et al., 2003). However, MudPIT technology still requires several hours of analysis for obtaining proteome-wide information. An orthogonal combination of capillary IEF (CIEF) with capillary RPLC (CRPLC) has been proposed (Chen et al., 2003) for more efficient analysis of proteomes. CIEF peak capacities of ~800 for intact proteins have been reported (Shen et al., 2001). A combination of capillary SCX with CRPLC resulted in a combined peak capacity of > 104 and the identification of proteins over a dynamic range of greater than eight orders of magnitude (Shen et al., 2004). Affinity-based separations have also been shown to be useful (Ficarro et al., 2002; Lee and Lee, 2004). Combinations of protein and peptide separation strategies have also been reported. Janini et al. (2003) separated intact proteins by liquid phase IEF, followed by tryptic digestion, a solid-phase extraction (SPE) clean-up and RPLC separation of clean peptides to characterize the yeast proteome. Another study (VerBerkmoes et al., 2002) suggests combining features of both top-down and bottom-up strategies for extensive proteome characterizations. MS-based identifications Developments in mass spectrometry with respect to the discovery of ‘soft’ ionization methods (in particular, matrix-assisted laser desorption ionisation (MALDI) and electrospray ionization (ESI)), and improvements in mass resolution, sensitivity and accuracy, have significantly enabled identification and quantification of proteomic expression. Consequently, a majority of the strategies and techniques in expression proteomics are dependent on mass spectrometry. Once the proteome is resolved using one of the separation strategies listed above, several mass spectral strategies can be adopted for the detection and subsequent identification of proteins. MALDI and ESI (more increasingly nano-


12 ESI) are the common ionization methods employed for the MS identification of separated proteins/peptides. For 2D-PAGE separated proteins, two main approaches can be followed to analyse and identify the excised protein spots. In ‘peptide mass fingerprinting’ (Henzel et al., 1993) the protein spots (bands) are subjected to in-gel digestion by a sequence-specific protease, usually trypsin, after destaining, reduction, alkylation and washing steps. This is followed by analysis of the eluted peptides by MALDI-MS (Shevchenko et al., 1996). The set of masses from the MS analysis is then compared with theoretically expected tryptic peptide masses in a database to identify the protein. There are issues related to differences in ionization efficiencies of different peptides (Zhu et al., 1995) and the influence of sample preparation conditions (Padliya and Wood, 2004) on the outcome of the analysis that require attention. Different amino acid termini of peptides affect their ionization efficiencies, and some work has been conducted to improve detection of these using derivatization techniques (Brancia et al., 2001). The second approach involves peptide sequencing using tandem mass spectrometry (MS/MS), followed by analysis of fragmented peptides. This approach usually uses (nano-)ESI-MS/MS. The peptides in liquid phase are electrosprayed into the mass spectrometer, a precursor ion corresponding to the ionized peptide mass is chosen using the first mass analyser (usually a quadrupole) and this is fragmented by the application of a collision gas (such as argon) under pressure to give product ions that are separated by a second mass analyser (usually a time of flight, ToF), thus generating a fragmentation pattern that can be used to sequence the peptide and in turn identify the protein. Peptide chemistry dictates that there is a propensity for certain fragments to occur in preference to others, enabling the deduction of rules for sequence identity. Tandem mass spectra are interpreted with computer assistance and database searches. This approach is usually applied to global peptide digests and uses strategies such as MudPIT for peptide separations prior to MS. Larger peptides often fragment efficiently providing long ion series, but owing to multiple charge distribution of the precursor ion intensity, may have poor sensitivity (Mann et al., 2001). More than 500 proteins (corresponding to about 30 % of the predicted number of ORFs) have been identified in the proteome of H. influenzae, using peptide mass fingerprinting (Langen et al., 2000). Protein identification has been reported even in cases where the genomic and proteomic database is not available as yet, which will be particularly valuable since complete genome sequences of several food pathogens are yet to appear. Mass spectrometric identification by peptide mass fingerprinting and by MS/MS combined with database search has been used to identify proteins from brewing yeast strains that have homology to those of Saccharomyces cereviseae from a protein database (Joubert et al., 2001). Mass spectrometry can also be performed after the first dimension of 2D-GE, by performing surface analysis on the IPG strips using MALDI-MS, to construct a virtual 2D gel (Loo et al., 2001). Strategies for combining MALDI-MS and ESIMS/MS have also been suggested for improving proteome coverage (Bodnar et al., 2003), and the use of multiple enzyme digestions (Choudhary et al., 2003) have been suggested for improving sequence coverage. A strategy based on the creation


13 and use of accurate mass and time tags (AMTs) in conjunction with LC-MS/MS has been shown to be effective in covering proteomes (Strittmatter et al., 2003). Once created, AMT database searches with modest mass and elution time criteria can provide proteomic information for approximately 1000 proteins in a single run of ~3 h. Analytical protein microarrays Microarray-based approaches involve miniaturization of standard assay procedures in multiple arrays to allow simultaneous analysis of multiple determinants/analytes. Such assays are very popular in transcriptomics, but have also been extended to proteome analyses. Microspots of ‘bait’ molecules are immobilized in rows and columns onto a solid support and exposed to samples containing the corresponding binding molecules. The complex formation within each microspot can be detected using readout systems based on fluorescence, chemiluminescence, electrochemistry, mass spectrometry or radioactivity. Largescale assessment of protein profiles can be carried out by the use of immunoassays on microarrays (Blagoev and Pandey, 2001; Schweitzer and Kingsmore, 2002). Antibodies immobilized in an array format onto specifically treated surfaces act as ‘baits’ to probe the sample of interest to detect proteins that bind to the relevant antibodies, using say fluorescence detection. Antibodies can be derived from polyclonal sera or can be hybridoma-derived mAbs, recombinant antibodies or antibodies selected from phage display libraries (Hust and Dubel, 2004). Other types of analytical arrays include autogen arrays, antigen arrays and peptide arrays. 3D matrices (like PAGE and agarose thin films) on glass surfaces, nanowells or plain glass chips can be used as substrates, with surface chemistries, including coating with thin nitrocellulose membrane or poly-L-lysine, reactive surfaces on glass that can covalently cross-link to proteins (Zhu and Snyder, 2003). A novel use of surface chemistry is represented by the ProteinChip technology of Ciphergen, where surface-enhanced laser desorption ionization (SELDI) MS is used. Microarray technology for the examination of proteins on a large scale is still in the developmental stage (Cutler, 2003) and there are several challenges to be considered, particularly with respect to reproducibility and quantification, compared with the technology used for nucleic acids. An additional benefit of protein arrays is the ability to detect protein–ligand and enzyme–metabolite binding (vide infra). Quantification of protein expression In addition to the detection and identification of expressed proteins, quantification provides an additional level of information that will be invaluable in the study and understanding of protein dynamics involved in cellular responses. Although highthroughput techniques for large-scale absolute quantification of the expressed proteins are yet to appear, several approaches for relative quantification have emerged. Relative quantifications with 2D-PAGE can be effected by difference gel electrophoresis (DIGE), which involves a two-colour fluorescent labelling system (with the spectrally distinct fluorescent dyes, Cyanine-2 (Cy2), Cyanine-3 (Cy3)


14 or Cyanine-5 (Cy5)) that allows two proteomes (say controlled and perturbed) to be differentially labelled and analysed in the same gel. Quantification can be achieved by an assessment of changes in the relative intensities between the two fluorescent images of the same gel, obviating many of the reproducibility problems associated with 2D-PAGE. Variations in expressed proteins between controlled and perturbed systems can be quantified, and linearity over at least a threefold range of protein abundances can be achieved (Lilley et al., 2002). However, the ambiguities of 2D-PAGE with respect to protein resolution (presence of multiple spots for the same protein and more than one protein in a single spot), the labourintensive procedures and difficulties in automation still limit practical implementations. Alternatively, methods based on stable isotope labelling open the possibility of quantification using MS, in a more reproducible and accurate manner. The methods rely on the principle that stable isotope incorporation shifts the mass of the peptides by a predictable amount. The ratio of the analyte between the isotope incorporated and the non-incorporated state can then be determined accurately by the measured peak ratio between the underivatized and the derivatized sample. Isotope labelling can be introduced pre-experiment, at the growth phase (metabolic or in vivo labelling), or post-experiment (chemical labelling) in vitro at the pre- or post-proteolytic digestion stage. Metabolic labelling can be effected by employing an isotopically depleted media enriched in 15N (Oda et al., 1999), or by using 13C-isotopically modified media, or by selective isotopically labelled amino acid incorporation (Ong et al., 2002), such as (5,5,5-2H3)leucine and (15N)methionine. Metabolic labelling is limited to cells that can be cultured in ‘controlled media’ and is not applicable to proteomes from tissues or body fluids. However, the early stage of isotope incorporation gives higher accuracy of quantification. In vitro pre-digestion approaches include incorporation of an isotopically coded affinity tag (ICAT) to cysteine residues in proteins and MS detection of differentially labelled digested peptides after affinity purification (Gygi et al., 1999). Several modified versions of the ICAT reagent have appeared to minimize the shortcomings of the original reagent and improve the efficiency of the technique (Tao and Aebersold, 2003). Acid-labile isotope-coded extractants (ALICE) (Qui et al., 2002) can also be used for differential labelling. These are reagents similar to ICAT but are designed to improve on the irreversible binding of peptides with the original ICAT approach. A major advantage with these approaches is the reduction in the complexity of the peptide digest for analysis by a factor of 10 (Sechi and Oda, 2003), but they are not applicable to quantification of proteins that do not have a cysteine residue. Other amino acid modifications can also be used for quantification purposes. These include (a) differential labelling of phosphoserine or phosphothreonine with isotopically labelled ethanediol after β-elimination of the phosphate moiety; (b) acylation of primary amines with isotopically differentiable reagents; and (c) selective introduction of the 2-nitrobenzenesulphenyl (NBS) moiety onto tryptophan residues using 13C labelled 2-nitrobenzenesulphenyl chloride (Kuyama et al., 2003).


15 Proteolytic labelling of peptides using H216O/H218O is an alternative strategy for incorporating differential isotopic labels for quantification (Mirgorodskaya et al., 2000; Yao et al., 2001). In an improvement on the ICAT strategy that reduces the effect of the labelling agent on the subsequent chromatographic and MS stage, Aebersold and colleagues used solid phase peptide capture and a photocleavable linker to isotopically label leucine residues in global peptide digests, after proteolytic digestion (Zhou et al., 2002). Non-isotopic taggings, post-digestion, have also been employed for quantification purposes. These methods rely on the difference in mass between the modified and the unmodified peptides and the utility of this in MS quantifications. Differential amidination of N-termini and lysine residues have been employed (Beardsley and Reilly, 2003). In this method, the modified and unmodified peptides differ by a methylene group rather than by isotope variants. Amidination increases the basicity of the peptide enhancing its MALDI MS ionization yields. This is therefore quantification using enhanced signal tags (QUEST) and can be used for quantification of proteomes. Another strategy is to use mass-coded abundance tagging (MCAT) which modifies the ε-amino group of lysine (Cagney and Emili, 2002). In a simplistic protocol (Wang et al., 2003), spectral intensity normalization using signals of molecules that do not change in concentration from sample to sample has been shown to be a useful method for quantification in proteomics.

1.3.2 Functional proteomics In the post-genome era, with the availability of several higher eukaryotic genetic sequences, it is becoming increasingly clear that the complexity of related organisms is not mediated by a dramatic increase in the number of genes but rather by a more complex network of protein–protein interactions and post-translational protein regulations. In addition to analysing the total cellular protein content (or protein expression), information regarding protein–protein interactions, protein localization and post-translational modifications are therefore required to assess and understand protein (and in turn cell) function, and hence form a significant part of proteomic analysis. Functional proteomics can be viewed to be providing this information. It involves mapping the cellular proteins with respect to their interactions in the cell in effecting cellular activities, in other words, detecting and identifying the nature of the protein in the cellular context, beyond protein expression that will be useful in defining its role (function) in the cell and in turn help in understanding cellular activities. Post-translational modifications Post-translational modifications (PTMs) are protein processing events in which the nascent (translated) protein is modified covalently in order to confer or abstract functionality, allowing for diversity in the regulation of protein function. These include proteolytic cleavage (e.g. active insulin from proinsulin), phosphorylation (e.g. kinase-mediated signalling cascades), glycosylation (e.g. excreted proteins, transcription factors), ubiquitinization, acetylation, methylation, etc. Although


16 more than several hundered PTMs have been documented (http://www.abrf.org/ index.cfm/dm.home), methods for monitoring them on a proteomic scale are still in the early days of development. Four basic strategies that can be used to varying degrees of success have been identified (Mann and Jensen, 2003) as being currently available for monitoring PTMs. These are (a) 2D-GE-based separations followed by MS identification, (b) affinity-based enrichment of modified proteins followed by MS of protein mixtures, (c) (LC-)LC-MS/MS of enzymatically digested proteins (peptide mixtures), and (d) selective derivatization of peptides followed by affinity purification and MS. MS forms a vital part in current techniques for identifying PTMs (Mann and Jensen, 2003; Schweppe et al., 2003). Phosphorylation, which is an abundant and important PTM, can be detected, identified and even quantified using several strategies (Kalume et al., 2003). These include strategies based on the following: • affinity purification using phosphospecific antibodies; • immobilized metal affinity chromatography (IMAC), which employs Fe3+ or Ga3+-chelated solid supports to selectively bind and enrich phosphopeptides (derivatization of other acidic groups by methyl esterification prevents nonspecific bindings and has been used to detect more than 1000 phosphopeptides and identify 383 phosphorylation sites from whole cell lysates of S. cerevisae, after proteolytic digestion (Ficarro et al., 2002)); • β-elimination and Michael addition to replace the phosphate group in serine and threonine by ethanediol, followed by introduction of a biotin-containing tag for affinity enrichment and subsequent MS detection and quantification (Oda et al., 2001); • solid phase capture of peptides containing pSer/pThr/pTyr on a support carrying immobilized iodoacetyl groups (Zhou et al., 2001); • neutral loss and precursor ion scanning (loss of a phosphate group, equivalent to 98 Da in MS/MS of phosphopeptides in the positive ion mode, and loss of PO3– (–79 m/z) in the negative ion mode). Glycosylation is another important PTM that is involved in several cellular functions such as differentiation, development and morphogenesis. Approaches to analyse proteome-scale glycosylation include: • enrichment of specific glycoproteins using sequential lectin affinity columns followed by protease digestion, the glycopeptides are recaptured and eluted using the same lectin columns, resolved by HPLC, and analysed by MALDIMS (Hirabayashi and Kasai, 2002); • isotope-coded glycosylation-site-specific tagging (IGOT), which involves a lectin affinity capture of glycopeptides generated by tryptic digestion of protein mixtures, followed by peptide-N-glycosidase-mediated incorporation of a stable isotope tag, 18O, specifically into the N-glycosylation site and subsequent identification by LC-LC-MS/MS (Kaji et al., 2003); • the use of hydrazide chemistry to identify and quantify N-linked glycoproteins. The methods consists of oxidizing the cis diol group of carbohydrates in the


17 glycoproteins to aldehydes, coupling the oxidized glycoprotein to solid supports by hydrazide chemistry, proteolysis of the immobilized glycoproteins, stable isotope labelling of glycopeptides, the specific enzymatic release of the formerly N-linked glycosylated peptides and subsequent analysis by LC-MS (/MS) (Zhang et al., 2003). It has also been possible to identify post-translational modifications such as loss of initiating methionine, acetylation, methylation and proteolytic maturation in yeast ribosomal proteins using direct MS analysis of intact proteins (Lee et al., 2002). Other strategies that utilize molecular recognition to induce measurable events (e.g. ribozyme activation; Vaish et al., 2003) are also being investigated. Protein–protein interactions and protein complexes The definition of protein function in the post-genomic era has to be considered in the context of its interactions with other proteins in the cell (Eisenberg et al., 2000). In fact, clues to the function of a protein can be obtained by assessing the proteins it interacts with, the concept of guilt-by-association (Oliver, 2000). It is now known that many cellular activities are carried out by multi-protein complexes, and that protein–protein interactions mediate many aspects of cell behaviour. On average, there are about five interacting partners per protein in the yeast (S. cereviseae) proteome (Grigoriev, 2003). Protein–protein interaction networks can be used to predict protein function (Vazquez et al., 2003). Currently, three major strategies can be identified for analysing protein–protein interactions: (a) the yeast two-hybrid system, (b) functional protein microarrays and (c) affinity capturing methods coupled to MS-based protein identification techniques. The yeast two-hybrid (Y2H) system (Fields and Song, 1989) is a simple and powerful genetic method to identify protein–protein interactions, and has been used extensively, for instance, in the analysis of Helicobacter pylori proteome, where over 1200 interactions were identified, connecting 46 % of the proteome (Rain et al., 2001). In Y2H, interaction between a ‘bait’ fusion (component of interest fused to a DNA-binding domain) and a ‘prey’ fusion (other proteins fused to a transcription-activating domain) re-constitutes a functional transcription factor, which in turn activates reporter genes or selectable markers. Although homodimeric and heterodimeric interactions can be detected, only binary or pairwise interactions are analysed. Functional protein microarrays involve over-expression of the protein(s) of interest or an entire proteome, their distribution in an addressable array format after appropriate purification steps, followed by assay for binding to other proteins. Proteins are attached to surfaces (glass microscope slides can be used) by direct covalent methods, through linkers, or by affinity tags and assayed (Zhu and Snyder, 2003). A high-throughput protein purification protocol has been employed by Snyder and colleagues (Zhu et al., 2001) to purify 80 % of the yeast proteome as full-length proteins and construct a microarray composed of >5800 individually cloned, over-expressed and purified proteins. The proteins were purified as GSTtags and attached to Ni-NTA-coated glass slides using HisX6 tags. When probed


18 with Cy3-labelled calmodulin, 33 novel calmodulin-binding partners were identified in addition to six already known. Synthetic peptide arrays (Bialek et al., 2003) can also be used to study protein–protein interactions. The Y2H and protein microarrays are useful in monitoring binary interactions, but do not allow the identification of higher-order complexes and their connectivity. The development of affinity capture techniques, tandem affinity purification (TAP) (Rigaut et al., 1999), for instance, has allowed progress to be made in purifying protein complexes. Here, tagged proteins are used as baits for highaffinity capture of complexes, followed by subsequent identification of the protein components using MS. The utility of such techniques has been demonstrated with the yeast proteome (Gavin et al., 2002; Ho et al., 2002). The TAP approach has been recently extended to higher eukaryotes in a strategy in which the TAP method is combined with double-stranded RNA interference (RNAi) to avoid competition from corresponding endogenous proteins while isolating and characterizing protein complexes from higher eukaryotic cells (Forler et al., 2003). The affinity capture approach is more physiological, because molecular assemblies made up of existing direct and cooperative interactions are monitored in vivo, rather than re-constituted ex vivo or in vitro as in Y2H or microarrays. However, the latter two approaches are more economical and easily amenable to automation. Since the approaches potentially give information at different levels (binary interactions and higher-order connectivities) they are complementary and an integration of the approaches would increase confidence in the findings (Dziembowski and Seraphin, 2004). Proximity ligation is a novel method that could be used to assess interacting protein partners (Gullberg et al., 2003). In principle, it consists of bringing together sets of probes that have bound their target protein. The probes are composed of one ligand-binding component capable of specifically binding the target molecule and one attached oligonucleotide. A connector oligonucleotide added in molar excess hybridizes to the DNA extension from adjacent probes, guiding enzymatic DNA-ligation. The ligated DNA sequence is then amplified using RT-PCR and detected. Probes that do not bind to the target do not result in the ligated DNA. Protein–carbohydrate interactions have special significance in host–pathogen interactions as they are important players in the process of immunity and are involved in protein trafficking and secretion. Carbohydrate-microarrays have been described (Wang et al., 2002) that will be useful in studying such interactions. Protein biochemical activities Development of efficient methods to clone sets of ORFs into plasmids for expression in an appropriate host has enabled approaches based on overexpressing the protein-coding genes of interest and screening the expressed proteins for biochemical activities of interest (Phizicky et al., 2003). Alternatively, functional microarrays can be constructed by arraying purified proteomes/sub-proteomes and screened for biochemical activities, as has been demonstrated by Snyder and colleagues (Zhu et al., 2000), who analysed the kinase–substrate binding affinities of over 100 yeast protein kinases using 17 different substrates. Similarly, Macbeath


19 and Schrieber (2000) have demonstrated the detection of antigen–antibody interactions, protein kinase activities and protein interactions with small molecules using functional microarrays. Peptide microarrays that can be used as substrates for detecting enzyme activities and as potential ligands for other protein interactions, and carbohydrate-based microarrays (Wang et al., 2002) to assay protein–carbohydrate binding activities have also been described. The functions of many enzymes, including kinases, phosphatases, and proteases are controlled by autoinhibitory domains and/or endogenous protein inhibitors. Chemical approaches have been introduced that utilize small-molecule probes for activity-based protein profiling that directly report on the integrity of enzyme active sites in complex proteomes (Speers and Cravatt, 2004). Protein subcellular localizations Currently, a common strategy to study protein localization is to fractionate and enrich subcellular contents (Dreger, 2003). But fractionation schemes designed to enrich a particular subcellular structure are not well suited for studying protein translocation, as information is often lost on the proteome content of the other structures. Chromatographic separation, density gradient centrifugation, epitopetagging and immunolocalization (Kumar et al., 2002) are some of the techniques used in subcellular proteomics, in addition to those employed for general protein profiling and protein complex identifications (see section 1.3.1 and this section above). Owing to the inherent presence of contaminants in chemical fractionation techniques, proper validation steps are essential to ascertain subcellular localizations. Cloning and heterologous expression of proteins as tagged fusion products at their ‘natural’ level is one way to ascertain the localization of a protein. Such techniques can be combined with in silico predictions based on protein primary structures to validate any findings with respect to protein localizations. Combination of computational methods to extract information from databases can be used in a way to complement each other to capture the core features of a protein that are intimately related to its localization in a cell (Chou and Cai, 2003). Studies on protein translocation require parallel investigations on the subcellular structures in order to monitor protein dynamics between the substructures of the cell. A molecular biology approach that minimizes perturbations to protein expression was adopted by Huh et al. (2003) to characterize protein subcellular localization in the budding yeast. A library of information on protein localization for the yeast in the resting state was created using a GFP-tagged yeast strain collection. Molecular biological approaches In addition to biochemical approaches discussed above, several molecular biological strategies have been devised and shown to be successful (Phizicky et al., 2003). The common theme in most strategies is the cloning of a genomic set of ORFs for subsequent expression in a homologous or heterologous system, followed by biochemical analysis of the expressed proteins. The biochemical analysis is often facilitated by the fusion of protein or peptide affinity tags, such as glutathione Stransferase (GST), His6, calmodulin-binding peptide, etc.


20 Ghaemmaghami et al. (2003) described the application of a yeast fusion library where each ORF is tagged with a high-affinity epitope and expressed from its natural chromosome location. Through immunodetection of the common tag, they were able to detect proteins such as transcription factors that are present at levels not readily detectable by other proteomic methods. A strategy based on conditional and rapid degradation of the protein of interest in vivo, so that the immediate consequences of bulk protein depletion can be examined, has been demonstrated to be useful for assessing protein function (Kanemaki et al., 2003). Ozawa et al. (2003) described a method that allows identification of novel proteins compartmentalized in mitochondria, based on reconstitution of split-enhanced green fluorescent protein by protein splicing and screening of cDNA libraries. Transposons, which are discrete segments of DNA that can relocate between genomic sites, can be used in developing strategies for dissecting proteomes (Hayes, 2003).

1.3.3 Structural proteomics Proteins have similarities at the structural level with respect to their 3D conformations that relate to their function. Proteins with analogous amino acid sequences often assume similar stable conformations, as common interactions govern protein folding and stability. The premise in structural proteomics is to extend this observation on a genome- or proteome-wide scale and to predict biochemical function of uncharacterized proteins based solely on structural homology to another protein of known function. Most of the natural domain sequences assume one of a few thousand folds, of which only a couple of thousand exist among naturally occurring proteins (Govindarajan et al., 1999). Yee et al. (2003) described the logistics involved in a subproteome-wide screening and gathering of structural data for the soluble (non-membrane) proteins of the archeaon Methanobacterium thermoautotrophicum. The genes of interest were cloned into vectors using genetic engineering techniques and the proteins over-expressed as fusion tags so that they can subsequently be purified using affinity chromatography and evaluated for structure determination by NMR and X-ray crystallography. Structure-based predictions on function were made using computational biology. Integration of structural information gathered at different levels of biological organization from atoms to cells would enable a comprehensive description of the multitude of interactions between molecular entities, in turn leading to the discovery of structural principles that govern all cellular processes (Sali et al., 2003). The structure–function relationship for protein complexes is less well documented than that for protein folds. The Protein Quaternary Structure (PQS) database currently contains ~10 000 structurally defined protein assemblies of presumed biological significance, derived from a variety of organisms (http:// pqs.ebi.ac.uk/pqs-doc.shtml), but there are no satisfactory estimates of the number of different non-covalent macromolecular complexes with a unique structure and biological function.


21 Current experimental methods for structural characterization of assemblies include: • X-ray crystallography, which is the most prolific technique and is still the ‘gold standard’ with respect to accuracy; • NMR spectroscopy, which is more suitable to study protein dynamics and interactions in solution; • electron crystallography (2D electron microscopy (EM)); • electron tomography, whose true potential lies in visualizing the assemblies in an unperturbed cellular context (tomograms of cells at molecular resolution are essentially 3D images of the cell’s entire proteome that reveal information about the spatial relationships of macromolecules in the cytoplasm); • chemical cross-linking and MS, to identify binary and higher-order protein contacts; • affinity purification and MS, to identify subunits in complexes; • fluorescence resonance energy transfer (FRET), to monitor protein interactions; • site-directed mutagenesis, which can reveal which subunits in a complex interact with each other; • Y2H system, to detect binary protein interactions; and • protein arrays, to detect functionally linked proteins.

1.4

Metabolome analyses

Analogous to the proteome, metabolome can be defined as the metabolite complement (the low molecular weight intermediates of metabolism) of a cell, in a particular physiological state (Oliver et al., 1998; Tweeddale et al., 1998). Quite like the proteome and unlike the genome, its composition is relative to the physiological state of the cell, with the potential to be more dynamic between different states. Metabolites are the currencies of the cell mediating cellular activities. Defining the metabolome, or even a metabolite, is not as easy as it appears. A metabolite can be classified based on its origin as ‘endogenous’ (i.e. arising from within the cell) or ‘exogenous’ (i.e. arriving from outside the cell), and based on its function as primary (central to the metabolic needs of the cell) or secondary (of less importance to the cellular machinery). The boundaries between these classifications are not entirely clear-cut and are subject to interpretation. Xenobiotics and the intermediates arising from their interaction with the cell could be classified as exogenous metabolites. Depending on the application, approaches to metabolome analysis have been termed differently. Metabolic profiling is a popular term, especially in clinical medicine, where the cellular metabolic effects in relation to a disease process, or in response to a drug or environmental stressors, are profiled. Some researchers, especially the ones addressing analysis of body fluids, prefer to use the term metabonome (and therefore metabonomics) to imply both exogenous and endogenous species detected in biofluids (Lindon et al., 2003). Plant researchers, at least more recently, appear to favour the classification suggested by Fiehn (2000) as metabolite target analysis (restricted to analysis of


22 metabolite(s) of, say, an enzyme system), metabolite profiling (analysis of a group of metabolites, of, say, a specific pathway), metabolomics (comprehensive analysis of the entire metabolome, under a given condition) and metabolic fingerprinting (classification of samples based on their biological relevance or origin). Within the microbial community, analysis of metabolic activities has been in the realms of metabolic engineering and biochemical engineers. More recently (Goodacre et al., 2004), metabolomics, metabolic profiling, metabolic footprinting and metabolic fingerprinting appear to be the preferred terms used for the analysis of cellular metabolites on a global scale. Changes in the quasi-steady state levels of metabolites reflect alterations in cells induced in response to environmental or developmental stimuli, or to a genetic mutation. Capturing these changes would require mapping the metabolites in the cell, both spatially and temporally. In a hierarchical sense (Fig. 1.2), changes in the endogenous metabolite composition in a cell would occur temporally after and consequent to proteomic expression. This is usually the case in response to a developmental stimulus or a genetic mutation. However, cellular response to environmental stimuli can be initiated at the metabolic level, when metabolites regulate genetic or proteomic expression. In such instances, for example when regulation is at the level of enzyme inhibitions, cellular responses to environmental stressors may not be reflected in genetic or proteomic expression profiles, as they take place post-expression, and analysis at the metabolome level would be required. Even otherwise, recent investigations and observations (Fell, 2001; Ideker et al., 2001; ter Kuile and Westerhoff, 2001) raise doubts as to whether transcriptomics and proteomics data would suffice in assessing biological function, pointing that metabolomic data may be beneficial and emphasizing the need for investigations at the metabolome level. Metabolites are characterized by a large number of chemically diverse analytes that vary over a wide concentration range, which presents unique challenges for analysis. Plants appear to produce a relatively more diverse range of metabolites, through their secondary metabolic pathways, and due at least in part to low enzyme specificities (Schwab, 2003). Microbes on the other hand seem to have less diversity in their metabolome composition. A recent structure-based exploration of the E. coli metabolome (Nobeli et al., 2003) revealed that a majority (85 %) of the metabolites have a molecular weight of less than 500, with nucleotides, carbohydrates and amino acids being the most prominent species. Techniques for the measurement of metabolites per se have been in existence since the early days of biochemistry. Usually this is accomplished by isolating the metabolite of interest from the sample milieu and analysing it. However, when the objective in metabolome analysis is to capture changes in the overall metabolite composition of the cell, the exercise of analysing several metabolites in parallel in a sample milieu that is chemically heterogeneous becomes challenging. In addition, rapid quenching of cellular activities is essential if quantitative levels of metabolites are to be captured in the analysis. In general, current biochemical strategies for metabolome analyses are an extension of those used to analyse specific metabolites and involve a combination of analyte separation techniques, such as gas or liquid


23

Fig. 1.4 A schematic overview of current metabolomic strategies. (FT, Fourier transform; DIESI, direct infusion electrospray ionization; TL, thin layer chromatography.)

chromatography or capillary electrophoresis, followed by detection using techniques such as NMR or MS (Fig. 1.4). An early attempt at metabolome analysis was made using two-dimensional thin-layer chromatography (2D-TLC) (Tweeddale et al., 1998). Two solvent systems were used, one in each of the two dimensions, to resolve the metabolite pool extracted from E. coli cells. The patterns of metabolites and spot intensities detected by phosphorimaging were found to differ reproducibly depending on culture conditions. Changes in spot intensities of 70 most abundant metabolites could be monitored. More recently, simultaneous resolution of over 90 spots has been reported with an improved extraction procedure (Maharjan and Ferenci, 2003). Although 2D-TLC is limited in the resolution for comprehensive highthroughput determinations on metabolomic scales, it is a fairly simple analytical technique affordable by most laboratories, and can be useful in monitoring prominent metabolomic changes. A more automatable separation and analysis strategy is the coupling of gas chromatography (GC) with MS. This has been the preferred route to analyse plant metabolomes, and has been in the realms of clinical chemistry for monitoring specific metabolite classes (metabolic profiling) (Niwa, 1986), and several metabolites usually employing multiple columns (Lefevere et al., 1989). Volatile metabolites can be directly monitored and many others are usually derivatized to volatile derivatives. In an initial report on analysing several metabolites in a single


24 chromatographic step, over 300 metabolites were quantified using the technique from leaf extracts (Fiehn et al., 2000) and metabolic phenotypes identified (Roessner et al., 2001; Schmelz et al., 2003). GC-MS data have been used to construct metabolic flux profiles for E. coli mutants in central carbon metabolism (Fischer and Sauer, 2003). Rapid sample throughput is achievable with the use of TOF-MS coupled to GC separations, and databases of up to 500 metabolites have been generated (Wagner et al., 2003). The application of 2D-GC or GC×GC (Phillips and Beens, 1999) coupled with TOF-MS should increase analyte resolution and enable high-throughput quantitative determinations (van Mispelaar et al., 2003). Liquid chromatography (LC) is another separation strategy that is frequently employed in analysing specific metabolic components that can also be used in conjunction with MS to develop strategies for investigating the metabolome. Buchholz et al. (2001) showed that about 20 E. coli intracellular metabolites, including nucleotides and co-factors at 20–500 µM levels could be quantitatively estimated using LC-ESI-MS with as little as 20 µl of sample. A variety of polar metabolite classes including oligosaccharides, glycosides, amino sugars, amino acids and sugar nucleotides could be analysed by employing hydrophilic interaction chromatography in conjunction with ESI-MS, as demonstrated with plant extracts (Tolstikov and Fiehn, 2002). The scope of LC techniques in metabolome analysis is still to be extensively investigated and unless analyte throughput is dramatically increased, their application in large-scale metabolic studies will remain limited. The combination of LCs in tandem or in parallel offers scope for increase in analyte throughput, as does miniaturization. Tan et al. (2003) recently reported a chip-based solid-phase extraction strategy for biological sample cleanup prior to ESI-MS. Such techniques could be developed for parallel and tandem separation of metabolites prior to MS analysis, enabling at least partial metabolome characterizations. A third separation technique that shows good promise for metabolome analyses is capillary electrophoresis (CE), because of the capability to provide highresolution separations in reasonable time with minimal sample consumption. In CE, metabolites can be separated based on size and charge, by applying voltage across buffer-filled capillaries. Detection is usually by UV/Vis or fluorescence techniques. Micellar electrokinetic chromatography, where micelles are used as pseudostationary phases has been reported for the separation of neutral and ionic metabolites (Terabe et al., 2001). Improved sensitivities up to three orders of magnitude when compared with conventional junctions have been achieved by employing a dynamic pH junction (Britz-McKibbin and Terabe, 2003). Several classes of metabolites, including catecholamines, purines, nucleosides, nucleotides, amino acids, steroids and coenzymes were measured. Submicromolar concentrations of carboxylic acid metabolites could be detected using photometric detection (Markuszewski et al., 2003). A more productive approach is the coupling of mass spectrometry to CE. Such a technique offers the separation capabilities of CE and the accurate determinations possible with MS detection techniques. Soga et al. (2002) demonstrated the utility of CE-ESI-MS in monitoring anionic


25 metabolites, detecting 27 such species from B. subtilis. More recently, the concurrent application of three CE-MS techniques, each for analysing cationic metabolites, anionic metabolites and nucleotides, enabled the group to monitor 1692 metabolites from B. subtilis extracts, including the detection of several previously uncharacterized metabolites (Soga et al., 2003). Quantification was performed by comparing peak areas against calibration curves generated using internal standardization techniques. The sensitivity of detection was reportedly high, with detections in the range of 40 zeptomoles (for adenine) to 350 attomoles (for glutamate) per cell possible. To cover the dynamic range of metabolite concentrations the MS scanning was carried over intervals of 30 m/z, each sample being analysed successively 33 times using an automatic injection sequence, while varying the m/z detection range between 70 and 1027 in both cation and anion modes. However, complete analysis of a sample took over 16 h. Nevertheless, given current technology, the potential of the technique is amply evident from this study alone. LC separations can also be combined with NMR to obtain metabolomic information (Wolfender et al., 2003). More comprehensive data can be obtained by coupling LC or CE separations to MS and NMR in tandem. However, generic optimal conditions for analysis by all the coupled techniques will not be easy to find. In some cases, it may not be essential to resolve all the metabolites and techniques capable of capturing global changes can be used. These are discussed below in section 1.5.

1.4.1 Metabolic networks As mentioned earlier, the metabolome is a dynamic entity. Metabolite levels in themselves do not give all the information required for deciphering the physiological activities of a cell. It is well known that physiological response to a stimulus is intertwined in a complex network of metabolic reactions. In addition to the quantitative levels of metabolites, the metabolic reaction rates or metabolic flux through a pathway also provide valuable information for understanding metabolic networks, providing a holistic perspective on metabolism. Biochemical and physiological knowledge about network organizations is essential for understanding cell function. The possibility of manipulating microbial growth in culture vessels and the balancing of extracellular consumption and secretion rates using stoichiometric equations have enabled the estimation of metabolic fluxes (Sauer et al., 1997; Varma et al., 1993). For multicellular organisms generic sample extraction strategies may not be able to capture compartmentalization of metabolic activities. In this regard, isotope tracer techniques have been in use to trace substrate intake and metabolic processing. 13Clabelled experiments allow for additional information that increases the reliability and resolution of flux balance analyses. Several research groups have concentrated on such techniques for characterizing local or global metabolic pathways, especially in the microbial world (dos Santos et al., 2003; Fiaux et al., 2003; Fischer and Sauer, 2003; Schmidt et al., 1998; Yang et al., 2003; Zhang et al., 2003).


26 Intracellular fluxes can be quantified using 13C-labelling experiments followed by measurement of isotopomers using either NMR or (GC) MS. Isotope labelling techniques have been used in combination with gene knockout experiments for detecting which pathways are active in vivo (Yang et al., 2003; Zhao et al., 2003). An alternative approach to understanding metabolic networks has been taken by researchers in plant sciences. Pair-wise metabolite–metabolite correlations constructed from measurement of cellular metabolites (Roessner et al., 2001) can be visualized by metabolite correlation networks (Kose et al., 2001) and can be used to interpret physiological conditions in plants (Steuer et al., 2003). Mathematical frameworks are required for describing properties of large metabolic networks (Papin et al., 2003). A comparative analysis on the metabolic networks of 43 diverse organisms shows that, despite significant variations in the individual constituents and pathways, metabolic networks have the same topological organization that complies with the principles of robust error-tolerant scale-free networks and may represent a common blueprint for large-scale organization of interactions among cellular constituents (Jeong et al., 2000).

1.5 Proteomic and metabolomic fingerprinting and footprinting In instances where a rapid turnover of information needs to be monitored, approaches that adopt short and simple protocols that can still provide the necessary information are highly desirable. Screening of samples/mutants for more elaborate investigations and identification/monitoring of biomarkers representative of specific physiological or disease states are some such instances. Approaches in which rapid characterizations of prominent changes can be captured in a reproducible manner would be useful in such instances. Fingerprinting, in which changes in intracellular components are captured and footprinting, where changes in extracellular components (secreted into the immediate environment of organisms, cells or tissues) are monitored ideally do not involve separation or sample clean-up operations (Fig. 1.4). Instead the system is directly observed using detection techniques that may be non-invasive (e.g. infrared spectroscopy, NMR) or invasive (e.g. MS). Since analytical resolution will be poor compared with more comprehensive methods of analysis, chemometric techniques (Lavine and Workman, 2002) and machine learning approaches (Mitchell, 1997) are used to extract or deconvolve the relevant information from the spectra. There are three major techniques that provide the kind of information and type of measurement associated with fingerprinting and footprinting: MS, NMR and vibrational spectroscopic techniques, such as Fourier-transformed infrared (FT-IR) spectroscopy and Raman spectroscopy (and, to a lesser extent, optical techniques such as fluorescence spectroscopy and electrochemical measurements). MS has been in use for the detection and analysis of small molecular weight chemicals, but its application to the analysis of proteins and nucleic acids was enabled by the advent of ‘soft-ionization’ methods such as ESI and MALDI. The


27 arrival of analysers such as quadrupole ion-traps, time-of-flight (ToF) and Fouriertransformed ion cyclotron resonance (FT-ICR) improved mass accuracy, resolution and sensitivity of measurements. Sample desolvation and ionization in ESI-MS takes place when a liquid sample is allowed to flow through a narrow capillary, the tip of which is connected to a voltage supply, to generate charged aerosols that can be desolvated by the assistance of temperature, and a coaxial flow of gas. In MALDI-MS, samples are presented as dried spots, mixed with a matrix. Pulsed application of a laser at a wavelength at which the matrix absorbs enables desorption of the sample–matrix co-crystals from the surface into the analyser as charged ions. MALDI-MS of whole cells (Claydon et al., 1996; Fenselau and Demirev, 2001; Vaidyanathan et al., 2002b) is particularly attractive because of its ability to characterize the proteome, albeit only a fraction of it, directly without 2D-GE separation and match these proteins to sequence databases (Demirev et al., 2001). Direct infusion and analysis of whole cells using ESI-MS (Goodacre et al., 1999; Vaidyanathan et al., 2001) showed promise for the rapid characterization of organisms, cells or tissues for fingerprinting purposes. Direct infusion of crude cell extracts in a flow injection mode has enabled the application of the technique in a high-throughput fashion for proteomic and metabolomic fingerprinting and footprinting (Allen et al., 2003; Castrillo et al., 2003; Kaderbhai et al., 2003; Vaidyanathan et al., 2002a). The capability for tandem mass spectrometry allows identification and characterization of the detected species (Vaidyanathan et al., 2002a). It is possible to detect several metabolites and macromolecular types, including proteins, phospholipids and glycolipids (Fig. 1.5). Improved mass accuracy is possible with the application of FT-ICR MS in conjunction with ESI (Aharoni et al., 2002). NMR is another technique that has been shown to have the potential for generating rapid metabolic fingerprints (Nicholson et al., 2002; Raamsdonk et al., 2001). It is less sensitive than MS, but can be used non-invasively, permitting in vivo measurements. It is based on the fact that nuclei such as 1H, 13C and 31P can exist at different energy levels in a strong magnetic field, as they possess nuclear spin. If such nuclei are subject to a magnetic field and pulsed with radiofrequency energy, the absorption and re-emission of energy as they change energy levels can be measured as chemical shifts, the NMR spectrum being a series of peaks representing the chemical environments within a molecule. It is thus valuable in getting structural information. The use of magic angle spinning (MAS) for high resolution enables minimization of sample inhomogenieties, making NMR more applicable to the analysis of biological samples. 1H, 13C, 31P NMR can also be used to trace metabolites along pathways. However, 1H NMR spectra can be complex due to several contributing analytes, and lengthy 2D NMR is necessary to attempt to assign the chemical shifts to specific metabolites. Improved methods for 1 H NMR acquisition for fingerprinting and footprinting include the application of 2D J-resolved spectroscopy (JRES), which can provide proton-decoupled projected 1D spectra (p-JRES) (Viant, 2003). 13C or 31P NMR are ideally suited to monitor biochemical activities, but 13C NMR would require growing cultures in media containing isotopically labelled substrates in order to enhance sensitivity of


28

Fig. 1.5 Direct infusion electrospray mass spectra of unfractionated cell extracts from three representative bacterial species, showing the detection of signals attributable to metabolites, proteins, phospholipids and glycolipids.

detection, and although 31P NMR can be used for in vivo studies, it would require high cell densities for sensitive measurements and only measures metabolites containing phosphorus. Vibrational spectroscopic techniques, which are also non-invasive albeit in the spectral sense of lesser sensitivity and resolution capabilities, can also be used. These techniques comprise those that are based on molecular bond vibrations to quantify chemical species, including near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopic techniques. Fourier-transformed (FT) MIR (or FT-IR) spectroscopic signals may be used to capture differences in biological samples that arise owing to differences in protein or metabolic profiles, and can be combined with chemometric methods of data extraction, as has been shown recently for detection of microbial spoilage in meat samples by FTIR (Ellis and Goodacre, 2002). Metabolite information obtained from FT-IR fingerprints of mutant strains may also be useful in evaluating and assessing gene function (e.g. yeast knockouts;


29 Oliver et al., 1998) or changes in physiology (e.g. in E. coli; Kaderbhai et al., 2003). The major advantage of this technique is its rapidity and ease of spectral acquisition, enabling non-invasive measurements to be made with little or no sample preparation. However, sufficient signal resolution is to be ascertained for the desired effect to be monitored in order to use spectral information as protein or metabolic fingerprints. To extract relevant information from metabolic fingerprinting and footprinting techniques, spectral preprocessing and chemometrics using unsupervised (such as principal component analysis) or supervised learning (such as artificial neural networks) techniques are usually resorted to (Allen et al., 2003; Goodacre, 2002; Kaderbhai et al., 2003; Nicholson et al., 2002; Raamsdonk et al., 2001).

1.6

Bioinformatics and in silico approaches

The availability of experimental data on proteomes and metabolomes, albeit in partial formats, enables the structuring of the data for future mining of information. The science of archiving such data into databases (Table 1.1), after proper curation, and that of retrieval of these data, forms the basis of the field of bioinformatics. Several in silico approaches have been developed and are increasingly appearing in the literature for mining the vast amount of information that is currently available on proteomes, and will hopefully be available for metabolomes in the future. Individual sequences of the protein of interest can be used to identify homologues with areas of local sequence similarity by pair-wise sequence alignments using programs such as BLAST. Related proteins can be grouped together in families, which contain one or more sequence motifs identifiable by the conservation of certain amino acids across all sequences. Database searches using these sequence motifs can be generated (e.g. Scansite, SMART, Pfam and InterPro) based on the type and relative location of the motifs or on the whole motif region itself, with greater sensitivity than pair-wise searches. An even more sensitive approach would be to evaluate how well a query sequence fits with an already solved 3D structure using methods such as threading. 3D to 1D threaders attempt to match 3D information predicted from a 1D amino acid query sequence with corresponding features of known 3D structures. A web-accessible virtual 2D database (Medjahed et al., 2003) computed on the basis of theoretical IEF points, molecular weights, abundance and tissue specificity of catalogued proteins can assist in the putative predictions of the identity and location of unknown and/or low abundance proteins in 2D-PAGE gels. For membrane proteins information available in databases usually concerns prediction of protein topology rather than its 3D structure. For these cases, several topology prediction algorithms such as TopPred, PHD, TMHMM, and MEMSAT exist. The reliability of such predictions can be improved by constructing reliability scores (Melen et al., 2003) that make it possible to estimate the likelihood that a given prediction is correct and can be used in conjunction with limited experimental information to provide high-quality topology models for entire proteomes. Since the process of accruing ‘omic’


30 Table 1.1

Proteome and metabolome databases

Database

Website

Comments

Proteome Bioknowledge Library 2D-PAGE Swiss-Prot/ TrEMBL PROSITE MIPS

http://www.incyte.com/control/research products/insilico/proteome http://us.expasy.org/ch2d/2d-index.html http://ca.expasy.org/sprot/sprot-top.html http://ca.expasy.org/prosite http://mips.gsf.de

EXProt Proteome Analysis

http://www.cmbi.kun.nl/EXProt http://www.ebi.ac.uk/proteome

InterPro PIR-PSD

http://www.ebi.ac.uk/interpro http://pir.georgetown.edu/pirwww/search/ textpsd.shtml http://pir.georgetown.edu/iproclass http://pir.georgetown.edu/pirwww/search/ pirnref.shtml http://www.bioinf.man.ac.uk/dbbrowser/PRINTS http://www.bioinf.man.ac.uk/dbbrowser/ALIGN http://www.biochem.ucl.ac.uk/bsm/cath http://protein.toulouse.inra.fr/prodom/current/ html/home.php http://blocks.fhcrc.org http://www.rcsb.org/pdb

Contains six volumes, one each for an organism, including one for human fungal pathogens Index to 2-D PAGE databases and services Protein sequence database Database of protein families and domains Information centre for protein sequences, including those for pathogenic bacteria A database for proteins with an experimentally verified function Comprehensive statistical and comparative analyses of the predicted proteomes of fully sequenced organisms A database of protein families, domains and functional sites Protein Sequence Database

iProClass PIR-NREF PRINTS ALIGN CATH ProDom

An integrated resource for structure/function-based classification Non-Redundant Reference Protein Database A compendium of protein fingerprints A compendium of protein sequence alignments Protein structure classification A comprehensive set of protein domain families

NIST BioCyc

http://www.nist.gov/srd http://biocyc.org

A service for biological sequence analysis Worldwide repository for the processing and distribution of 3D biological macromolecular structure data Contains structural domain definitions for all protein chains in the PDB A central repository of protein sequence and function NCBI’s sequence similarity search tool designed to support analysis of nucleotide and protein databases Chemical compound database of relevance to metabolomics Of relevance to metabolomics

KEGG

http://www.genome.ad.jp/kegg/ligand.html

Of relevance to metabolomics

Protein Data Bank (PDB) 3Dee UniProt BLAST

http://www.compbio.dundee.ac.uk/3Dee http://www.pir.uniprot.org http://www.ncbi.nlm.nih.gov/BLAST


31 information and the creation of databases is a rather recent occurrence, information on relatively few organisms is available in databases. Consequently, there is difficulty in the applicability of conventional protein identification strategies to proteomes of organisms with unsequenced genomes. However, the emerging interplay of MS and bioinformatics is widening the scope of proteomics even to organisms with unsequenced genomes, enabling cross-species protein identifications (Liska and Shevchenko, 2003). Apart from protein identification and sequence-related information, functional and contextual information of proteomes is a major pursuit in the post-genomic era. A sequence annotation server (Bioverse – http://bioverse.compbio. washington.edu) that provides a web-based interface to allow users to submit protein sequences and obtain functional, structural and contextual annotations, has been described (McDermott and Samudrala, 2003). At present, a proteome consisting of up to 15 000 proteins can reportedly be processed within a day. Functional information regarding protein-modifying enzymes and protein interaction domains can be had from peptide libraries, generated using methods employing either synthetic or encoded (e.g. by phage display) approaches. Recent developments (Turk and Cantley, 2003) enable the identification of interaction partners and substrates for proteins on the basis of their peptide selectivity profiles. Proteome-wide post-translational modifications can also be captured by appropriate database search algorithms, as shown for the characterization of intact Helicobacter pylori (Demirev et al., 2001). A number of bioinformatic strategies have been reported for mining protein–protein interactions, useful in inferring protein function (Bock and Gough, 2003; Chen and Xu, 2003; Lu et al., 2003; Wojcik and Schachter, 2001). Bock and Gough described an algorithm, the phylogenetic bootstrap used to train a learning system to recognize correlated patterns of primary structure within protein interaction pairs taken from the human gastric pathogen H. pylori. They successfully used it to predict interactions in a phylogenetically related pathogen, C. jejuni. A hybrid approach that combines information from gene ontology, functional domain and pseudo amino acid composition data and the construction of intimate sorting algorithm (ISort) has been described that complements each technique for the successful prediction of protein subcellular localizations in prokaryotes and eukaryotes (Chou and Cai, 2003). Exploiting the fact that peroxisomal matrix proteins have a peroxisomal targeting signal (PTS1) at their extreme C-terminus, consisting of three amino acids – SKL – or a conservative variant, a scheme for the prediction of peroxisomal localization of proteins has been developed employing neural network and support vector machine training (Emanuelsson et al., 2003). Compared with proteomic applications, bioinformatic efforts for studying metabolomes are relatively few. The need for databases containing metabolite profiles of organisms comprising visualization schemes that combine biological context with the underlying chemistry have been discussed (Mendes, 2002). Researchers in plant metabolomics are constructing spectral formatting tools (Duran et al., 2003) and visualization frameworks (Kose et al., 2001) from experimental metabolomics GC-MS data, and have developed pair-wise


32 correlation networks that would represent snapshots of the physiological state of a cell (plant system in the demonstrated case) at a given point in time (Steuer et al., 2003). In silico approaches using convex analysis based on a detailed stoichiometric model in combination with metabolomic data can be used to elucidate the function or functional class of orphan genes, as demonstrated with a simplified yeast model (Forster et al., 2002). In a study that represents the first 2D structurebased anatomy of a metabolome, 2D structures of 745 metabolites of the E. coli metabolome, as accessible from the EcoCyc and KEGG databases, have been characterized by EMBL researchers (Nobeli et al., 2003). Although classification of the metabolites into biochemically relevant groups was not entirely possible without group overlaps, the utility of the approach in assessing structure-based metabolite preferences of protein domains, and in characterizing metabolic pathways was demonstrated. Database predictions for the existence of a metabolic pathway in an organism on the basis of its annotated genome, in combination with firmly established data from the biochemical literature, has revealed the similarity of metabolic network organization to the inherent organization of non-biological systems (Jeong et al., 2000). Following experience with genomic and transcriptomic data, strategies for capture, storage and dissemination of proteomic data are being considered for generating proteome repositories (Taylor et al., 2002). The difficulty of dealing with this profusion of binary electronic formats has led to the design of proteomics information-gathering systems that are based on metadata formats, rather than on the original raw data (Fenyo and Beavis, 2002). Finally, similar strategies for capturing metadata are being adopted for metabolomics (Hardy and Fuell, 2003).

1.7 Applications in understanding pathogen behaviour Pathogenic proteomes have been investigated largely by the use of conventional 2D-PAGE-MS technology. Other techniques have not been extensively adopted, which perhaps reflects the chronological development of techniques in the field and the availability of 2D-PAGE in a relatively established accessible format. Despite its limitations, 2D-PAGE has proved invaluable in revealing information that has not been accessible through genomic or pre-genomic techniques, and for confirming observations made using those techniques. It is estimated that about 75 % of a proteome can be measured by this technology (Cordwell et al., 2001). There is much evidence of its utility, as will be evident from the discussion below, but there are other strategies that have been shown to be useful as well. These are summarized in Table 1.2. Greater variability at proteome level, compared with genomic or transcriptomic levels, has been noted (Arevalo-Ferro et al., 2003; Ramnath et al., 2003), indicating the value of investigations at the proteomic level. 1.7.1 Proteome databases and comparative proteomics Analogous to the generation of genomic and transcriptomic databases, proteome


33 databases have also been set up (e.g. SWISS-2D-PAGE database) to mine proteomic information that is increasingly becoming available (Table 1.1). Because of its dynamic nature, there are different aspects of a proteome that can be recorded, relating to the expressed proteome, the functional proteome or to the structural proteome. The widespread use of 2D-PAGE technology has resulted in the availability of 2D-PAGE reference maps for expressed proteomes of several microbes, including pathogens, under different experimental and physiological conditions. In many instances, these are usually partial proteomes corresponding to extractable proteins, under specified conditions. For example: • A 2D-PAGE reference map of Shigella flexneri was described, in which 488 protein spots were detected, of which 388 were identified (Liao et al., 2003). • A partially annotated proteome reference map of Listeria monocytogenes was presented by partial fractionation of membrane and cytosolic proteins, in which 261 spots were detected, of which 33 were identified (Ramnath et al., 2003). • A reference map of Vibrio cholerae was presented with the detection of 1715 spots and the identification of 40 proteins (Hommais et al., 2002). Rain et al. (2001) presented the first protein–protein interaction map by screening 261 H. pylori proteins, using the Y2H system, observing 1200 interactions connecting about 46 % of the proteome. Proteins mapped on 2D arrays under standard conditions can be visually compared with those from a variety of test conditions, with the aim of assessing strain variability, environmental influences or the effects of genetic manipulation. The relative protein abundances between the compared proteomes can assist in identifying protein candidates that may be specific for one or more test conditions. For instance, comparison of the extracellular proteins (secretome) of two strains of Staphylococcus aureus enabled the discovery of new members of the SarA and σB regulon , implicating them as components of a complex network of gene regulation that plays an essential role in the coordinated synthesis of extracellular proteins (Ziebandt et al., 2001). There are other instances when comparative proteomics has contributed to understanding pathogen behaviour, as is evident from the discussion below.

1.7.2 Pathogen survival mechanisms and host–pathogen interactions A key aspect that characterizes a pathogen, and one that will lead to a better understanding of pathogen behaviour, is in unravelling its ability to colonize hosts, and how it can ward off the host defences, survive and proliferate in an environment meant for its destruction. Differences in protein expression (as monitored by using 2D-PAGE) have been observed between planktonic and biofilm modes of growth in both obligate and opportunistic pathogens including C. jejuni (Dykes et al., 2003), Pseudomonas aeruginosa (Arevalo-Ferro et al., 2003), Brucella melitensis (Eschenbrenner et al., 2002) and Actinobacillus actinomycetemcomitans (Fletcher et al., 2001). In general, biofilm-associated bacterial cells, including those of human pathogens, are more resistant to stress conditions than their


34 Table 1.2

Proteomic and metabolomic strategies and their outcomes demonstrated for the study of pathogen behaviour

Pathogen

Proteomic/metabolomic strategy

Outcomes/findings

Reference

Candida albicans

Heparin agarose chromatography

Niimi et al. (1999)

Clostridium perfringens Chlamydia pneumoniae

2D-PAGE 2D-PAGE

Escherichia coli E. coli, Haemophilus influenza, Campylobacter jejuni, Helicobacter pylori Helicobacter

2D-PAGE in silico approaches

Detection of a minor group of low-abundance proteins. (130–200 kDa) that were differentially synthesized during glucose-induced morphogenesis. Identification of proteins associated with the VirR/VirS system Elementary bodies (EB) are capable of metabolizing energy and providing fuel for protein expression and active transport of proteins at the very beginning of the developmental cycle. Involvement of bgl and trx in pathogen survival. Detection of modules from sequence databases – structural proteomics.

H. pylori H. pylori Listeria monocytogenes

L. monocytogenes L. monocytogenes

SELDI protein chip

Shimizu et al. (2002) Vandahl et al. (2001) Baglioni et al. (2003) Le Bouder-Langevin et al. (2002)

Cell surface associated response to bile stress. Different patterns Hynes et al. (2003) are generated in response to bile stress among various pathogenic Helicobacter species. Yeast two-hybrid system Protein–protein interaction map. Rain et al. (2001) 2D-PAGE, Immunoproteomics of infection and relation to gastric disease. Haas et al. (2002) immunoblotting 310 antigenic proteins recognized, nine of which were newly identified. Isolation of pathogenInlA- and InlB-phagosomes are enriched in distinct specific Pizarro-Cerda et al. containing phagosomes; proteins. (2002) 2D-PAGE Identification of a putative new cellular factor that could be involved in the internalization or early post-internalization survival of the pathogen within host cells. Evidence to suggest that machinery involved in the maturation of phagosomes, regardless of the precise mechanism of phagocytosis, is highly conserved. 2D-PAGE Partial proteome reference map. Ramnath et al. (2003) 2D-PAGE SecA2-dependent secretion of autolytic enzymes promotes Lenz et al. (2003) pathogenesis.


Mycobacterium tuberculosis

Mycobacterium

2D-liquid phase electrophoresis (2D-LPE) 1D SDS gels coupled to µCap-LC-nanoESIMS/MS Functional microarrays

Salmonella

2D-PAGE

S. Typhimurium

2D-PAGE

Shigella flexneri Staphylococcus aureus

2D-PAGE 2D-PAGE

Vibrio cholerae (2002)

2D-PAGE

M. tuberculosis

CE-ESMS


35

T-cell antigens defined.

Covert et al. (2001)

Proteomic profiling of the membrane constituents.

Gu et al. (2003)

Kinome analysis of host response. Hestvik et al. (2003) Detection of host proteins involved in regulation of apoptotic pathways, cytoskeletal arrangement, calcium signalling and macrophage activation in mycobacterial infections. Evidence to suggest an active attenuation of macrophage defence through mycobacterial inhibitionof PKCε and possible interference with host calcium signalling. A signal transduction system (PhoPQ) implicated in virulence Adams et al. (2001) controls motility of the pathothen. Gene expression is globally altered in the presence of sublethal Bader et al. (2003) concentrations of different structural classes of antimicrobial peptides. Cationic peptides (like CAMP) might serve as a molecular signature of an animal environment and induce virulence. 2D-proteome map. 388 proteins identified Liao et al. (2003) Ziebandt et al. (2001) Discovery of new members of the SarA and σB-regulon in the secretome (extracellular proteins) of S. aureus. Both SarA and σB are components of a very complex network of gene regulation and play an essential role in the highly coordinated synthesis of extracellular proteins. Existence of different alterations in the functioning of cell Hommais et al. envelope components during the onset of inorganic acid tolerance response in the pathogen. Detection of sugar nucleotides involved in the biosynthesis of Soo et al. (2004) pseudaminic acid, a sialic acid-like sugar, observed on the flagellin of some pathogenic bacteria.

36 planktonic counterparts. However, in C. jejuni despite the elicitation of some putative stress proteins, it was observed that biofilm cells are less resistant to stress than their planktonic counterparts and may lack a sophisticated adaptive stressresistance response (Dykes et al., 2003), indicating that the physiological condition of food-contaminating campylobacters may play an important role in their subsequent ability to survive and cause disease. For enteric pathogens, the low pH of the gastric system is one of the most potentially harmful stresses encountered by the organism, and requires adaptive measures for its survival and virulence. In Salmonella, low pH has been shown (using 2D-PAGE analysis) to result in the transcriptional down-regulation of flagellar genes, establishing that a signal transduction system implicated in virulence controls motility of this pathogen at low pH, and suggesting that Salmonella cells may be non-motile in very low pH environments in the host such as the stomach, perhaps conserving ATP for survival of the pathogen (Adams et al., 2001). In Vibrio cholerae, it has been noted (again by 2D-PAGE analysis) (Hommais et al., 2002) that the accumulation levels of several proteins known to be involved in the organization and functioning of membranes, including lipopolysaccharide, were decreased in response to mild acidic pH, as also observed for E. coli. Such modifications in cells grown at mild acid pH could constitute a signal for the outbreak of the acid tolerance response, which is known to protect cells at extreme pH for several hours. Enteric bacteria, such as E. coli, are frequently subjected to changes in their environment and the bgl operon may represent one of those systems by which microbial populations respond to environmental stress. A comparative proteome analysis (using 2D-PAGE) between three E. coli strains, one of which was a bgl spontaneous mutant, revealed up-regulation of thiolperoxidase (Tpx), an antioxidant enzyme that removes peroxides and H2O2, suggesting the possible involvement of bgl and tpx in the survival of the pathogen during infection (Baglioni et al., 2003). Oxidative stress-related differential expression of proteins in H. pylori has also been reported (Baek et al., 2004). Most of the proteomic studies in relation to H. pylori have focused on H. pylori itself rather than the host cells. In the study by Baek et al. (2004), the involvement of oxidative stress in the pathogenesis of H. pylori-induced gastric diseases including inflammation, ulceration and carcinogenesis, was demonstrated by comparative proteomic analysis of the human gastric mucosa from normal subjects and diseased patients. Differentially expressed proteins by H. pylori in the human gastric mucosa were captured. In P. aeruginosa and in many other Gram-negative bacterial pathogens, expression of virulence factors is not constitutive but is regulated in a cell densitydependent manner. The microorganisms therefore utilize a quorum-sensing (QS) mechanism to monitor the size of the population. A comparative proteomic study of the intracellular, extracellular and surface protein fractions of P. aeruginosa parent and mutant strains demonstrated the utility of proteomic investigations (Arevalo-Ferro et al., 2003). It was shown that inactivation of the P. aeruginosa PA01 QS system affects the cell’s protein composition more strongly than its transcriptome, indicating that a major part of the QS-regulation occurs at the post-


37 transcriptional level. Quantitative proteomics using the ICAT strategy (section 1.3.1) enabled the identification of several QS proteins induced by magnesium limitation (Guina et al., 2003). It was revealed that the synthesis of Pseudomonas quinolone signal, which interacts with QS, is increased during in vitro cultivations in low magnesium and increased in P. aeruginosa isolates from airways of infants with cystic fibrosis. A comparative proteomic study with a Burkholderia cepacia parent and a mutant strain deficient in cep (a QS system) (Riedel et al., 2003) showed at least 55 differentially expressed proteins that could be associated with QS, indicating that QS is a global gene regulation system in B. cepacia, and that a number of apparently unrelated cellular functions including the production of extracellular hydrolytic enzymes, swarming motility and biofilm formation, are cep-regulated. Several studies involving proteomic approaches have been demonstrated to contribute to the understanding of host–pathogen interactions. Isolation of pathogen-containing phagosomes has been a useful technique to approach trafficking during parasite–host cell interactions. Using this approach, a global proteomic characterization of early listerial phagosomes containing latex beads coated independently with two invasion proteins of Listeria monocytogenes, InlA and InlB, revealed a putative new cellular factor that could be involved in the internalization or early post-internalization survival of L. monocytogenes within host cells (Pizarro-Cerda et al., 2002). In another study (Lenz et al., 2003), it has been shown that SecA2 (an auxiliary secretory protein that is conserved in several pathogenic Gram-positive bacteria) is required for persistent colonization of host tissues by L. monocytogenes. Proteomic analysis using 2D-PAGE-MS enabled the identification of 17 proteins that suggest SecA2-dependent secretion has evolved in part to promote the secretion of autolysins with important contributions to L. monocytogenes virulence, and implicating SecA2 as a possible Gram-positive bacterial equivalent of the better characterized specialized secretory systems in Gram-negative bacterial pathogens. A microarray proteomic approach, in which an array of phospho-specific antibodies covering kinases and other signalling proteins from major known eukaryotic signalling networks was used, revealed changes in host signalling pathways that have reportedly not been previously described, including host proteins involved in regulation of apoptic pathways, cytoskeletal arrangement, calcium signalling and macrophage activation (Hestvik et al., 2003). Cationic antimicrobial peptides (CAMP) represent a conserved and highly effective component of innate immunity. During infection, the Gram-negative pathogen Salmonella enterica serovar Typhimurium induces different mechanisms of CAMP resistance that promote pathogenesis in animals. Using 2D-PAGE-MS and nucleic acid microarrays, it has been demonstrated that Salmonella Typhimurium globally alters gene expression in the presence of sublethal concentrations of different structural classes of CAMP, and that this stress response leads to important phenotypic changes, including resistance to oxidative stress and antimicrobial peptides (Bader et al., 2003). Proteasomes degrade intracellular proteins. The application of bioinformatic tools enabled Kesmir et al. (2003) to predict the


38 degradation of pathogen proteomes and find that the immunoproteasome generates peptides that are better ligands for the major histocompatibility complex (MHC) binding than peptides generated by the constitutive proteasome and show that the immunoproteasome is a more specific enzyme than the constitutive proteasome.

1.7.3 Drug resistance and drug development The earliest modification event during eukaryotic mRNA synthesis, the 5' cap acquisition, is a molecular target for antifungal drugs. The inefficacy of using this molecular target has been pointed out in a study that used comparative proteomics of wild and an mRNA capping enzyme-deficient mutant (De Backer et al., 2000). The pleiotropic effects one might expect upon the deletion were reflected in the large number of differentially expressed proteins with a globally reduced level of protein expression, but the overall functionality of the pathogen and its ability to cause infection remained intact, and the levels of a number of proteins were also significantly increased in the proteome of the mutant, including that of a heat shock protein, suggesting that the pathogen could easily survive under these less favourable growth conditions. In another study involving a drug-resistant H. pylori strain, proteome analysis revealed increased expression of proteins associated with resistance of oxidative stress, despite global down-regulation of several other proteins (McAtee et al., 2001). Western blot analysis of 2D-PAGE maps coupled to protein identification by MS has been shown to be useful in identifying 53 surface antigens of Chlamydia pneumoniae elementary bodies, several of which tested positive for the inhibition-of-infection assay, when screened with mouse sera (Grandi, 2003), demonstrating the potential of proteomics in identifying vaccine candidates. Proteomics can be used as a tool to study drugs of unknown mechanism of action, as shown for antifungals, where those with a common mechanism of action have been shown to have comparable effects at the proteome level (Bruneau et al., 2003).

1.7.4 Virulence and antigenic determinants Proteomic analysis has also proved useful in identifying virulence determinants, as in Clostridium perfringens (Shimizu et al., 2002), Francisella tularensis (Sjostedt, 2003) and Mycobacterium tuberculosis (Smith, 2003), and for identifying surface antigenic proteins, as in C. pneumoniae (Bruneau et al., 2003; Montigiani et al., 2002), H. pylori (Haas et al., 2002), Bacillus anthracis (Ariel et al., 2003) and M. tuberculosis (Covert et al., 2001). A 2D-PAGE-MS-based proteomic approach enabled Adams et al. (2001) to establish that a signal transduction system implicated in virulence controls the motility of Salmonella. The search for virulence associated factors of bacterial pathogens is considered to be an important step in vaccine discovery. In addition to in vivo expression technology (IVET), signature tagged mutagenesis (STM) and DNA microarrays, proteomic approaches are useful in determining virulence and antigenic factors, as has been demonstrated


39 by Grandi (2003) for the elucidation of C. pneumoniae surface protein subproteome and for the identification of potential vaccine candidates. As several of the virulence and antigenic factors are associated with the cell surface, proteomic strategies that accommodate the analysis of membrane proteins are well suited for these purposes. In silico approaches and bioinformatics tools are also being explored for the identification and development of vaccines (Capecchi et al., 2004; Zagursky et al., 2003).

1.8 Future trends There is a drive to direct significant analytical efforts towards capturing changes in proteomes and the metabolomes, thanks to an increasing realization that not all transcribed genes contribute to cell function and that cellular activities are mediated by processes beyond transcription. Current analytical efforts are more focused on unravelling the mysteries of the proteome. Even here, several challenges remain to be surmounted with respect to increasing the number of proteins detected per analysis, the sensitivity of detection to identify proteins of low abundance, the dynamic range of analysis and miniaturizing large-scale protein separation strategies to increase sample throughput and proteome completeness, improvements which will all be addressed in the near future. The importance of looking at protein turnover within cells is being realized (Pratt et al., 2002) and the development of strategies to analyse these are appearing (Cargile et al., 2004) and should form the subject matter of a greater number of investigations in the near future. Greater efforts will also be expended in developing technologies for comprehensive estimation of protein function as compared with those for protein expression. Efforts towards creating and maintaining public repositories of proteomic data (e.g. OPD, http://bioinformatics.icmb.utexas.edu/OPD) and standardization of protocols to enable inter-laboratory comparisons will increasingly appear. At the metabolome level, the diversity and dynamic nature of the metabolome pose considerable challenges for future analytical efforts in developing rapid, high-throughput technologies. Current technologies often enable only a portion of the metabolome to be analysed. Future efforts will have to be directed towards increasing the size and range of metabolites analysed. For this to happen, technical challenges with respect to reliable and comprehensive metabolite extraction strategies will have to be addressed in the near future. Non-invasive techniques that can provide reliable information in a rapid, high-throughput format will receive greater attention for devising fingerprinting strategies. As with proteomic and transcriptomic data before that, repositories of data and databases for reproducible metabolomic information will follow suit. The need for integrating datasets is increasingly being recognized (Ge et al., 2003; Reed et al., 2003), and the availability of ‘omic’ information at different levels will enable combination of such information, as demonstrated by researchers in plant metabolomics, for instance, with pair-wise transcript-metabolite correlation analyses (Urbanczyk-


40 Wochniak et al., 2003), for deciphering cellular functionality. The trend towards automation of analysis, and even automated experimentation (King et al., 2004), towards more precise and efficient analytical efforts will receive increasing attention. Proteomic and metabolomic technologies will increasingly find application in unravelling pathogen survival mechanisms, and the details of host cell interactions, enabling a better understanding of pathogen behaviour and the development of methods to prevent and cure pathogenic diseases. Since a majority of surface antigens are either proteomic or metabolomic in origin, these technologies will be useful in characterizing biomarkers for pathogenic detection and identification, and in drug development efforts. The generation of well-curated, stable databases should pave the way for the application of proteomic and metabolomic information in pathogen studies, which will undoubtedly help in finding solutions to questions arising at the genomic level, and will in turn pose questions that will eventually lead to a better understanding of pathogen behaviour. Analytical developments in proteomic and metabolomics will also be fine tuned by the questions arising from their application to pathogen studies.

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53

2 Mechanistic modelling of pathogen stress response Y. Le Marc and J. Baranyi, Institute of Food Research, UK and T. Ross, University of Tasmania, Australia

2.1

Introduction

As discussed in other chapters, the behaviour of microbial populations (growth, survival or decline) is determined by the effects of the physicochemical environment in the food (e.g. temperature, pH, water activity) both in terms of those conditions at any particular time and also the sequences and rates of change of environmental conditions experienced by the microbial cells. It would be of immense practical value to be able to predict the effect of environmental conditions in foods on the fate of microbial populations, and this is the goal of ‘predictive microbiology’, i.e. to develop quantitative (mathematical) models to enable estimation of the microbial response to environmental conditions in foods over time. Comprehensive reviews of the subject are available in McMeekin et al. (1993) and McKellar and Lu (2004). Currently, models are essentially descriptive, although mechanistic forms and interpretations have been suggested, particularly for the effects of temperature on growth rate, or for the dynamics of lag phase resolution. While the effects of stress on microbial population dynamics are well documented, and physiological corollaries of these stresses described, their translation into mathematical models is not yet well developed. This chapter will describe basic principles of modelling of microbial population dynamics in foods and provide an overview of how the physiology of stress may be incorporated in predictive microbiology models. While the influence of stress


54 responses in the kinetics of inactivation of spores and vegetative microbial cells has been well documented (for reviews see e.g. Cerf et al., 1996; Mafart, 2000), this chapter will concentrate on approaches to modelling the effects of stressful environments on lag and growth rate responses.

2.2

Mathematical modelling of microbial population kinetics

Modelling of microbial growth, or inactivation, is usually carried out in two stages. First, growth or death curves are generated at different levels of the environmental factors of interest (temperature, pH, water activity, preservatives, etc.) and fitted with a ‘primary’ model to estimate the bacterial rate of population change, time delays before those changes are evident (e.g. lag time, ‘shoulders’) and particularly in the case of growth, the final population densities observed. Then, the effects of environmental conditions on the parameters of the primary model are described by a ‘secondary model’. Predictions of the microbial population changes in food over time are therefore obtained by combining both primary and secondary models. In the case of conditions allowing growth, by taking into account the temperature of storage and the physical properties of the food product (pH, water activity, etc.) and the physiological state of cells at the time, the lag time and growth rate can be estimated from the secondary models. These values are input into the primary model to predict the bacterial population growth kinetics.

2.3 Primary models 2.3.1 Dynamics of bacterial population growth Dynamics of a bacterial population of size x as a function of time can be seen as a succession of three phases (Fig. 2.1): 1. A lag phase which is associated with the physiological adaptation of the cells to their new environment, prior to growth. 2. An exponential growth phase where the logarithm of the bacterial population increases linearly with time. The slope of this linear portion, when the natural logarithm of the bacterial concentration is plotted against time, is called maximum specific growth rate (µmax). If the log10(x) is plotted, as is more usual, then the slope of that curve is ~2.3 times less than µmax (ln(10) = 2.30). 3. A stationary phase related to the exhaustion of available nutrients and/or accumulation of inhibitory or end-products. The lag time is usually defined as the intersection between the tangent to the exponential growth phase and the lower asymptote (ln(x0)) (see Fig. 2.1). A standard growth curve is characterized by: • the inoculum size (x0);


55

Fig. 2.1 (a) Schematic representation of a bacterial growth curve: (1) lag phase; (2) growth phase; (3) stationary phase. (b) The characteristic parameters of a bacterial growth curve.

• the lag time, lag; • the maximum specific growth rate, µmax; • the bacterial concentration at the stationary phase, xmax. A variety of models have been proposed to describe the growth kinetics of a bacterial population (Fig. 2.2). In this section, we will review some of the most commonly used models. Primary models can be divided into two categories. The first category consists of functions that can be used to describe the bacterial growth kinetics because their sigmoid shapes are similar to a bacterial growth curve. The


56

Fig. 2.2 Same experimental growth curve fitted with different primary models: (a) Gompertz model; (b) trilinear model; (c) logistic model with delay and rupture and (d) Baranyi model (CFU = colony-forming unit).

second category consists of models based on the differential equation which describes the exponential growth of the microorganisms in the exponential growth phase. 2.3.2 Gompertz model First, the Gompertz model was probably the most popular primary model. It can be written as: log x(t) = A + C exp{–exp[– B(t – M)]}

[2.1]

where A is the lower asymptotic value as t decreases to –∞, C is the difference between the upper and lower asymptote, M is the time at which the specific growth rate is maximum, and B is related to the growth rate at M, as shown below. The parameters A, C, B and M can be used to estimate the bacterial growth parameters. Lag is usually described by M–1/B but, more correctly (McMeekin et al., 1993) is given by: lag = M – 1/B{1 – exp[1 – exp(BM)]} and

specific growth rate = BC/e ≈ BC/2.718


57 Although used extensively, some authors (Whiting and Cygnarowicz-Provost, 1992; Baranyi, 1992; Ross, 1993; Dalgaard et al., 1994; Membré et al., 1999) reported that the Gompertz equation systematically overestimated growth rate compared with the usual definition of the maximum growth rate. Note that there is also no correspondence between the lower asymptote and the inoculum size x0. Baty et al. (2004), while comparing the ability of different primary models to estimate lag time, found that the Gompertz model is perhaps the least consistent. Nevertheless, this model was for a long time the most widely used to fit bacterial curves, notably by the Pathogen Modelling Program (Buchanan, 1993), current versions of which are still available without cost (see http://www.arserrc.gov/mfs/ pathogen.htm). 2.3.3 Models based on the differential equation dx/xdt = µ max A new generation of primary models has been introduced by Baranyi and Roberts (1994) based on the differential equation that describes the exponential growth of microorganisms during the exponential growth phase: dx ––– = µmax xdt

[2.2]

These models are attractive for two reasons. They are usually defined by a set of differential equations and therefore can easily be used to describe the bacterial growth under dynamic conditions. Moreover, unlike the Gompertz, their parameters (in particular µmax) are consistent with the usual definitions adopted by microbiologists. Equation 2.2 does not describe the lag phase and the stationary phase. Therefore, it was necessary to introduce two new functions α(t) and u(t,x) to describe, respectively, the adaptation of the cells to their actual environment and the slowing down of the growth at the end of the growth phase, and the stationary phase: dx ––– = µmaxxα(t)u(t,x) dt

[2.3]

Different approaches have been proposed to describe α(t) and u(t,x). The simplest model is derived from eqn 2.2 by considering α equal to 0 during the lag phase (no growth) and equal to 1 during the growth phase (cells grow with a rate equal to the maximum growth rate) and u(x) equal to 1 during the growth phase and equal to 0 during the stationary phase (no growth). This can be formalized by the following equations:  0 if t ≤ lag α(t) =   1 if t > lag

[2.4]

 1 if t ≤ ts u(t,x) = u(t) =   0 if t > ts

[2.5]

and


58 where ts is the time at which the bacterial population reaches the stationary phase. In that case, eqn 2.3 defines the well-known tri-linear model or three-phase linear model (Buchanan et al., 1997) and can be written as:         

dx –– = 0 dt dx –– = µmaxx dt dx –– = 0 dt

if t ≤ lag if t > lag and t ≤ ts

[2.6]

if t > ts

To describe the slowing down of the bacterial population u(t,x) at the end of the growth phase, Baranyi and Roberts (1994) proposed the use of a logistic inhibition function: [1 – (x/xmax)], where xmax represents the maximum cell density achieved. Then, assuming the same definition for α(t) as in eqn 2.4, eqn 2.3 can be written as:       

dx –– = 0 dt  dx x  –– = µmax  1 – –––– x dt xmax  

t ≤ lag [2.7] t > lag

This equation was first presented by Jason (1983) and was represented as the ‘logistic model with delay and rupture’ by Rosso et al. (1996). The model of Baranyi and Roberts (1994) introduced a gradual adjustment to the environment during the lag time. The model is defined by the following set of differential equations:       

dx –– = µmaxq dt  dx q(t) x  –– = –––––– µmax  1 – –––– x dt 1 + q(t) xmax  

[2.8]

where q(t) is a measure of the mean physiological state of the cells at time t and with the initial conditions: x(0) = x0 and q(0) = q0. Note that the lag time does not appear directly in eqn 2.8. In this model, the lag is a derived parameter, and it is affected by the actual environment and q0, the mean initial physiological state of the cells (Baranyi and Pin, 2004). The quantity q(0) = q0 depends on the pre-history of the cells. As the cells adapt to their actual environment, q(t) increases exponentially at a rate assumed to be the same as the specific rate that the cells exhibit in the exponential phase under the same conditions. The factor q(t)/[1+q(t)] in the second equation describes the gradual


59 adaptation of the cells to their new environment and plays the same role as α(t) in eqn 2.4. Thus, α0 = q0/(1 + q0) is another quantification of the initial state of the bacterial population rescaled between 0 and 1. Like q0, it expresses the suitability of the cells to grow in the new environment: if its value is 0, then the lag time is infinite; if α0 = 1, then growth will start immediately, without lag. To make the curve-fitting procedure more stable, Baranyi and Roberts (1994) proposed a transformation of q0:  q0  = – ln(α0) h0 = – ln  ––––––  1 + q0 

[2.9]

The authors demonstrated mathematically that h0 is the product of the lag time and maximum specific growth rate (Baranyi and Roberts, 1994): h0 = µmax • lag

[2.10]

This parameter describes an interpretation of the lag first formalized by Robinson et al. (1998). Using the terminology of Robinson et al. (1998) h0 may be regarded as the ‘work to be done’ by the bacterial cells to adapt to their new environment before commencing exponential growth at the rate µmax, characteristic of the organism and the environment. The duration of the lag, however, also depends on the rate at which this work is done, which is often assumed to be µmax. Stresses (e.g. thermal, osmotic, acid) can, however, significantly increase the amount of work to be done by the bacteria, while less optimal environments decrease the rate at which that ‘work’ is completed. This treatment is valid as long as the work to be done does not increase as the environment becomes less optimal or more stressful. It has been demonstrated, however, that this is not always true as will be discussed in section 2.4.1. Nonetheless, as we will see below, these concepts of ‘initial physiological state’ and ‘work to be done’ are key elements that will enable integration of the effects of stress into predictive microbiology models for microbial growth.

2.4

Secondary modelling: growth rate modelling

Secondary models relate the effect of environmental factors on primary model parameters such as bacterial growth rate and lag time. Initially, most studies dealt with the effect of the environmental conditions on the specific growth rate of the microorganisms because this is generally considered an intrinsic characteristic of an organism in a specific environment. More recently, systematic studies on the effect of environmental changes on lag time or inactivation rates have begun (e.g. Mellefont and Ross, 2003; Mellefont et al., 2003, 2004). This section describes the most commonly used classes of models applied within predictive microbiology. We can distinguish between two different approaches. The first approach consists of describing simultaneously the effects of the environmental factor through a polynomial function. This method has probably been the most extensively used within predictive microbiology. In the second approach, environmental factors are individually modelled. A general model


60 describing the combined effects of the factors is elaborated from these individual modules. This approach is notably applied in the development of the increasingly popular square-root and cardinal parameter-type models.

2.4.1 Polynomial models Polynomial models describe the growth responses (µmax, lag) or their transformations (sqrt(µmax), ln(µmax), …) through a polynomial function. An example of a polynomial model is that proposed by McClure et al. (1993), for Brochothrix thermosphacta: ln(µmax) = a0 + a1T + a2pH + a3(%NaCl) + a4T • pH + a5T(%NaCl) + a6pH(%NaCl) + a7T 2 + a8pH 2 + a9(%NaCl)2

[2.11]

Polynomial models allow, in almost every case, the development of a model describing the effect of any environmental factor to be taken into account, including interactions between factors. Moreover, the fit of polynomial models does not require the use of advanced techniques such as non-linear regression. The disadvantages of polynomial models lie in the high number of parameters and their lack of biological significance.

2.4.2 Square-root type models Temperature was the first environmental factor taken into account in square-root type models. Based on the observation of a linear relationship between temperature and the square root of the bacterial growth rate in the suboptimal temperature range, Ratkowsky et al. (1982) proposed the following function: √µmax = b(T – Tmin)

[2.12]

where b is a constant, T is the temperature and Tmin is the theoretical minimum temperature for growth. This equation was extended to describe the effect of temperature in the entire temperature range allowing bacterial growth, the socalled ‘biokinetic range’ (Ratkowsky et al., 1983; see Fig. 2.3): √µmax = b(T – Tmin){1 – exp[c(T – Tmax)]}

[2.13]

where Tmax is the theoretical maximum temperature for growth and c is a model parameter without biological meaning. These models were extended to take into account other environmental factors such as pH and water activity. McMeekin et al. (1987) proposed the following equation to describe the combined effects of temperature (suboptimal range) and water activity on the growth rate of Staphylococcus xylosus: √µmax = b(T – Tmin)√(aw – aw min)

[2.14]

where awmin is the theoretical minimum water activity for growth. More recently,


61

Fig. 2.3 Simulation of equation describing the effect of temperature on the square root of the bacterial growth rate. Model parameter: b = 0.03, Tmin = –2 ºC, Tmax = 45 ºC, c = 0.2.

square-root models have also been expanded to include, for example, the effects of CO2, (Devlieghere et al., 1998) or pH and lactic acid concentration (Presser et al., 1997; Ross et al., 2003). 2.4.3 The Gamma concept Zwietering et al. (1992) proposed a model called the ‘Gamma model’, describing the growth rate relative to its maximum value at optimal conditions for growth:

µmax = µoptγ (T)γ (pH)γ (aw)

[2.15]

where µopt is the growth rate at optimum conditions, and γ (T), γ (pH), γ (aw) are the relative effects of temperature, pH and water activity, respectively. The concept underlying this model (‘Gamma concept’) is based on the following assumptions: • The effect of any factor on the growth rate can be described as a fraction of µopt, using a function (γ ) normalized between 0 (no growth) and 1 (optimum condition for growth). • The environmental factors act independently on the bacterial growth rate. Consequently, the combined effects of the environmental factors can be obtained by multiplying the separate effects of each factor (see eqn 2.15). Ross and Dalgaard (2004) considered that while this apparently holds true for growth rate under conditions where growth is possible, environmental factors do interact synergistically to govern the biokinetic ranges for each environmental factor. At optimal conditions for growth, all γ terms are equal to 1 and therefore µmax is equal to µopt. The γ terms proposed by Zwietering et al. (1992) for the normalized effects of temperature, pH and water activity are given in eqns 2.16 to 2.18.  T – Tmin  2 γ (T) =  –––––––   Topt – Tmin 


[2.16]

62 pH – pHmin γ (pH) = –––––––––– pHopt – pHmin

[2.17]

aw – aw min γ (aw) = ––––––––– 1 – aw min

[2.18]

γ -Type terms for pH and lactic acid effects on growth rate were also included in square-root type models by Presser et al. (1997) and Ross et al. (2003). Introduced by Rosso et al. (1993, 1995), the cardinal parameter models (CPMs) were also developed according to the Gamma concept. The relative effects of temperature, pH and water activity on the bacterial growth rate are described by a general model called CMn: 0

X≤X

0

X ≥ Xmax

min  (X – Xmax)(X – Xmin)n  CMn(X) =  ––––––––––––––––––––––––––––––––––––––––––––––––––Xmin< X < Xmax  (X –X )n–1{(X –X )(X–X )–(X –X )[(n–1)X +X –nX)]} opt min opt opt max opt min  opt min [2.19]

where X is temperature, pH or water activity. Xmin and Xmax are, respectively, the values of X below and above which no growth occurs. Xopt is the value of X at which bacterial growth is optimum. n is a shape parameter. As for the ‘Gamma model’ of Zwietering et al. (1992), CMn(Xopt) is equal to 1, CMn(Xmin) and CMn(Xmax) are equal to 0. For the effects of temperature and pH, n is set to 2 and 1, respectively (Augustin and Carlier, 2000a,b; Le Marc et al., 2002; Pouillot et al., 2003; Rosso et al., 1993, 1995). Figure 2.4 shows the effect of pH on the bacterial growth rate as described by the cardinal pH model. For the effects of water activity, n is set to 2 (Augustin and Carlier, 2000a,b; Rosso and Robinson, 2001) or alternatively to 1 (Le Marc, 2001). The combined effects of the environmental factors are also obtained by multiplying the relative effects of each factor. Thus, the CPM for the effects of temperature, pH and water activity on µmax can be written as:

µmax = µopt CM2(T)CM1(pH)CM2(aw)

[2.20]

or alternatively:

µmax = µopt CM2(T)CM1(pH)CM1(aw)

[2.21]

In many ways, CPMs resemble the square-root model. Responses predicted by the two types of models can be almost identical (Oscar, 2002; Ross and Dalgaard, 2004; Rosso et al., 1993, 1995). The advantages of the CPMs lie in the lack of structural correlation between parameters and the biological significance of all parameters (Rosso et al., 1995). Several attempts have been made to include in CPMs the effects of organic acids (Augustin and Carlier, 2000a,b; Coroller et al., 2003; Le Marc et al., 2002) or other inhibitory substances (Augustin and Carlier, 2000a,b).


63

Fig. 2.4 Simulation of the model µmax= µopt*CM1(pH) describing the effect of the pH on the bacterial growth rate. Model parameters: µopt = 1 h–1, pHmin = 4; pHopt = 7, pHmax = 9.

2.4.4 Artificial neural networks ‘Black boxes’ such as artificial neural networks are an alternative to the models above and have been used to develop secondary growth (or lag time) models (Garcia-Gimeno et al., 2002, 2003; Geeraerd et al., 1998; Najjaar et al., 1997). For further details on the principles of this method in the context of predictive microbiology, consult Hajmeer et al. (1997) and Hajmeer and Basher (2003a,b). To date, few attempts have been made to integrate the effect of stress in the models by using this modelling technique (Cheroutre-Vialette and Lebert, 2000; CheroutreVialette et al., 1998).

2.5

Secondary modelling: lag time models

As previously said, predictive microbiology studies have mainly focused on growth rate modelling. Comparatively less work has been done on lag time modelling. When lag time models are developed, predictions are usually poor (Baranyi, 2002; McKellar et al., 1997). One reason for this is that the bacterial lag time depends not only on the actual environmental factors but also on preincubation conditions and on the physiological history of the cells (Baranyi et al., 1995; McMeekin et al., 1993; Mellefont and Ross, 2003; Swinnen et al., 2003). There is usually a lack of knowledge of the physiological state of bacterial cells in a food product. It may vary from cells in exponential phase to cells damaged but under repair, or damaged but unable to reproduce because of the ineffectiveness of repair (McMeekin et al., 1997). In order to obtain safe predictions, the lag time can be set to zero in simulations. This corresponds to a ‘worst case’ situation and leads to ‘fail-safe’ predictions, but might not correspond to realistic situations encountered in the food industry. Two different approaches have been applied to develop lag time models: models where lag time is modelled independently from the growth rate and lag


64 time models which are derived from the growth rate model. In the first approach, the methodology used is the same as the one applied for the growth rate modelling. Lag times are derived from bacterial curves by fitting a primary model to growth curves. The obtained lag times are then related to the environmental factors through a secondary model. The inconvenience of this approach is that lag time is only related to the environmental factors. If pre-incubation conditions vary, then lag times may deviate significantly from the model predictions. In the following section, we will focus on the second approach, which is based on the assumption of proportionality between the lag time and the inverse of the growth rate.

2.5.1 Relative lag time Many authors (e.g. Baranyi and Roberts, 1994; Chandler and McMeekin, 1985; Cooper, 1963; Fu et al., 1991; Rosso, 1995; Smith, 1985) observed that for the same pre-incubation conditions, lag times are inversely proportional to growth rates (thus proportional to generation times):

µmax • lag = K

[2.22]

where K is a constant assumed to depend only on the bacterial strain and cell history. This observation supports the assumption that to adapt to their new environment, cells have to perform a certain amount of work (depending on the physiological state of the cells) and that the rate at which that work is performed is related to the maximum specific growth rate (µmax). According to this assumption, for example, the lag is longer at low temperature, not because of an increase of the amount of work the cells have to carry out before they can divide, but because this work is carried out more slowly (Baranyi and Pin, 2004). This relation between lag times and growth rates (eqn 2.22) has been expressed in various manners by researchers. Mellefont and Ross (2003) proposed the concept of a relative lag time (RLT) defined by the ratio of the lag and the generation time for a same cell history. RLT reflects the physiological state of microorganisms introduced into a new environment as well as the difference between their actual and previous environment, and can be interpreted as the amount of work cells have to do to change their physiology to be able to grow in their new environment (Ross and Dalgaard, 2004): lag –––––– = RLT Tgeneration

[2.23]

By replacing the generation time in eqn 2.23 by ln(2)/µmax, it can be seen that the RLT is another parameterization of the h0 parameter of the Baranyi model:

µmax • lag –––––– = RLT, µmax • lag = h0 = RLT ln(2) ln(2)


[2.24]

65

Fig. 2.5 Simulation of the Growth Predictor for the growth of E. coli at 20 ºC, pH 6 in the presence of 3 % NaCl. Simulations were performed for different α0 values: α0 = 0.1 (solid line), α0 = 0.01 (dotted line) and α0 = 0.001 (dashed line).

Rosso (1995) expressed the same concept through the definition of a ‘minimum lag time’. At optimum conditions for growth, the growth rate is optimum and eqn 2.22 implies that the lag time is at a minimum value. Therefore, this equation can be written as:

µmax • lag = K = µopt • lagmin

[2.25]

Thus, the lag time model can be deduced from the growth rate model by the following equation:

µop • lagmin lag = –––––––– µmax

[2.26]

Numerous authors (Alavi et al., 1999; Mellefont and Ross, 2003; Mellefont et al., 2003; Robinson et al., 1998) showed the limits of the assumption of a constant ratio between the lag time and the inverse of the growth rate. Ross and Dalgaard (2004) stressed that this assumption is more relevant to changes in environmental temperature. When additional variables such as NaCl or low pH are to be considered, Robinson et al. (1998) found that the observed lag time is longer than expected according to the assumption of a constant RLT, as might be expected from the physiological changes required to adjust to acid or osmotic stress as discussed elsewhere in this book. Nonetheless, although it should be used with caution, the assumption of a constant RLT appears to be useful to create lag time models (Ross and Dalgaard, 2004). 2.5.2 Conclusion Accurate predictions of lag time are a key factor to the quality of predictions in predictive microbiology. In order to illustrate this, Fig. 2.5 shows different


66

Fig. 2.6 Schematic representation in two dimensions of the space of the environmental variables as divided into the ‘Model’ (M), ‘Growth’ (G), ‘Uncertainty’ (U) and ‘No growth’ (NG) regions.

predictions of the growth of E. coli obtained from the Growth Predictor software (see www.ifr.ac.uk/safety/growthpredictor) for the same environmental conditions but with different values for the ‘initial physiological state’ (α0) of the inoculum. As previously discussed, the α0 value is a parameter describing the initial physiological state of the cells; the more stressed the bacterial population, the closer to zero the α0 value is and the longer the lag time. An α0 value of 0.001 leads to a 1 log increase within 21 days. But for α0 values of 0.01 and 0.1, the predicted log increases are 1.9 and 2.9, respectively. Therefore, providing a realistic input for the initial physiological state of the population is a key point for accurate predictions, especially when the initial bacterial population has been submitted to a stress.

2.6

Validation and prediction of bacterial growth

2.6.1 Interpolation region Predictions from models should not be made beyond their interpolation region unless the models have a strong mechanistic basis (Draper and Smith, 1981). Baranyi et al. (1996) proposed a definition of the interpolation region through the concept of a minimum convex polyhedron (MCP) of the experimental design.


67 According to this definition, the interpolation region is the minimum polyhedron that would encompass all the combinations of environmental variables specifically tested to develop the model. The further that a combination of environmental conditions is outside of the MCP, the more likely it is that the prediction will deviate from the observation. That concept was extended by Le Marc et al. (2004) to define ‘Growth’, ‘Uncertainty’ and ‘No growth’ regions (Fig. 2.6).

2.6.2 Bias and accuracy factor Ross (1996) proposed a technique to evaluate the reliability of predictive models through two indices of performance: the bias and accuracy factors: bias factor = 10Σlog(ypred/yobs)/n

[2.27]

bias factor = 10Σlog(ypred/yobs)/n

[2.28]

where y is the studied response (i.e. generation time, lag time, growth rate,…) and n the number of observations. Perfect agreement between the predictions and observations leads to a bias and an accuracy factor of 1. Bias factors of 0.9 and 1.1 indicate that the model respectively underestimates and overestimates the observation on average by 10 %. As individual over and underestimations may cancel out (leading to a bias factor of 1), Ross (1996) suggested the accuracy factor (eqn 2.28) to be used conjointly with the bias factor. An accuracy factor of 1.2 indicates that the predictions are on average 20 % larger or 20 % smaller than the observations. Note that the bias and accuracy factors were refined by Baranyi et al. (1999).

2.6.3 Simulation of bacterial growth in fluctuating conditions Predictions in fluctuating conditions are usually obtained by resolving the differential equations that define the primary model. Several examples can be found in the literature (e.g. Baranyi et al., 1995; Bovill et al., 2000; Rosso, 1995). For example, to validate the FoodMicroModel growth model for L. monocytogenes and Salmonella in fluctuating temperature, Bovill et al. (2000) resolved the Baranyi model according to changes of temperature (Fig. 2.7):       

dq –– = µmax[T(t),pH,aw]q dt  dx q(t) x  –– = –––––– µmax[T(t),pH,aw]  1 – ––––  x dt 1 + q(t) xmax  

[2.29]

In eqn 2.29, the maximum growth rate µmax depends on the pH and water activity and the dynamically changing temperature T(t). In this method, it is implicitly assumed that µmax varies instantaneously with the change of the temperature. In


68

Fig. 2.7 Simulation of the growth of L. monocytogenes in pâté in fluctuating temperatures and comparison with experimental plate counts. (Adapted from Bovill et al., 2000.)

particular, it is assumed that changes in the environmental conditions do not induce an intermediate lag time caused by the stress due to the change of environmental factors. While this assumption seems to be valid in the case of smooth changes, several authors reported induced lag times when sudden and abrupt changes were applied during the growth (Baranyi et al., 1995; Swinnen et al., 2003; Zwietering et al., 1994). This point will be discussed in further detail in section 2.7.3.

2.7

Modelling the effects of stress

Bacterial cells exposed to sublethal stress often require an adaptation or a recovery period, prior to recommencement of exponential growth. Most of the models for microbial growth have been developed using uninjured and unstressed cells. This allows the predictive models to deal with a ‘worst case’ situation and is supposed to provide ‘fail-safe’ predictions but might not be relevant for many situations encountered in the food industry. Conversely, models for microbial inactivation have usually used cells pre-adapted to stressful conditions to ensure ‘fail-safe’ predictions of survival. The duration of the recovery period after stress depends on a number of factors: species, the nature of stress, its intensity and duration. The physiological state of the cell population before stress and its adaptation to stress also play a role. Both complexity of the issue and implications of resistance can be shown in the results of O’Driscoll et al. (1996) who demonstrated that L. monocytogenes cells which have developed an acid tolerant response (ATR) also showed an increased resistance to osmotic or thermal shock. Because of the complexity of the problem


69 and our poor understanding of the mechanisms of adaptation, resistance and repair, it is not surprising that most mathematical models do not incorporate the influence of stress. Nevertheless, several models have attempted to take into account the effect of cell history (e.g. Augustin et al., 2000c; Bréand et al., 1997, 1999). Although some results seem to indicate that stress may influence subsequent growth rates (Cheroutre-Vialette et al., 1998; Membré et al., 1999; Walker et al., 1990), it is usually generally agreed that the maximum specific growth rate depends on environmental factors but is independent of cell history. Consequently, most of the studies have dealt with the effect of stress on bacterial lag time. Although many studies have focused on the effect of stress on lag times of a bacterial population, recently the distribution of individual lag times after stress was also considered. In this section, techniques used within predictive microbiology to incorporate history into predictions and an overview of published results and models will be presented.

2.7.1 Estimating the physiological state of the bacterial population As discussed in the previous section, the lag duration depends both on the actual environmental factors and the initial physiological state of the cells. One technique to take into account the stress in the model predictions consists in evaluating experimentally this ‘physiological state’ of the surviving population after stress. This can be done by fitting reference growth curves obtained after stress to estimate the RLT, h0 or lagmin. For example, once the lag and the maximum growth rate are calculated, h0 can be deduced by eqn 2.8. The estimated values are then introduced in the models in order to predict the lag time induced by the stress. An example of this technique illustrates the prediction of the subsequent growth of C. perfringens in bulked meat during cooling. The ‘work to be done’ (h0) before cell division after various heating processes typical of those encountered in the industry was estimated by fitting the model of Baranyi and Roberts (1994) to experimental growth curves. For the heating processes tested, the same h0 value of 6.9 was found to be suitable to describe this ‘work to be done’. This value for h0 was combined with the growth rate model of Plowman et al. (2004) to predict the kinetics of C. perfringens during cooling. Comparison between observations and predictions are proposed in Fig. 2.8. This work was part of the UK Food Standards Agency project B14009 aimed at producing and validating a user-friendly software tool to describe the kinetic behaviour of C. perfringens during the cooling of bulked meats. Although the initial ‘physiological state’ of cells can be experimentally estimated, the technique can be costly and tedious. If the stress applied is modified, so might be the ‘physiological state’ of the microorganisms at the end of stress. Therefore, this ‘physiological state’ should theoretically be experimentally reestimated each time the stress process is modified. The alternative to the experimental estimation of the ‘work to be done’ is the development of models describing the effects of stress (its nature, length and intensity) and the ‘work to be done’ prior to re-growth.


70

Fig. 2.8 Comparison between experimental data and simulations of the kinetics of C. perfringens in bulked meat under different cooling profiles. The bacterial growth was simulated according to the secondary model of Plowman et al. (2004) by using the primary model of Baranyi and Roberts (1994). To describe the ‘work to be done’, an h0 value of 6.9 was used, derived from previous experiments in meat. Each of the lines represents predictions for different starting concentrations of spores (high, medium, low); and circles, squares and diamonds represent experimental observations for those respective spore concentrations (respectively high, medium, low).


71 2.7.2 Modelling the effects of heat treatment conditions on the time prior to re-growth Bréand et al. (1997, 1999) proposed a model to describe the effect of temperature of stress and stress duration on the lag time of L. monocytogenes which survived a mild heat treatment. As expected, the authors found that that the higher the stress temperature and the longer the stress duration, the smaller the number of surviving cells. More surprisingly, the authors exhibited a two-phase relationship between the length of stress and lag time. First, lag time increases with stress duration and then decreases to a steady threshold value. Note that similar results were obtained by Laurent et al. (1999) for spores of Bacillus cereus and Mellefont et al. (2003) for several Gram-negative organisms. These results imply that, in terms of food safety, lengthening stress duration is not automatically the best procedure to ensure the safety of a product (Bréand et al., 1997).

2.7.3 Intermediate lag time and normal physiological range Fluctuations in the environmental conditions that occur during the growth should theoretically require a certain ‘work to be done’ prior to re-growth and therefore induce an intermediate lag time prior to re-growth. However, predictions in dynamic conditions are usually based on the assumption of an instantaneous adaptation of the microorganisms to the new conditions. In other words, the current models are unable to describe any intermediate lag time. This assumption of an instantaneous adaptation seems relevant when changes in environmental conditions are smooth, but abrupt changes during bacterial growth may result in an intermediate lag time (Zwietering et al., 1994; Baranyi et al., 1995; Swinnen et al., 2003). The first attempt to integrate the effect of magnitude of temperature change in the predictive models was made by Zwietering et al. (1994) assuming that the length of the intermediate lag phase is a quarter of the lag time normally found at the post-shift temperature. From a more mechanistic point of view, the existence of these transient growth periods has been related to the notion of normal physiological range (NTPR, Ng et al., 1962). The normal physiological range is defined as the temperature range within which the bacterial cells can adapt themselves to a new environment without additional lag time. From the results of Ng et al. (1962), if incubation temperature shifts within the normal physiological temperature range, no transient period (i.e. induced lag) is expected. However, shifts from within the normal physiological range to temperatures either above or below that normal range (or vice versa) induce an intermediate lag time. Experimental results of various researchers (e.g. Mellefont et al., 2003; Swinnen et al., 2003) are consistent with the normal temperature range concept except that RLTs associated with temperature upshifts were less than those associated with equivalent temperature downshifts. For example, the NTPR for Escherichia coli ranges approximately from 20 to 37°C. Swinnen et al. (2003) noticed that a shift from 15 to 25°C (initial temperature outside the NTPR) induces an intermediate lag time but that a shift of the same amplitude within the NTPR (25–35 °C) does not result in any intermediate lag time.


72 The NTPR is usually defined as the ‘linear region’ of the Arrhenius plot (ln(µmax) versus 1/T). The determination of the lower and upper limits of the NTPR is often made ‘by eye’ and may vary from one to another depending on what researchers consider as the ‘linear region’ of the Arrhenius plot. To define the NTPR, Mellefont et al. (2003) proposed a procedure which consists first of fitting growth rates obtained at different temperatures to an Arrhenius-type model (McMeekin et al., 1993; Ratkowsky et al., 1995; Ross, 1997). Then, the boundary of the normal physiological temperature range is arbitrarily defined by the upper and lower limits of the temperature at which the growth rate deviates by 30 % from a purely Arrhenius plot. Although arbitrary, this definition allows a reproducible and objective method to determine the NTPR (Mellefont and Ross, 2003). While no concept such as a normal physiological temperature range has been formally proposed for other environmental factors (e.g. pH, water activity,), it was shown that the limits of the normal physiological temperature range depend on the level of these factors. For example, Krist et al. (1998) found that the normal temperature range was narrower at low water activity than at high water activity. The concept of normal temperature range should be integrated into models to determine whether an intermediate lag time should be predicted or the assumption of an instantaneous adaptation of the microorganisms is sufficient to describe the bacterial growth. Difficulties in modelling the effects of amplitudes of change on intermediate lag times are numerous: the mechanisms involved are complex, there are numerous factors that should be taken into account (amplitude of change, precedent and actual environmental conditions…). Moreover, the normal physiological temperature range varies according to the level of other factors. Nevertheless, there is little doubt that models will be proposed in the near future.

2.7.4 Effect of stress on single cell lag times Baranyi (1998) demonstrated that deterministic models ignoring the variability among single cells are not suitable to describe the lag time of populations consisting of fewer than some hundred cells. In this respect, as food (when contaminated) is usually contaminated with low numbers of pathogens, the development of models describing the lag time of single cells is of particular interest (Baranyi, 1998; Francois et al., 2003). In recent years, the distribution of individual lag times has been increasingly studied (e.g. Elfwing et al., 2004; Francois et al., 2003; Guillier et al., 2004; Métris et al., 2003; Robinson et al., 2001). These studies were facilitated by the use of new techniques for observing bacterial growth including time to turbidimetric detection and ‘flow chambers’. Guiller et al. (2004) studied the effect of nine stress treatments applied within the food industry including organic acids, heat shock, starvation and alkaline shocks. The mean of the variability of individual lag times was found to increase with the mean of single cell lag times. This is a general trend also observed in other studies. Métris et al. (2003) showed that the mean and the variability of individual lag times both increase with the intensity of acid and osmotic stress. Results of


73 Elfwing et al. (2004) suggest a linear increase of the mean and the variance of individual lag time of L. innocua cells with the duration of sublethal heat shock. Gamma distributions (Métris et al., 2003; Francois et al., 2003) or normal distributions (Francois et al., 2003) were usually found appropriate to fit the experimental distributions of bacterial cells’ lag times. So far, no predictive model has been proposed to relate the intensity or length of stress to the parameters (mean, variance) of the Gamma or normal distributions experimentally observed. Again, there is little doubt that models will soon be developed.

2.8

Conclusions

In this chapter, some of the main techniques currently used in predictive microbiology are discussed. Until recently, studies were focused mostly on the predictions of the bacterial growth rate. As they are usually developed with unstressed cells, growth rate models do not take into account the effects of stress on bacterial population growth kinetics. However, as shown in this chapter, the effect of stress on the bacterial lag time can be taken into account through parameters related to the physiological state of cells after shock (h0, α0, RLT or lagmin). Moreover, recent studies such as those involving stochastic modelling of single cell lag times, systematic studies of the effect of ‘environmental shock’ and measures of sublethal injuries on lag times, should result in a new generation of models that can predict the effects on growth of stress and injury within bacterial populations.

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77 ROSS T (1996), Indices of performance evaluation of predictive models in food microbiology. J Appl Bacteriol, 81, 501–8. ROSS T AND DALGAARD P (2004), Secondary models. In: Modelling Microbial Responses in Food. McKellar R C, Lu X (Eds), CRC Press, Boca Raton, FL. ROSS T, RATKOWSKY D A, MELLEFONT L A AND MCMEEKIN T A (2003), Modelling the effects of temperature, water activity, pH and lactic acid concentration on the growth rate of Escherichia coli. Int J Food Microbiol, 82, 33–43. ROSSO L (1995), Modélisation et microbiologie prévisionnelle: Elaboration d’un nouvel outil pour l’agro-alimentaire. Thèse. Université Claude Bernard, Lyon I, France. ROSSO L AND ROBINSON T P (2001), A cardinal model to describe the effect of water activity on the growth of moulds. Int J Food Microbiol, 63, 265–73. ROSSO L, LOBRY J R AND FLANDROIS J P (1993), An unexpected correlation between cardinal temperatures of microbial growth highlighted by a new model. J Theor Biol, 162, 447–63. ROSSO L, LOBRY J R, BAJARD S AND FLANDROIS J P (1995), A convenient model to describe the combined effects of temperature and pH on microbial growth. Appl Environ Microbiol, 61, 2, 610–6. ROSSO L, BAJARD S, FLANDROIS J P, LAHELLEC C, FOURNAUD J AND VEIT P (1996), Differential growth of Listeria monocytogenes at 4 and 8 ºC: consequences for the shelf life of chilled products. J Food Prot, 59, 944–9. SMITH M G (1985), The generation time, lag time and minimum temperature for growth of coliform organisms on meat, and the implications for codes of practice in abattoirs. J Hyg (Camb.), 94, 289–300. SWINNEN I A M, BERNAWERTS K, GYSEMANS K AND VAN IMPE J F M (2003), Quantifying microbial lag phenomena due to a sudden rise in temperature: a systematic study. In: Predictive Modelling in Foods – Conference Proceedings, Van Impe J F M, Geerarerd A H, Leguerinel I, Mafart P (Eds), Kathoelieke Universiteit Leuven/BioTec, Belgium, pp. 212–214. WALKER S, ARCHER P AND BANKS J B (1990), Growth of Listeria monocytogenes at refrigeration temperatures. J Appl Bacteriol 68, 157–62. WHITING R C AND CYGNAROWICZ-PROVOST M (1992), A quantitative model for bacterial growth and decline. Food Microbiol, 9, 269–77. ZWIETERING M H, DE WIT J C AND VAN’T REIT K (1992), A decision support system for prediction of the microbial spoilage in foods. J Food Prot, 55, 973–9. ZWIETERING M H, DE WIT J C, CUPPERS H G A M AND VAN’T REIT K (1994), Modeling of bacteriaI growth with shifts in temperature. Appl Environ Microbiol, 60, 204–13.


78

3 The development of pathogenicity C. Gyles, University of Guelph, Canada

3.1

Introduction

Bacteria were present on earth over 3 billion years ago, long before animals. Their adaptability is legendary as they have been recovered from a wide range of extreme environments and they have been known to survive in a resting state for as long as 250 million years. One of the environments to which some bacteria have become adapted is the human body. In most cases bacteria and their human host have a mutually beneficial relationship, but in a small percentage of cases bacterial activity results in harm to the host. Much research has been conducted on these pathogenic bacteria and the mechanisms by which they have acquired the ability to alter host physiology. Pathogen evolution has involved the complete range of known genetic changes in bacterial genomes – point mutations, insertions, and deletions of DNA fragments, and plasmid transfer (Table 3.1) (Dobrindt and Hacker, 2001). Pathogenicity refers to the ability of a species of bacteria to cause disease, and virulence refers to the degree of pathogenicity of strains or subgroups within a species. The acquisition of new DNA by horizontal transfer through transduction, conjugation, or transformation is probably the most significant contributor to rapid bacterial evolution towards pathogenicity and increased virulence. The presence of mutator strains in bacterial populations adds to the arsenal of mechanisms for rapid evolution. The recognition of massive variation within species attributable to insertions of large blocks of genes is, in fact, challenging the traditional concept of species and clonality among bacteria (Escobar-Paramo et al., 2004). Changes in the food industry impact on pathogen development by selection pressure, by


79 Table 3.1

Mechanisms of genetic adaptation

Mechanism

Significance

Point mutation Homologous recombination

Change in DNA sequence, revertible Integration of horizontally acquired genes, rearrangement of DNA, deletion of DNA, inversion, duplication Uptake of DNA from the organism’s environment Horizontal transfer of genes, transfer of plasmids, transfer of chromosomal DNA, transfer of conjugative transposons Horizontal gene transfer by bacteriophages Insertion, deletion, altered gene expression, DNA elements inversion Rearrangement of DNA, gene transfer Horizontal transfer of large blocks of DNA

Transformation Conjugation Transduction Movement of transposons or insertion sequence (IS) Integron activity Insertion of pathogenicity island Adapted from Dobrindt and Hacker (2001).

facilitating transfer of DNA from one organism to another, and by transfer of pathogens from the food processing environment to susceptible hosts. This latter element, transmissibility, may be a key element in the survival strategy of pathogenic bacteria (Kingsley and Baumler, 2000; Anita et al., 2003; Ewald, 2004).

3.2

Pathogenicity and virulence

Horizontally transferred DNA has contributed significantly to both pathogenicity and virulence, and genome analyses are aiding our understanding of these developments. For example, the sequences of the genomes of two strains of O157:H7 Escherichia coli have identified the existence of a large number of insertions compared with the genome of E. coli K12 (Hayashi et al., 2001; Perna et al., 2001). The basic gene order of the genomes of the O157:H7 and K12 organisms is conserved and the two organisms share 4.1 megabase (Mb) of DNA. Superimposed on this core are a large number of strain-specific insertions and deletions. The E. coli O157:H7 genome has almost 1 Mb more DNA than E. coli K12 and is characterized by insertions having atypical base composition and called ‘O-islands’ (Perna et al., 2001). Approximately 180 of these islands have been described, and many of these have been identified as pathogenicity islands (PAIs) that encode established or putative virulence factors. Considerable variation has been recognized among strains of O157:H7 E. coli, and much of this variation has been attributed to insertions and deletions (Kudva et al., 2002). The genome sequence for a uropathogenic E. coli has confirmed the themes identified in the comparison of the O157 and K12 genomes, namely that there is a conserved core that encodes predominantly housekeeping functions and that the pathogenic E. coli possesses additional gene clusters that were acquired from foreign sources and are responsible for pathogenicity (Welch et al., 2003). There is strong evidence that virulence confers some advantage to at least some


80 bacteria. One line of evidence in support of this contention is that evolution from non-pathogenic to pathogenic E. coli has occurred in parallel manner in both enteropathogenic E. coli (EPEC) and enterohemorrhagic E. coli (EHEC) (Reid et al., 2000). Investigations and analyses by these researchers have shown that the same virulence genes have repeatedly been acquired sequentially at different times and places – indicating that the orderly acquisition of virulence genes confers some selective advantage on the E. coli that acquire them. A similar situation exists in Yersinia, in which the high pathogenicity island (HPI) has been acquired independently by Y. pestis and Y. pseudotuberculosis (Buchrieser et al., 1998; Hare et al., 1999). HPI has been disseminated to other pathogens including several pathotypes of E. coli (Schmidt and Hensel, 2004). Although bacteria such as EHEC O157:H7 have been shown to evolve towards increasing virulence (Reid et al., 2000), there is considerable debate and a great deal of uncertainty about the advantages gained by many bacteria as a consequence of their being pathogenic. In the case of enteric pathogens in which diarrhea is a common feature, copious volumes of fluid feces containing large amounts of the bacterium contribute to spread to new hosts. This is evident, for example, in outbreaks of salmonellosis or E. coli O157:H7 infections in which person-toperson spread occurs following ingestion of contaminated food or water. In some pathogens, such as certain Salmonella, the opportunity to establish the carrier state in a percentage of infected individuals ensures long-term survival and continuing transmission to new hosts.

3.3

Genetic exchange and the development of pathogenicity

The genetic changes that result in pathogenicity or increased virulence fall into one of two categories, namely, changes in existing bacterial DNA and acquisition of new DNA from other microorganisms. Changes in the bacterium’s DNA typically result in small changes that accumulate over a long period of time, although recombination events can sometimes cause large changes to occur. Mutations are highly significant in bacteria because their high numbers and short generation times allow for mutations to occur at relatively high frequency. Acquisition of foreign DNA, on the other hand, often involves addition of large amounts of DNA that can rapidly and profoundly affect the ability of bacteria to cause disease. Opportunities for acquiring foreign DNA depend on the availability of the DNA and the bacterial mechanisms for receiving the DNA. Thus, another potential value of enteric pathogenicity is that it tends to bring large numbers of the pathogen into contact with the massive pool of bacterial DNA present in the intestine. This provides for acquisition of a large variety of genes and subsequent selection of organisms that have acquired genes that enhance survival and/or transmissibility. The following sections examine the mechanisms by which foodborne bacterial pathogens alter their DNA in ways that contribute to pathogenicity. 3.3.1 Mutator strains Mutator strains develop mutations at rates that are much higher than those found in


81 other bacteria and thereby provide for accelerated rates of evolution. A background level of mutations occurs because of errors in DNA replication. This rate is low because of the many genes whose products review the accuracy of the process and correct errors. However, populations of bacteria include strains known as mutator strains, characterized by mutations in the genes that monitor and repair errors in DNA replication. In an impressive study published in 1996, LeClerc et al., showed that the frequency of hypermutable strains among a collection of E. coli and Salmonella strains ranged from 1.4 to 6.7 %. This rate is considerably higher than the 10–5 to 10–6 frequencies reported in laboratory strains of these species. The hypermutable strains had increases in mutation frequencies that were 100 to 1000 times greater than those of the controls. Seven out of nine of these mutators that were investigated had mutations in the mutS gene, resulting in defective methyldirected mismatch repair (MMR). The mutS mutants contribute to increased mutations in two ways. There is an increased rate in mutation of the DNA in the organism’s genome and there is an increased rate of acceptance of foreign DNA, because of inactivation of the MMR system that normally blocks recombination of non-homologous sequences (Matic et al., 1995; LeClerc et al., 1996; Horst et al., 1999; Kotewicz et al., 2003). It appears that there is positive natural selection for these mutator strains, presumably because of their greater capacity for adaptive mutation (Kotewicz et al., 2003). These mutators are proposed to contribute to rapid evolution through a series of acquisitions of advantageous traits and subsequent rescue from the mutS background by horizontal transfer of wild-type mutS genes (Kotewicz et al., 2003). This process is likely to be of particular benefit in environments that change rapidly and may lead to selection of mutations that permit the bacterium to evade or counter host defence mechanisms and/or take advantage of a new niche. This may be the reason that the frequency of mutator strains of E. coli and Salmonella is so much higher in natural populations than in laboratory strains (Li et al., 2003). Mutator strains are at some disadvantage, since deleterious mutations arise about 10 000 times as frequently as beneficial ones, and this phenomenon could drive the mutators to extinction (Li et al., 2003). Yet, the mutators occurred at a frequency considerably higher than that of deleterious mutations (LeClerc et al., 1996). These observations led Cebula’s laboratory (LeClerc et al., 1996; Kotewicz et al., 2003; Li et al., 2003) to postulate that the mutator state must be a temporary one that allows the bacterium first to acquire beneficial mutations, then avoid the disadvantages of the mutator state by loss or suppression of the mutator allele. The amounts of MutS in a wild-type E. coli O157:H7 and in the laboratory strain K12 were measured during growth in exponential, transitional, and stationary phases (Li et al., 2003). The amount of MutS in the stationary phase of both organisms was reduced compared with the levels in the exponential and transitional phases. However, the reduction for the O157:H7 culture was 26-fold, compared with 4.5fold for the K12 bacterium. The authors suggest that this may be a mechanism whereby the pathogen was predisposed to the advantages of the mutator phenotype during the stationary phase.


82 3.3.2 Recombination Recombination is critical for many of the significant changes in DNA that affect pathogenicity. Genes that are acquired by horizontal transfer consist of genes that have homology with genes in the organism and those that are foreign to the organism (Ochman, 2001). Genes with high homology tend to be donated by closely related organisms and to result in exchange with the existing gene. Genes foreign to the organism often result in new capabilities and are frequently the means by which rapid adaptation to new environments is achieved. These genes may be parts of plasmids or phage genomes. Genetic exchange is particularly important in the intestinal tract, in which there is a rich pool of DNA from a wide range of bacteria. It is therefore not surprising that enteric pathogens such as EHEC, Salmonella and Campylobacter jejuni have remarkable variability in their genomes. Evidence of extensive recombination is recognizable throughout chromosomal and plasmid DNA in these species. Improved methodologies have contributed to our understanding of the nature of the relationships among bacteria and of the way in which pathogens have evolved. Until recently, multilocus enzyme electrophoresis (MLEE) was the method used to assess clonality and relationships among bacteria (Ochman, 2001). This technique involves comparison of electrophoretic mobilities of several proteins representing allelic variations at selected loci and is very effective but time consuming and technically demanding. Recently, advances in sequencing techniques that resulted in less costly and rapid nucleotide sequencing heralded a movement towards use of multilocus sequence typing (MLST). This method involves determination of the sequences of approximately 450 bp (base pair) regions of housekeeping genes identical or similar to those selected for MLEE. The genome sequences of over 200 bacteria have now become available and have permitted the most detailed analyses of evolution of virulence.

3.3.3 Pathogenicity islands PAIs are mosaic-like blocks of DNA found in both Gram-negative and Grampositive bacteria that include at least one virulence gene, are often inserted near a transfer RNA (tRNA) gene, are associated with mobile genetic elements, are usually not present in non-pathogenic strains of the species, and usually have a G + C percentage and codon usage that differ from those of the core genome (Hacker and Kaper, 2000; Boerlin, 2004; Schmidt and Hensel, 2004). PAIs are typically large regions of DNA, ranging from 10 to 200 kb and are frequently unstable, presumably because of deletions. Most PAIs have a chromosomal location, but they may also be found on plasmids or in bacteriophages. Virulence factors that are encoded by PAIs include Type III secretion systems (TTSS), adhesins, toxins, and iron uptake systems (Hacker and Kaper, 2000). Table 3.2 shows features of PAIs in foodborne bacterial pathogens. Type III secretion systems include proteins that are related to certain proteins involved in flagellar export, and the two systems appear to have had a common ancestor (Gophna et al., 2003).


83 Table 3.2

Features of pathogenicity islands (PAIs) in foodborne bacterial pathogens

Bacterium

PAI

Virulence-related functions

Size (kb)

Insertion site

EHEC O157:H7

LEE

Type III secretion, adherence, re-organization of enterocyte cytoskeleton Invasion Yersiniabactin synthesis and transport Type III secretion, effectors (yops) Type III secretion, epithelial cell invasion, apoptosis Type III secretion, monocyte invasion Invasion, survival in monocytes Invasion, survival in monocytes

43

selC

46 43

selC asnT

47 40

plasmid centisome 63 valV

ETEC Tia Yersinia enterocolitica HPI Yersinia enterocolitica Yop Salmonella SPI-1 Salmonella

SPI-2

Salmonella Salmonella

SPI-3 SPI-4

Salmonella Vibrio cholerae

SPI-5 VPI

Enteropathogenesis TCP-adhesin, ToxT regulator, accessory colonization factor (Acf) Vibrio cholerae VPI-2 Encodes neuraminidase Staphylococcus aureus SaPI1 Toxic shock syndrome toxin-1, superantigen Listeria monocytogenes LIPI-1 Listeriolysin, phospholipases, metalloproteases, ActA regulator Listeria ivanovii LIPI-1 Listeriolysin, phospholipases, metalloproteases, ActA regulator LIPI-2 Internalins, sphingomyelinase

40 17 25 7 39.5

selC 92 min on S. Typhimurium serT ssrA

57.3 15.2

ser ?

9

?

9

?

18

?

Adapted from Hacker and Kaper (2000).

The LEE (locus for enterocyte effacement) pathogenicity island is an example of a PAI that has been studied extensively and shown to be critical for virulence. Acquisition of the LEE appears to be a major factor in the evolution of EPEC, from which EHEC evolved. Different sites of insertion of the LEE in the chromosome mark two major lineages of EPEC and EHEC in which mutations and recombination have occurred in the LEE to create divergence within these lineages (Wieler et al., 1997; Sperandio et al., 1998; Tarr and Whittam, 2002). The LEE is essential for virulence of EHEC O157:H7, in which it encodes a type III secretion system and effector proteins that result in an attaching and effacing lesion characterized by reorganization of the host cell cytoskeleton and intimate adherence of the bacteria to the enterocyte (Table 3.2). The bacterial adhesin that mediates the intimate attachment is called Eae (E. coli attaching and effacing) or intimin. The eae gene and gene product have been extensively investigated and shown to possess a highly conserved N-terminus and a variable C-terminus, resulting in 14 major types of intimin (Blanco et al., 2004). Tarr and Wittam (2002) investi-


84 gated nucleotide sequences from a number of the LEE genes and the nature of amino acid changes in intimin in five O111:H8 strains that had been isolated over a 40-year period. They determined that the LEE in this serotype was a mosaic of sections found in other E. coli and that the extracellular (EC) C-terminal domains of intimin showed a selection for amino acid changes that resulted in a change in charge. The authors suggested that these observations might reflect selection for diversification, possibly as a mechanism for evading immune surveillance in the host. In addition to the LEE, several other PAIs have been identified in O157:H7 (Schmidt and Hensel, 2004). These PAIs encode a wide range of potential virulence factors including urease, Iha (IrgA homologue adhesin), a macrophage toxin, an RTX-like exotoxin, fimbriae and invasion genes. Human enterotoxigenic E. coli (ETEC) have also been shown to possess at least one PAI, named Tia (Fleckenstein et al., 2000, 2002). Functions associated with genes in this locus include adherence to and invasion of intestinal epithelial cells, and promotion of release of heat-labile (LT) enterotoxin from the bacteria (Fleckenstein et al., 1996; Mammarappallil and Elsinghorst, 2000). Attachment to and invasion of human intestinal epithelial cells in culture is mediated by Tia, a 25 kDa heparin-binding outer membrane protein. As ETEC are non-invasive, it is not clear how this property of Tia contributes to virulence of ETEC, but the promotion of LT secretion by proteins such as LeoA (Fleckenstein et al., 2000) may promote development of diarrhea. Highly pathogenic strains of Yersinia (Y. pestis, Y. pseudotuberculosis and highly virulent Y. enterocolitica) carry the HPI, a region of DNA that is involved in iron acquisition (Carniel et al., 1996; Carniel, 1999, 2001). In Yersinia enterocolitica biotype 1B (serotypes O:8, O:13, O:20, O:21), HPI is a 43 kb region that is required for synthesis and transport of the siderophore yersiniabactin and for mouse virulence (Rakin et al., 1999). Interestingly, HPI is stable in Y. enterocolitica but unstable in Y. pestis. A second PAI found in Yersinia is the yop regulon that is a discrete segment of the 70 kb virulence plasmid pYV (Cornelis and Wolf-Watz, 1997). This PAI encodes proteins that form a TTSS as well as associated effector proteins. Both temperature and cell contact function to activate the TTSS and the transfer of effector proteins from the bacterium. After contact with host cells, Yersinia spp. secrete at least 14 Yop proteins through the TTSS; 4 of these are known to be injected into host cells and 3 are secreted into the extracellular milieu (Mecsas and Strauss, 1996; Cheng and Schneewind, 2000). The injected molecules YopE and YopH exert a profound effect on certain cells of the immune system, acting on macrophage proteins and preventing the macrophages from phagocytosing and killing the Yersinia. In Salmonella there are at least five pathogenicity islands called Salmonella pathogenicity islands 1–5 (SPI 1-5) (Table 3.2). SPI-1 is present in all Salmonella and confers on Salmonella the ability to invade enterocytes (Mills et al., 1995; Galan, 2001). The genes carried by SPI-1 encode a TTSS and effector proteins that induce cytoskeletal changes resulting in uptake of Salmonella by enterocytes and are responsible for apoptosis of the enterocytes. These genes are induced on initial


85 contact of the bacteria with the host enterocytes. SPI-2 also encodes a TTSS and transfers effector proteins that are essential for survival of Salmonella in macrophages. An important feature of the SPI-2 effectors is their ability to interfere with NADPH oxidase-dependent oxidative killing of Salmonella in vacuoles. Interestingly, the TTSSs encoded by SPI-1 and SPI-2 secrete effectors encoded within these SPIs as well as proteins encoded at other loci (Knodler et al., 2002). The SPI-3 and SPI-4 genes also contribute to survival in macrophages. SPI-5 was first identified in Salmonella Dublin but has been shown to be present in a wide range of Salmonella serovars (Wood et al., 1998; Amavisit et al., 2003). The genes carried by SPI-5 contribute to intestinal fluid secretion and inflammation. One of the genes encoded by SPI-5 is secreted by the SPI-1 TTSS and another is secreted by the SPI-2 TTSS (Knodler et al., 2002). Two PAIs have been described in Vibrio cholerae. The V. cholerae pathogenicity island (VPI) is an approximately 40 kb gene cluster that encodes a type 4 toxin co-regulated pilus (TCP) that is essential for virulence, and ToxT, which regulates a number of virulence genes including the genes for cholera toxin (CT) and for TCP. VPI is actually the genome of a phage called VPIM (Karaolis et al., 1999). Interestingly, TCP, encoded by phage VPI, serves as receptor for the filamentous CTX phage that carries the genes for CT. A novel feature of the VPI is that it can excise from the chromosome at attL and attR sites, which can join to produce a circularized extrachromosomal product (Rajanna et al., 2003). VPI-2 is a 57.3 kb gene cluster that includes the nanH gene encoding neuraminidase (Jermyn and Boyd, 2002). Neuraminidase cleaves sialic acid from oligosaccharides on enterocytes to increase the number of receptor molecules to which CT may bind. Both PAIs are required for a V. cholerae to be an epidemic strain. SaPI1 is a Staphyloococcus aureus pathogenicity island that carries the tst gene for toxic shock syndrome toxin (TSST-1) and genes for enterotoxins (Lindsay et al., 1998; Novick, 2003). SaPI1 is a defective phage and it can be transferred to other strains by helper phages. In Listeria monocytogenes, there is a 9 kb pathogenicity island (LIPI-1) which contains six critical virulence genes (Chakraborty et al., 2000). The six genes (hly, actA, mpl, plcA, plcB and prfA) are required for intracellular survival and for cellto-cell transfer of the bacterium. The gene hly encodes listeriolysin O (LLO), which lyses the phagocytic vacuole and releases the organisms into the cytoplasm. LLO is aided by the metalloprotease Mpl, the phospholipase C PlcB and the phosphatidyl-inositol specific phospholipase C PlcA. ActA is a protein that accumulates at one pole of the bacterium and orchestrates actin-based motility that allows the organism to move inside the host cell. PlcB is required for lysis of the double membrane that is formed when a cellular protrusion containing Listeria penetrates an adjoining cell. PrfA is a transcriptional activator that regulates expression of all six genes in the cluster. A similar PAI exists in L. ivanovii. A second PAI found in L. ivanovii is called LIPI-2 and is an unstable cluster which contains genes for internalins and a species-specific sphingomyelinase (GonzalezZorn et al., 2000). The availability of genome sequences, microarray technology and developments


86 Table 3.3

Virulence plasmids in bacterial foodborne pathogens

Bacterium

Plasmid

Virulence factors (genes)

Enterotoxigenic E. coli (ETEC)

Enterotoxin

Heat-labile enterotoxin (LT) (elt) Heat-stable enterotoxin a (Sta) (estA) Enteroaggregative heat-stable enterotoxin (EAST) (astA) CFAI – CFAIV, cs1-cs7, type IV pilus longus, PCFO159, PCFO166 Extracellular serine protease P (espP), catalase peroxidase (katP), LPS biosynthesis (msbB2), toxin, adhesin (toxB), RTX hemolysin (ehx) Yops Enhanced replication in macrophages, ADP-ribosyl transferase (spvRABCD) Epithelial cell adhesion and invasion Exfoliative toxin B (etb) Staphylococcal enterotoxins D, J Clostridium perfringens enterotoxin (cpe) Beta, beta2, epsilon, tau, lambda

Fimbriae Enterohemorrhagic E. coli pO157 (EHEC) O157:H7 Yersinia enterocolitica Salmonella Campylobacter jejuni Staphylococcus aureus Clostridium perfringens

pVY Virulence plasmid pVir pETB Enterotoxin Enterotoxin Toxins

in bioinformatics have increased opportunities for studies of bacterial pathogenicity at the genome level (Schoolnik, 2002). For example, the genome sequence of Campylobacter jejuni has revealed that there are no pathogenicity islands or prophages in the strain that has been sequenced (Parkhill et al., 2000; Linton et al., 2001). This observation has led to the speculation that the considerable genotypic diversity of C. jejuni is likely due to its natural competence (Wassenaar and Blaser, 1999; Dorrell et al., 2001). 3.3.4 Plasmids and virulence Several virulence genes in bacterial foodborne pathogens are located on plasmids (Table 3.3). The plasmids may be self-transmissible or they may be mobilizable by other plasmids. The factors that are most frequently plasmid-encoded are toxins and adhesins, but invasiveness is also plasmid-mediated in several foodborne bacterial pathogens. In ETEC, the essential virulence factors (enterotoxins and colonization fimbriae) are encoded by genes on plasmids. The genes for LT (eltAB) may be found alone or along with the gene for STa (estA) on plasmids that vary in size (Echeverria et al., 1986). There is good evidence that eltAB is a part of a small block of foreign DNA that was transferred to E. coli (Schlor et al., 2000). The eltAB genes are flanked by small conserved regions of DNA upstream and downstream that share the same G + C content (38 %) as the eltAB genes. Adjacent to these conserved regions are highly variable regions containing partial insertion sequence (IS) elements. The estA gene is located within transposon Tn1681 and is flanked by inverted repeats of the insertion sequence IS1 (So and McCarthy,


87 1980). In some ETEC strains, the estA gene was present on a plasmid that also carried the genes for the CFA/I fimbriae. The plasmid was non-self-transmissible but was transferred by a second plasmid that was present in the wild-type strain (Reis et al., 1980). The EAST-1 enterotoxin is also plasmid-encoded, and the gene (astA) has been reported in some ETEC as well as in EHEC O157:H7. In EHEC O157 there is a 92 kb virulence plasmid which carries several genes that are believed to contribute to virulence (Burland et al., 1998). The products of these putative virulence genes include ToxB, which contributes to bacterial adhesion; KatP, a catalase-peroxidase; Ehx, an RTX hemolysin; a type II secretion system, and proteins that are involved in lipid A biosynthesis. Yersinia enterocolitica possesss the pYV virulence plasmid that encodes a TTSS and effector proteins and is essential for virulence (Carniel, 2002). This system allows effector Yersinia proteins (Yops) to be translocated into host cells following contact of the bacteria with the cells. The Yops inhibit phagocytosis, induce apoptosis and, overall, promote the maintainance of the bacteria in an extracellular environment in host tissues. Certain serovars of Salmonella, including Typhimurium, Enteritidis, Dublin, Pullorum, Gallinarum and Choleraesuis, possess an approximately 90 kb virulence plasmid (Poppe et al., 1989; Gulig, 1990). The spv genes on this plasmid are needed for severe invasive disease. C. jejuni strain 81–176 and certain other C. jejuni have recently been reported to carry a 37 kb plasmid, pVir, which contributes to virulence (Bacon et al., 2000, 2002). Genes on this plasmid are involved in promoting adherence to and invasion of intestinal epithelial cells. In Staphylococcus aureus, some strains carry the pETB plasmid that encodes exfoliative toxin B and an ADP-ribosyl transferase (Yamaguchi et al., 2001). The gene for Staph. aureus enterotoxin D, a serotype of enterotoxin frequently associated with food poisoning, is found on a plasmid which also carries the gene for enterotoxin J (Zhang et al., 1998). The gene (cpe) for the enterotoxin of Clostridium perfringens may be either plasmid or chromosomal in its location (Miyamoto et al., 2004). Strains of type A C. perfringens implicated in food poisoning usually have the chromosomal cpe gene, but strains with plasmid-encoded enterotoxin have occasionally been recovered from outbreak strains (Tanaka et al., 2003). The beta, beta-2, tau and epsilon toxins are also plasmid-encoded (Katayama et al., 1996; Ohtani et al., 2003).

3.3.5 Bacteriophages and the development of pathogenicity Bacteriophages may affect bacterial pathogenicity in a variety of ways. These phages may include virulence genes along with genes needed to carry out phage functions; they may regulate virulence genes; they may provide a mechanism for release of virulence factors from the bacterium; and they may transfer small or large blocks of virulence-related DNA from one organism to another (Table 3.4). Zoonotic pathogens that reside in the intestinal tracts of animals have ready access to a vast pool of genetic material in the rich flora of the large intestine, a site that is also rich in bacteriophages. Many bacteriophages have the ability to insert in the


88 Table 3.4

Phage-encoded virulence factors of bacterial foodborne pathogens

Bacterium

Virulence property

Phage

EHEC O157:H7

Verotoxin, a cytotoxin responsible for damage to vascular endothelium and for the severe sequelae of infection

Lambdoid

Salmonella

Superoxide dismutase SodC – protects against superoxide produced by macrophages, GtgE – required for mouse virulence GipA – growth or survival in Peyer’s patch SopE – stimulates cytoskeletal reorganization

Gifsy-2

Vibrio cholerae

Gifsy-1 SopEΦ

Cholera toxin (CT) – ADP-ribosyl transferase that causes hypersecretion of electrolytes and water Toxin-co-regulated pilus (TCP) – essential for intestinal colonization ToxT, TcpP, TcpH – virulence gene regulators

CTXΦ

Staphylococcus aureus

Staphylococcal enterotoxins – superantigens Exfoliative toxin A – proteolytic cleavage of desmoglein 1 Chemotactic inhibitory protein – inhibits chemotaxis

ΦSa3ms ΦETA

Clostridium botulinum

C3 exoenzyme – ADP-ribosyl transferase in type C and D strains

Tox+ phages

VPIΦ VPIΦ

chromosome of bacteria and become an addition to the normal complement of genes at the disposal of the bacterium. The bacterial host is said to be lysogenized by the bacteriophage and the quiescent bacteriophage is referred to as a prophage. In many cases the phage genome includes virulence genes that contribute to the bacterium’s ability to invade host tissues, to injure the host, or to evade host immune mechanisms (Boyd and Brussow, 2002). Accumulation of phages in a bacterium is limited by factors such as superinfection immunity, in which a resident prophage excludes acquisition of identical or closely related phages, and by occupation of potential integration sites. Phage-encoded virulence genes sometimes encode potent protein toxins. These were first recognized over 50 years ago when the diphtheria toxin was shown to be encoded by the β-phage in Corynebacterium diphtheriae (Freeman, 1951). Phageencoded virulence genes that are relevant to food safety include genes for verotoxins and an enterohemolysin in EHEC, in which certain lambdoid phages that lysogenize the bacteria possess the genes for verotoxin (VT) and also play important roles in regulating the production of VT. Induction of the phage in EHEC is critical for VT production and release from the bacteria into the intestinal tract. Certain antibiotics can induce prophages in the genome of O157:H7 E. coli to become virulent and lyse the host cell, resulting in a massive increase in production of VT and release of this toxin into the environment of the bacteria (Kimmitt et al., 2000). Genes for enterotoxins, exfoliative toxin and leukocidin in Staph. aureus, and the gene for the C1 toxin in Clostridium botulinum, are other examples of virulence genes


89 found in bacteria of importance in foodborne disease. In Staph. aureus, some differences in virulence and in type of disease are attributable to phage-encoded genes (Boyd and Brussow, 2002). Genome sequences for two strains of E. coli O157:H7 have demonstrated the extent to which bacteriophages play roles in pathogenesis, genome rearrangements and strain diversity (Hayashi et al., 2001; Perna et al., 2001). In E. coli O157:H7, for example, more than 50 % of the 1.3 Mb of DNA that is found in O157:H7 but not in E. coli K12 is made up of bacteriophage DNA. In both the Sakai and EDL 933 strains of E. coli O157:H7, there are 18 prophages as well as prophage-related elements (Hayashi et al., 2001; Perna et al., 2001). Most of these bacteriophages are lambdoid phages. The presence of several related phage genomes in the same bacterium is a mechanism for promoting recombinations in the bacterium. Differences in bacteriophage content contribute substantially to the considerable variation that exists among strains of O157:H7 E. coli (Ohnishi et al., 2002). Type III secretion systems that are found in a wide range of Gram-negative bacteria that include plant, animal, and human pathogens may have been transferred by bacteriophages. The TTSSs are critical for virulence of the bacteria that possess them. The LEE PAI, which has already been discussed, encodes a TTSS and effector proteins. Salmonella possess two TTSSs located in pathogenicity islands SPI-1 and SPI-2. Several effector proteins secreted by a TTSS in Salmonella enterica are phage encoded. These are the GogB, Sse1, and SspH1 proteins encoded by genes in the λ-like Gifsy phages and SopE encoded by the P2-like SopEPhi. Unlike other effector proteins, SopE is found in only a limited number of Salmonella isolates. This protein activates the Rho GTPases CDC 42 and RacI in host epithelial cells and contributes to reorganization of the cytoskeleton and the entry of Salmonella into these non-phagocytic cells. Transfer of these phages among Salmonella serovars has generated diversity in effector proteins in these serovars (Mirold et al., 2001). These phages play a major role in creation of new epidemic clones and in the adaptation of Salmonella to specific animal hosts. Mirold et al. (1999) examined a collection of 160 Salmonella Typhimurium isolates for the presence of sopE and found that 43 of them carried the sopE gene and that the occurrence of this gene was restricted to a small number of phage types. Of 25 DT204 isolates that were examined, all 25 carried this gene. Of 36 phage types that were investigated, only 4 phage types were positive, and only 2 phage types (DT49 and DT204) had all isolates positive. Interestingly, phage type DT204 is an epidemic clone of Salmonella Typhimurium that persisted in cattle and humans during the 1970s and 1980s. Transfer of virulence genes among bacteriophages of different families enhances the frequency of transfer of the genes among Salmonella, which contains many prophages that will cause superinfection immunity. An example of this is the sopE gene cassette (the sopE-moron), which is organized as an independent transcriptional unit (Mirold et al., 1999). Thus the sopE gene has been found not only in the P2-like SopEPhi, but also in λ-like phages related to the Gifsy-phages. The gene is present in the SopEPhi in Salmonella Typhimurium and Salmonella


90 Typhi and in λ-like phages in Salmonella Dublin, Enteritidis, Hadar, and Gallinarum (Mirold et al., 1999). It appears that the sopE moron integrates into the phage by homologous recombination involving its flanking sequences. Lysogenic conversion, involving the acquisition of phages which become part of the bacterial genome, combined with exchange of virulence gene cassettes among phages of the same or unrelated family, are important mechanisms for transfer and reassortment of virulence proteins in Salmonella. Phage-encoded superoxide dismutase (SOD) is another virulence factor that has been reported in Salmonella and E. coli. The genes sodCI and sodCIII are carried by the Gifsy-2 and Fels-1 phages, respectively. The products of these genes probably aid in protecting Salmonella against the destructive oxidative burst in macrophages. Other enzymes encoded by phage genes are neuraminidase in Salmonella and staphylokinase in Staph. aureus. Lysogenic conversion may alter the surface antigen and the serovar of Salmonella enterica. Bacteriophages are not only associated with conversion of serovars but are likely to play a role in host range determination (Rabsch et al., 2002a,b). Location of bacteriophage-encoded virulence genes sometimes provides clues as to their evolution and/or regulation. Virulence genes in bacteriophages in Grampositive bacteria tend to be located near the phage attachment site, suggesting that they arose from imprecise phage excision (Boyd and Brussow, 2002). These genes typically have a GC content that is different from their hosts – further evidence that they probably became incorporated into the phage genome during excision from a foreign host. This pattern is not repeated in Gram-negative bacteria. In the case of the stx genes in Shiga toxin-producing E. coli (STEC), for example, the genes are found between the Q antitermination gene and the lysis genes. This arrangement is strategic. It allows for regulation of transcription of the toxin genes by the phage and for release of toxin when the bacterium is lysed following induction. As noted in the section on PAIs, certain bacteriophages are PAIs.

3.3.6 Vesicle-mediated gene transfer The importance of DNA transfer of virulence genes by membrane vesicles is not known, but it seems likely that this phenomenon occurs in nature and may be of practical importance. Membrane vesicles that are produced by many Gramnegative bacteria include DNA as well as lipopolysaccharide (LPS), periplasmic proteins, phospholipids and RNA (Gankema et al., 1980; Kadurugamuwa and Beveridge, 1999; Kolling and Matthews, 1999; Yaron et al., 2000). Yaron et al. (2000) showed by electron microscopic examination that the DNA in vesicles of E. coli O157:H7 was present as linear DNA, open-circle plasmid DNA and larger rosette-like structures. They demonstrated that several genes, including the virulence genes eae, stx1, stx2, and hlyCA, were transferred by transformation from isolated vesicles to E. coli and that the stx genes were transferred to Salmonella Enteritidis recipients. Fragment of phage 933W were also detected in transformed E. coli, and the authors suggested that the entire phage may have been transferred.


91 Both E. coli and Salmonella transformants expressed cytotoxicity for Vero cells, indicative of Stx activity.

3.4 The food processing environment and the evolution of pathogenicity There is very little information available on the contribution of the food processing environment to the development of bacterial pathogenicity. However, the food processing environment probably plays a significant role in evolution of pathogenicity by the selection it imposes on bacteria and by the opportunities that processed foods provide for transmission of pathogens to humans. If traits that are advantageous in the food processing environment are also advantageous in the setting in which the bacteria cause disease, then selection for pathogenicity may occur. Bacteria that survive stresses such as cold, heat and acidity during food processing may be better adapted to survive acid and other stresses in the stomach and intestines of the human host. The food processing environment is also important for its contribution to transmissibility, a major factor in pathogen survival and evolution. Centralized processing and wide distribution of foods with pathogens may promote transmissibility of foodborne pathogens. The food processing environment may promote gene transfer among bacteria. Transfer of plasmid-borne virulence and drug resistance genes has been demonstrated from a clinical Enterococcus faecalis to a food strain of this organism (Cocconcelli et al., 2003). The researchers determined the frequency of transfer of one plasmid carrying aggregation substance and tetracycline resistance and a second plasmid carrying vancomycin resistance in models of fermented sausage and cheese. They showed a high rate of transfer of the tetracycline resistance plasmid, particularly in sausage, in which the frequency of transfer was 10–3/ recipient bacterium. A similar rate of transfer occurred with the vancomycin resistance plasmid in fermented sausages. Biofilm formation in food processing equipment may favour the survival of bacteria that are able to conjugate. Bacteria in the food processing environment frequently exist as biofilms, which confer on the member organisms greater resistance to physical and chemical measures designed to remove them from surfaces. However, a few studies have sought to assess the effect of the biofilm state on gene transfer. Christensen et al. (1998) found that there was a low frequency of plasmid transfer in a three-species model biofilm community in a flow chamber biofilm system. The plasmid conferred a metabolic advantage on the recipient bacteria and became established primarily by growth of the donor strain rather than by frequent transfer to recipient organisms. In contrast, Hausner and Wuertz (1999) determined that rates of conjugation in a defined bacterial biofilm were high when determined by confocal laser scanning microscopy and measurement of cellular biovolumes. It is not clear whether the differences in the results from these studies were due to the use of different methods of detection of plasmid transfer or to the use of different biofilm and plasmid systems. However, both


92 studies demonstrated that new properties may be acquired in biofilms through transfer of plasmids by conjugation. Interestingly, conjugative plasmids are capable of promoting biofilm formation. Ghigo (2001) showed that E. coli K12 with the F plasmid rapidly formed biofilms and that this capacity was lost on removal of the F plasmid. The environment, including the food processing environment, may play a role in evolution that goes beyond selection of mutants that arise spontaneously. The notion that mutations arose at random without being influenced by selection appears to have been settled by the fluctuation experiments of Luria and Delbruck in 1943 and the replica plating studies of the Lederbergs (1952). However, Cairns (1988) provided evidence that presence of lactose led to late-appearing lactosepositive revertants of a Lac- strain of E. coli. Cairns considered that the bacterial population had a mechanism for favoring mutations that were advantageous. Hall (1990) refers to these directed mutations as Cairnsian mutations. Hall also provided evidence to support the occurrence of Cairnsian mutations (Hall, 1991, 1998). These studies support the view that the environment is an important contributor to the genetic changes that occur.

3.5

Predicting and controlling pathogenicity

Predicting pathogenicity is fraught with uncertainty, but investigations of changes in pathogens over time allow certain trends to be recognized. Understanding the molecular mechanisms that drive the capacity of bacteria to adapt and change, and understanding how bacteria benefit from increasing virulence are critical aspects of approaches to predicting and controlling pathogenicity. Gaining a better understanding of circumstances that are mutually beneficial to pathogen and host is one approach to controlling foodborne disease caused by pathogenic organisms. The goal may not be to eliminate the pathogens but to divert them to niches and/or lifestyles that prevent harm to the human host. Hall and colleagues have developed an in vitro evolution method for predicting the potential of specific genes to evolve in a particular way (Barlow and Hall, 2002; Hall, 2004). When the model was applied to experimental evolution of the TEM-1 β-lactamase gene, the amino acid substitutions that resulted in an extended spectrum phenotype were similar to those that have occurred in nature. The researchers also predicted that one change that has not yet been detected is likely to arise naturally. These approaches suggest that although evolution of bacteria is much more complex than evolution of individual genes, accuracy in prediction of bacterial evolutionary changes is likely to improve considerably.

3.6 Future trends Several future trends will impact on detection of pathogens in foods and on the decisions that need to be made with respect to regulation and to research.


93 • Improved methodologies for detecting foodborne bacterial pathogens will lead to recognition of a higher percentage of food samples that are found to contain pathogens. However, this will not be accompanied by an increase in disease due to these pathogens, as the changes will not be a reflection of increased frequency of contamination. • Food safety practices inside and outside the food industry that are not pathogenspecific will increasingly be recognized as a logical approach to the control of foodborne pathogens. • More attention will be focused on viral pathogens in foods based on increasing recognition of their significance. • Dramatic advances will continue to be made in studies of evolution of bacterial pathogens, including foodborne pathogens. Kingsley and Baumler (2000) argue convincingly that improved understanding of the mechanisms acquired by bacteria that have enabled them to overcome host defences and be maintained in new environments may allow us to predict behaviour of known pathogens and of pathogens that might emerge in the future. These studies must involve consideration of bacterial adaptability, host defences, selective pressures, transmissibility, and competition among microorganisms. For example, the potential consequences of removing competing organisms or of eliminating a source of specific host immunity need to be taken into account. As an example, Baumler et al. (2000) suggest that eradication of Salmonella serovars Pullorum and Gallinarum from poultry flocks in Europe and North America three decades ago may have resulted in loss of flock immunity against the O9 antigen (serogroup D1) and may have paved the way for infection with another D1 Salmonella, namely Salmonella Enteritidis. This exchange resulted in replacement of the poultry-adapted Salmonella serovars by the zoonotic Salmonella Enteritidis serovar.

3.7

Sources of further information and advice

The reference list includes several reviews that will be valuable to readers interested in further information on development of pathogenicity in foodborne pathogens. Additional sources of information are: HACKER J, KAPER J B (2002), Pathogenicity Islands & the Evolution of Pathogenic Microbes, Springer-Verlag Telos. HACKER J, KAPER J B, COMPANS R W, COOPER M , ITO Y, KOPROWSKI H, MELCHERS, F, OLDSTONE M, OLSNES, S, POTTER M (2002), Pathogenicity Islands & the Evolution of Pathogenic Microbes, Volume II, Springer-Verlag. HACKER J, KAPER J B, Pathogenicity Islands and Other Mobile Virulence Elements, American Society for Microbiology, Washington, DC. http://www.bacteriamuseum.org/niches/evolution/mutation.shtml http://www.farmasi.uit.no/~knielsen/Evolbact.html http://www.ibmsscience.org/general/pathogens.htm


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4 Host–pathogen interactions A. Roberts and M. Wiedmann, Cornell University, USA

4.1

Introduction

The establishment of a foodborne infection is contingent upon the outcome of the interplay between the virulence mechanisms of the pathogen and the defense mechanisms of the host. In any discussion of foodborne infectious disease, therefore, both pathogen and host must be addressed. While the pathogens that cause foodborne disease in humans are many and varied, we have chosen to limit our discussion in this chapter to the interactions that occur between human host and bacterial pathogen. While great strides have been made in recent years to understand the epidemiology and pathogenesis of foodborne disease-causing viruses and parasites (for review see Koopmans and Duizer, 2004 and Slifko et al., 2000), much more is known about the bacteria that cause foodborne disease. Furthermore, even though viral pathogens cause higher numbers of known human foodborne infections (approximately nine million per year in the United States as compared to approximately four million caused by bacteria), bacterial foodborne pathogens give rise to the majority of fatal infections (72 % caused by bacteria versus 7 % caused by viruses) (Mead et al., 1999). This chapter will begin with an overview of human host defense mechanisms against foodborne pathogens and, next, will explore some of the mechanisms pathogenic foodborne bacteria use to cause human disease. Our discussion will end with a review of several host factors that can modulate virulence gene expression and an exploration of novel strategies to prevent infection.


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4.2 Host defense mechanisms and pathogen survival strategies 4.2.1 Non-specific human host defense mechanisms There are multiple human host defense mechanisms acting all along the human gastrointestinal tract that help prevent infection by foodborne bacterial pathogens. These include non-specific defenses against all bacteria as well as defenses specific to particular pathogens that are part of an adaptive immune response. Some of the body’s non-specific defenses include the acidic pH and proteolytic enzymes in the stomach, detergents and flushing action in the small intestine, as well as competitive microflora in the large intestine. The human host also combats foodborne bacterial pathogens using innate and adaptive immune responses generated via interaction between pathogen and the intestinal mucosa. In this way, the human intestine acts as a key player in the development of healthy immune functioning. A major component of intestinal immunity, responsible for generating a specific adaptive immune response to foodborne pathogens, is gastrointestinal-associated lymphoid tissue (GALT), which includes the Peyer’s patches in the intestines and their underlying lymph tissue. When a foodborne bacterium enters a human host through consumption of contaminated food, it must survive a variety of physiological stresses before reaching its target host tissue and initiating infection. Even while still in the mouth, bacteria encounter antibodies and degradative enzymes such as lysozyme and salivary peroxidase. The first significant defense that foodborne pathogens encounter, however, is the highly acidic pH of the human stomach (Audia et al., 2001; Kirstila et al., 1994). The hydrochloric acid in the gastric juice of the stomach kills most ingested pathogenic microorganisms. For example, when 109 CFU per ml of Lactococcus lactis was fed to rats, only 7 % of the bacteria survived in the stomach (Drouault et al., 1999). The importance of stomach acidity as a protective mechanism is evidenced by repeated findings that persons or laboratory animals with hypochlorhydria as a result of antacid, histamine H2 antagonist, or proton pump inhibitor treatment are more susceptible to foodborne infections from several foodborne pathogens, including Listeria monocytogenes, Vibrio cholerae, Salmonella, Campylobacter, and Enterobacter (Czuprynski and Faith, 2002; Dinsmore et al., 1997; Schuchat et al., 1992). Furthermore, elderly adults are believed to be at a greater risk for foodborne infections, in part due to their decreased production of stomach acid (Russell et al., 1993). Interestingly, several studies have demonstrated that the killing action of gastric acid is enhanced if nitrite is available (Benjamin et al., 1994; Dykhuizen et al., 1996; Xu et al., 2001). Nitrite is found in saliva as a result of the reduction of nitrate by bacteria in the mouth, and is added to the gastric juice when saliva is swallowed (Duncan et al., 1997). 4.2.2 Pathogen survival strategies Many foodborne pathogens are able to survive passage through the normally


101 acidic stomach of a healthy adult as a result of several acid survival mechanisms. Ingestion of pathogens with certain foods may offer protection against the killing effects of stomach acid. Waterman and Small, for example, demonstrated that several enteric pathogens, including Shigella flexneri, Salmonella, Campylobacter jejuni and V. cholerae, were able to survive significantly better when inoculated onto high-protein foods, such as ground beef or boiled egg white, and exposed to media acidified to pH 2.5 for 1 h than when exposed alone to acidified media (Waterman and Small, 1998). Certain foods may protect pathogens by buffering stomach acidity, but other foods protect by unknown mechanisms (Peterson et al., 1989). In addition to the protective action of some foods, many enteric bacteria, commensal as well as pathogenic, are able to survive passage through the gastric acid because they possess a variety of inherent acid resistance mechanisms. Many foodborne pathogens, including V. cholerae, Salmonella enterica Typhimurium, Shigella spp., L. monocytogenes and Escherichia coli among others, have an acid tolerance response (ATR) that is induced at moderately acidic pH (i.e. 4.5 to 5.8 for Salmonella) and that allows the bacteria to survive subsequent exposure to more severe acidic pHs (for review see Foster, 1999; Merrell and Camilli; 2002; Smith, 2003). For example, the foodborne pathogen L. monocytogenes can survive for up to 80 min at pH 3.0, but its survival time at the same pH increases to over 120 min if it has been previously acid adapted for 1 h at a pH of 5.8 (Davis et al., 1996). Interestingly, the identification of ATR systems in many foodborne pathogens has implications for food preservation; for example, a possible unintended side effect of low pH-based food preservation methods might be an induction of an ATR in contaminating pathogens and a subsequent increase in their acid resistance and overall fitness (Koutsoumanis et al., 2003). If an enteric pathogen survives passage through the stomach and succeeds in reaching the intestine, it must confront many additional specific and non-specific host defense mechanisms designed to prevent infection. Pathogens must be able to survive assault by various enzymes and detergents, as well as adhere to the host so that they are not flushed away in the small intestine. Paneth cells lining the intestines secrete antimicrobial molecules and proteolytic enzymes such as defensins and lysozyme, which attack bacterial cell walls (Masschalck and Michiels, 2003; Salzman et al., 2003). Similarly, the detergent bile is emptied into the upper region of the small intestine by the gall bladder and, in addition to its role in the digestion of fats, serves to kill bacteria by disruption of their lipid membranes (Bourlioux et al., 2003; Rince et al., 2003; Salyers and Whitt, 1994). Certain foodborne pathogens such as L. monocytogenes and Salmonella species, however, have evolved mechanisms that enable them to survive in environments with high concentrations of bile salts (Begley et al., 2002; van Velkinburgh and Gunn, 1999). The pathogen L. monocytogenes, for example, has three genes that are likely involved in bile salt degradation that are missing in the closely related nonpathogen Listeria innocua (Glaser et al., 2001). These three genes are likely used in the intestinal environment as well as in the gall bladder itself, where Hardy et al. (2004) recently demonstrated that L. monocytogenes can replicate extracellularly. Other substances encountered by bacteria along the gastrointestinal tract that help


102 prevent infection include the high-affinity iron-binding protein, lactoferrin and the superoxide radical-producing protein, lactoperoxidase (Salyers and Whitt, 1994). In addition to surviving the physiological challenges posed by a chemical and enzymatic assault, pathogenic microorganisms must find a way to adhere to the intestinal wall if they are to avoid being flushed out of the body and if they are to reproduce. Several mechanisms that pathogens use to adhere to the intestinal wall will be discussed later, but it should be noted here that the intestine has evolved a variety of ways to prevent the adherence of pathogens. One such mechanism is the flushing action of the small intestine, which creates a turbulent environment where it is difficult for bacteria to adhere. Similarly, intestinal epithelial cells and the outer mucous layers lining the intestine are frequently sloughed off, so that many bacteria that do attach are washed away before they have the opportunity to cause infection. Another protective factor that helps prevent colonization by pathogens is the complex microflora found primarily in the colon (Bourlioux et al., 2003). This microflora, in part, protects the host by competing with pathogens for limited nutrients and adhesion sites, but also exerts protective effects by a variety of other mechanisms including the creation of a physiologically restrictive environment and the production of antimicrobial compounds such as bacteriocins (Bourlioux et al., 2003). There is also some evidence that interspecies communication that occurs by means of quorum-sensing autoinducer-2 molecules (AI-2) may play a role in modulating virulence (Anand and Griffiths, 2003; Duan et al., 2003; Jeon et al., 2003). Finally, the gastrointestinal mucous membranes themselves contain many features meant to defend against infection by pathogenic microorganisms. Chief among these are the tissues and lymphoid cells that make up the GALT.

4.2.3 The gastrointestinal mucosa as a defense The mucosal surfaces of the intestine have been described as the body’s largest immune organ (Anon, 1994). The adult human gastrointestinal tract has up to 300 m2 of mucosal surface and approximately 1010 immunoglobulin-producing cells per meter in the small intestine. These cells account for approximately 80 % of all immunoglobulin-producing cells in the body (Salminen, 1999). The primary sites along the gastrointestinal tract where the immune system encounters foreign antigens are the Peyer’s patches, a collection of follicles composed of M cells and their associated lymphoid cells. Because they are naturally phagocytic, M cells can take in bacteria and other antigens and pass them to underlying macrophages or other antigen-presenting cells where they are processed and presented to T and B cells. In this way, M cells sample antigens from the intestinal lumen and help activate an appropriate immune response (Clark and Jepson, 2003). One outcome of antigen sampling via M cells is the production of secretory immunoglobulin A (sIgA) by GALT B cells. Secretory IgA is transported into the intestinal lumen where it coats many intestinal bacteria to both prevent their adhesion to the intestinal mucosa and to target them for opsonization (Brandtzaeg, 2003; Mestecky and Russell, 2003). The portfolio of antigens encountered by the intestinal immune system is extraordinarily large and complex, and includes food antigens, antigens


103 from beneficial commensal bacteria, as well as antigens from pathogens. The details of how the human body regulates its intestinal immune response to distinguish between benign and harmful foreign antigens remain largely a mystery. However, the vast immune potential of the intestinal mucosa as well as the problems associated with its malfunction (such as food allergies and inflammatory bowel diseases) makes continued research on the intestinal immune system a priority.

4.3

Pathogenic cell adhesion and invasion

4.3.1 Adhesion Just as the human host has evolved multiple mechanisms to prevent infection by pathogenic microorganisms, the microorganisms themselves have evolved diverse and effective mechanisms that thwart their host’s defense mechanisms to enable successful infection. As previously mentioned, for example, many bacteria have evolved stress response systems that enable them to survive highly acidic conditions such as those encountered during passage through the human stomach. After reaching the intestine, an essential step in any successful foodborne infection is pathogen adhesion. Adherence prevents the pathogen from being flushed away and allows it to begin the infectious process that will enable its reproduction and propagation. Foodborne bacteria have evolved multiple mechanisms of adherence. One adherence mechanism that bacteria use is to attach using long filamentous structures known as pili or fimbrae. Pili often bind to glycolipid and glycoprotein receptors on host epithelial cells (Salyers and Whitt, 1994; Taylor, 1991). Some foodborne pathogenic bacteria have specific cell surface proteins that bind to specific host cell receptors. The bacterial surface protein internalin (InlA) of L. monocytogenes, for example, uses the intestinal epithelial cell surface protein E-cadherin as its receptor (Mengaud et al., 1996). Pace et al. (1993) reported the activation of epidermal growth factor (EGF) receptor upon Salmonella enterica Typhimurium invasion of cultured epithelial cells, suggesting that Salmonella may use the EGF receptor for adherence. Others, however, have found that Salmonella Typhimurium can invade cultured cells that lack the EGF receptor and can invade the gastrointestinal tracts of mice expressing an EGF receptor with reduced activity, thus suggesting that the EGF receptor has a limited role, if any, as a Salmonella receptor (Jones et al., 1993; McNeil et al., 1995). Much remains unknown about the specific ligand–receptor interactions between bacteria and their host cells, and about other mechanisms of and structures involved in adhesion. Another bacterial adherence mechanism that has only relatively recently been recognized as playing an important role in the pathogenesis of some foodborne infections is adhesion through the formation of biofilms. A biofilm is a layer of bacterial cells attached to a solid surface and surrounded by a thick polysaccharide matrix (Donlan and Costerton, 2002). The cells in the most basal layer of the biofilm are attached to the host structure, while cells in the outer layers of the


104 biofilm attach themselves to the polysaccharide matrix or to the bacterial cells beneath them. Adherence via a biofilm is a very effective mechanism used by bacteria to avoid several of the host defenses aimed at preventing enteric infection. For example, the majority of bacteria in a biofilm are protected under several layers of extracellular polysaccharide matrix and, therefore, are not easily flushed away or easily accessed by secretory antibodies, bacteriocidal enzymes, phagocytic immune system cells or antibiotics (Donlan and Costerton, 2002). Adherence via biofilms can contribute to persistence of an infection. Prouty et al. (2002) for example, demonstrated that Salmonella enterica can form biofilms on gall stones and thus cause chronic infection and be continually shed. Finally, the changes in gene expression that occur as a result of living in a biofilm often enhance stress resistance and may facilitate virulence. For example, biofilm-producing strains of Enterococcus faecalis were able to survive inside peritoneal macrophages 24 h longer than strains that were unable to produce biofilms (Baldassarri et al., 2001), while Salmonella Typhimurium grown as a biofilm were recovered from the spleens of intraperitoneal infected mice up to 5 days post-infection in greater numbers than cells grown planktonically (Turnock et al., 2002).

4.3.2 Prevention of adhesion Because adhesion is a requisite step for any foodborne infection, it stands to reason that preventing bacterial adhesion is a principal way to prevent foodborne infections. A key contributing factor to the prevention of adhesion by foodborne pathogens is the resident competitive microflora in our large intestine. These resident microorganisms are very diverse and most cannot be cultured ex vivo (Hooper and Gordon, 2001). Using traditional culture techniques if possible and, more recently, molecular approaches such as 16S ribosomal RNA gene sequencing, members of the Bacteroides, eubacteria, bifidobacteria, anaerobic Gram-positive cocci, clostridia, lactobacilli, Escherichia, methanogens, fusobacteria, enterobacteria, Veillonella, staphylococci, Proteus and Pseudomonas, among others, have been identified in the intestine (Bourlioux et al., 2003). The intestinal microflora prevents adhesion by pathogenic organisms by competing with them for limited space and nutrients, as well as by producing antimicrobial molecules (Bourlioux et al., 2003). The importance of the resident intestinal microflora can be seen clearly in the symptoms manifested when the balance of microflora is acutely disturbed; for example, diarrhea is a common side effect of antibiotic therapy. Similarly, infants less than 1 year old are susceptible to gastrointestinal botulism if they are fed foods such as honey that contain Clostridium botulinum spores (Tanzi and Gabay, 2002). While healthy adults are not affected by ingesting Cl. botulinum spores, infants are susceptible because the competitive microflora in their gut is not well enough developed to successfully out-compete the botulinum spores.

4.3.3 Invasion The next requisite step for many, but not all, foodborne bacterial pathogens is


105 invasion of the target host tissue. Some foodborne pathogens such as V. cholerae, C. jejuni, and some strains of E. coli adhere to host intestinal tissue and cause disease extracellularly via toxin production. In the case of cholerae infection, for example, the cholera toxin induces water release into the intestinal lumen, causing watery diarrhea and facilitating dissemination of the bacteria (Sack et al., 2004). Similarly, in E. coli O157:H7 infections, production of shiga-like toxins causes tissue fluid loss and severe damage to blood vessels (LeBlanc, 2003). Other foodborne bacterial pathogens, however, have evolved to invade the intestinal tissues in order to access the protected intracellular environment and escape destruction by the host’s humoral immune system, to access nutrient-rich areas of the host’s body, and to facilitate their spread within the mammalian host. Examples of foodborne bacteria capable of invading human host cells include Salmonella enterica, Sh. flexneri and L. monocytogenes. Facultatively intracellular foodborne pathogens have evolved numerous mechanisms to exploit eukaryotic host cell function in ways that will promote their own uptake into host cells or their cell-to-cell spread. Some pathogens, for example, such as Salmonella enterica, Shigella species and Yersinia entercolitica, are able to exploit the natural phagocytic ability of M cells in order to reach the underlying cells, blood and lymph (Neutra et al., 2003). Other bacteria interact with epithelial host cell receptors in ways that interfere with normal host cell signaling processes and result in underlying cytoskeletal actin rearrangements. The effect of such actin rearrangement resulting from the interaction of Salmonella Typhimurium with its host cell receptors, for example, is host cell membrane ruffling that facilitates bacterial uptake (Francis et al., 1993). Actin rearrangements can be initiated by bacteria in the host cell cytosol as well. The facultative intracellular pathogens L. monocytogenes and Sh. flexneri, upon entry into the cytosol, express the protein analogs ActA and IcsA, respectively, that initiate a cascade of cellular protein interactions culminating in the formation of actin comet-like tails at the bacterial pole that propels the bacteria into neighboring cells (Cossart, 2000; Gouin et al., 1999). Another well-known example of a pathogenic bacterium that modifies host cell signaling processes is enteropathogenic E. coli (EPEC). An EPEC cell attaches to a host intestinal epithelial cell using bundleforming pili and then uses a Type III secretion apparatus to inject its own receptor, TIR (translocated intimin receptor), into the epithelial cell. Injection of TIR causes actin rearrangements in the host cell that result in the formation of a membrane pedestal-like structure known as an attaching and effacing (A/E) lession. Binding between the E. coli surface protein intimin and its injected receptor TIR creates an intimate attachment (Clarke et al., 2003; Frankel et al., 1998).

4.4

Factors affecting virulence expression

Even before a foodborne pathogen adheres to its target host tissue, it begins to sense a variety of signals that indicate to the bacterium that it has entered a host environment. The signals induce many changes in bacterial gene expression,


106 including changes that prepare the bacterium to survive host-associated stress conditions, and changes that begin the infectious processes. Some of these signals are well known and commonly studied, but many of the factors that influence stress response and virulence gene expression remain a mystery. Three signals that pathogenic bacteria often use to sense their environment are temperature, the concentration of trace elements, and molecules produced by other bacteria. The environment of a mammalian host is typically 37 ºC and iron-limited, for example, and pathogenic bacteria have evolved mechanisms to sense both characteristics (Braun, 2001; Hurme and Rhen, 1998; Ratledge and Dover, 2000; Winzer and Williams, 2001). A well-known example of an iron-sensing mechanism by a pathogen occurs in Corynebacterium diptheriae. The diptheria toxin repressor DtxR is active only when it is bound to iron. Upon entering the iron-limited environment of a human host, DtxR becomes inactive and diptheria toxin is expressed (Boyd et al., 1990). Another example of a trace element sensing system is found in Salmonella Typhimurium where the PhoP–PhoQ two-component regulatory system senses and responds to Mg2+ concentrations, and regulates the expression of the majority of virulence genes. Specifically, the sensing of low Mg2+ concentrations is thought to indicate to the bacterium an intracellular environment (such as the phagosome), whereas the sensing of high Mg2+ concentrations indicates an extracellular environment (Chamnongpol et al., 2003; Groisman, 1998, 2001). By monitoring its environment, Salmonella Typhimurium is able control the temporal and spacial expression of virulence genes. Temperature is another signal frequently used by pathogens to sense their environment. One interesting and recently discovered mechanism by which a pathogen senses temperature occurs in L. monocytogenes and depends on the secondary structure of its global virulence gene regulator’s messenger RNA. Specifically, at temperatures ≤ 30 ºC, a stem loop structure is present in the messenger RNA of positive regulatory factor A (prfA) that masks its ribosome binding site and prevents translation. However, the stem loop structure in the prfA transcript melts at 37 ºC, unmasking the ribosome binding site and allowing translation of PrfA and subsequent positive regulation of virulence genes (Johansson et al., 2002). Similarly, the Yersinia entercolitica pYV virulence plasmid has been shown to contain intrinsic secondary structural elements that melt at 37 ºC, allowing transcription of the virulence gene activator virF (Rohde et al., 1999). Finally, many foodborne bacterial pathogens have quorum-sensing systems that allow them to sense the local concentration of autoinducer and initiate a cell density dependent response, which is often the induction of virulence gene expression. A detailed discussion of quorum-sensing systems in foodborne pathogens is beyond the scope of this chapter, but evidence for such systems have been found in Salmonella Typhimurium, E. coli O157:H7, Campylobacter spp. and Helicobacter pylori (Anand and Griffiths, 2003; Cloak et al., 2002; Joyce et al., 2000; Sperandio et al., 2003; Surette and Bassler, 1999).


107

4.5

Blocking infection

Although prevention of foodborne disease is preferable to treating it, we are still far from achieving this admirable goal. Furthermore, resistance to the frontline antibiotics is spreading among foodborne pathogens, making treatment of infections more difficult (McDermott et al., 2002; White et al., 2002). Continued research into prevention and treatment strategies for foodborne pathogens is, thus, a necessity. Some of the alternative strategies to blocking infection being pursued include the development of human vaccines against foodborne pathogens as well as the implementation of vaccination programs in food production animals. Other strategies involve the use of probiotic microorganisms or the development of novel chemotherapeutic agents.

4.5.1 Vaccines Because of its large surface area, the gastrointestinal mucosa has great immunological potential. Unfortunately, this potential has remained largely untapped in mucosal vaccine development. Currently, for example, there are only three mucosal vaccines in use: for polio, cholera and typhoid fever (Anon, 1994). While mucosal vaccines are desirable over parenteral vaccines for a number of reasons – ease of administration and the production of a strong immune response at the site of infection, for example – they have several shortcomings that need to be overcome. Current mucosal vaccines must be given in large doses relative to parenteral vaccines; they often require boosters and the immunity they produce often fades quickly (Anon, 1994). Furthermore, any orally administered mucosal vaccine for a foodborne disease must be able to withstand the same adverse conditions the pathogen itself withstands in its journey to the intestine: namely, the acidic pH of gastric fluid, bile salts and proteolytic enzymes. Two strategies for safely delivering vaccine antigens to the intestinal mucosa are currently receiving much research focus. The first strategy being pursued is the use of avirulent bacterial and viral vectors to deliver recombinant DNA vaccines to the intestinal mucosa. For many years, the pathogens L. monocytogenes and S. enterica have been the model vectors for much recombinant DNA vaccine research, but attenuated strains of Shigella, Yersinia, invasive E. coli, Mycobacterium bovis-BCG and V. cholera have also been used (Kochi et al., 2003; Loessner and Weiss, 2004) Attenuated vaccinia virus has been the choice agent for viral vector vaccine development (Legrand et al., 2004; Slobod et al., 2004). Another vaccine approach worth mentioning is the use of bacterial S-layers as combined antigen carriers and adjuvants. For example, S-layers from the bacterium Bacillus alvei conjugated to capsular oligosaccharides from Streptococcus pneumoniae have been shown to elicit a protective antibody response in mice (Jahn-Schmid et al., 1996). Finally, a more recent strategy for delivering vaccine antigens to the intestinal mucosa has been to encapsulate bacteria, viruses, or antigenic complexes in microspheres made of biodegradable biopolymers. The ability of encapsulated antigens to elicit strong local and systemic mucosal


108 immune responses has been promising in animal models of disease (Nechaeva, 2002; Ren et al., 2002). Another application of vaccines for the prevention of human foodborne diseases is the vaccination of food production animals. Ideally, vaccination of food production animals would be able to both prevent disease in the animals and eliminate the shedding of the pathogen into the environment or animal products meant for human consumption. Recent work by Potter et al. (2004) demonstrated progress in meeting both goals; specifically, they showed that vaccination of cattle with secreted proteins from E. coli O157:H7 resulted in significant reductions in the numbers of E. coli shed in the animals’ feces, the duration of fecal shedding, and the numbers of animals that shed. Similarly, several groups have been able to reduce the intestinal and caecal colonization with and fecal shedding of pathogenic Salmonella spp. in chickens by orally administering virulence-attenuated Salmonella vaccine strains or vaccine antigens (Li et al., 2004; Methner et al., 1997).

4.5.2 Probiotics As mentioned previously, an important way to prevent foodborne infection is to prevent pathogen adhesion. The resident microflora of a healthy adult serve this purpose well by competitively excluding pathogens. Scientists, interested in developing novel ways to block infection, have tried to enhance the natural abilities of our normal microflora to defend us against pathogens by developing probiotics. In brief, probiotics can be defined as bacteria such as lactobacilli, bifidobacteria and enterococci that are part of the normal intestinal microflora and that have a beneficial effect on the health of the host (Fioramonti et al., 2003). The exact mechanisms by which probiotic microorganisms aid the host are varied and not entirely understood. Aside from their role in competing against pathogens for limited space and nutrients, one way probiotics are thought to protect the host is through promoting the maturation and integrity of the gut, for example, by reducing paracellular permeability and mucus degradation. Another way probiotic microorganisms are known to protect against pathogens is via their ability to stimulate intestinal mucosal immunity (Blum and Schiffrin, 2003; Fioramonti et al., 2003). Probiotic microorganisms also help maintain intestinal homeostasis, but the mechanisms they use to do so are not clear (Blum and Schiffrin, 2003). In spite of our inadequate understanding of the mechanisms by which probiotic microorganisms exert their beneficial effects, evidence exists that supports their use in the treatment of various human health problems ranging from foodborne infections, diarrhea, cancer, food allergies, and inflammatory bowel disease (for reviews see Reid et al., 2003a,b; Salminen, 1999). Despite their medical promise, many problems remain concerning the use of probiotics. Chief among these concerns is the general lack of large-scale trials to test the safety and efficacy of probiotic use (Tannock, 2003). Regarding the safety of probiotics, specific concerns have emerged surrounding the use of enterococci as probiotic organisms because certain species possess virulence determinants and/or anti-


109 biotic resistance genes, and certain species and strains are important nosocomial pathogens (Franz et al., 2003; Kayser, 2003). Other problems concerning the use of probiotics include the scant evidence demonstrating positive benefits of probiotic usage by adults with normal intestinal health and the challenges that have been encountered in attempts to apply probiotics to the manufacture of functional foods (Salminen, 1999; Shah, 2001).

4.5.3 Novel antimicrobials targeting virulence processes Motivated by the growing problem of antibacterial resistance among pathogens, researchers have become increasingly interested in identifying new targets for antimicrobial therapy as a novel way to block infection. Processes involved in bacterial virulence are attractive targets for many reasons, one being that an antimicrobial targeting virulence would be specific for virulent organisms in the process of infection, and would spare non-virulent commensal microorganisms. Also, an antimicrobial specific for virulence processes might, at least in theory, slow the acquisition and spread of resistance. Several structures and processes have been suggested as potential antimicrobial targets, including, for example, the signaling molecules and protein receptors used in quorum-sensing systems, the sensor and regulatory proteins found in type III secretion systems, and the transpeptidase enzyme, sortase, responsible for linking cell surface proteins to the cell wall in Gram-positive bacteria. The potential of these virulence targets for antimicrobial chemotherapy as well as the challenges of virulence target identification and drug design are described extensively in the review by Alksne and Projan (2000). Despite the theoretical advantages of antimicrobials that target structures or processes involved in bacterial virulence, many practical problems remain. For example, although virulence processes are often well conserved, the specific proteins that carry those processes out may not be. Thus, even if an effective antimicrobial were to be designed against a specific virulence process, it is unclear whether the drug would be effective against multiple pathogens (broadspectrum) and, therefore, be marketable (Alksne and Projan, 2000). Even before a drug is manufactured or its marketability is considered, however, the identification of all of the signals and mechanisms inducing virulence is a formidable task.

4.6 Future trends Although great strides have been made in recent years in our understanding of the pathogenic mechanisms of and host responses to many foodborne bacterial pathogens, there is still much that remains to be learned about host–pathogen interactions, including the role of bacterial stress survival mechanisms, particularly acid tolerance and acid survival mechanisms. Importantly, because foodborne infections are established at the host–pathogen interface, most often in the intestine, research directed towards understanding the complex communications and interactions between the gut microflora and the intestinal


110 mucosa, and between different members of the gut microflora, must be a priority. Results from such work will not only contribute to a better understanding of pathogen virulence mechanisms and host intestinal defenses, but improve our understanding of how probiotics may enhance human health. Included in studies on the normal gut microflora and probiotics should be efforts to identify all microbiological members of the microflora in both healthy and diseased states, efforts to understand intra- and interspecies quorum-sensing mechanisms, as well as efforts to identify natural and potentially useful antimicrobial compounds such as bacteriocins. Research on host defense mechanisms must continue with efforts to understand the generation and maintenance of mucosal immunity. Armed with more information about the intestinal ecosystem and the interactions between all its members, we will be able to pursue research aimed at developing novel approaches to foodborne disease therapy. Another current trend that will continue to necessitate attention is the identification and control of emerging or re-emerging foodborne pathogens. There are many factors that allow a bacterial species or strain to emerge as a foodborne pathogen. Among these are changes in food manufacturing processes and consumption patterns. For example, there has been an increased demand in recent years for cold-stored, ready-to-eat (RTE) food products (Tillotson, 2002). These foods, if contaminated in the processing plant or in the home with a potential pathogen, may transmit disease because they are consumed without cooking. Listeria monocytogenes, for example, has been described as an emerging pathogen, in part due to its frequent association with RTE foods. L. monocytogenes is able to grow in processed foods stored at refrigeration temperatures and, accordingly, the risk of listeriosis increases alongside increased consumption of RTE foods stored under refrigeration for extended time periods (FDA, 2003). Another factor that may have contributed to the emergence or re-emergence of some foodborne pathogens is a decreased immunity of some populations, particularly those in countries with high standards of hygiene (Koopmans and Duizer, 2004). For example, the incidence of hepatitis A disease is lower and outbreaks are more rare in underdeveloped countries because people there become infected, clear the infection, and develop immunity while still children (Mast and Alter, 1993). Changing agricultural practices have contributed to the emerging or re-emerging foodborne pathogens. For example, a considerable increase in the number of cases of Salmonella Enteritidis occurred in Europe and the Americas beginning in the early 1960s, which has been proposed to be the result of the eradication of the avian pathogens Salmonella Pullorum and Salmonella Gallinarum from domestic fowl (Baumler et al., 2000). Another future trend is the development of new approaches to improving foodborne disease prevention. The most effective way to prevent foodborne disease is to eliminate pathogens from foods. How this is best accomplished, however, is a matter of much debate and requires much more study. One approach is to eliminate human pathogens from food production animals on the farm through administration of either antibiotics or vaccines. The efficacy of antibiotic and/or vaccination programs as well as their impact on the environment and human health


111 is a matter of concern for many. Another approach towards the elimination of pathogens from foods focuses on effective killing of pathogens during food processing and on the elimination of post-processing contamination in food processing plants, in restaurants and in the home. The implementation of Hazard Analysis and Critical Control Point (HACCP) programs and good manufacturing practices (GMP) in food processing plants has repeatedly been shown to reduce bacterial contamination of processed foods (FDA, 2003). Similarly, programs such as the Fight Bac! campaign designed by the Partnership for Food Safety Education to educate the public about food safety may help prevent foodborne disease, especially if the programs undergo assessment for effectiveness (Anderson et al., 2004; Medeiros et al., 2001). Effective approaches to the prevention of foodborne disease undoubtedly will vary with the pathogen and with the food, but those that are most effective will incorporate preventative measures both before and during food processing and will have an interdisciplinary and collaborative foundation.

4.7


Many relevant reviews have been cited throughout the text of this chapter. There are also several informative textbooks that have been written containing information about host–pathogen interactions relevant to foodborne disease. One excellent text is Microbial Pathogenesis and the Intestinal Epithelial Cell, edited by Gail Hecht (2003). Other texts include Molecular Aspects of Host–Pathogen Interactions, edited by McCrae et al. (1997), Cellular Microbiology: Bacteria– Host Interactions in Health and Disease by Henderson et al. (1999) and Molecular Infection Biology: Interactions Between Microorganisms and Cells, edited by Hacker and Heeseman (2002).

4.8 References ANON (1994), CVI Forum, 2–3. ALKSNE L E AND PROJAN S J (2000), Curr Opin Biotechnol, 11, 625–36. ALPUCHE ARANDA C M, SWANSON J A, LOOMIS W P AND MILLER S I (1992). Proc Natl Acad Sci USA, 89, 10079–83. ANAND S K AND GRIFFITHS M W (2003), Int J Food Microbiol, 85, 1–9. ANDERSON J B, SHUSTER T A, HANSEN K E, LEVY A S AND VOLK A (2004), J Am Diet Assoc, 104, 186–91. AUDIA J P, WEBB C C AND FOSTER J W (2001), Int J Med Microbiol, 291, 97–106. BALDASSARRI L, CECCHINI R, BERTUCCINI L, AMMENDOLIA M G, IOSI F, ARCIOLA C R, MONTANARO L, DI ROSA R, GHERARDI G, DICUONZO G, OREFICI G AND CRETI R (2001), Med Microbiol Immunol (Berl), 190, 113–20. BAUMLER A J, HARGIS B M AND TSOLIS R M (2000), Science, 287, 50–2. BEGLEY M, GAHAN C G AND HILL C (2002), Appl Environ Microbiol, 68, 6005–12. BENJAMIN N, O’DRISCOLL F, DOUGALL H, DUNCAN C, SMITH L, GOLDEN M AND MCKENZIE H (1994), Nature, 368, 502.


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114 PROUTY A M, SCHWESINGER W H AND GUNN J S (2002), Infect Immun, 70, 2640–9. RATLEDGE C AND DOVER L G (2000), Annu Rev Microbiol, 54, 881–941. REID G, JASS J, SEBULSKY M T AND MCCORMICK J K (2003a), Clin Microbiol Rev, 16, 658– 72. REID G, SANDERS M E, GASKINS H R, GIBSON G R, MERCENIER A, RASTALL R, ROBERFROID M, ROWLAND I, CHERBUT C AND KLAENHAMMER T R (2003b), J Clin Gastroenterol, 37, 105–18. REN J M, ZOU Q M, WANG F K, HE Q, CHEN W AND ZEN W K (2002), World J Gastroenterol, 8, 1098–102. RINCE A, LE BRETON Y, VERNEUIL N, GIARD J C, HARTKE A AND AUFFRAY Y (2003), Int J Food Microbiol, 88, 207–13. ROHDE J R, LUAN X S, ROHDE H, FOX J M AND MINNICH S A (1999), J Bacteriol, 181, 4198– 204. RUSSELL T L, BERARDI R R, BARNETT J L, DERMENTZOGLOU L C, JARVENPAA K M, SCHMALTZ S P AND DRESSMAN J B (1993), Pharm Res, 10, 187–96. SACK D A, SACK R B, NAIR G B AND SIDDIQUE A K (2004), Lancet, 363, 223–33. SALMINEN S (1999), Food Technol, 53, 66–77. SALYERS A AND WHITT D (1994), Bacterial Pathogenesis: A Molecular Approach, ASM Press, Washington, DC. SALZMAN N H, CHOU M M, DE JONG H, LIU L, PORTER E M AND PATERSON Y (2003), Infect Immunol, 71, 1109–15. SCHUCHAT A, DEAVER K A, WENGER J D, PLIKAYTIS B D, MASCOLA L, PINNER R W, REINGOLD A L AND BROOME C V (1992), JAMA, 267, 2041–5. SHAH N P (2001), Food Technol, 55, 46–53. SLIFKO T R, SMITH H V AND ROSE J B (2000), Int J Parasitol, 30, 1379–93. SLOBOD K S, LOCKEY T D, HOWLETT N, SRINIVAS R V, RENCHER S D, FREIDEN P J, DOHERTY P C. AND HURWITZ J L (2004), Eur J Clin Microbiol Infect Dis, 23, 106–10. SMITH J L (2003), J Food Prot, 66, 1292–303. SPERANDIO V, TORRES A G, JARVIS B, NATARO J P AND KAPER J B (2003), Proc Natl Acad Sci USA, 100, 8951–6. SURETTE M G AND BASSLER B L (1999), Mol Microbiol, 31, 585–95. TANNOCK G W (2003), Curr Issues Intest Microbiol, 4, 33–42. TANZI M G AND GABAY M P (2002), Pharmacotherapy, 22, 1479–83. TAYLOR R K (1991), J Chemother, 3 Suppl 1, 190–5. TILLOTSON J E (2002), Nutr Today, 37, 36–38. TURNOCK L L, SOMERS E B, FAITH N G, CZUPRYNSKI C J AND LEE W A (2002), Comp Immunol Microbiol Infect Dis, 25, 43–8. VAN VELKINBURGH J C AND GUNN J S (1999), Infect Immunol, 67, 1614–22. WATERMAN S R AND SMALL P L (1998), Appl Environ Microbiol, 64, 3882–6. WHITE D G, ZHAO S, SIMJEE S, WAGNER D D AND MCDERMOTT P F (2002), Microbes Infect, 4, 405–12. WINZER K AND WILLIAMS P (2001), Int J Med Microbiol, 291, 131–43. XU J, XU X AND VERSTRAETE W (2001), J Appl Microbiol, 90, 523–9.


115

5 Factors affecting stress response C. Dodd, The University of Nottingham, UK

5.1

Introduction: heterogeneity in cellular response to stress

Although in an exponentially growing bacterial culture there is an assumption of homogeneity, in fact cells are growing and dividing in a non-synchronous manner. This heterogeneity in expressed phenotype may become vital when cells are challenged with a stress condition: many cells within the population may die but a small proportion will survive because their exact physiological conditions at the time of exposure fitted them for survival. That such survivors show only transient physiological variation can be demonstrated by re-growing the surviving cells to form a new population. When challenged with the same stress the new population will show the same level of loss as with the first exposure. A detailed review of the reasons for population diversity in this regard has been produced by Booth (2002). When a population of cells of Escherichia coli is exposed to a sublethal stress such as moderate heat treatment (56 ºC), freeze–thaw injury, osmotic or chemical shock, a proportion of the cells die but some can be recovered on growth media. The proportion of the cells that can be recovered is dependent on a number of factors, including the level of stress imposed, the nature of the recovery conditions and the phase of growth which the cells were in when the stress was imposed. In particular, stationary phase cells show a greater tolerance to stress than exponentially growing ones. Such a response is seen in other facultative anaerobes such as Salmonella and Staphylococcus aureus but is not seen in strictly fermentative organisms such as Streptococcus mutans. Campylobacter jejuni does not mount this enhanced stress resistance phenotypic stationary-phase response but shows a reduced resistance in stationary phase (Kelly et al., 2001).


116 Table 5.1 Gene

Genes under rpoS control in Escherichia coli* Function

Reference

Genes associated with stress response katG, katE Catalase hydroperoxidase HPI, HPII respectively; Loewen and Triggs (1984) H2O2 protection otsAB Trehalose-6-phosphate synthase and trehalose-6- Kaasen et al. (1992) phosphate phosphatase; synthesis and metabolism of trehalose; protection against thermal and osmotic stress treA (osmA) Trehalose metabolism: periplasmic trehalase; Kaasen et al. (1992) growth on trehalose in high osmolarity medium csiD Putative regulatory protein involved in production Loewen and Henggeof heat resistance proteins Aronis (1994) htrE Unknown protein giving protection against Loewen and Henggethermal and osmotic stress Aronis (1994) xthA Exonuclease III; DNA repair by H2O2 and Sak et al. (1989) near-UV, hence stress resistance gorA Glutathione reductase; response to oxidative stress dinB Error-prone DNA polymerase Pol IV Layton and Foster (2003) aidB Repair of methylation damage to DNA Loewen and Henggeinduced during anaerobiosis or low pH acetate Aronis (1994) treatment; rpoS dependent in exponential phase Genes associated with cell wall and shape bol A Regulatory protein for cell shape dacC; ftsQAZ dacC

Penicillin-binding protein 6; cell wall stability

ftsQAZ

Cell division proteins involved in septum formation Putative cell-division protein; affects cell shape

ficA

Genes associated with stationary phase metabolism appY Anaerobically induced – linked to arcAB and fnr expression; regulatory protein for hyaABCDEF and cyxABappCBA operons hyaABCDEF Hydrogenase I; oxidation of hydrogen as electron operon donor in anaerobic respiration cyxAB (appCB)Cytochrome oxidase (membrane) operon appA Acid phosphatase (periplasmic enzyme); uptake of non-transportable organophosphates, works at pH 2.5 not 4–6 therefore assists in acid resistance pex Carbon starvation proteins dps Regulatory protein for starvation-induced proteins (23 proteins) glgS Glycogen synthesis


Lange and HenggeAronis (1991) Lange and HenggeAronis (1991) Wang et al. (1991) Loewen and HenggeAronis (1994) Atlung et al. (1989) Bronsted and Atlung (1994) Atlung and Bronsted, (1994) Atlung and Bronsted (1994) McCann et al. (1991) Altuvia et al. (1994). Hengge-Aronis and Fischer (1992)

117 Table 5.1 cont’d Gene

Function

Reference

Genes of uncertain function osmB Lipoprotein membrane-located Jung et al. (1990) osmY Major constituent of periplasmic space in highLoewen and Henggeosmolarity/stationary phase; capsule formation? Aronis (1994) cfa Cyclopropane fatty acid synthase Loewen and HenggeAronis (1994) wrbA Suggested repression of trp operon under Yang and Somerville starvation conditions (1993) mcc genes Microcin C7 synthesis; peptide antibiotic Diaz-Guerra et al. inhibiting protein synthesis; competitive (1989) advantage in starvation conditions? csiE Unknown function Loewen and HenggeAronis (1994) csiF Unknown function Loewen and HenggeAronis (1994) Genes associated with virulence csgBA Curli subunit induced in low osmolarity conditions; curli fibres may play a role in host intestinal tissue adhesion via fibronectin binding spv genes Virulence in Salmonella yst Virulence in Yersinia enterocolitica Unknown? Albuminase, caseinase and elastase activity in V. vulnificus

Olsén et al. (1993) Kowarz et al. (1994) Iriate et al. (1995) Hulsmann et al. (2003)

*Based on Rees et al. (1995).

5.2

Cellular stage of growth and bacterial resistance

In E. coli, entry into the stationary phase of growth is accompanied by a host of cellular changes. Cells become shorter and rounder, the mode of metabolism alters, there are changes in membrane and cell wall structure, and cells show increased resistance to a number of stresses including oxidative, near-UV irradiation, acid and osmotic stress and starvation. Central to these changes is the expression of a central regulator of stationary phase gene expression, the sigma factor σs or RpoS. Levels of this sigma factor increase at the onset of stationary phase and, when bound to the core RNA polymerase, the sigma factor confers specificity for a large series of genes and operons under its control (Table 5.1) whose expression is upregulated. Many genes under its control are themselves regulatory proteins and so RpoS is at the start of a cascade system. Typical of this is bolA which regulates the expression of a number of other genes: dacC (penicillin-binding protein 6) produces a transpeptidase involved in the cross-linkage of peptidoglycan and hence cell wall stability; ftsQAZ genes encode the cell division proteins involved in septum formation. Up-regulation of bolA increases expression of these genes leading to the observed increase in cell wall stability, resistance to penicillin and change in cell shape.


118 Although thought of as a stationary phase factor, RpoS is actually primarily associated with the transition phase from late exponential to early stationary phase, with only some genes increasing their expression in stationary phase. Not all genes under RpoS control are expressed at the same time and other regulatory factors can also be involved in their expression. Examples of early expression genes are otsAB and bolA whose expression increases in the transition period; intermediate expression genes include osmY, tre and glsS with osmB and spv being expressed later at the beginning of stationary phase. appY is expressed 2 h into stationary phase while dps is a real stationary-phase gene, its expression increasing up to three days later following onset of starvation. In addition to this, it has become increasingly recognised that high levels of active RpoS can be induced in exponential phase in response to a range of stress factors and thus rpoS is more appropriately considered as a stress regulon. This is discussed in more detail in the next section of this chapter. Many of the genes under RpoS control are involved in stress response and their induction provides an explanation for the greater resistance of stationary-phase cells to oxidative, osmotic, thermal, near-UV and starvation stresses. In some instances RpoS is the sole regulator of these genes but in others there is a network of regulatory factors. The catalase genes katG and katE are examples of this; katE expression is solely under rpoS regulation and hence its expression correlates with the levels of RpoS in the cell. katG, in contrast, is also part of another response regulon under the control of oxyR (see section below) and there is an interdependence of these regulators involved in its expression. Hence cellular levels of catalase activity are regulated in a complex manner and different genes may be involved at different stages of growth. One recently suggested role for rpoS as a stress response regulon is in the generation of stationary-phase adaptive mutations. Lombardo et al. (2004) have suggested that when cells are growing under growth-limiting conditions, the processes of adaptive point mutation and adaptive amplification occurring in the cell are under RpoS control. As such mutations may allow growth under unfavourable conditions, their generation can be considered as a stress response. Because DinB, the error-prone DNA polymerase Pol IV, is required for adaptive point mutation (McKenzie et al., 2001), this could account for the role of RpoS in this process. However, DinB is not required for adaptive amplification and so RpoS must play some other, as yet undefined, role in this response. In many other Gram-negative bacteria an rpoS gene has been identified. Among the Enterobacteriaceae, Salmonella has a similar range of genes to E. coli under RpoS control, with specific virulence genes, the plasmid encoded spv genes being under its control (Table 5.1). These genes are found associated with Salmonella enterica subspecies enterica serovars such as Typhimurium, Enteritidis and Choleraesuis and appear to be needed for systemic infection and intracellular multiplication in the liver and spleen. Virulence genes under RpoS control have also been identified in Yersinia. In Vibrio vulnificus, Hulsmann et al. (2003) showed that an rpoS mutant strain was not able to survive a range of environmental stresses, including exposure to


119 hydrogen peroxide, hyperosmolarity and acidic conditions. Park et al. (2004) have also shown the need for rpoS for the ability of exponentially growing cells of Vibrio vulnificus to survive in the presence of H2O2. In addition Hulsmann et al. (2003) showed that albuminase, caseinase and elastase activity were eliminated in their rpoS mutant and an additional two hydrolytic activities (collagenase and gelatinase) showed reduced activity compared with the wild type. Motility was also severely diminished. Thus, as well as having a role in stress response as is seen in the Enterobacteriaceae, rpoS may also have a role in virulence in V. vulnificus.

5.3

Response regulators

Although originally considered as a gene induced in stationary phase, it has become increasingly recognised that active RpoS levels are regulated by a range of factors including osmotic stress, starvation, temperature stress and acid stress, and thus rpoS is more appropriately considered as a stress regulon. Initially it seemed that a reduction or cessation of growth was the unifying signal for the action of all these stress responses. However, the induction in late exponential phase occurs with no change in growth rate, and heat shock produces accelerated growth. Thus it now seems unlikely that there is a single signal moderating the change and that different processes involved in the regulation of RpoS levels are affected by different environmental signals. A significant body of research has examined the complex network of mechanisms by which rpoS is regulated and several detailed reviews of the area have been published (Loewen and Hengge-Aronis, 1994; Hengge-Aronis, 2002; Gottesman, 2004). In this chapter only a brief overview of key control factors (based on these reviews) will be presented.

5.3.1 Regulation of RpoS Regulation of active RpoS levels occurs at all three possible stages: transcription, translation and post-translation. Transcription is increased in response to a decrease in growth rate; controlled reduction in growth rates leads to inversely related increases in transcription of five to ten-fold. Abrupt cessation of growth, e.g. via starvation, however, only increases transcription two-fold. Several promoters are involved in rpoS transcription (nlpDp1, nlpDp2, rpoSp) but the last is believed to produce the major mRNA and moreover is induced during stationary phase, while the others are not. At the translational level, an increase in translation is seen by a shift to low temperatures, hyperosmolarity, acidic pH stress or when a certain cell density is reached, e.g. during late exponential phase. Translation is regulated via secondary structure formation at the 5' end of the rpoS mRNA. The translation initiation region is base-paired with an upstream region of the mRNA (‘internal upstream anti-sense’ region; Hengge-Aronis, 2002), which results in the formation of this secondary structure, blocking the binding of the ribosome. Hence under noninduced conditions, while fair amounts of rpoS mRNA are detectable, the levels of


120 RpoS are hardly detectable. Under induction conditions, changes occur to the secondary structure, allowing more frequent translation to be initiated. Initiation of translation appears to need two key factors: the protein Hfq and small non-coding RNA regulators. Hfq is a histone-like protein that acts as an RNA chaperone, binding to the secondary structure of the mRNA. Hfq appears to act in concert with the small non-coding RNA regulators; the latter have a common feature of being complementary to the upstream anti-sense region and activating translation of rpoS by base pairing to this region. One example is the small nontranslated RNA regulator, DsrA. This binds to an internal region at the 5' end of the rpoS mRNA, which includes the transcription initiation region; effectively this is an ‘anti-antisense’ activity, which is stimulated by the presence of the Hfq protein. This activity improves ribosome accessibility to the initiation site, perhaps by changing mRNA conformation or perhaps by encouraging ribosome binding. The DsrA promoter is active only at low temperatures (107 CFU/ml significantly reduced the time taken to induce RpoS activity in a population of exponential phase Salmonella, with the induction time being halved at 108 CFU/ ml. This was not solely due to a starvation effect (Aldsworth et al., 1999a) and may be an effect of reduced oxygen levels induced by the competitive flora (Aldsworth et al., 1998b). In line with these findings, Komitopoulou et al. (2004) demonstrated that redox potential regulates RpoS levels in Salmonella Typhimurium. A drop in the oxidation/reduction potential of the growth medium occurred simultaneously with RpoS induction and entry into stationary phase. Artificially decreasing the redox potential earlier during growth reduced the time to RpoS induction and the organism entered the stationary phase prematurely. In contrast, under high redox conditions, Salmonella grew unaffected, but on entering stationary growth phase, RpoS induction did not occur. This was confirmed by greater sensitivity of such cells to heat stress. Aldsworth et al. (1998c) also showed that when different humectants (lactose, sucrose, glycerol) were used to induce osmotic stress, RpoS expression may become co-linear with growth or even precede the exit out of lag phase. The same authors showed that a similar effect could be produced with concentrations of nitrite >500 ppm. In E. coli, polyamines, putrescine and spermidine, which are present in almost


122 all living organisms, also play a protective role in H2O2-induced oxidative damage during normal aerobic growth by regulating expression of ahpC, katG and katE genes (Hengge-Aronis, 2002; Jung and Kim, 2003). In the absence of polyamines the induction of both oxyR and rpoS gene expression has been shown to be affected.

5.3.2 Other E. coli response regulators Although RpoS is a major global regulator for stress response in the cell, it must be remembered that it is not the sole regulatory system and that some genes are also regulated by other mechanisms. As discussed above, the expression of many of the H2O2-inducible genes is regulated by OxyR in E. coli (Zheng et al., 2001). Members of the OxyR regulon with established functions in the oxidative stress response are katG, ahpCF, gorA (glutathione reductase), grxA (glutaredoxin 1), trxC (thioredoxin 2), fur (repressor of iron uptake), dps, oxyS, agn43 (protein of the outer membrane) and fhuF (ferric reductase). Thus, the expression of any individual gene may sometimes be under RpoS control and sometimes under that of a different regulator.

5.3.3 Stress response regulation in other bacteria The control of RpoS as a response regulator is also different when organisms outside the Enterobacteriaceae are considered. In particular, in Pseudomonas the interaction of RpoS with acyl-homoserine lactone(AHL)-based quorum-sensing systems has received particular interest. A full review of this is outside the scope of this chapter but useful references that cover this area are Venturi (2003) and Bertani and Venturi (2004). In Gram-positive organisms, the gene most closely serving the function of rpoS is sigma B; this has been shown to be involved in the regulation of stress response (for example in Bacillus and Listeria; Völker et al., 1994; Becker et al., 1998) and in virulence of Staphylococcus (Bischoff et al., 2001).

5.4

The role of free radicals in stress responses

In a number of studies examining the induction of genes involved in responding to stresses of a varying nature, the authors have reported the induction of oxidative stress protection systems even when the organisms were not exposed to oxidative stress. Privalle and Fridovich (1987) reported that E. coli expresses superoxide dismutase (SOD) when exposed to heat stress, Belkin et al. (1996) showed that in the same organism catalase was induced when challenged with ethanol, while Armstrong-Buisseret et al. (1995) demonstrated that Staph. aureus expresses alkyl hydroperoxide reductase (AhpFC) when subjected to osmotic shock. Deficiencies in any of the protective mechanisms against oxidative stress lead to multiple stress sensitivity in bacterial cells; for example E. coli deficient in SOD was reported to be sensitive to heat, as well as O2–. (Benov and Fridovich, 1995).


123 In 1997 Dodd et al. advanced a theory suggesting that a major cause of damage in sublethally injured cells is intracellularly produced free radicals; this effect was termed the bacterial suicide response. The central tenet of the suicide response is that, when injured, actively growing cells produce a burst of free radicals, which causes oxidative damage to the cell. It is this that causes cellular injury and death and not the imposed stress per se. This occurs because, under sublethal stress, cells suffer a growth arrest but their metabolism continues to function for a time. As a consequence of this uncoupling of energy production from energy utilisation, a burst of excess free radicals occurs that is lethal to the cell. This response occurs regardless of the nature of the stress causing injury and is generic in nature. Exponential phase cells, because of their greater metabolic flux, are more susceptible to the effect than stationary phase ones and hence incur greater damage in response to sublethal injury. Aldsworth et al. (1998a, 1999b) used lucigenin, a chemiluminescent superoxide probe, to demonstrate that when E. coli was exposed to heat stress at 56 ºC for 2 min or 20 % ethanol, a burst of reactive oxygen species (ROS) is produced. A similar response was obtained from Staph. aureus under the same conditions, but not from the strictly fermentative organism Streptococcus mutans, indicating this is a response of respiratory organisms. This work was also confirmed using a chromogenic superoxide probe, the tetrazolium salt MTT (Aldsworth et al., 1998a). Aldsworth et al. (1999b) also discuss the likely pathways involved in the suicide response and the possible mechanisms of damage, although these have still to be confirmed. The suicide response hypothesis was proposed by Dodd et al. (1997) to explain the protective behaviour of a competitive microflora, at a density of 108 CFU/ml or greater, in enhancing the survival of an exponential phase population of 105 CFU/ml Salmonella Typhimurium when subjected to heat shock or to freeze–thaw injury (Duffy et al., 1995; Aldsworth et al., 1998b) which Aldsworth et al. (1998b) demonstrated was due to an RpoS-independent protective mechanism. However, the theory did explain other observations regarding stress response including the expression of oxidative stress genes in response to a variety of stresses as discussed above. The authors also suggested that the theory would explain the need to recover damaged cells under reduced nutrient conditions and the phenomenon of substrateaccelerated death reported by Postgate and Hunter (1964) and Postgate (1976). Bloomfield et al. (1998) suggested that the suicide response could explain the viable non-culturable phenotype attributed to organisms that have undergone extreme nutrient and/or other stress conditions and that will then not reculture under their normal conditions of culture, although cells can be shown to maintain metabolic activity. They suggested that organisms returned to ideal nutrient and temperature conditions for growth after such exposure would induce a burst of free radicals that the cell was not equipped to deal with, and so would fail to grow. Recent work by Kong et al. (2004) on the viable but non-culturable (VBNC) state in Vibrio vulnificus confirms the involvement of ROS species and the need for catalase for recovery, although here it would appear that the ROS are produced in the culture media and are active exogenously.


124

5.5 Summary: consequences of stress response and resistance From the work discussed in this chapter, it can be seen that there are a variety of factors affecting the expression of RpoS-dependent stress response and resistance. While entry into stationary phase is the most widely recognised mechanism, the induction of resistances following a variety of environmental stresses can also lead to induction of this stress regulon gene system. As such, the use of sublethal levels of inimical processes presents a particular problem. Exposure to sublethal levels of one stress will switch on resistance in surviving cells to a whole host of other stresses and will, in some instances, induce expression of virulence genes. Thus cells surviving one sublethal stress will be primed to survive subsequent different stresses and may be more virulent. There are obvious disadvantages in this for the food industry. However, the move towards minimal processing to improve quality of foods may mean that there is a greater risk of this occurring.

5.6


A very useful review on stationary phase gene expression and the role of RpoS can be found in Hengge-Aronis (1996). Other useful reviews have been indicated in the chapter where appropriate.

5.7 References ALDSWORTH T G, DODD C E R AND STEWART G S A B (1998a), ‘Bacterial self destruction – the ‘suicide response’ explored’, in Roda A, Pazzagli M, Kricka L J and Stanley P E, Bioluminescence and Chemiluminescence: Perspectives for the 21st Century, Chichester, John Wiley & Sons, 263–266. ALDSWORTH T G, SHARMAN R L, DODD C E R AND STEWART G S A B (1998b), ‘A competitive microflora increases the resistance of Salmonella typhimurium to inimical processes: evidence for a suicide response’, Appl Env Microbiol, 64, 1323–7. ALDSWORTH T G, CARRINGTON C, FLEGG, J, STEWART G S A B AND DODD C E R (1998c), ‘Bacterial adaptation to environmental stress: the implications for food processing’, Leatherhead Food RA Food Ind J, 1, 136–144. ALDSWORTH T G, DODD C E R AND STEWART G S A B (1999a), ‘Induction of rpoS in Salmonella typhimurium by nutrient-poor and depleted media is slower than that achieved by a competitive microflora’, Lett Appl Microbiol, 28, 255–7. ALDSWORTH T G, SHARMAN R L AND DODD C E R (1999b), ‘Bacterial suicide through stress’, Cell Mol Life Sci, 56, 378–83. ALTUVIA S, ALMIRÓN M, HUISMAN G, KOLTER R AND STORZ G (1994), ‘The dps promoter is activated by OxyR during growth and by IHF and σs in stationary phase’, Mol Microbiol, 13, 265–72. ARMSTRONG-BUISSERET L A, COLE M B AND STEWART G S A B (1995), ‘A homologue to the Escherichia coli alkyl hydroperoxide reductase AhpC is induced by osmotic upshock in Staphylococcus aureus’, Microbiology, 141, 1655–61. ATLUNG T AND BRONSTED L (1994), ‘Role of the transcriptional activator AppY in regulation of the cyx appA operon of Escherichia coli by anaerobiosis, phosphate starvation and growth phase’, J Bacteriol, 176, 15414–22.


125 ATLUNG T, NIELSEN A AND HANSEN F G (1989), ‘Isolation, characterization, and nucleotide-sequence of appY, a regulatory gene for growth-phase-dependent gene-expression in Escherichia coli’, J Bacteriol, 171, 1683–1691. BECKER L A, ÇETIN M S, HUTKINS R W AND BENSON A K (1998), ‘Identification of the gene encoding the alternative sigma factor σB from Listeria monocytogenes and its role in osmotolerance’, J Bacteriol, 180, 4547–54. BELKIN S, SMULSKI D R, VOLLMER A C, VAN DYK T K AND LAROSSA R A (1996), ‘Oxidative stress detection with Escherichia coli harboring a katG'::lux fusion’, Appl Env Microbiol, 62, 2252–6. BENOV L AND FRIDOVICH I (1995), ‘Superoxide dismutase protects against aerobic heat shock in Escherichia coli’, J Bacteriol, 177, 3344–3346. BERTANI I AND VENTURI V (2004), ‘Regulation of the N-acyl homoserine lactone-dependent quorum-sensing system in rhizosphere Pseudomonas putida WCS358 and cross-talk with the stationary-phase RpoS sigma factor and the global regulator GacA’, Appl Env Microbiol, 70(9), 5493–502. BISCHOFF M, ENTENZA J M AND GIACHINO P (2001), ‘Influence of a functional sigB operon on the global regulators sar and agr in Staphylococcus aureus’, J Bacteriol, 183(17), 5171–9. BLOOMFIELD S F, STEWART G S A B, DODD C E R, BOOTH I R AND POWER E G M (1998), ‘The viable but non-culturable phenomenon explained?’, Microbiology, 144, 1–3. BOOTH I R (2002), ‘Stress and the single cell: intrapopulation diversity is a mechanism to ensure survival upon exposure to stress’, Int J Food Microbiol, 78, 19–30. BOUGDOUR A, LELONG C AND GEISELMANN J (2004), ‘Crl, a low temperature-induced protein in Escherichia coli that binds directly to the stationary phase sigma subunit of RNA polymerase’, J Biol Chem, 279, 19540–50. BRONSTED, L AND ATLUNG T (1994), ‘Anaerobic regulation of the hydrogenase 1 (hya) operon of Escherichia coli’, J Bacteriol, 176, 5423–8. DIAZ-GUERRA L, MORENO F AND SANMILLAN J L (1989), ‘appR gene product activates transcription of microcin C7 plasmid genes’, J Bacteriol, 171, 2906–8. DODD C E R, SHARMAN R L, BLOOMFIELD S F, BOOTH I R AND STEWART G S A B (1997), ‘Inimical processes: bacterial self-destruction and sub-lethal injury’, Trends in Food Sci Technol, 8, 238–41. DUFFY G, ELLISON A, ANDERSON W, COLE M B AND STEWART G S A B (1995), ‘The use of bioluminescence to model the thermal inactivation of Salmonella typhimurium in the presence of a competitive microflora’, Appl Env Microbiol, 61, 3463–5. HENGGE-ARONIS R (1996), ‘Regulation of gene expression during entry into stationary phase’, in Neidhardt F C, Curtiss R, Ingraham J L, Lin E C C, Low K B, Magasanik B, Reznikoff W S, Riley M, Schaechter M and Umbarger H E, Escherichia and Salmonella: Cellular and Molecular Biology, Washington, ASM Press, 2nd edition, vol 1, 1497– 1512. HENGGE-ARONIS R (2002), ‘Signal transduction and regulatory mechanisms involved in control of the σs (RpoS) subunit of RNA polymerase’, Micro Mol Biol Rev, 66, 3373– 95. HENGGE-ARONIS R AND FISCHER D (1992), ‘Identification and molecular analysis of glgS, a novel growth-phase-regulated and RpoS-dependent gene involved in glycogen-synthesis in Escherichia coli’, Mol Microbiol, 6, 1877–86. HULSMANN A, ROSCHE T M, KONG I S, HASSAN H M, BEAM D M AND OLIVER J D (2003), ‘RpoS-dependent stress response and exoenzyme production in Vibrio vulnificus’, Appl Env Microbiol, 69, 6114–20. IRIATE M, STAINER I AND CORNELIS G R (1995), ‘The rpoS gene from Yersinia enterocolitica and its influence on expression of virulence factors’, Infect Immun, 63, 1840–7. JUNG I L AND KIM I G (2003), ‘Transcription of ahpC, katG, and katE genes in Escherichia coli is regulated by polyamines: polyamine-deficient mutant sensitive to H2O2-induced oxidative damage’, Biochem Biophys Res Com, 301, 915–22.


126 JUNG J U, GUITIERREZ C, MARTIN F, ARDOUREL M AND VILLAREJO M (1990), ‘Transcription of osmB, a gene encoding an Escherichia coli lipoprotein, is regulated by dual signals’, J Biol Chem, 265, 10574–81. KAASEN I, FALKENBERG P, STYRVOLD O B AND STRØM A R (1992), ‘Molecular cloning and physical mapping of the otsBA genes, which encode the osmoregulatory trehalose pathway of Escherichia coli: evidence that transcription is activated by KatF (AppR)’, J Bacteriol, 174, 889–98. KELLY A F, PARK S F, BOVILL R AND MACKEY B M (2001), ‘Survival of Campylobacter jejuni during stationary phase: evidence for the absence of a phenotypic stationary-phase response’, Appl Environ Microbiol, 67(5), 2248–54. KOMITOPOULOU E, BAINTON N J AND ADAMS M R (2004), ‘Oxidation–reduction potential regulates RpoS levels in Salmonella Typhimurium’, J Appl Microbiol, 96(2), 271–8. KONG I-S, BATES T C, HULSMANN A, HASSAN H, SMITH B E AND OLIVER J D (2004), ‘Role of catalase and oxyR in the viable but non-culturable state of Vibrio vulnificus’, FEMS Micro Ecol, 50, 133–142. KOWARZ L, COYNAULT C, ROBBESAULE V AND NOREL F T I (1994), ‘The Salmonella typhimurium katF (rpoS) gene – cloning, nucleotide-sequence, and regulation of spvR and spvABCD virulence plasmid genes’, J Bacteriol, 176, 6852–60. LANGE R AND HENGGE-ARONIS R (1991), ‘Growth-phase regulated expression of bolA and morphology of stationary-phase Escherichia coli cells are controlled by the novel sigma factor σs’, J Bacteriol, 173, 4474–81. LAYTON J C AND FOSTER P L (2003), ‘Error-prone DNA polymerase IV is controlled by the stress-response sigma factor, RpoS, in Escherichia coli’, Mol Microbiol, 50, 549–61. LOEWEN P C AND HENGGE-ARONIS R (1994). ‘The role of the sigma factor σs (KatF) in bacterial global regulation’, Ann Rev Microbiol, 4853–80. LOEWEN P C AND TRIGGS B L (1984), ‘Genetic-mapping of katF, a locus that with katE affects the synthesis of a second catalase species in Escherichia coli’, J Bacteriol, 160, 668–75. LOMBARDO M J, APONYI I AND ROSENBERG S M (2004), ‘General stress response regulator RpoS in adaptive mutation and amplification in Escherichia coli’, Genetics, 166, 669–80. MCCANN M P, KIDWELL J P AND MATIN A (1991), ‘The putative σ actor KatF has a central role in the development of starvation-mediated general resistance in Escherichia coli’, J Bacteriol, 173, 4188–94. MCKENZIE G J, LEE P L, LOMBARDO M J, HASTINGS P J AND ROSENBERG S M (2001) ‘SOS mutator DNA polymerase IV functions in adaptive mutation and not adaptive amplification’, Mol Cell, 7, 571–9. OLSÉN A, ARNQVIST A, SUKUPOLVI S AND NORMARK S (1993), ‘The RpoS sigma factor relieves H-NS-mediated transcriptional repression of csgA, the subunit gene of fibronectin binding curli in Escherichia coli’, Mol Microbiol, 7, 523–36. PARK K J, KANG M J, KIM S H, LEE H J, LIM J K, CHOI S H, PARK S J AND LEE K H (2004), ‘Isolation and characterization of rpoS from a pathogenic bacterium, Vibrio vulnificus: role of sigma(S) in survival of exponential-phase cells under oxidative stress’, J Bacteriol, 186, 3304–12. POSTGATE J R (1976), ‘Death in macrobes and microbes’, in Gray T R G and Postgate J R, The Survival of Vegetative Microbes (Society for General Microbiology Symposium Series), Cambridge University Press, 1–18. POSTGATE J R AND HUNTER J R (1964), ‘Accelerated death of Aerobacter aerogenes starved in the presence of growth-limiting substrates’, J Gen Microbiol, 34, 459–73. PRIVALLE C T AND FRIDOVICH I (1987), ‘Induction of superoxide dismutase in Escherichia coli by heat shock’, Proc Natl Acad Sci USA, 84, 2723–6. REES C E D, DODD C E R, GIBSON P T, BOOTH I R AND STEWART G S A B (1995), ‘The significance of bacteria in stationary phase to food microbiology’, Int J Food Microbiol, 28, 263–75. SAK B D, EISENSTARK A AND TOUATI D (1989), ‘Exonuclease III and the catalase


127 hydroperoxidase II in Escherichia coli are both regulated by the katF gene product’, Proc Natl Acad Sci USA, 86, 3271–5. VENTURI V (2003) ‘Control of rpoS transcription in Escherichia coli and Pseudomonas: why so different?’, Mol Microbiol, 49(1), 1–9. VÖLKER, U, ENGELMANN S, MAUL B, RIETHDORF S, VÖLKER A, SCHMID R, MACH H AND HECKER M (1994), ‘Analysis of the induction of general stress proteins of Bacillus subtilis’, Microbiology, 140, 741–52. WANG X D, DEBOER P A J AND ROTHFIELD L I (1991), ‘A factor that positively regulates cell-division by activating transcription of the major cluster of essential cell-division genes of Escherichia coli’, EMBO J, 10, 3363–72. YANG W, NI I AND SOMERVILLE R I (1993), ‘A stationary phase protein of Escherichia coli that affects the mode of association between the trp repressor protein and operator-bearing DNA’, Proc Natl Acad Sci USA, 90, 5796–800. ZHENG M, WANG X, TEMPLETON L J, SMULSKI D R, LAROSSA R A AND STORZ G (2001), ‘DNA microarray-mediated transcriptional profiling of the Escherichia coli response to hydrogen peroxide’, J Bacteriol, 183, 4562–70.


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6 Cross-protective effects of bacterial stress L. Rodriguez-Romo and A. Yousef, Ohio State University, USA

6.1

Introduction

Stress can be broadly defined as any deleterious factor or condition, i.e. physical, chemical, or biological, that adversely affects microbial growth or survival (Vorob’eva, 2004; Yousef and Courtney, 2003). When applying this practical definition to food processing, it can be assumed that many preservation treatments are considered stressful, and therefore they may significantly influence the behavior of pathogenic and spoilage microorganisms present in foods. The effect of stress on microbial populations may vary depending on the magnitude of the stress. Sublethal stress (or simply, stress) alters cells’ metabolic activities unfavorably. Consequently, this type of stress commonly leads to cell injury and growth retardation or temporary cessation. Exposure to severe (i.e. lethal) stress causes irreversible damage to microbial cells and thus decreases their viability. Exposing microorganisms to a stress may induce adaptation to subsequent lethal levels of the same type of stress. This phenomenon is referred to as stress adaptation. Microbial adaptation to a stress may also enhance cell tolerance to multiple lethal stresses. This multiple stress-adaptation response is known as cross-protection. Foodborne pathogens, as well as other microorganisms, are often exposed to stresses that cross-protect them against the lethality of various preservation factors (Table 6.1). Therefore, cross-protective responses are of paramount importance when evaluating the efficacy of intervention strategies to achieve food safety and to preserve the quality of food products.


129

6.2

Microbial stress adaptation and cross-protection

Microbial cells sense stress (e.g. disruption of ribosomes as a result of heat or changes in cell membrane fluidity during cold shock), and this triggers protective responses against the sensed deleterious conditions. These responses involve physiological adaptations, which counterbalance stress damage and allow the cells to continue their growth and ensure their survival. Microorganisms also respond to stress induced by inherent physiological change. Entry into the stationary phase, for example, triggers a general stress response, resulting in microbial resistance to multiple stresses. Responses of microorganisms to stress include: (i) synthesis of protective proteins that participate in damage repair, cell maintenance, or eradication of stress agents, (ii) transient increase in resistance to lethal factors, (iii) transformation of cells to a latent state, e.g., formation of spores or induction of viable-but-notculturable state, (iv) evasion of host’s defense mechanisms, and (v) adaptive mutations (Yousef and Courtney, 2003). 6.2.1 Industrial significance and safety implications Food is processed or preserved by application of physical, chemical or biological factors; these include heating, drying, freezing and the addition of approved preservatives. Thermal pasteurization and sterilization are the most frequently used methods to preserve foods owing to their efficacy and reliability in ensuring food safety. Conventional preservation technologies, although effective to produce safe food, may result in products with reduced nutritional and sensory quality, and therefore decreased consumer acceptability (Abee and Wouters, 1999; Lado and Yousef, 2002). Alternative food preservation technologies have been developed, and in some cases applied, to produce safe foods of high quality. These emerging technologies include high-pressure processing, ionizing radiation, pulse electric fields, and ultraviolet (UV) radiation, among others (Lado and Yousef, 2002; Sofos, 2002). During food production and processing, foodborne microorganisms may encounter a wide variety of stresses including (i) physical treatments, e.g. heat, pressure, electric pulses, ultrasonic waves, light/radiation, and osmotic shock, (ii) added chemicals, e.g. acids, salts, and oxidants, and (iii) biological stresses such as competition, bacterial metabolites, and antagonism (Abee and Wouters, 1999; Beales, 2004). Microorganisms adapted to these stresses may survive or even proliferate under conditions that could have ordinarily eliminated them. Therefore, adaptation of pathogenic microorganisms, under food production and processing conditions, represents a significant food safety risk. Microorganisms can attach to surfaces of food processing equipment to form biofilms, which consist of bacterial populations aggregated in a protective matrix of polysaccharides and deposits of nutrients and minerals (Poulsen, 1999). Cells in biofilms are exposed to starvation, dehydration and oxidative stress, which could induce stress adaptation and cross-protective responses (Ravishankar and Juneja, 2003). In a recent study, factories processing ready-to-eat foods were sampled over a period of three years to detect Listeria spp. and Escherichia coli in


130

Table 6.1 Sublethal stresses

Examples of adaptive responses leading to cross-protection in microorganisms previously exposed to sublethal stress Cross-protective response to lethal stresses Heat

Heat

+ +

+ +

+

+

+ + + + + + Cold

+ +

+

+ + + Acid

Microorganism

References

Lactobacillus johnsonni Lactococcus lactis Listeria monocytogenes Clostridium perfringens Bacillus cereus Escherichia coli E. coli O157:H7 Shigella flexneri Pseudomonas

Walker et al. (1999) Broadbent and Lin (1999) Lou and Yousef (1997) Garcia et al. (2001) Browne and Dowds (2002) Chow and Tung (1998) Wang and Doyle (1998) Tetteh and Beuchat (2003) Park et al. (2001)

Lc. lactis Lc. lactis B. cereus Bacillus weihenstephanensis C. perfringens

Panoff et al. (1995) Broadbent and Lin (1999) Periago et al. (2002b) Periago et al. (2002a) Garcia et al. (2001)

Lactobacillus acidophilus Bacillus subtilis L. monocytogenes

Lorca and Valdez (2001) Browne and Dowds (2002) Ravishankar et al. (2000)

Cold Freezing Freeze- Osmotic Acid Ethanol Oxidative shock drying

+ +

+ +

+ +


131

+ + + + + +

+ + +

+

+ +

+ + Osmotic

+ + + +

+ +


+

+

L. monocytogenes Salmonella Typhimurium Salmonella Typhimurium Salmonella spp. Vibrio parahaemolyticus E. coli E. coli O157:H7, Salmonella enterica L. monocytogenes E. coli O157:H7 E. coli O157:H7

Faleiro et al. (2003) Leyer and Johnson (1993) Tosun and Gönül (2003) Bacon et al. (2003) Wong et al. (1998) Rowe and Kirk (2001)

Lactobacillus paracasei Lb. acidophilus B. cereus B. cereus L. monocytogenes

Desmond et al. (2001) Kim et al. (2001) Browne and Dowds (2001, 2002) Periago et al. (2002b) Faleiro et al. (2003)

Mazzotta (2001) Ryu and Beuchat (1998) Duffy et al. (2000)

132 the environment. Although both microorganisms were detected at low incidence rates (0.08–0.35 %), prevalent strains were identified. It was suggested that these prevalent strains could have resulted from adaptation to multiple processing stresses, which included low temperature, wide pH range, fluctuating nutrient supply and moisture level, and cleaning and disinfection, among others (Holah et al., 2004).

6.3

Types of microbial stress adaptation

The mechanisms of bacterial defense against adverse environmental conditions can be divided into two classes, limited and multiple adaptive responses (De Angelis and Gobbetti, 2004). A limited or specific adaptive response results from microbial exposure to a sublethal dose of a physical, chemical or biological stress, which protects cells against subsequent lethal treatment with the same stress (De Angelis and Gobbetti, 2004; Sanders et al., 1999). A multiple adaptive response, also known as cross-protection, occurs when bacterial cells adapt to an inherent physiological condition or an environmental factor, which results in microbial protection against subsequent lethal treatments, including stresses to which the microorganism had not been previously exposed (De Angelis and Gobbetti, 2004; Hecker et al., 1996; Juneja and Novak, 2003; Pichereau et al., 2000). This crossprotection involves the induction of the general stress response, and it is triggered by a variety of stress conditions such as cell starvation, exposure to high or low temperatures, high osmolarity, and low pH (Hengge-Aronis, 1999; Pichereau et al., 2000). The activation of the general stress response is characterized by reduced growth rate or induced entry into stationary phase. The regulation of the general stress response has been well characterized in several microorganisms, and is under the control of the alternative sigma factors, σS and σB, in E. coli and other Gramnegative bacteria, and in Bacillus subtilis and other Gram-positive bacteria, respectively (Hengge-Aronis, 1999; Price, 2000).

6.4

Regulation of cross-protective responses

The regulation of cross-protective responses is mediated by the rpoS, a gene that encodes the σS RNA-polymerase subunit of E. coli, and other bacteria such as Shigella flexneri, and Salmonella enterica serovar Typhimurium (Abee and Wouters, 1999; Hengge-Aronis, 2000; Komitopoulou et al., 2004). Although the regulation of the general stress response has been studied in a variety of microorganisms, the regulation mechanisms covered in this section will refer to E. coli, an organism in which these mechanisms have been well characterized. During rapid growth, microbial cells not exposed to any particular stress have hardly detectable levels of σS. Exposure of these cells to stress (e.g. entry into stationary phase, high osmolarity, high or low temperature) results in rapid σS


133 accumulation, to high levels, and subsequent expression of more than 50 genes involved in stress adaptation (Hengge-Aronis, 2000). The regulation of rpoS, which determines the cellular concentration of σS, occurs at multiple levels, including transcription, translation, and post-translational modifications (i.e. σS proteolysis), with the level of control being dependent on the type of stress affecting the cells (Abbe and Wouters, 1999; Hengge-Aronis, 2000; Venturi, 2003). In general, sudden microbial exposure to lethal stresses, which requires a rapid response (i.e. a shocking stress), involves σS proteolysis-mediated regulation, while gradual stress exposure usually requires stimulation of rpoS transcription or translation (Hengge-Aronis, 2000; Ihssen and Egli, 2004). Enhanced cellular accumulation of σS occurs during microbial growth in rich medium, while cells are in transition from late exponential phase to stationary phase (Abee and Wouters, 1999; Ihssen and Egli, 2004). At the transcriptional level, the two-component system, cAMP and its receptor protein, the catabolite regulatory protein (CRP), act as negative regulators of rpoS. Conversely, small molecules such as guanosine-3',5'-bispyrophosphate (ppGpp), homoserine lactone and polyphosphate may enhance its transcription (Hengge-Aronis, 2000; Venturi, 2003). Translational control involves a series of complex mechanisms in which stress conditions such as high osmolarity, low temperature or entry into late exponential phase stimulate the translation of rpoS mRNA (Hengge-Aronis, 1999). It has been suggested that these stresses can play an important role in stabilizing the mRNA secondary structure, allowing its accessibility to ribosomes, and therefore enhancing its translation (Hengge-Aronis, 2000). Activation of rpoS mRNA-translation requires the presence of Hfq, a small mRNA binding-protein that stabilizes the secondary structure of the polynucleotide. Translation of rpoS can also be enhanced by the stabilization of the mRNA with a small RNA fragment (DsrA RNA) in cells stressed by temperature downshifts (Hengge-Aronis, 1999). Control at post-translational level involves regulation of the sigma factor proteolysis rate. In cells growing exponentially, the levels of σS are very low because of its continuous proteolysis. Sudden stresses including carbon starvation, shift to low pH, high temperature and high osmolarity prevent σS proteolysis and permit its accumulation in the cells to trigger the general stress response. Proteolysis of σS requires ClpXP protease, which is regulated by the RssB protein. The level of phosphorylation or dephosphorylation of RssB, influenced by the stresses already mentioned, determines its affinity for σS and the subsequent recognition of the σS– RssB complex by the ClpXP protease (Hengge-Aronis, 1999, 2000). The activation of the general stress response, mediated by σS, results in the expression of stress-adaptive genes, including bolA (involved in controlling cell morphology), cfa (involved in cyclopropane fatty acid synthesis), uspB (involved in ethanol resistance), and katE and katG (encode catalases), among many others (Abee and Wouters, 1999). Sensitivity of bacteria defective in the rpoS gene to a series of stresses such as heat shock, oxidative environment, starvation, acid, ethanol and UV radiation, provides additional, and indisputable, evidence of the role of σS in the control of the general stress response (Farewell et al., 1998; Hengge-Aronis, 1996).


134

6.5

Stresses that induce cross-protection

6.5.1 Heat Sublethal heat stress will be defined as the stress resulting from the exposure of cells to temperatures greater than the maximum for growth and lower than that causing considerable cell death. Response to heat stress is most obvious when this stress causes minimal (less than one log) reduction in cell population. Microorganisms are usually exposed to heat stress in the environment and during food processing. This stress causes damage in the macromolecular structure of bacterial cells and results in protein denaturation, which in turn affects microbial growth owing to disruption of metabolic activities (Russell, 2003). Bacteria respond to heat by triggering a universal protective response, generally known as the heat shock response. This response consists of the transient over-expression of a set of heat shock proteins, which protect the cells against heat damage as well as against other stresses. Heat shock proteins play a key role in repairing injured cells and participate as molecular chaperones (e.g. DnaK and GroEL) in the folding of denatured proteins (Vorob’eva, 2004). Other heat shock proteins have an ATP-dependent protease activity (e.g. ClpP), and are involved in the degradation of heat-damaged proteins (Krüger et al., 2001). Additionally, microorganisms adapt to mild heat by changing the fluidity of their cell membranes, as a result of the increase in the saturation level and the length of their fatty acids (Juneja and Novak, 2003). The transcription of the majority of the heat shock proteins in E. coli is under the control of the alternative sigma factor, σ32 (Rosen and Ron, 2002). In addition, another major sigma factor, σE, is involved in the regulation of heat-induced genes in the periplasmic space of the same microorganism (Alba and Gross, 2004; Raivio and Silhavy, 2000). In B. subtilis, the induction of the heat shock response involves several regulatory groups that include (i) the HrcA-CIRCE system, which controls the major chaperone genes, (ii) the general stress response controlled by the sigma factor, σB, and (iii) the genes encoding for the Clp protease system under the control of CtsR (Rosen and Ron, 2002; Yousef and Courtney, 2003). Several stress conditions other than heat can trigger the synthesis of heat shock proteins, and therefore induce cross-protection. These stresses include changes in pH or osmolarity, ultraviolet irradiation and the presence of substances such as ethanol, antibiotics, aromatic compounds and heavy metals (Ramos et al., 2001). Synthesis of heat shock proteins after microbial exposure to other stresses could be explained by the presence of a common stress-sensing mechanism in the cells, which involves the detection of accumulated abnormal proteins in the cytoplasm (Ramos et al., 2001; Wawrzynów et al., 1995). Cross-protective responses, induced by heat, have been previously reported for lactic acid bacteria (Table 6.1) (De Angelis and Gobbetti, 2004; Walker et al., 1999). Earlier studies suggested that stressing Lactobacillus johnsonii, a starterculture bacterium, with heat treatments at 55 ºC for 45 min increased its survival under subsequent freezing conditions (Walker et al., 1999). Similarly, thermal treatments of Lactococcus lactis, at 39 or 42 ºC for 25 min, increased its tolerance to freezing (–60 ºC), and lyophilization (Broadbent and Lin, 1999).


135 Evidence of the cross-protective effect of heat in foodborne pathogens has been previously reported (Table 6.1). Lou and Yousef (1997) indicated that stressing Listeria monocytogenes with heat (45 ºC for 60 min) protected the cells against subsequent exposure to lethal concentrations of ethanol, hydrogen peroxide and sodium chloride. Lin and Chou (2004) observed a similar behavior in the same microorganism under comparable, sublethal, stress conditions. These researchers also indicated that thermal stress at a higher temperature and shorter time (48 ºC for 10 min) than those used by Lou and Yousef protected L. monocytogenes against sodium chloride, but decreased the resistance of the pathogen to lethal concentrations of hydrogen peroxide. In a different study, Garcia et al. (2001) observed that stressing Clostridium perfringens, at 50 ºC for 30 min, cross-protected the bacterium to subsequent cold shock at 10 ºC. Similarly, Chow and Tung (1998) reported that stressing E. coli with a thermal treatment, at 42 ºC for 30 min, increased microbial survival during subsequent freezing at –80 ºC. Other studies demonstrated that Bacillus cereus, previously exposed to mild heating (43 ºC for 20 min), had enhanced resistance to lethal treatments at increased temperature (49 ºC) or acid (pH 4.6) conditions (Browne and Dowds, 2001, 2002). Similarly, heating E. coli O157:H7 at 48 ºC for 10 min increased the tolerance of the microorganism to subsequent exposure to pH 2.5 for at least 6 h (Wang and Doyle, 1998). Tetteh and Beuchat (2003) reported that stressing Sh. flexneri with heat (48 ºC for 15 min) caused an increase in the tolerance of the microorganism to acid conditions. The researchers suggested that this crossprotection could result in enhanced survival of Shigella in high-acid foods. In a different study, Park et al. (2001) observed that heat shocking Pseudomonas, at 42 ºC for 10 min, cross-protected the cells against subsequent lethal treatments with increased heat (46 ºC) or ethanol (20 %).

6.5.2 Cold Bacteria react to cold stress by inducing an adaptive response, known as the cold shock response. This response permits cells to survive under low temperatures, and could cross-protect them to different stresses. Therefore, adaptation of microorganisms to low temperatures represents a food-safety risk since refrigeration is one of the most common methods used to preserve foods (Garcia et al., 2001; Russell, 2002). The cold shock response consists of a series of physiological adjustments that include changes in the cell membrane fluidity, as a result of the increase in unsaturation or the decrease of chain length of its fatty acids, synthesis of protective DNA- and RNA-binding proteins and importation of compatible solutes (Beales, 2004; Yousef and Courtney, 2003). The cytoplasmic membrane, nucleic acids and ribosomes are involved in sensing temperature variations in microbial cells, and temperature downshifts trigger the synthesis of up to 50 different coldinduced proteins (Phadtare et al., 2000; Russell, 2002). The response of microorganisms to cold stress involves the over-expression of two types of proteins, the cold shock proteins (Csps) and the cold acclimation


136 proteins (Caps). A sudden drop in temperature induces the rapid, and transient, synthesis of Csps. Conversely, Caps are synthesized for extended time periods under continuous microbial growth at low temperatures; the expression of both protein types, however, can overlap during stress adaptation (Graumann and Marahiel, 1996; Panoff et al., 1998; Yousef and Courtney, 2003). The cold shock response has been well characterized in E. coli and its Csps fall into two classes, I and II. Class I Csps are expressed at very low levels at 37 ºC, and are induced and over-expressed after a temperature downshift to 15 ºC. These class I proteins include the major cold shock protein, CspA (a RNA- and DNA-binding chaperone), ribosomal binding factors (e.g. RbfA, CsdA), and transcriptional termination and antitermination factors (e.g. NusA) (Beales, 2004; Inouye and Phadtare, 2004; Phadtare et al., 2000), among many others. On the other hand, class II Csps are present in cells at 37 ºC, and are induced at moderate levels ( 14 days), sometimes bloody, low-grade fever, little/no vomiting.

Thought to be high – 25 Aggregative adherence fimbriae and 38 % of volunteers (AAFs), haemolysin, enterosuffered diarrhoea after aggregative heat stable toxins ingesting 108 and 1010 (EAST1), α-haemolysin activity, organisms, respectively. plasmid-encoded toxin (Pet), membrane associated proteins (MAPs).

Enteroinvasive E. coli (EIEC)

Shigella-like dysentery, mostly in developing countries.

Profuse, non-bloody diarrhoea and dysentery, fever, chills, headache, cramps, muscular pain.

Membrane-lysing toxin (invasion High – 108 required to cause diarrhoea in healthy plasmid antigens or Ipas), plasmidadults. Can be lower (e.g. encoded enterotoxin (Shigella 106) in low-acid enterotoxin or Sen). conditions.

Enteropathogenic E. coli (EPEC)

Infantile diarrhoea in developing countries, traveller’s diarrhoea, vomiting and fever.

In infants, severe diarrhoea, vomiting cramps, fever, lasting >14 days. In adults, watery stools containing mucous (not bloody), vomiting, nausea, cramps, fever.

Variable, some strains Attaching and effacing (e.g. intimin), cause disease with doses type three secretion systems (e.g. of around 106 cells. secretory proteins such as ESPs, Other strains require SEPs), haemolysins (Ehly 1 and 2), bundle-forming pili (BFP), EPEC higher doses (e.g. 1010) to cause disease. adherence factor (EAF) some produce enteroaggregative heat stable toxin (EASTI), lymphostatin.


253

Enterotoxigenic E. coli (ETEC)

Traveller’s diarrhoea Cholera-like (watery) and infantile diarrhoea diarrhoea, dehydration, in developing countries. vomiting, fever.

High – at least 107 cells necessary to cause disease in adult feeding trials.

Coli surface antigens (CSs), colonisation factor antigens (CFAs), putative colonisation factors (PCFs), curli, heat labile enterotoxins (LTs), heat stable enterotoxins (STs), enteroaggregative heat stable toxin (EAST1).

Enterohaemorrhagic E. coli (EHEC)

Shigella-like dysentery, haemolytic uraemic syndrome (HUS), thrombotic thrombocytopaenic purpura (TTP).

Thought to be very low, e.g. 10–100 cells, from outbreak studies (feeding trials not ethical because of potential for severe disease).

Vero cytotoxins (VTs or SLTs), attaching and effacing (e.g. intimin), type three secretion systems (e.g. secretory proteins such as ESPs, SEPs), EHEC haemolysin (Ehx), enteroaggregative heat stable toxin (EAST1), lymphostatin (non-O157 strains).

1

From Kothary and Babu (2001).


Watery diarrhoea, cramps, vomiting, haemorrhagic colitis (bloody diarrhoea), no fever, ischaemic colitis, chronic illness (e.g. kidney failure).

254 pedestal structures at the point of attachment. Protein (e.g. tyrosine) kinases are also thought to be involved in EPEC pathogenesis, by causing phosphorylation of proteins that results in prolonged cell-signalling in infected cells, and death of those cells (Williams et al., 1997). Among EPEC strains, there are significant differences in the virulence factors of the same and different serogroups, reflecting the different combinations of virulence factors found in the distinct clonal groups. EPEC strains are not known to produce enterotoxins and are not invasive. Although they are one of the most studied extracellular intestinal pathogens, the molecular mechanism(s) by which they cause diarrhoea are still unknown. Strains similar to EPEC are also found in rabbits, where they cause similar symptoms and lesions to those observed in human EPEC disease. ETEC and host factors ETEC-associated disease occurs in adults from industrialised countries travelling to developing countries (traveller’s diarrhoea) and also in young children in those regions. These organisms possess a wide array of adhesins (see section 10.2.1 above) that take the form of surface fimbriae and attach to the mucosa of the small intestine. The receptors are not known but are likely to be oligosaccharide residues on glycoproteins or glycolipids, and interactions between adhesins and receptors are host-specific. Therefore the adhesins that infect and cause disease in domestic new-born animals (piglets, calves and lambs) are different from human strains (Robins-Browne and Hartland, 2002). ETEC do not cause local inflammation but cause major dysfunction of electrolyte and water transport, resulting in low-grade cholera-like symptoms, by secreting two types of enterotoxin (LT and ST, see section 10.2.4). Other symptoms include vomiting, diarrhoea, cramps and nausea. Most clinical isolates of ETEC secrete only ST, with LT- and ST-secreting strains being the next most common. The intracellular accumulation of cAMP and cGMP, resulting from the activities of LT and ST respectively, promotes altered electrolyte transport by enterocytes. As a consequence, there is increased secretion of chloride by crypt cells and reduced absorption of sodium and chloride ions by villous cells. Accumulation of electrolytes in the lumen results in water ingress, from the osmotic gradient created, and if the volume exceeds the absorptive capacity of the intestine, the excess is evacuated as watery diarrhoea. LT-induced diarrhoea is also mediated by influencing prostoglandin metabolism and stimulating neurotransmitters of the enteric nervous system (Mourad and Nasser, 2000). The principal reservoir for strains causing human disease are humans, and faecal contamination of food and water is the main route of transmission. For other animals, contamination of the environment also plays a role. EIEC and host factors EIEC are closely related to Shigella species with which they share a number of key determinants. A number of these factors are encoded by a large 220 kb plasmid. They cause non-bloody diarrhoea and dysentery that is indistinguishable from that caused by S. flexneri and less severe than disease caused by S. dysenteriae. These


255 organisms cause disease mostly in developing countries, in the form of outbreaks and sporadic cases. EIEC are invasive bacteria that localise in the large intestine (colon) and penetrate epithelial cells there. After penetration, cell death occurs, releasing bacterial cells which avoid phagocytosis by inducing apoptosis in macrophages, and then enter erythrocytes. Some EIEC express IpaB, which is encoded on the virulence plasmid and this is known as a membrane-lysing toxin. The pattern of invasion, multiplication and intercellular spread provides EIEC with a distinct advantage, allowing cells to avoid host-defence mechanisms but still have access to unlimited pools of host-cell nutrients. Diarrhoea caused is typically watery and in more severe cases manifests as dysentery. The exact mechanism through which diarrhoea is caused remains to be determined, and it has been postulated that up-regulation of nitric oxide synthase and cyclooxygenase-2 by EIEC can modulate chloride secretion and barrier function in intestinal epithelial cells (Resta-Lenert and Barrett, 2002). Transmission of EIEC-related disease appears to be through contaminated food and water. EaggEC and host factors As a group of pathogenic E. coli, EaggEC (or EAEC) is rapidly gaining recognition as an important type. These organisms cause acute and persistent diarrhoea in travellers, children and adults in both developed and developing nations, and are a major cause of diarrhoea in HIV patients. Clinical signs include watery mucoid diarrhoea (sometimes bloody) with low-grade fever and little or no vomiting. These organsisms are distinguished by their unique ability to produce a pattern of aggregative adherence on cultured human colon (Hep-2) cells. The pathogenesis of disease caused by this group has only been recently established and is complex (Huang et al., 2004). Adherence to intestinal mucosa is the first step in pathogenesis and there are multiple determinants that play a role in EAggEC-related disease (see section 10.2.1). The second stage involves induction of a mucous layer forming a biofilm and this may be a cause for the mucoid stools and persistent symptoms found in EaggEC patients. An inflammatory response is then induced, accompanied by mucosal toxicity and intestinal fluid secretion. DAEC and host factors DAEC cause diarrhoea primarily in children between the ages of 1 and 5. Typical symptoms include mild diarrhoea without blood. These organisms do not produce ST or LT and do not invade epithelial cells. DAEC strains are characterised by their diffuse adherence to Hep-2 cells. This pathogroup is less well characterised than the other groups. A subgroup of DAEC designated Afa/Dr DAEC has recently been associated with recurrent gastrointestinal infections. Previously, this subgroup was only known to be associated with UTIs. In intestinal disease, adhesin binding to apical surfaces is thought to take place by recognising the decayaccelerating factors (DAF) CD55 and CD66e (carcinoembryonic antigen, CEA) associated with brush-borders. CD66e is a member of the CEA-related cell adhesion molecules (CEACAM) family, comprising seven members. Adhesion is followed by microvillar injury accompanied by cytoskeletal F-actin disassembly.


256 Some Afa/Dr DAEC strains are now known to exert a proinflammatory signal in intestinal epithelial cells (Bétis et al., 2003), and interaction of these organisms with polymorphonuclear leukocytes (PMNs) is thought to increase virulence by PMN apoptosis, through agglutination and by diminishing their phagocytic capacity (Brest et al., 2004). EHEC and host factors EHEC have gained notoriety in recent years because of their association with severe disease and relatively high mortality rates in particular subgroups of the population. This pathogroup has been responsible for large outbreaks of food and waterborne disease and is known by the prototype serovar O157:H7. EHEC produce one or two Vero cytotoxins (see section 10.2.4) and cause haemorrhagic colitis that sometimes develops into more serious conditions that include haemolytic uraemic syndrome (HUS) in children, causing kidney failure and its equivalent, thrombotic thrombocytopaenic purpura, in adults. Not all VT-producing strains (VTEC or shiga toxin-producing E. coli or STEC) cause haemorrhagic colitis, and the severity and nature of illness caused is related to the virulence determinants present within each strain. Possession of other virulence determinants (e.g. those that are encoded by the LEE pathogenicity island) influences the clinical outcome of infection with these organisms. Initial symptoms of those who become ill (some ‘infected’ individuals are asymptomatic) include severe abdominal pain, vomiting and diarrhoea. Within a few days, the diarrhoea becomes streaked with blood or grossly bloody but there is no fever. Some individuals go on to develop more serious conditions, e.g. ischaemic colitis and chronic illness. For example, up to 8 % of children develop HUS. The current model of infection proposes that VT, and perhaps endotoxin produced in the gut, are taken up, inducing proinflammatory cytokines that up-regulate Gb3 receptors on vascular endothelial cells. Outbreaks and sporadic cases occur in both industrialised and developing countries. In many regions, disease caused by EHEC is predominantly caused by O157:H7 strains, but in some regions, other serovars are the leading cause of EHEC-associated disease. Of particular concern with this pathogroup is the ability to cause infection with extremely low numbers of cells (e.g. 10). The primary reservoir of EHEC is thought to be cattle, but sheep and goats are also known to harbour this pathogen. EHEC have been associated with diarrhoeal disease in calves. As evidenced by outbreaks of disease caused by meat products, strains that are found in cattle are not host-specific. In adult cattle, EHEC do not appear to cause overt disease. Carriage and excretion rates in cattle harbouring the organism vary and will depend on a number of factors (Meng et al., 2001). At any one time, there may be between 10 and 60 % of animals harbouring VTEC and a smaller proportion (e.g. 3 % of the total) will be EHEC. Not all VTEC found in farm animals will be capable of causing disease in humans. Current thinking suggests that there are four major complexes or clonal groups comprising: O157:H7/H– ; O26:H11, O111:H8; O113:H21, O91:H21; O103:H2, O45:H2. Particular regions of the globe show patterns of emergence that appear to


257 be unique, e.g. 20–25 % of isolates in Germany are sorbitol +ve. The most common non-O157 serotypes in Germany are O26:H–, O103:H–, O111:H– and O145. In Italy, the HUS cases caused by O26 strains now outnumber those caused by O157:H7. Also, the O118 serogroup appears to be endemic in calves in Germany.

10.3.2 Environment interactions Even though the primary natural reservoir of E. coli is known to be the gastrointestinal tract of warm-blooded animals, it would be a mistake to think that the organism is not found in and capable of growth in other environments. In this respect, there are no special requirements for growth of E. coli. Indeed, since some pathotypes such as EHEC are capable of infection at very low levels, growth is almost irrelevant and survival is a key determinant of its ability to cause disease. Since cattle, sheep and goats are major reservoirs of EHEC, there will be large numbers excreted into the environment and survival therefore becomes an important consideration. Many of the recent research studies carried out on survival of E. coli in different environments tend to use E. coli O157:H7 as the test organism because of its predominance in EHEC-related disease, the serious nature of disease caused by EHEC and the low infectious dose for human illness. As a consequence of this, much of the work referred to below relates directly to this serovar. Nevertheless, despite some claims made about its ‘unusual’ ability to survive extreme conditions, there is little evidence to suggest that O157:H7 should be regarded as atypical. It is quite likely that many other serovars and types of E. coli behave in the same way and are equally able to survive in what we might regard as ‘stress’ environments. Interaction with the soil environment There are a number of studies demonstrating that E. coli can survive for relatively long periods of time in the environment outside animal hosts. Generally speaking, survival rates depend on the conditions in which the organism finds itself. For example, survival in soil is influenced by soil type, pH, water levels and presence of a rhizosphere and competition (Young and Burns, 1993). Avery et al. (2004) recently showed that naturally occurring E. coli originating from cattle, sheep and pigs were able to survive for up to 162 days during November on grass, with average decimal reduction times (D-values) of between 26 and 36 days. In a study following survival of a O128:B12 strain on fields of rye-grass, Sjogren (1995) showed survival on the surface for up to 41 days but interestingly, and perhaps not surprisingly, found that the organism penetrated some distance into the soil and was recoverable after 13 years. Some other studies in soil show shorter times of survival, e.g. Taylor and Burrow (1971) could not recover E. coli O139:K82 after 10 days when applied to land in cattle slurry. Some studies have provided evidence to support the view that the soil enviroment is able to support in situ growth. Byappanahalli and Fujioka (2004) showed that E. coli were capable of establishing themselves as minor populations in soil microflora in Hawaii. The ‘virulence’ of E. coli present in the soil environment is difficult to estimate


258 or measure. Several studies have detected the presence of virulence factors in isolates recovered directly from soil environments. Since most of the pathogroups require relatively high levels of organism to produce infection in humans, these high levels would have to be present in soil to pose a risk to public health, and this is unlikely to occur. However, for EHEC, where the infectious dose is low, even low numbers constitute a risk. This risk is confirmed by the occurrence of outbreaks of EHEC-related disease associated with handling soil and consuming fresh produce that has been in contact with soil (Ackers, 1998; Morgan et al., 1988). Interaction with water and waste environments There are a number of studies describing the fate of E. coli in water. These reports show that, as in soil, E. coli survives for many days (Rice et al., 1992) and sometimes months (Hancock et al., 1998; Wang and Doyle, 1998), depending on temperature. Worryingly, there is some evidence that starvation conditions may promote resistance to chlorine (Lisle et al., 1998). E. coli can persist in animal waste (e.g. faeces) for many days and months (Kudva et al., 1998; Wang et al., 1996).) Water has been identified as a vehicle for transmission in outbreaks of EHECassociated disease (Geldrich et al., 1992; Paunio et al., 1999). As with soil, it is unlikely that other pathogroups are likely to pose a risk in water unless conditions allow growth (e.g. through presence of nutrients and favourable temperatures) or there are substantial contamination levels. From these outbreaks it is clear that E. coli retain their virulence potential in conditions that do not allow growth. There is evidence that the VT2-carrying microorganisms are present at frequencies of 1 per 1000 faecal coliform colonies in municipal sewage and 1 per 100 faecal coliform colonies in animal wastewaters. Persistence in these environments is probably very important for maintaining the cycle of infection for EHEC. The significance of environmental persistence for other pathotypes that do not have an animal host other than humans and have a relatively high infectious dose is unclear. The presence of ‘free’ virulence factors, such as VT-encoding bacteriophage, in aquatic environments supports the view that the environment provides ‘pools’ of genes that may be acquired by E. coli at some point in time. If acquisition of those genes confers some advantage to the organism e.g. in establishing itself in a particular environment, then the new genotype is more likely to persist for longer and spread. Processing environment interactions In the food and water industries, E. coli are used to indicate faecal contamination and their presence, although unwanted, does not normally constitute a risk to public health. For that reason, specifications for foods will often have a limit for the highest levels of E. coli acceptable in a product. Since E. coli are normal inhabitants of the gut of warm-blooded animals, they may be present from time to time on untreated foods of animal origin, as a result of contamination from the gastrointestinal tract. E. coli are also used as ‘index’ organisms for presence of


259 enteric pathogens such as salmonellae. As such, E. coli is sometimes a poor index organism since it not believed to survive as well as salmonellae and other foodborne pathogens in certain food processing environments (e.g. dry conditions). Under refrigeration conditions, growth of E. coli is minimal and since it indicates faecal contamination, it is considered a good indicator in raw meat and poultry. There is no good indicator of pathogenic E. coli. The pathogroup of E. coli of most concern in food processing environments is EHEC. The environments of concern are those involving intake of raw materials of animal origin (e.g. raw meat or unpasteurised milk) or vegetables and fruit that may have come into contact with faecally contaminated soil. Since transport of these perishable materials is usually under refrigeration conditions and of short duration, there is little opportunity for multiplication to occur. In many food manufacturing environments, risk management procedures such as Hazard Analysis Critical Control Point (HACCP) should be in place. These will, if working effectively, identify where hazards (such as EHEC) may be present and have control measures in place to eliminate the hazard. There have been changes in performance standards (e.g. setting a target of 5 log10 reduction for EHEC) that will ensure certain ‘at risk’ products are free of the pathogen. As with the other environments above, if EHEC are able to survive in a food manufacturing environment, they will still pose a risk to human health. Coordinating regulation of virulence gene expression Like other enteric pathogens, E. coli has to adapt in order to survive the different environments it encounters so that it can eventually multiply. In the host environment, the organism has to synthesise virulence factors that allow it to colonise in the presence of host defence mechanisms. Expression of these virulence factors has to be controlled and the organism is able to sense the environment. Full characterisation of the events that occur is not yet available but there is knowledge from in vitro experiments allowing important factors to be identified. These factors are sometimes referred to as inimical stresses and include pH, temperature, osmolarity, iron concentration, sugars, starvation and amino acids. A higher temperature is the first environmental change that E. coli is likely to sense when it enters the body. Some proteins involved in virulence are expressed only at higher temperatures (e.g. 37 ºC). Iron availability is also an indicator of the host environment for pathogenic E. coli. Iron is often limited inside the body and low concentrations of iron induce expression of virulence factors such as haemolysin and VTs. Since E. coli first has to pass through the low pH environment of the stomach before it can reach more favourable environments, it is able to invoke an acid tolerance response and expression of adhesins is inhibited. In the small intestine, there are usually higher concentrations of monoamines and this triggers production of VTs in EHEC. Presence of particular combinations of carbon and nitrogen sources (e.g. glucose and amino acids) and osmolarity indicate to the bacterium that it is in a specific location within the gastrointestinal tract, and the organism is able to sense these. The expression of virulence genes is controlled by global regulator proteins, such as the cAMP receptor protein (CRP), the leucine


260 responsive protein (Lrp), the histone-like protein (H-NS), the sigma factor RpoS, the integration host factor (IHF), the Fur protein and the bacterial adaptive response gene product BarA. The mechanisms by which these regulators influence production of various virulence factors in E. coli in the host have been reviewed by Harel and Martin (1999). Quorum-sensing, a phenomenon that provides a means for cell-to-cell signalling and coordinating gene expression in response to cell population density, may also play a role in regulation of virulence genes in E. coli O157:H7 (Anand and Griffiths, 2003).

10.4 Types of stress affecting pathogenic strains and response mechanisms Escherichia coli and other enteropathogenic microorganisms possess an impressive ability to survive harsh conditions encountered in different environments, including the natural environment and within their hosts. Their disease-causing abilities are known to be directly related to their ability to adapt and survive in different environments. There are a number of different types of stress that microorganisms are likely to encounter and these may well be the same or similar in different environments. For example, low pH conditions are present in the host stomach, decaying organic matter, some foods and in the macrophage phagosome. On exposure to different stresses, E. coli undergoes a programmed molecular response resulting in preferential gene expression, adapted mutations and changes in cell morphology. For many bacteria, these responses are modulated by specific sigma (σ) factors or regulators. The specific genes affected comprise regulons (large numbers of coordinately controlled genes) that encode for proteins responsible for protection of the cell. These responses can be subdivided into general responses and stress specific responses. The general stress response (GSR) regulon is a large group of genes (more than 50 in E. coli) that collectively provide different functions enabling the cell to survive osmotic shock, thermal stress, pH stress, oxidative stress and nutrient depletion (Hengge-Aronis, 2000). This regulon is coordinately regulated by the product of the rpoS gene, σs, which is an alternative sigma-subunit of RNA polymerase (Loewen et al., 1998). The various stresses that E. coli is likely to encounter, and the respective responses induced, are described below. 10.4.1 Low pH and acid type One of the most frequently encountered stresses encountered by gut-inhabiting organisms such as E. coli is low pH. During passage through the gastrointestinal tract, E. coli will be exposed to low pH (e.g. pH 1.5–3.5 in the stomach) and also to volatile fatty acids present in the intestine and faeces. In foods, the organism may encounter organic acids and low pHs, used for preservation. It is critical that the organism is able to sense these harsh environments and respond to allow survival. The acid stress response of some enteric pathogens, including E. coli, has been


261 studied in detail over the past 15 years or so. Interpretation of these studies is made more difficult by the use of different test media, cells in different stages of growth and different pre-exposure and exposure conditions. In addition, different terminologies have been used, including acid resistance (AR), acid tolerance (AT), acid habituation (AH) and acid tolerance response (ATR). The three key elements of cell function that may be affected by low pH conditions are capacity for nutrient acquisition and energy production, cytoplasmic pH homeostasis and protection of proteins and DNA (Booth et al., 2002). The various studies that have been published on the subject of acid tolerance provide good evidence that there is no single response governed by a global regulator but that there are three systems that use different and sometimes overlapping mechanisms to overcome this stress. These are the oxidative (or glucose-repressed) system, the arginine-dependent system and the glutamate-dependent system (Castanie-Cornet et al., 1999). The mechanism of the oxidative system is not understood. The two amino acid-dependent systems are thought to consume protons that leak into the cell during acid stress, by decarboxylation of the amino acids that are transported into the cell. There is some overlap between the oxidative system and the glutamate-dependent system (see Audia et al., 2001). The glutamate system is thought to be the more effective of the three and involves at least three genes, gadA, gadB and gadC, which encode a γ-aminobutyrate (GABA) antiporter. The arginine-dependent system is affected by the cAMP receptor protein (Crp) and adenylate cyclase (Cya), in addition to RpoS. The mechanism by which amino acid-dependent systems help protect E. coli is likely to be alkalinisation of the periplasm. There are other mechanisms that are also important in protection. RpoS may also play a role in changing the cyclopropane fatty acid content of membranes in E. coli, and this has been implicated in increased acid tolerance (Brown et al., 1997). Control of σs is not well understood but is thought to involve the intracellular molecule guanosine 3',5'-bispyrophosphate (ppGpp) (Hirsch and Elliot, 2002). Several E. coli small RNAs are also known to coordinate stress responses or virulence factors (Gottesman, 2002). Recent work by Lease et al. (2004) has provided evidence that dissimilatory reductase (DsrA) RNA plays a regulatory role in acid resistance and suggested that this may enhance the virulence of pathogenic E. coli. Protection of DNA and cytoplasmic proteins is essential for organisms to survive rapid shifts to acidic pH environments. It is postulated that HdeA and B form associations with unfolded periplasmic proteins at low pH, thereby preventing their aggregation (Gajiwala and Burley, 2000). HdeA and B proteins are expressed from the hdeAB operon, which is under the control of RpoS and the H-NS protein. It is interesting to note that both RpoS and H-NS are targets of DsrA RNA. Other proteins, such as DNA-binding proteins (e.g. Dps), are also thought to be important for acid tolerance (Choi et al., 2000; Nair and Finkel, 2004). With low pH environments being encountered in different situations, and evidence of multiple systems of acid resistance, an interesting question that has been asked is ‘when does E. coli invoke these systems?’ Foster’s group in the USA has provided evidence that σs and gadC (involved in the glutamate-dependent


262 system) are required for survival through the bovine gastrointestinal tract (Audia et al., 2001). It is interesting that survival studies in apple juice (referred to as ‘cider’ in the USA) have shown that the two amino acid-dependent systems are not required, but the σs-dependent system is (Price et al., 2004). Evidence for the importance of pre-adaptation in enhancing survival at low pH appears to be equivocal, with some studies (e.g. Berry et al., 2004) showing this is important and others (e.g. McClure and Hall, 2000) showing that no advantage is conferred. Results appear to depend on a number of considerations including strains chosen, time of pre-adaptation exposure and acid type used. There is recent evidence that overall acid resistance is dependent on the source or lineage of strains tested (Saridakis et al., 2004), with strains of bovine origin surviving exposure to HCl better than strains of porcine or human origin. The generally held view that organic acids (e.g. lactic acid, acetic acid) are more lethal than inorganic acids (e.g. HCl) is supported by published work. However, different acid types have different effects and interpretation of the studies requires careful consideration.

10.4.2 Reduced water activity Turgor pressure is considered to be the driving force for cell extension, growth and division (Csonka, 1989) and in order to generate this, bacterial cells need to maintain an internal osmotic pressure higher than the external medium. In the different environments encountered by E. coli, osmolarity can vary dramatically, ranging from 0.06 M (0.036 % w/w) NaCl in aqueous environments to 0.3 M (0.18 % w/w) in the lumen, to much higher concentrations in foods. There are several strategies employed by bacteria for adaptation to high osmolarity. One is to increase salt (KCl) in the cytoplasm; another is accumulation, by synthesis and/or uptake of compatible organic solutes. Uptake of K+ is mediated through the TrkAG(H)E and KdpFABC systems in E. coli. The first is regulated at the level of transport activity. Uptake of K+ results in alkalinisation of the cytoplasm which is thought to signal increased glutamate synthesis. The Kdp system is regulated at the transcription and enzyme activity levels. The hypothesis for Kdp is based on a drop in turgor pressure, inducing autophosphorylation of Kdp kinase, which phosphorylates an aspartate residue in KdpE, activating expression of the kdpFABC operon (Jung et al., 1997). Compatible solutes are highly soluble molecules that do not carry any net charge and do not interact with proteins, nucleic acids and other vital cellular functions. The most widely utilised solute present in prokaryotes is glycine betaine (N,N,N-trimethyl glycine), and other osmolytes include proline and carnitine. Studies with E. coli have shown that in minimal media, potassium is the primary osmolyte accumulated under conditions of high osmolarity (or osmotic stress or reduced water activity, aw). However, in complex media, large amounts of betaine and smaller amounts of proline are accumulated, and this is accompanied by a large increase in the volume of cytoplasmic water (Cayley et al., 1992). In E. coli, like other members of the Enterobacteriaceae, this occurs via the gene products of proU and proP. Choline is also transported into the cell during osmotic stress and is


263 converted to betaine by the low-affinity systems BetT and ProU. BetA oxidises choline to glycine betaine aldehyde via choline dehydrogenase and BetB then converts this to betaine via glycine betaine aldehyde dehydrogenase. However, direct uptake of betaine and proline is energetically more favourable, and if these compounds are present in the external environment during osmotic stress, synthesis within the cell is inhibited in E. coli (Dinnbier et al., 1988). ProP and ProU are osmoregulated permeases. In E. coli, ProP transports betaine and proline and this permease is characterised by 12 transmembrane domains, presence of an extended central hydrophilic loop and a carboxy-terminal extension that is thought to form an α-helical coiled-coil (Culham et al., 1993). This coil is thought to play a role in osmosensing and activating ProP. ProU is a multicomponent binding-protein-dependent system that belongs to a family of ATP-binding cassette transporters (also referred to as traffic ATPases). The components of this system are encoded by an operon containing proV, proW and proX. Regulation of these genes is thought to be through two promoters. DNA supercoiling has also been implicated in proU expression and also through intracellular K+ concentration. These two systems have been reviewed in more detail by Sleator and Hill (2001). ProU has a much higher affinity for glycine betaine than proline. E. coli and other Gram-negative bacteria possess three uptake systems for proline, PutP, ProP and ProU (Wood, 1988) and only the latter two of these are invoved in osmoregulation, as described above. However, unlike betaine, ProP is the major contributor to osmoprotection by proline. Further strategies involved in osmoregulation do not involve compatible solutes. These include the two outer membrane porin proteins OmpC and OmpF. Increased expression of OmpC and decreased expression of OmpF is induced in high external osmolarity, enabling diffusion of small hydrophilic molecules across the cell membrane (Csonka, 1989). Other mechanisms include membrane adjustment and solute efflux and water efflux systems. The best characterised of the solute efflux systems are MscL and MscS (Levina et al., 1999). Water efflux is mediated by aquaporins, which have been recognised in higher plants and animals for some years but have only recently been identified in bacteria, including E. coli. These various strategies contribute to the survival of E. coli in high osmolarity environments, but the role of osmoprotectant systems in virulence is less clear. There have been relatively few studies that have looked at this aspect of osmoregulatory systems in E. coli. ProP may play a role in colonisation in uropathogenic E. coli, and it has been proposed (Gowrishankar and Manna, 1996) that ProU plays a role in virulence of pathogenic E. coli. Since OmpC has been implicated in invasion of epithelial cells in Shigella flexneri, it is possible this may also play a role in virulence in invasive diarrhoeagenic E. coli.

10.4.3 Non-lethal heat shock Non-lethal heat shock induces the ubiquitous heat shock response which involves production of various heat shock proteins (HSPs). This occurs primarily at the


264 level of transcription, involving the product of the rpoH (htpR gene) which is a sigma factor designated σ32 (Yura et al., 1993). This sigma factor binds to core RNA polymerase, recognising heat shock promoters. The increase in concentration of σ32 seen with higher temperatures is due to enhanced rate of translation of the rpoH mRNA. At high temperatures, σ32 becomes more stable and is translated at a higher rate. This results in transient accumulation of the σ32 protein and a corresponding increase in the rate of translation from heat shock promoters that are recognised by σ32 RNA polymerase (Morita et al., 2000). Approximately 30 proteins are associated with the heat shock regulon. A second heat shock system controlled by σE (σ24) has also been identified in E. coli (Erickson and Gross, 1989). This system senses and coordinates responses to thermal stress in the periplasm (Mecsas et al., 1993), regulating expression of htrA, htrC and rpoH. The major proteins have been identified as DnaK, DnaJ, GrpE and GroEL and are molecular chaparones protecting the cell from the detrimental effects of heat such as damage to DNA replication, RNA transcription, flagella synthesis and UV mutagenesis (Yura et al., 1993). They prevent protein denaturation and reactivate partially denatured proteins. The induction of heat shock response and temperature regulation of virulence gene expression (seen at body temperatures) in E. coli are distinct. Induction of virulence genes is directly coupled to temperature and does not decrease unless temperature is lowered, whereas the initial large increase in transcription of heat shock genes is transient. This is then followed by an adaptation phase when the level of induction falls to a lower steady-state value.

10.4.4 Response to oxidative stress Oxidative stress response is known to be important for the expression of adherence and invasion factors, and a number of regulatory systems for this have been identified in E. coli. These include the Fnr (fumarate-nitrate reductase)-dependent system, induced in anaerobic conditions and the cytochrome d oxidase system induced in semi-anaerobic conditions. The Fnr system is known to activate several genes including frd, which codes for fumarate reductase, dms, which encodes dimethyl sulphoxide-trimethylamine-N-oxide reductase, and nar, encoding nitrate reductase. A number of specific antioxidant roles are coordinately controlled by the soxRS regulon. The transcriptional induction of at least 12 promoters is coordinated by soxRS, and activation of this regulon, reviewed by Demple (1996), confers resistance to oxidants, a broad range of antibiotics and immune cells that generate nitric oxide. The DNA-binding protein Dps has also been shown to be involved in response to oxidative stress. Dps is among the most abundant proteins in stationary-phase E. coli, reaching up to 200 000 molecules per cell. Dps is also regulated by OxyR and σ70 in response to oxidative stress. Most of the studies looking at effects of Dps have focused on its role in oxidative damage protection, particularly to peroxides (Martinez and Kolter, 1997).


265 10.4.5 Metal ion stress Since free iron is extremely limited in the tissues and fluids of mammalian systems and iron is also an essential element for bacterial growth, enteric pathogens have developed systems that respond to extracellular levels of iron. As mentioned already, low iron concentration leads to increased synthesis of virulence determinants in EPEC (Calderwood and Mekalanos, 1987). Coordinate expression of iron-regulated genes involves the Fur protein as repressor and iron as the corepressor.

10.4.6 Response to other stresses and cross-protective effects There are a number of other factors that have been associated with inducing responses in bacteria including cold-shock, chemicals (e.g. disinfectants), alternative physical preservation processes such as ultra-high pressure treatment, oxidative stress, UV and gamma irradiation, and iron and copper toxicity. A number of systems already described have general effects and will also afford protection to some of these other factors. The systems known to have general protective effects include RpoS and Dps. RpoS has a central role in stress response (Rees et al., 1995). Dps has recently been shown to confer protection against UV and gamma irradiation, iron and copper toxicity, thermal stress and acid and base stress. This is thought to be achieved through a combination of functions associated with protein–DNA binding and chromosome compaction, metal chelation, ferroxidasae activity and regulation of gene expression (Nair and Finkel, 2004). Alternative sigma factor σ32 proteins are now known to be induced by hydrogen peroxide, ethanol, heavy metals, bacteriophage infection, DNA-damaging agents and the presence of abnormally folded polypeptides (Georgopoulos et al., 1990).

10.5 Summary: improving risk assessment and control in food It is evident from work carried out in recent years that there are close links between general stress response mechanisms, such as the alternative sigma factor RpoS, and expression of virulence factors in the host and survival in different environments. This started with early studies showing that bacterial infection and pathogenesis required controlled expression of the genes involved and recognition of attenuation of virulence in regulatory mutants. The availability of whole genome sequences has allowed us to compare the genetic material in different bacteria and carry out analyses of bacterial gene expression using sophisticated techniques. It is apparent that these studies have often been carried out in the context of a particular niche, in an attempt to explain and describe the interaction between the bacterium and that environment. Through comparison of microbial responses of the same organism to different niches, it is now clear that an organism may not reveal its full range of characteristics until it is exposed to a new niche or set of niches. Accordingly, if the risks posed by pathogens such as E. coli are to be properly assessed and the organisms controlled in vehicles of transmission such as


266 foods, then we need to develop approaches that enable us to understand behaviour in different environments. For dangerous pathogens such as EHEC, with low infectious doses, we cannot afford to allow even low numbers to be present in foods that are consumed.

10.5.1 Where virulence is important for risk assessment Virulence is important for a number of different elements of risk assessment. One of the first steps in risk assessment is hazard identification, which, in the case of diarrhoeagenic E. coli, involves evaluation of epidemiological, medical and food microbiological data. The epidemiological aspects will include sources of the organisms (e.g. animal reservoirs) and characteristics of strains coming from these environments. Medical aspects will include consideration of disease(s) caused, which will include understanding of the virulence traits. Microbiological data will include typical vehicles associated with disease outbreaks and potential vehicles. This is followed by exposure assessment, which determines the frequency and numbers of a pathogen that the consumer is exposed to. This includes some quantitative estimation of the prevalence and numbers in raw materials, and prediction of the fate of target organisms during stages of processing, storage, transportation and final preparation. Also included are the frequency and amount of food consumed. The stress response of E. coli to different environments is particularly relevant to this part of risk assessment. It is critical that the models used to predict the behaviour/fate of E. coli in different environments are able to predict the behaviour in real situations, and if stress responses are shown to be relevant in particular environments, then the models must be able to reflect these responses. It is also clear that strains involved in human disease outbreaks have proven virulence characteristics and other features that may allow them to survive better in particular conditions. For example, Buchanan and Edelson (1996) studied a number of EHEC strains and concluded that pH-dependent and pH-independent stationary-phase acid-tolerant phenotypes may exist. Interestingly, they also observed that three of the strains that were pH-independent acid tolerant (no need to induce acid tolerance by pre-exposing to low pH) were associated with large foodborne outbreaks. The same authors also concluded that pre-adaptation of cells is important for survival studies with acid foods. The third stage in risk assessment is hazard characterisation or dose response assessment, linking exposure estimates to adverse health consequences. For diarrhoeagenic E. coli there are data available from human volunteer studies for some of the pathogroups causing disease in humans. This allows the linking of severity of illness to dose ingested. For EHEC, this has not been possible because of the nature of disease caused and so epidemiological studies have provided valuable information in this respect. Nevertheless, great uncertainties still exist about dose response and it is fair to say that many of the human volunteer studies have been carried out with healthy individuals who are probably not representative of the more susceptible people who might be exposed to the pathogen in real situations. Other differences between human volunteer studies and real situations


267 may be the protection by foods in real situations and the virulence potential of ingested organisms. Only through a comprehensive understanding of factors important in influencing virulence potential and infection can it be possible to predict the behaviour of pathogenic forms in real situations. The final stage of risk assessment is risk characterisation, which combines information from the exposure assessment and hazard characterisation stages. The many uncertainties and variations in responses estimated can result in very conservative outputs, often because assumptions have been made and ‘worst case’ scenarios are chosen because they are ‘fail safe’.

10.5.2 Control in food Control of diarrhoeagenic E. coli in foods is now relatively well understood. Before the advent of EHEC, the controls in place for infectious pathogens in foods were capable of also controlling pathogenic E. coli. There was nothing different about these organisms compared to other enteric pathogens such as salmonellae that meant they had to treated differently. However, when EHEC emerged in the early 1980s and was associated with outbreaks of disease that were atypical, compared with other E. coli, it was clear that existing control measures were inadequate. The outbreaks causing most concern included those associated with comminuted cooked meat products (e.g. burgers), fermented meats and apple juice (called cider in the USA). Since these outbreaks, the processes used for the various commodities affected have had new controls put in place, setting higher performance standards (e.g. 5 log10 reduction in apple juice processing and fermented meat production) to ensure that products are free from EHEC at the point of consumption. More recently, there have been an increasing number of fresh foods associated with EHEC-related disease. These include seed sprouts, fresh fruit and salad vegetables. For these foods, it is more difficult to ensure that foods are free of EHEC since there are few intervention methods that can be applied without affecting taste and texture attributes that need to be retained for consumer preference.

10.5.3 Gaps in knowledge for risk assessment For the different pathogroups of E. coli described, there are a number of gaps in knowledge for risk assessment. Compared with the other pathotype of E. coli, there is relatively little information on infectious dose for EHEC and EaggEC. Much of the data available for EHEC is based on studies on one serovar, O157:H7. These studies, in particular prevalence, incidence and survival studies in animal reservoirs, environmental studies and foods, have all been made easier by the ability to selectively recover O157 using a relatively simple culturing approach involving sorbitol MacConkey (SMAC) agar plates. We are relatively ignorant about the non-O157:H7 serovars that are able to cause equally serious disease in humans. Many of these non-O157 isolates possess the full complement of virulence factors necessary to cause disease in humans. It is anticipated that there is likely to be an


268 increase in prevalence of non-O157 serogroups in human disease. There are probably pathotypes of E. coli that currently go unrecognised because we have not yet been able to isolate and characterise them. In addition, there are probably many virulence determinants that have not been recognised as such. Even though approaches to looking for pathogens in human and other animal reservoirs are improving by focusing on presence of virulence determinants in different strains, if we have not identified important virulence markers in the first place, potentially virulent strains will go undetected. It would be beneficial to understand why the infectious dose appears to differ among different pathotypes and to understand the mechanisms and virulence determinants that are essential for initial adherence and colonisation. Although the infectious dose associated with EPEC-related disease is usually with high numbers of cells, outbreaks in neonatal wards in hospitals (e.g. Bettelheim et al., 1983) and the ease of spread suggest that the infectious dose may be very low in some circumstances. There are other examples (e.g. Rowe et al., 1970, describing an outbreak of disease caused by ETEC) of rapid spread of disease where this appears to conflict with infectious dose study data. It is clear that natural conditions may differ from experimental ones and that it would not make sense to carry out further human volunteer studies to gain better understanding of the initial steps in pathogenesis, so alternative methods have to be developed, preferably without the use of animal models. Survival modelling is an important aspect of predictive modelling for pathogens such as E. coli O157:H7, particularly for products that do not rely on conventional intervention strategies such as pasteurisation. This is of particular concern in fresh foods. In such products, it may only be possible to deliver safe products by using combinations of factors, such as decontamination/washing regimes and placing heavy reliance on good agricultural practices to ensure these products remain free of even low levels of pathogens.

10.6 Future trends 10.6.1 Animal reservoirs Animal reservoirs are an important source of pathogenic E. coli. Animals, and ruminants in particular, are known to harbour different pathotypes, including ETEC, EHEC and EPEC, that sometimes cause disease in young animals. The first bacteria to develop in calves are E. coli and streptococci, and the initial sites of localisation are the forestomachs (rumen, omasum and reticulum). Colonisation occurs in hours, a number of strains will predominate at different times and most are not long-term residents. Surveys that look at E. coli in cattle have shown a wide variety of serovars and virulence determinants present in these populations. Occasionally, strains will cause disease in animals but these are not always detected by routine examination of faeces or intestinal contents (Pearson et al., 1999). The early identification of particular serovars, such as O118:H16 (Wieler et al., 1998) and O26 (Pearson et al., 1999), that possess potent combinations of


269 virulence determinants in cattle, and other animal populations can provide valuable information about future pathogens in humans. It is interesting and worrying to note that these non-O157 enterohaemorrhagic serovars have also been detected recently in cattle in South America. O26 seems to be the most prevalent serogroup found in young cattle in a number of regions around the world (Geue et al., 2003; Wani et al., 2003). There are some recent indications that pigs may also be a source of EHEC (Notario et al., 2000; Paiba et al., 2000; Prado, 2000). To date it has generally been assumed that VTEC of porcine origin were only relevant to pigs and unlikely to cause disease in humans. This is no longer the case. The prevalence of VTEC and EHEC in farm animals can be relatively high. A number of factors have been shown to influence the incidence, carriage and shedding of E. coli O157:H7 in cattle and sheep. It has been claimed that types of feed and fasting also affect shedding. The situation with regard to feeding practices remains unclear for E. coli O157:H7, although there is good evidence that for some Enterobacteriaceae, e.g. salmonellae, fasting results in proliferation. Fasted calves appear to be more susceptible to lower inoculation doses and shed significantly more E. coli O157:H7 than calves maintained on a normal diet (Rasmussen et al., 1999). It is critical to consider the importance of confounding factors when evaluating the risk factors in these experiments. Because of the current anomalies, it is felt that more work is required before any changes in management practices are implemented. Our current understanding of the ecology of EHEC in the ruminant gut is poor, and to achieve any control over the proliferation within the gut, more knowledge is needed. Given the distribution and presence of different species and survivability, eradication of pathogenic E. coli, including E. coli O157:H7 is not currently feasible. Use of ‘clean’ water/feeds, prevention of cross-contamination and multiplication, and sanitation are important. Owing to the low levels of organisms that can cause harm, testing for the presence of the pathogen will not be an effective means of control. Improved hygiene prior to slaughter is important for minimising opportunities for contamination.

10.6.2 Human health aspects Diarrhoeal diseases caused by E. coli continue to be a major public health problem worldwide. One of the most common causes of infantile diarrhoea is EPEC. This pathogroup still causes high rates of morbidity and mortality (up to 30 % case fatality rate) among children in developing countries, despite the improved understanding of pathogenesis. Several hundred thousand children die from EPEC-related disease each year (Nataro and Kaper, 1998). In developed countries, the major concern is EHEC. Mead et al. (1999) estimated that there were about 73 000 cases of O157:H7-related disease, 36 000 cases of non-O157:H7 VTEC, 79 000 cases of ETEC and 79 000 cases of other diarrhoeagenic E. coli-related illness in the US per year. The number of cases associated with non-O157 serovars appears to be generally increasing and some of this increase could be due to improved detection/


270 identification and reporting. The food types associated with EHEC-related illness are increasing, and now include unpasteurised milk/cream/cheese, ground beef, fermented meats, meat other than beef (venison, lamb, mutton), apple juice, mayonnaise, yoghurt, lettuce, (handling of) raw potatoes, radish and alfalfa sprouts, melon, cooked maize, fish roe and water. ETEC-associated disease has been linked to French Brie, curries, turkey and mayonnaise and EIEC has been linked to French Brie, Camembert cheese and canned salmon.

10.6.3 Food industry practices Control of pathogenic E. coli in foods is principally through thermal processing. E. coli O157:H7 and other diarrhoeagenic E. coli are not unusually heat resistant and will be inactivated with the current recommended processes for foods (e.g. 70 ºC for 2 min in the UK). Survival of E. coli and other members of the Enterobacteriaceae in fermented foods that are not heat-processed is possible and has resulted in outbreaks of foodborne illness. Intrinsic (e.g pH) and extrinsic (e.g. storage temperature) conditions will determine the rate of survival in these foods. Survival is better at lower temperatures of storage. If raw materials are likely to be contaminated with Enterobacteriaceae and no subsequent process exists for reducing them to acceptable levels, e.g. cooking, then control of the raw material is absolutely essential. E. coli O157 or other pathogenic E. coli are not known to be common food factory environmental contaminants, and so there should be little opportunity for post-process contamination providing good hygienic practice and good manufacturing practice are used.

10.7


There are excellent sources of information available from different sources. Many of the recent textbooks on E. coli tend to focus on EHEC O157:H7-related aspects. These include Verocytotoxigenic E. coli (eds Duffy G, Garvey P, McDowell A, Trumbull: Food and Nutrition Press, 2001), Escherichia coli O157 in Farm Animals (eds. Stewart C S, Flint H J, New York: CABI Publishing, 1999), Escherichia coli: Mechanisms of Virulence (ed. Sussman M, Cambridge: Cambridge University Press, 1997).

10.8 References ABRAHAM S N AND JAISWAL S (1997), Type-1 fimbriae of Escherichia coli. In Escherichia coli: Mechanisms of Virulence, ed M Sussman, pp. 169–92. Cambridge: Cambridge University Press. ABRAHAM S N, JONSSON A B AND NORMARK S (1998), Fimbriae-mediated host-pathogen crosstalk. Curr Opin Microbiol, 1, 75–81. ACKERS, M L, MAHON B E, LEAHY E, GOODE B, DAMROW T, HAYES P S, BIBB W F, RICE D H, BARRETT T J, HUTWAGNER L, GRIFFIN P M AND SLUTSKER L (1998), An outbreak


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279

11 Campylobacter: stress response and resistance S. Park, University of Surrey, UK

11.1

Introduction

Many foodborne bacterial pathogens are considered to be robust organisms and this is thought to reflect a requirement to survive the inimical conditions imposed by both food processing and food preservation practices. In terms of incidence, Campylobacter jejuni and C. coli are leading causes of foodborne illness worldwide, yet among this category of pathogens, they possess a unique sensitivity to environmental stress (Table 11.1). Although a number of key regulatory functions have been identified (Table 11.2), these pathogens also appear to lack many of the well-characterised adaptive responses that can be correlated with resistance to stress in other foodborne pathogens (Park, 2000). The aim of this chapter is to highlight the unusual physiology of campylobacters (C. jejuni and C. coli) and relate this to their role as foodborne pathogens. 11.1.1 Taxonomy of the genus Campylobacter The genus Campylobacter belongs to the epsilon subclass of proteobacteria and comprises slender, spirally curved, Gram-negative rods which display a characteristic corkscrew-like darting motility. However, unlike many other proteobacteria, members of this genus neither ferment nor oxidise carbohydrates and, consequently, they are considered to be asaccharolytic. Campylobacters are also considered to be microaerophilic, though some species are able to grow aerobically and some under anaerobic conditions.


280 Table 11.1 The limits of growth and temperature sensitivity of C. jejuni (compared with other common foodborne bacterial pathogens) Organism

Listeria monocytogenes Staphylococcus aureus Salmonella Typhimurium Escherichia coli Campylobacter jejuni

Temperature (ºC)

Typical Minimum Minimum Oxygen D-value aw pH requirement at 55 ºC

Min

Opt

Max

0

37

45

4.5

0.92

4.4

Facultative

7.0

37

48

3.0

0.83

4.0

Facultative

5.2 35–43 46 7–10 35–40 44–46

4.7 5.5

0.93 0.95

3.8 4.4

Facultative Facultative

30

1.0

0.987

4.9

Microaerophilic (5–10 % O2)

42–43 45

Data taken from International Commission on Microbiological Specifications for Foods (1996).

11.1.2 Campylobacters and foodborne disease Members of the genus have been recognised as agents of disease for nearly a century and, consequently, it is remarkable that it was not until 1972 that campylobacters were recognised as a significant cause of foodborne illness (Dekeyser et al., 1972). In the first full year of reporting (1978) only 6500 isolations were recorded. However, the incidence of Campylobacter enteritis has risen dramatically on an almost annual basis ever since and, today, C. jejuni and C. coli, in particular, are regarded as the leading causes of bacterial foodborne illness worldwide. In England, for example, an all-time peak of 58 059 cases of Campylobacter infectious intestinal disease were reported in 1998 (Tam, 2001). Remarkably, the true incidence of campylobacteriosis within any population may be considerably higher because of significant under-reporting. In the UK, which has sophisticated epidemiological surveillance systems, it has been established that for every case that is reported, 7.6 uncharacterised cases occur within the community (Wheeler et al., 1999). In light of these figures, and considering that the average cost per case presenting a general practitioner is £315 (Roberts et al., 2003), the socio-economic impact of Campylobacter infection is profound. In this context, it has even been suggested that the incidence of infectious intestinal disease as a whole will only be reduced if Campylobacter is specifically targeted (Adak et al., 2002). A peculiarity of Campylobacter infection is that despite the high incidence of infection, outbreaks are generally rare. As an example of the sporadic nature of infection, between 1992 and 1994, only 21 general outbreaks of Campylobacter infection were reported, and just 706 people were infected as a consequence of these, despite the fact that Campylobacter was the commonest enteric pathogen


281 Table 11.2 Protein

Known regulatory functions in C. jejuni Function

Reference

Oxidative stress PerR Negative regulator of the response to peroxide stress van Vliet et al. (1999) Stationary phase/starvation CsrA Carbon storage regulator (putative) Heat shock HspR Negative regulator of the heat shock response (putative) HrcA Negative regulator of the heat shock response (putative) RacR/ Two-component regulatory system that regulates RacS expression of genes during temperature shift (37–42 ºC)

Parkhill et al. (2000) Parkhill et al. (2000) Parkhill et al. (2000) Bras et al. (1999)

Motility and virulence FlgS/FlgR A two-component regulatory system that regulates Wosten et al. (2004) the fla regulon RpoN σ54: An alternative σ factor which regulates expression Carrillo et al. (2004) in the basal body, hook and flagella filament Wosten et al. (2004) FliA σ28: An alternative σ factor which regulates expressionCarrillo et al. (2004) of flagella filament biosynthesis and postWosten et al. (2004) translational modification FlhA Involved in the coordination and regulation of late Carrillo et al. (2004) flagella genes and certain virulence factors Iron homeostasis Fur Ferric uptake regulator: represses iron uptake systems in the presence of ferric iron

van Vliet et al. (1998)

Global regulation RpoD σ70: The primary σ factor Crp/Fnr Member of the catabolite gene activator protein/ anaerobic regulatory protein family

Wosten et al. (1998) Parkhill et al. (2000)

isolated from humans in England and Wales during this period (Pebody et al., 1997). The genus Campylobacter comprises 15 species, 12 of which have been associated with human disease (Lastovica and Skirrow, 2000), with C. jejuni being the predominant cause of Campylobacter infectious intestinal disease and accounting for 92 % of Campylobacter-associated intestinal illness. However, while C. coli accounts for only 8.1 % of Campylobacter infections in humans, the health burden is still considerable, and in 2000 alone, C. coli accounted for 25 000 cases of intestinal disease (Tam et al., 2003a). 11.1.3 Clinical aspects of Campylobacter infection The diseases caused by C. jejuni and C. coli are clinically indistinguishable and


282 since both organisms are commonly found in the gastrointestinal tract of a variety of animals, wild and domesticated, the diseases are essentially foodborne zoonoses. The manifestation of the disease differs markedly between industrialised and developing nations, and although the incidence of Campylobacter infections in developing countries is much higher than in industrialised nations, the symptoms are mostly mild and characterised by a non-inflammatory watery diarrhoea. In contrast, in industrialised nations, the usual manifestation of Campylobacter enteritis is acute inflammatory diarrhoea which generally occurs one to seven days after infection. Symptoms include bloody diarrhoea and fever, and although the acute diarrhoea lasts only two to three days, abdominal pain can persist up to seven days and a recurrence of symptoms can occur in 25 % of cases after this point. The disease is usually self-limiting, but complications such as appendicitis, bacteraemia, meningitis and other extraintestinal infections can occur. A number of sequelae are also associated with C. jejuni infection. Of these, Guillain–Barre Syndrome (GBS), an acute inflammatory demyelinating polyneuropathy, is the most fully characterised and it is estimated that approximately 0.1 % of the patients suffering from acute C. jejuni gastroenteritis subsequently develop this illness (Nachamkin et al., 2000). While Campylobacter infectious intestinal disease is not the sole predisposing factor for GBS, it is estimated that C. jejuni infection is responsible for 15 % of all GBS cases in England (Tam et al., 2003b), and in the USA as many as 1300 cases of GBS may be attributable to prior infection with this pathogen (Mishu and Blaser, 1993).

11.2 Campylobacters in the food supply 11.2.1 Growth requirements and limitations imposed Unlike many other bacterial foodborne pathogens, campylobacters have demanding growth requirements (Table 11.1) and this places unique limitations on the range of food environments in which these bacteria can multiply. In particular, the organisms have a restricted temperature growth range, and although they grow optimally at 42 ºC, they do not grow at temperatures below 30 ºC. Furthermore, the organisms are generally considered to be microaerophilic, that is they are unable to grow in the presence of air and grow optimally in atmospheres containing 5 % oxygen. This combination of growth requirements places significant limits on the ability of campylobacters to multiply outside of warm-blooded hosts and consequently, unlike most other bacterial foodborne pathogens, these bacteria are not normally capable of multiplication in food during either processing or storage. Campylobacters are also highly susceptible to a number of other environmental stresses and are generally less able to tolerate inimical conditions than other foodborne pathogens (Table 11.1). The responses of campylobacters to particular stresses are considered on an individual basis later in the chapter.


283 11.2.2 The major vehicles for foodborne transmission Many domestic and wild animals such as poultry, swine, sheep, cattle, dogs and cats carry Campylobacters in their gastrointestinal tracts without displaying overt symptoms, although disease symptoms may arise in young animals (Blaser et al., 1980). Consequently, the gastrointestinal tract of certain warm-blooded animals is considered to be the natural habitat of Campylobacter species and it is perhaps not surprising then, that the majority of human Campylobacter infections result from the ingestion of contaminated foods of animal origin. In particular, given the high rate of contamination in commercially raised chickens, and the ease with which poultry meat becomes contaminated (Herman et al., 2003), the main vehicle for C. jejuni in most industrialised nations is generally assumed be undercooked poultry or foods tainted by cross-contamination. At the retail level contamination of poultry is certainly high, and a number of recent studies have shown carriage to be 57 % (Wilson, 2002) and 68 % (Harrison et al., 2001). In the latter study, campylobacters were even isolated from the external surface of packaging in 3 % of the samples. Once poultry harbouring C. jejuni is introduced into the kitchen, it inevitably serves as a focal point for contamination, and routine cleaning with detergent and hot water has no effect on the frequency of contamination (Cogan et al., 1999). While much emphasis has been placed on the role that poultry plays in the transmission of C. jejuni, it is now evident that the sources and vehicles of Campylobacter infection are numerous and that other routes of transmission (Sopwith et al., 2003; Siemer et al., 2004) are also significant. Unpasteurised milk, for example, is regularly implicated as a vehicle for infection (Evans et al., 1996; Peterson, 2003) and contaminated drinking water, in which C. jejuni can survive for extended periods, has been the cause of several large outbreaks of Campylobacter infection (Said et al., 2003). More unusual vehicles for foodborne campylobacteriosis include garlic butter (Zhao et al., 2000), milk that has been contaminated following the action of birds pecking through the aluminium bottle tops (Riordan et al., 1993), stir-fried food (Evans et al., 1998) and tuna salad (Roels et al., 1998). More recently, on the basis of epidemiological data, bottled mineral water has been proposed as a significant vehicle for Campylobacter infection (Evans et al., 2003). Finally, pet animals are also considered to be a source of infection (Sopwith et al., 2003) and contact with puppies with diarrhoea has been shown to cause gastroenteritis in children (Blaser et al., 1980; Saeed et al., 1993). It is perhaps not surprising then that recent typing studies have revealed that populations of veterinary and human isolates of Campylobacter overlap (Manning et al., 2003).

11.2.3 The association of campylobacters with animals and physiological adaptations to these hosts The gastrointestinal tract of certain warm-blooded animals is thought to be the natural habitat and environmental reservoir for Campylobacter species. For example, C. jejuni is particularly associated with avian hosts, including poultry.


284 When poultry is experimentally inoculated with campylobacters, colonisation occurs largely in the ceca, large intestine and cloaca but generally is confined to the intestinal mucous layer in the crypts of the intestinal epithelium at these locations (Beery et al., 1988). This represents a highly specialised environment, and it is apparent that C. jejuni has evolved specialised strategies that allow it to exploit this restricted ecological niche. The optimal growth temperature for the organism (42 ºC), for example, correlates with that of the avian gut and not that of the mammalian gut (37 ºC). The mechanism of motility also appears to have evolved specifically to enable campylobacters to colonise the mucous layer of the gastrointestinal tract of animals since the polar flagellum and the spiral shape of Campylobacter species endow cells with a characteristic corkscrew-like motility. This enables campylobacters to remain motile in mucus, a highly viscous environment that rapidly paralyses other motile rod-shaped bacteria (Ferrero and Lee, 1988; Shigematsu et al., 1998). Chemotactic mechanisms are also attuned to direct campylobacters to this niche since the organisms are attracted towards mucin and, more specifically, fucose, a constituent of mucin (Hugdahl et al., 1988). As a consequence of this, colonising cells of C. jejuni can readily associate with intestinal mucus. The fact that fully motile, but non-chemotatic, mutants do not colonise the intestines of animal models (Takata et al., 1992) demonstrates the importance of chemotaxis in the life cycle of this pathogen. Campylobacters will also encounter oxygen gradients within sections of the poultry gut, with the lumen of the ceca and large intestine being strictly anaerobic, and therefore, unlikely to support the growth of campylobacters, and parts of the intestinal mucous layer being microaerobic and, therefore, supportive of growth. The fact that C. jejuni demonstrates aerotaxis (Hazelerer et al., 1998), chemotaxis with air or oxygen as a stimulus, suggests that in addition to substrate taxis, this pathogen is able to move towards environments with more favourable oxygen concentrations, such as the intestinal mucous layer. This mechanism is likely to be a further adaptation for a life cycle spent primarily in the gastrointestinal tracts of animals. The basis of the aerotaxis mechanism is not yet known, but it is notable that an analysis of the genome sequence reveals two matches to possible aerotaxis (Aer) receptor proteins (Marchant et al., 2002) and these may be involved in this process. An established feature of Campylobacter physiology is that the organisms are asaccharolytic. While this attribute precludes a large number of potential carbon and energy sources that may be utilised within the chicken gut, the substrates required for growth in the gut and, therefore colonisation, have not yet been identified. A recent study has, however, demonstrated that L-serine catabolism is important for the growth of C. jejuni in vivo since mutants that lack the serine catabolic enzyme L-serine dehydratase fail to colonise chickens (Velayudhan et al., 2004). Consequently this amino acid may be an important growth substrate in vivo. Exposure to iron limitation and bile is also an inevitable consequence of growth in the gastrointestinal tract and it appears that C. jejuni possesses mechanisms to cope with these stresses. Iron limitation results in the expression of a number of


285 transport systems for iron-containing siderophores and host-derived compounds. These systems, their regulation and their role in maintaining iron homeostasis are discussed in detail in van Vliet et al. (2002). Bile comprises a mixture of bactericidal detergents and the resistance to this agent is important during bacterial infection as reduced capacity to tolerate bile has been correlated with a loss of colonisation in a number of enteric pathogens (Lacroix et al., 1996; Bina and Mekalanos, 2001). Multidrug efflux transporters contribute to the intrinsic resistance of bacteria to many antimicrobial agents and have been implicated as a protective mechanism against bile salts (Zgurskaya, 2002). A multidrug efflux pump, designated CmeABC, has recently been characterised in C. jejuni. Since inactivation of the system results in a 4000-fold decrease in the minimum inhibitory concentration (MIC) for bile (Lin et al., 2002), it obviously plays an important role in the resistance of C. jejuni to this agent, and since deficient mutants also fail to colonise the gut of chickens, it also has an important role during colonisation (Lin et al., 2003). The ability of C. jejuni to become established in the gastrointestinal tract of chickens is also believed to involve binding of the bacterium to the surface of host epithelial cells. The outer membrane protein CadF, which facilitates the binding of campylobacters to fibronectin, is thought to be responsible for this since a deficient mutant is incapable of colonising the ceca of newly hatched chicks (Ziprin et al., 1999) . Campylobacter coli is also associated with the gastrointestinal tract of warmblooded animals but is thought to have a particular association with swine. This has been highlighted by a recent study, which demonstrated that of 660 isolates of Campylobacter recovered from pigs, 95.7 % were C. coli (Guevremont et al., 2004). However, it is not clear at present whether the prevalence of C. coli in pigs, and C. jejuni in poultry reflects host specificity and particular bacterial adaptations to these different gastrointestinal tracts, since C. coli strains isolated from pigs are excellent colonisers of poultry (Ziprin et al., 2002) and, conversely, C. jejuni may also be found commonly in pigs (Harvey, et al., 1999b).

11.3 Stress responses in food and the environment 11.3.1 The response to low temperature Campylobacters are unable to grow below 30 ºC and consequently will not multiply during food processing or food storage. Temperature, however, impacts dramatically on the survival of these pathogens in food. Chilling is known to promote the survival of C. jejuni (Chan et al., 2001) but, nevertheless, this process will generate stress and the organism must be able to respond to this in order to survive. For example, proteins are known to precipitate in cold (Nicholson and Scholtz, 1996) and cell membrane fluidity decreases. The ability of many bacteria to replicate at temperatures far below that required for optimum growth is associated with the production of characteristic cold shock proteins (Ermolenko and Makhatadze, 2002), some of which are thought to act as RNA chaperones,


286 which block the formation of secondary structures in mRNA (Yamanaka et al., 1998). The profile of protein expression in C. jejuni, however, is similar at 37, 20 and 4 ºC and the induction of specific cold shock proteins has not been observed (Hazeleger et al., 1998). The failure of C. jejuni to elicit the characteristic cold shock response is reflected by the content of the genome sequence, which lacks any genes encoding members of the CspA family of cold shock proteins (Parkhill et al., 2000). This may be an explanation for the inability of C. jejuni to grow at low temperatures. This deficiency in the general cold shock response may also explain the observation that unlike other microorganisms, which show a gradual reduction in growth rate near the minimal growth temperature, C. jejuni shows a dramatic and sudden growth rate decline near the lower temperature limit (Hazeleger et al., 1998). Despite its inability to grow at low temperatures, C. jejuni is still metabolically active at temperatures far below its minimal growth temperature and also remains motile. As a consequence of this, the organism is still able to move to favourable environments at temperatures as low as 4 ºC (Hazeleger et al., 1998). The synthetic pathway for the osmotically compatible solute trehalose is induced on exposure to cold in E. coli, and is also essential for viability at low temperatures (Kandror et al., 2002). Similarly, glycine betaine transport, while osmotically regulated, is also cryo-activated in Listeria monocytogenes (Mendum and Smith, 2002) and it too confers cryotolerance (Ko et al., 1994). Consequently, there appears to be a link between osmoprotection and cryotolerance. An analysis of the genome sequences suggests that C. jejuni does not possess any of the cryotolerance mechanisms described above and, consequently, alternative mechanisms may therefore operate. However, since substantial variability in the resistance of different strains to chilling has been reported (Chan et al., 2001), it is possible that some cryotolerance mechanisms are strain specific and that these are not present in a genome-sequenced strain but may be found in other isolates. If poultry products are frozen, C. jejuni rapidly loses viability (Humphrey and Cruickshank, 1985). Nevertheless, campylobacters can still be isolated from frozen meats and poultry products (Fernandez and Pison, 1996; Humphrey and Cruickshank, 1985). In this situation, several factors, including ice nucleation and dehydration, are implicated in the freeze-induced injury of bacterial cells. Reactive oxygen intermediates are also generated during freeze–thawing, and these may also contribute to lethal cell damage (Park et al., 1998). In this context, superoxide dismutase (SOD)-deficient mutants of C. jejuni are highly susceptible to freezing and thawing, and since the resistance of these cells to freeze–thaw is restored by freezing in the absence of oxygen, it is likely that superoxide radicals are generated during this process and that SOD is important in the resistance of campylobacters to these damaging agents (Stead and Park, 2000).

11.3.2 The response to elevated temperatures Campylobacters are sensitive to heat and readily inactivated by pasteurisation treatments and domestic cooking processes. The physiological events that take place during adaptation to elevated temperature are well documented from studies


287 with other bacteria but are not yet completely understood in campylobacters. Bacterial cells exposed to temperatures above that optimal for growth generally respond by eliciting a characteristic heat shock response involving the synthesis of proteins able to act as chaperones or ATP-dependent proteases (Arsene et al., 2000). At least 24 proteins are preferentially synthesised by C. jejuni immediately following heat shock (Konkel et al., 1998) and some have been identified as GroELS, DnaJ, DnaK and Lon protease (Konkel et al., 1998; Thies et al., 1998, 1999a,b). The fact that dnaJ mutants are severely retarded in growth at 46 ºC and are also unable to colonise chickens suggests that the heat shock response plays an important role in both thermotolerance and colonisation (Konkel et al., 1998). More recently genomic microarrays have been used to measure global changes in gene expression, in response to a temperature increase from 37 to 42 ºC (Stintzi, 2003). This relatively modest transition in temperature causes transient changes in the expression levels of approximately 20 % of all C. jejuni genes in the short term. Since many of the genes that are down-regulated during this period encode ribosomal proteins, it is predicted that a short growth arrest occurs during heat shock. After this transient phase, only a small subset of genes remain differentially expressed and, not surprisingly, many of these encode chaperones and heat shock proteins. Although it is clear that campylobacters are able to elicit a heat shock response similar to that observed in other bacteria, the regulatory mechanisms governing this response have yet to be studied in detail. There are potentially three regulatory systems controlling the induction of the heat shock response in C. jejuni. RacRS has been previously characterised as a two-component regulatory system, and is required for the differential expression of proteins at 37 and 42 ºC (Bras et al., 1999). The genome also contains genes encoding homologues of HrcA and HspR, negative regulators of the heat shock response in Bacillus subtilis (Schulz and Schuman, 1996) and Streptomyces coelicolor (Bucca et al., 1997), respectively, and it is likely that these are involved in regulating aspects of the heat shock response. Indeed, putative consensus recognition sequences for HrcA binding have been identified upstream of the groESL and dnaK operons of C. jejuni (Thies et al., 1999a,b), providing further support for a regulatory role for the HcrA orthologue. The fact that C. jejuni possesses a number of different heat shock regulatory proteins suggests that this organism may be able to regulate distinct groups of heat shock genes in response to particular types of stress.

11.3.3 The response to stationary phase and starvation Generally when bacteria enter into the stationary phase and/or encounter starvation, distinctive structural and physiological changes occur that result in increased resistance to heat shock, oxidative, osmotic and acid stress (Rees et al., 1995). For many foodborne pathogens, this adaptive process has an important bearing on the ability of the organisms to survive the stresses encountered during food processing. The central regulator for many of these stationary phase-induced changes in a number of Gram-negative bacteria is the alternative σ-factor RpoS


288 which, accordingly, is critical for the survival of the bacterial cell in stationary phase and following exposure to many types of environmental stress (Loewen et al., 1998). An analysis of the C. jejuni NCTC 11168 genome sequence (Parkhill et al., 2000) indicates that RpoS is absent from this organism, and this raises the possibility that, unlike the situation observed in other Gram-negative bacteria, campylobacters do not elicit an RpoS-mediated phenotypic stationary phase response. Indeed, stationary phase cultures of C. jejuni are actually more sensitive to mild heat stress and oxidative stress than those containing exponential phase cells (Kelly et al., 2001). This observation reflects the absence of RpoS and, in view of the increased sensitivity of cells from this growth phase (Kelly et al., 2001), it seems unlikely that there is an alternative regulator responsible for generating stationary phase resistance, at least in the strains of C. jejuni studied to date. RpoS is responsible for the regulation of many stationary-phase inducible genes. However, the expression of certain genes is also repressed in this growth stage, and the CsrA/CsrB (carbon storage regulator) forms a global regulatory system that controls the expression of some of these repressed genes (Romeo, 1998). In Escherichia coli, for example, glycogen catabolism, gluconeogenesis, glycolysis, motility and adherence are modulated by CsrA, which acts by binding to and destabilising specific mRNAs. CsrA also controls genome-wide changes in gene expression in Salmonella enterica serovar Typhimurium, but in this case, it also regulates invasion genes (Lawhon et al., 2003). C. jejuni possesses a homologue of CsrA (Cj1103) and, given the lack of other key starvation and stationary-phase regulators, it is possible that it plays a role in modulating gene expression in C. jejuni in response to starvation and/or growth phase. The response of bacteria to starvation often depends upon which individual nutrient has become depleted. Carbon, phosphorus or nitrogen starvation, for example, elicits individual responses in enteric bacteria (Spector and Foster, 1993). The response of C. jejuni to starvation is very poorly understood. Protein synthesis does occur during the first hours of starvation (Cappelier et al., 2000), but it is not clear whether this is the result of a specific induced stress response or is just a continuation of non-adaptive protein synthesis. Many bacteria synthesise storage polymers when faced with starvation and these play roles during periods of starvation and stress. E. coli and various other bacteria, for example, are able to synthesise glycogen when a specific nutrient is limiting but when carbon is in excess. During carbon limitation glycogen can then be metabolised and used as a carbon or energy source. Similarly, massive accumulations of polyphosphate, containing hundreds of phosphate residues linked by high-energy phosphoanhydride bonds, are generated in response to amino acid starvation in E. coli (Kuroda et al., 1997). While the exact function of this polymer is not yet known, it is essential for the induction of RpoS-mediated gene expression in the stationary phase in E. coli (Shiba et al., 1997) and may serve as an important energy and phosphate store in starved cells of Helicobacter pylori (Nilsson et al., 2002). C. jejuni seems unable to produce stores of glycogen since genes encoding enzymes for this pathway are not represented in the genome sequence. However,


289 the bacterium is able to produce polyphosphate (S.F. Park, unpublished), although the exact function of this polymer is not yet known.

11.3.4 Dynamic populations in the stationary phase Once a culture has reached stationary phase, the total cell population does not generally increase in number. Nevertheless, growth does not appear to cease completely since ageing cultures of E. coli have been shown to comprise highly dynamic and fluctuating cell populations (Finkel and Kolter, 1999). In this situation, mutation within a small subpopulation of the surviving cells gives rise to alleles able to confer a competitive advantage in the prevailing environment. As a consequence, the stationary phase culture is taken over by cell types that possess advantageous mutation(s). These mutants, with increased fitness, possess the socalled growth advantage in stationary phase (GASP) phenotype. This phenotype has been associated with loss of function of the global regulators, RpoS (Zambrano et al., 1993) and Lrp (Zinser and Kolter, 1999). Although C. jejuni lacks both RpoS and Lrp (Parkhill et al., 2000), a similar process, which results in the evolution of fitter mutants, may operate in this species since fluctuating changes in the heat resistance of ageing cultures of C. jejuni have been linked with the emergence and growth of new subpopulations of cells (Kelly et al., 2001). Indeed, variant cell types purified from this growth phase display much lower rates of viability loss in the early stationary phase and a small increase in resistance to aeration, peroxide challenge and heat. However, these variants do not demonstrate a convincing GASP phenotype since the strains fail to out-compete the original strain in mixed culture experiments (Martinez-Rodriguez et al., 2004). Nevertheless these variants are clearly better adapted for survival in the stationary phase and their emergence may represent an alternative mechanism for stationary phase survival.

11.3.5 The response to oxidative stress Exposure to oxygen is unavoidable for foodborne pathogens and inevitably leads to the formation of reactive oxygen intermediates (ROIs), such as superoxide radicals. If these highly reactive agents are not neutralised, lethal damage to nucleic acids, proteins and membranes may ensue. Many prokaryotic cells are able to induce the synthesis of specific anti-oxidant enzymes in response to distinct types of oxidative stress, The paradigms established in E. coli are the soxRS regulon, which coordinates the induction of at least 12 genes in response to superoxide in E. coli (Demple, 1996) and the oxyR regulon, which coordinates the response of the cell to peroxide (Storz et al., 1990). Although it is possible to grow campylobacters in the presence of air under certain conditions (Jones et al., 1993), the organisms are generally considered to be microaerophilic, implying an inherent sensitivity towards oxygen and its reduction products. Consequently, cellular defences against the damaging effects of oxidative stress play an important role in the survival of these bacteria during exposure to air. A number of enzymes play specific roles in the oxidative defence system of


290 campylobacters. An alkyl hydroperoxide reductase (AhpC), which is induced under iron limitation, detoxifies alkyl hydroperoxides, but not hydrogen peroxide, by reducing these compounds to the corresponding alcohol. This enzyme also contributes to aerotolerance (Baillon et al., 1999). In contrast, catalase, the expression of which also responds to iron limitation, detoxifies hydrogen peroxide (Grant and Park, 1995) but does not contribute to aerotolerance (Purdy et al., 1999). The deleterious effects of exposure to superoxide radicals are counteracted by the activity of SOD, and both C. jejuni and C. coli possess a single iron cofactored SOD (Pesci et al., 1994; Purdy and Park, 1994). This enzyme has been shown to play a key role in the defence against oxidative stress and aerotolerance (Purdy and Park, 1994; Purdy et al., 1999). SOD-deficient Campylobacter mutants are also less able to survive in milk, on poultry meat, and to survive freezing (Purdy et al., 1999; Stead and Park, 2000), indicating that SOD is an important determinant in the ability of campylobacters to survive in food. Furthermore, since mutants lacking this enzyme are also less able to colonise animal models (Purdy et al., 1999) and less able to invade mammalian cells in vitro (Pesci et al., 1994), SOD may also play an important role during infection. The key regulators of oxidative stress defence enzymes in E. coli and Salmonella Typhimurium, namely SoxRS and OxyR, are not present in C. jejuni (Parkhill et al., 2000), thus the recognition and response to oxidative stress in this organism are not mediated by these archetypal mechanisms. However, an alternative regulator, termed PerR, which has also been identified as a negative regulator of the peroxide responsive regulon in B. subtilis (Bsat et al., 1998), mediates at least part of the response to oxidative stress in campylobacters since it has been shown to regulate the iron-dependent gene expression of both AhpC and KatA (van Vliet et al., 1999).

11.3.6 The response to hypo-osmotic stress The introduction of the bacterial cell into water leads to a massive influx of water due to osmosis. Unless the bacterium is able to react quickly to prevent or reverse this process, cell lysis will rapidly ensue. Campylobacters are able to survive in water for extended periods (Thomas et al, 2002) but the degree of survival appears to be strain dependent (Cools et al., 2003). However, little is known about the mechanisms by which these organisms counter hypo-osmotic stress. In many bacteria, the response to hypo-osmotic shock involves both solute and water efflux. Mechanosensitive or stretch-activated channels are the primary routes for this expulsion and consequently are often activated following the transition from high to low osmolarity (Levina et al., 1999). In E. coli three types of mechanosensitive channel have been identifed: MscM (M for mini), MscS (for small) and MscL (for large) (Berrier et al., 1996). No experimental evidence concerning the molecular response of C. jejuni to low osmolarity has been published. However, H. pylori, which is closely related to C. jejuni, possesses a homologue of an MscL channel (Kloda and Martinac, 2001). Other than the protein Cj0238, which exhibits weak similarity to this, there are no obvious genes


291 in the genome sequence of NCTC 11168 that could encode alternative mechanosensitive channels in C. jejuni. Certain bacteria also possess aquaporins. These are specific channels that allow the rapid influx/efflux of water and, accordingly, they are also thought to play an essential role in maintenance of turgor during fluctuations in osmolarity (Calamita, 2000). Again there is no obvious homologue for this class of protein in C. jejuni according to the genome sequence.

11.3.7 The response to hyperosmotic stress and desiccation Campylobacters are very sensitive to desiccation and accordingly do not survive well on dry surfaces (Fernandez et al., 1985). The stresses encountered during desiccation are intimately linked with osmotic stress and, consequently, the lack of desiccation tolerance may be a reflection of their limited capacity to respond to elevated osmolarities. Campylobacters are more sensitive to osmotic stress than other bacterial foodborne pathogens and will not grow in sodium chloride concentrations of 2 % (Doyle and Roman, 1982). For comparison, Salmonella Typhimurium and L. monocytogenes will grow in concentrations of sodium chloride of 4.5 % and 10 %, respectively (International Commission on Microbiological Specifications for Foods, 1996). In the absence of any empirical information relating to osmoregulation, the genome sequence is the only source for information on the osmoregulatory capacity of C. jejuni. The most rapid response to osmotic upshock in many bacteria is the accumulation of potassium. Generally this is mediated by the activation of lowand high-affinity transport systems. C. jejuni contains at least one potassium transporter, encoded by two adjacent genes (Cj1283 and Cj1284), which resembles the KtrAB transporter previously characterised in Vibrio alginolyticus (Nakamura et al., 1998). It is not known, however, whether this system plays any role in osmoregulatory potassium transport. The Kdp system is a high-affinity ATPdriven potassium transport system, originally characterised in E. coli, which is involved in potassium transport during osmotic upshock. This system comprises the catalytic subunit (KdpB), the potassium translocating subunit (KdpA), a stabilising peptide (KdpF) and an unknown function (KbpC). At the promoterdistal end of the operon are the kdpDE genes, which encode a sensor kinase and response regulator responsible for the osmotically inducible nature of the operon (Altendorf et al.,1992 ) While the C. jejuni genome contains genes encoding the KdpB (Cj0677) catalytic subunit, other anticipated Kdp components do not appear to be functional. For example, Cj0679 encodes a truncated copy of KdpD, lacking the C-terminal two-component histidine kinase domain, while KdpA (Cj0676) and KdpC (Cj0678) are pseudogenes. Furthermore, KdpE and KdpF both appear to be completely absent. The exact role of this seemingly redundant system in C. jejuni NCTC 11168 (Parkhill et al., 2000) is not yet known but it is possible that the operon is intact in other strains. Long-term resistance to osmotic stress in other bacteria is correlated with mechanisms for the synthesis or transport of compatible solutes (Kempf and


292 Bremer, 1998). It is clear from an analysis of the C. jejuni genome sequence (Parkhill et al., 2000) that, while this organism may possess a low-affinity transporter for proline and glycine betaine similar to ProP (Cairney et al., 1985), it does not possess any previously characterised high-affinity transporters for known compatible solutes such as the ProU system for betaine transport (Stirling et al., 1989). In addition, C. jejuni lacks the capacity to synthesise compatible solutes by known pathways and accordingly does not contain the osmoregulatory betaine (Lamark et al., 1991) and trehalose (Strom and Kaasen, 1993) biosynthetic pathways seen in other bacteria. In conclusion, therefore, the apparent limited capacity for the accumulation of compatible solutes may explain the sensitivity of campylobacters to osmotic stress and desiccation.

11.3.8 The response to acid/alkali stress Compared with other foodborne pathogens, C. jejuni appears to be unusually sensitive to low pH (Cuk et al., 1987). However, acid tolerance may be strain dependent as some strains appear to have increased acid resistance compared with commonly used strains such as 81116 and NCTC 11351 (Murphy et al., 2003). One such acid-tolerant strain, CI120, exhibits an adaptive tolerance response to acid, which requires de novo protein synthesis, and which can also be induced by sublethal exposure to acid and aerobic conditions (Murphy et al., 2003). However, it is not clear at present whether this is a specific response to acid or aerobic conditions or whether this is an aspect of a more general stress response. In this context, it should be noted that proteins induced during exposure to alkaline pH have been identified as heat shock proteins (Wu et al., 1994).

11.3.9 Growth limitations imposed by oxygen and the microaerophilic nature of C. jejuni When C. jejuni is present in the mucous layer of the chicken gastrointestinal tract, it is likely to be exposed to a microaerobic atmosphere that is required for optimal growth. However, if the organisms become detached, they may enter the lumen of the colon which is strictly anaerobic. Conversely during survival on food, the organisms are likely to be exposed to aerobic conditions. C. jejuni is generally recognised as being a microaerophile and thus would not be expected to replicate either in air or in the anaerobic environment of the lumen of the gut. A number of recent studies have highlighted the delicate nature of the relationship of C. jejuni with oxygen and offer possible explanations for the microaerophilic nature of this organism. An analysis of the genome sequence reveals the presence of genes encoding a number of terminal reductases which should allow the use of a wide range of electron acceptors to oxygen such as fumarate, nitrate and nitrite. The presence of these systems suggests at least a potential to grow anaerobically. However, while these compounds stimulate growth in environments in which the rate of oxygen transfer is severely limited, they do not allow growth in the complete absence of oxygen. Consequently, these alternative respiratory pathways may


293 contribute to energy conservation, but not growth, under oxygen-limited conditions in vivo (Sellars et al., 2002). The inability of the organism to grow in the absence of oxygen may be explained by the presence of an oxygen-requiring ribonucleotide reductase and, consequently, an oxygen requirement for DNA synthesis (Sellars et al., 2002). In contrast, C. jejuni also possesses oxygen-sensitive catabolic enzymes such as the oxygen-labile L-serine dehydratase (Velayudhan et al., 2004) and this and other oxygen-sensitive enzymes may conceivably contribute to the sensitivity of the organism to air. In conclusion, it is apparent that C. jejuni possesses enzymes with conflicting requirements for oxygen, and this aspect of Campylobacter physiology may limit the growth of the organism to all but a very narrow band of oxygen concentrations.

11.3.10 Quorum-sensing Quorum-sensing, or the control of gene expression in response to cell density, allows individual bacterial cells to communicate and coordinate actions through the production and detection of extracellular signalling compounds (Bassler, 1999). Such systems also play important roles in virulence and the formation of complex communities of bacteria. For example, through this mechanism, pathogens are able to synchronise production of specific proteins needed for infection processes. This process can occur via the sensing of acyl homoserine lactone derivatives as is the case for many Gram-negative bacteria (Fuqua et al., 2001) or via the production of signalling peptides in Gram-positive organisms (Kleerebezem et al., 1997). More recently, another quorum-sensing pathway, which is widespread in both Gram-positive and Gram-negative bacteria, has been characterised (Surrette et al., 1999). This is based on LuxS which generates the signalling compound 4-hydroxy-5-methyl-3(2H)-furanone (Schauder et al., 2001). Although there is some debate as to whether all bacteria that possess LuxS use the pathway as a cell-to-cell signalling compound (Winzer et al., 2002a,b), it has been implicated in a number of cell signalling roles (Ohtani et al., 2002; Stevenson and Babb, 2002). While C. jejuni does not contain any gene predicted to encode an acyl homoserine lactone synthase and, thus, is unlikely to produce acyl homoserine lactone-based signalling molecules, it does contain LuxS and generates the cognate signalling molecule (Elvers and Park, 2002). However, the role of this signalling pathway is not yet clear.

11.3.11 Strain-dependent variations in tolerance From the above discussion it is apparent that C. jejuni does not possess many of the adaptive responses that have been established for model organisms such as E. coli and B. subtilis. Indeed, the unusual sensitivity of these organisms to stress is a striking feature compared with other foodborne pathogens (Table 11.1) and this seems to reflect low numbers of adaptive mechanisms and, consequently, a limited capacity for recognising and responding to environmental stress (Park, 2000).


294 However, since the physiology of C. jejuni is still poorly understood and the survival strategies that operate in food and the environment are not yet fully known, it is possible that alternative survival strategies do exist. In this context, C. jejuni appears to be an unusually diverse bacterial species as evidenced by variation in virulence (Everest et al., 1992; Pickett et al., 1996; Harvey et al., 1999; Purdy et al., 2000). Also, it is clear from the above discussion that tolerance to particular stresses varies greatly among Campylobacter isolates. For example, acid tolerance may be strain dependent (Murphy et al., 2003) and substantial variability is found in the ability of strains to survive prolonged periods of chilling (Chan et al., 2001) and in water (Cools et al., 2003). Furthermore, the resistance of isolates to the inimical conditions imposed by processing also varies substantially (Newell et al., 2001) and to such an extent that while many strains and subtypes are introduced into the processing environment, only the most resistant types are able to survive processing and therefore persist in the food chain (Newell et al., 2001). It is apparent then that different strains of campylobacters may have very different abilities to resist stress and this implies that strains can have important differences in genetic content. In this context, the publication of the genome sequence for one strain of C. jejuni, namely NCTC 11168 (Parkhill et al., 2000) has provided a reference point for comparing the genetic content of campylobacter strains. While chromosome heterogeneity in enteric bacteria has resulted from acquisition and deletion of large segments of DNA with changes in gene order or small changes being rare (Ochman and Bergthorsson, 1998), differences in the genetic content of campylobacters are much more extensive and are not confined to large rearrangements. In this context, as much as 21 % of genes in the genome strain are thought to be dispensable as they are absent or highly divergent in other strains (Dorrell et al., 2001). A recent study has pointed to the presence of a 1385 set of core genes that are indispensable and suggested that while the number of variable genes is still high, it may be lower than previously thought and varies from 2.6 % (40 genes) to 10.2 % (163 genes) (Pearson et al., 2003). Nevertheless, both these studies illustrate the extensive genetic diversity among C. jejuni strains and highlight the presence of additional genetic material in strains and, thus, the potential for strain-specific mechanisms of stress tolerance.

11.3.12 Adaptation through genetic evolution The presence of 25 actively polymorphic homonucleotide tracts in the genome of strain NCTC 11168 (Parkhill et al., 2000) and of numerous pseudogenes containing one frameshift mutation at potentially polymorphic regions suggest that differential expression of some genes in C. jejuni can be mediated by mutation in these hypervariable tracts. Indeed, a number of these regions have been shown to display variation and polymorphism and this can even be detected within single colonies (Wassenaar et al., 2002). Furthermore, phase variation of flagellin gene expression in C. coli has been correlated with high-frequency, reversible insertion and deletion frameshift mutations in a short homopolymeric tract of thymine


295 residues in the gene encoding the flagella regulatory protein FlhA (Park et al., 2000). Switching in certain genes may mean that only a limited number of cells in any population would survive a given stress, or be able to grow in a particular environment, but once selection had been applied these cells would survive to perpetuate the resistant phenotype. Similar genetic events might mediate the emergence of resistant strains in the stationary phase (see section 11.3.3). In addition, the emergence of variants better able to tolerate low temperatures, and which take over cultures during incubation at temperatures that would not normally allow growth, may be a manifestation of this mechanism (Kelly et al., 2003). The ease with which campylobacters can adapt via genetic variation has been highlighted by two recent studies that have investigated differences in the genotype and phenotype of variants of the genome strain (NCTC 11168). One study compared the original deposit of this strain made in 1977 with the version of the strain used as the template for the genome sequence (Gaynor et al., 2004). Extensive laboratory passage and storage, from the time of deposit, has led to dramatic differences in phenotype, such that, while the variant from the original deposit colonises animal models, the variant from which the genome sequence originates does not. Microarray analysis has identified differences in gene expression in the variants in flagellar and chemotaxis-related genes and genes encoding proteins needed for metabolism under oxygen limitation. Thus, it is possible that the genome strain has adapted to laboratory growth through an increase in oxygen tolerance, but that in doing so it has lost the ability to respond to oxygen limitation. The second such study examined two variants of NCTC 11168, deposited in different culture collections, one from the National Collection of Type Cultures, UK, and the other version from the American Type Culture Collection. This study again revealed marked differences in colonisation and virulence potential, and also highlighted down-regulated expression in genes encoding late flagella structural components and virulence factors in the genomesequenced variant (Carrillo et al., 2004). Both these studies highlight the mutable nature of C. jejuni, which can be manifested simply by laboratory subculture, and the ease with which this pathogen can adapt to multiple environmental niches by mutation and genetic evolution.

11.4 The pathogenesis of Campylobacter infection The pathogenesis of C. jejuni and C. coli infection reflects both the susceptibility of the host and the virulence of the infecting strain. One of the most notable manifestations of this is the difference in disease syndromes observed between developing and developed countries (see section 11.1.3). Generally, however, the mechanisms by which campylobacters induce disease are not clearly understood, but on the basis of empirical evidence at least two mechanisms for gastrointestinal illness have been postulated: (i) intestinal adherence and toxin production; and (ii) bacterial invasion and proliferation within the intestinal mucosa. In addition, since


296 a common feature of Campylobacter-generated enterocolitis is a localised acute inflammatory response which also leads to tissue damage, it has been suggested that this reaction may be responsible for many of the clinical symptoms of the illness (Ketley,1997).

11.4.1 Strain-dependent variations in virulence As is the case for stress tolerance, the efficiency with which Campylobacter strains invade cultured human cells also varies greatly (Everest et al., 1992; Harvey et al., 1999a) and this implies strain-specific differences in virulence. A further example of this variation in virulence comes from a study that has shown that the possession of certain plasmids by a small subset of strains is correlated with increased virulence (Bacon et al., 2000). Conversely, it is possible that certain strains of C. jejuni or C. coli may not be capable of causing disease in humans since the distributions of genotypes from poultry and humans are not necessarily the same (Korolik et al., 1995; Clow et al., 1998) and, consequently, there may be strains that present a low risk to human health (Siemer et al., 2004). In contrast, however, there appears to be little, if any, marked difference in the genetic content between strains that are responsible for GBS and those that are not associated with this sequelae, and microarray analysis of these strains suggests significant genomic heterogeneity among these isolates (Leonard et al., 2004).

11.4.2 Toxins Toxin production has been widely reported in Campylobacter strains and the nature of these has been summarised in detail by Wassenaar (1997) and Pickett (2000). However, only one such toxin has been fully substantiated by the publication of the genome sequence (Parkhill et al., 2000), namely the cytolethal distending toxin (CDT) (Pickett et al., 1996). This well-characterised toxin causes cultured eukaryotic cells to become blocked in the G2 phase of the cell cycle (Whitehouse et al., 1998) and this subsequently generates cytoplasmic distension and, ultimately, chromatin fragmentation and cell death. The active subunit of this toxin, CdtB, exhibits features of type I deoxyribonucleases and thus DNA damage mediated by this activity may cause cell cycle arrest (Lara-Tejero and Galan, 2000). Although the genes encoding this protein seem to be universally present in this species (Pickett et al., 1996), the role of CDT in C. jejuni pathogenesis has not yet been determined empirically. The toxin may be active on rapidly dividing and differentiating cells within the crypts of the intestine and, therefore, could potentially lead to loss of function or erosion of the epithelial layer and thereby generate the symptoms of diarrhoea. Since certain strains still retain some toxigenic activity when CDT-negative mutants have been analysed (Pickett et al., 1996; Purdy et al., 2000), it is possible that additional toxigenic activities are present in some strains of C. jejuni.


297 11.4.3 Interaction with eukaryotic cells of epithelial cells: nitrosative and oxidative stress The exact mechanism by which C. jejuni causes illness is not yet fully established. Certain strains are known to be able to invade intestinal epithelial cells and this has been implicated as a possible disease-causing process (Wooldridge and Ketley, 1997). However, an understanding of the mechanisms of invasion and interaction of campylobacters with these cells is far from complete. Detailed consideration of these aspects may be found in Hu and Kopecko (2000) and Konkel et al. (2000). Once invasion has been accomplished, SOD is thought to contribute to the survival of C. jejuni in epithelial cells as SOD-deficient mutants are more sensitive to host killing than wild-type strains (Pesci et al., 1994). Although factors that combat reactive oxygen species enable C. jejuni to persist inside host epithelial cells, catalase, which provides resistance to hydrogen peroxide, does not appear to play a role in intra-epithelial cell survival but may contribute to survival in macrophages (Day et al., 2000). Nitric oxide (NO) and/or its redox products form a key component of the inducible defence of intestinal cells against microbial infection (Witthoft et al., 1998) since NO and its reaction products, particularly peroxynitrite, have strong bactericidal activities. As a result of this antimicrobial mechanism, NO synthesis is markedly increased in patients with infective gastroenteritis (Enocksson, et al., 2004; Forte et al., 1999). Consequently, during infection and invasion, campylobacters will inevitably be exposed to significant concentrations of NO. Despite the likely importance of this antimicrobial mechanism, the interaction of these pathogens with NO is only starting to be considered. The most fully understood mechanisms for detoxification of NO in other bacteria involve the inducible bacterial flavoheamoglobin (Hmp) of E. coli (Gardner et al., 1998) and flavorubredoxin (Gardner et al., 2002). In the presence of oxygen, Hmp detoxifies NO by acting as an NO dioxygenase (Gardner et al., 1998) and affords protection of respiration (Stevanin, et al., 2000). Under anaerobic conditions, in the absence of Hmp activity, flavorubredoxin serves as an oxygenindependent NO reductase (Gardner et al., 2002). C. jejuni expresses a haemoglobin (Cgb) which exhibits a haem pocket and structural signatures in common with Hmp, and vertebrate and plant globins, but does not possess the reductase domain seen in the flavohaemoglobin. Nevertheless, since a Cgb-deficient mutant of C. jejuni is hypersensitive to nitric oxide-releasing compounds, but not to peroxides or superoxide (Elvers et al., 2004), it is clear that Cgb plays a vital role in NO detoxification and may therefore be important during infection.

11.5 Future trends Campylobacteriosis, being the most common manifestation of bacterial foodborne disease, has profound social and economic consequences worldwide. However, even today, a few years after the publication of the complete genome sequence for C. jejuni NCTC 11168 (Parkhill et al., 2000), we are only just beginning to


298 understand the physiology and virulence mechanisms of this pathogen and how these differ from features of other well-characterised pathogens. One of the most striking features, revealed by the completion of the genome sequence, is the low number of regulatory functions compared with other foodborne pathogens, which suggests that the ability to respond to stress is more limited than is seen in other foodborne bacteria. However, it has become increasingly evident that strains differ markedly in their genetic content and also exhibit significant differences in virulence and tolerance to environmental stresses. Consequently, certain strains may possess additional genetic material that enhances tolerance to stress and/or virulence. The characterisation of these strain-dependent differences by whole genome sequencing of other strains or by subtractive hybridisation techniques (Ahmed et al., 2002) will be vital if we are to understand these species as a whole. Our understanding of how campylobacters cause illness is far from complete, and consequently this aspect of Campylobacter biology will remain an intense area of future research. Since one of the features of enterocolitis is a localised acute inflammatory response, one area of research likely to be very fruitful is the response of the host and host cells to infection. The coincidental completion of the human genome sequence, and the availability of human genome arrays, should catalyse these investigations. Indeed, some initial studies have shown that a range of proinflammatory cytokines and chemokines are induced in host cells (Mellits et al., 2002; Jones et al., 2003). The characterisation of motility and the flagellum has been a major area of Campylobacter research for at least two decades. Nevertheless, this is still an area of major research and striking findings are still being made. For example, the Campylobacter flagellin protein has recently been shown to be one of the most extensively glycosylated bacterial proteins identified to date, and this results from two independent glycosylation systems (Szymanski et al., 2003). Furthermore, the N-linked glycosylation pathway, which has recently been described in C. jejuni, happens to be the first description of such a system in a bacterium (Wacker et al., 2002). While the function of these glycoprotein biosynthesis systems is not yet fully known, campylobacters provide a unique model system for the future elucidation and exploitation of these pathways. The development of control strategies has been hampered by a lack of understanding of the physiology and virulence of these pathogens. The completion of the genome sequence and the availability of microarrays will allow the response of campylobacters to environmental stress to be mapped in intricate detail. Indeed, using these technologies, the response of C. jejuni to elevated growth temperatures has already be studied (Stinzi, 2003). However, from the information that we have derived for these pathogens already, it is clear that the organisms do not obey many of the physiological paradigms established for model organisms such as E. coli and B. subtilis and, consequently, it is likely that novel mechanisms of adaptation exist, for example through phase variation and genetic evolution. The identification and characterisation of these will also form an important focus of future research.


299

11.6


Campylobacter, 2nd edition, is a 545 page book that is dedicated to the biology of these pathogens and, as such, is one of the most comprehensive and detailed accounts of Campylobacter biology (see Park, 2000 for details). Obviously, it contains far more information than could reasonably be considered here and consequently is an excellent source for further study. Readers will also find many smaller but useful reviews that focus on individual aspects of these pathogens such as: pathogenicity and virulence (Bereswill and Kist, 2003; Wassenaar and Blaser, 1999), toxins (Pickett, 2000; Wassennaer, 1997), physiology and survival in food (Park, 2002), iron acquisition and homeostasis (van Viet et al., 2002), typing and detection (On, 1996; Wassenaar and Newell, 2000) and glycosylation systems (Szymanski et al., 2003). The completion of the complete genome sequence of one strain of C. jejuni represents a major step forward in our understanding of this pathogen. Information regarding this pathogen and tools for examining this information may be found at http://www.sanger.ac.uk/Projects/C_jejuni/ and http:/ /campy.bham.ac.uk/).

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12 Bacillus cereus: factors affecting virulence C. Nguyen-the and V. Broussolle, Institut National de la Recherche Agronomique, France

12.1

Introduction

Bacillus cereus is a spore-forming, Gram-positive bacterium and a frequent cause of foodborne poisoning. Its spores enable it to survive heat, dry conditions, a wide range of sanitation procedures and food processing treatments. It is ubiquitous, common in soil, and widely disseminated over food production chains. Therefore, spores of B. cereus contaminate a wide range of raw material used in food processing, and in most instances they will not be eliminated. The major questions for food safety are whether B. cereus spores can be kept at the lowest level possible along the food production chain and whether B. cereus spores will be able to multiply in foodstuffs to reach hazardous levels before food consumption. Although it is well established that the presence of B. cereus in foods can lead to foodborne poisoning, the virulence of B. cereus is still a subject of debate. As a pathogen, B. cereus is involved not only in foodborne poisoning but also in various severe clinical human infections, septicaemia, meningitis, gingival and ocular infections. In contrast, it also appears as a beneficial bacterium, used as a probiotic for humans (Hoa et al., 2000; Kniel et al., 2003) and animals and proposed as a plant growth promoter. The relation between numbers of B. cereus in foods and development of foodborne poisoning is far from clear.


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12.2 Taxonomy of Bacillus cereus With five related species (Bacillus thuringiensis, Bacillus anthracis, Bacillus weihenstephanensis, Bacillus mycoides, Bacillus pseudomycoides), B. cereus forms a fairly homogeneous group (Drobniewski, 1993; Lechner et al., 1998; Nakamura, 1998), which we could refer to as B. cereus sensu lato (Guinebretière and Sanchis, 2003). Bacteria belonging to B. cereus sensu lato comprise large rods (>1.0 × 3–5 µm) with ovoid, central to subterminal spores. DNA composition is fairly homogeneous with a G + C of 33 to 36 mol% (Guinebretière and Sanchis, 2003). They can grow under anaerobic conditions and under the presence of lysozyme. They do not ferment mannitol and most strains produce a lecithinase. Distinction of species within B. cereus sensu lato can be achieved by motility (B. anthracis and B. mycoides are not motile), haemolysis on sheep blood agar (negative for B. anthracis and weak for B. mycoides), presence of a parasporal crystal for B. thuringiensis, sensitivity to penicillin and presence of plasmids pXO1 and pXO2 for B. anthracis (Claus and Berkeley, 1986), and rhizoid colonies on agar media for B. mycoides and B. pseudomycoides. B. weihenstephanensis grow at 7 ºC, not at 43 °C, and are characterised by specific signatures in the 16S rRNA and in the cold shock protein A (cspA) sequences (Lechner et al., 1998). The distinction among these species within B. cereus sensu lato has important implications for food and human safety: ability to cause foodborne diseases for B. cereus, toxicity towards insects and use as a biopesticide for B. thuringiensis, agent of anthrax for B. anthracis, and ability to grow at low temperature for B. weihenstephanensis. However, these separations are not so clear cut. Psychrotrophic B. cereus without the B. weihenstephanensis signature have been isolated (Anderson Borge et al., 2001; Stenfors and Granum, 2001). The distinction between B. cereus and B. thuringiensis is particularly difficult because it relies only on the production of the parasporal crystal. A strain of B. thuringiensis without this feature would be indistinguishable from B. cereus. In addition, diarrhoeal foodborne poisoning caused by B. thuringiensis has been described (Jackson et al., 1995). It is therefore not currently possible to exclude the possibility that species other than B. cereus within B. cereus sensu lato could be a foodborne hazard. The 16S rRNA sequences of the six species constituting B. cereus sensu lato share 99 % similarity (Ash et al., 1991; Ash and Collins, 1992), indicating their close proximity. However, DNA–DNA hybridisation percentages below 70 % were reported within strains belonging to the same species for B. cereus, B. mycoides and B. thuringiensis, and reciprocally, strains belonging to different species of the group B. cereus sensu lato showed percentages over 70 % (Guinebretière and Sanchis, 2003). These results indicate that genomic species may not correspond to the currently recognised taxonomical entities. Phylogenetic approaches using multilocus enzyme electrophoresis and amplified fragment length polymorphism (Hegalson et al., 2004; Hill et al., 2004) suggest that B. anthracis is a very monomorphic species, whereas B. cereus and B. thuringiensis are very diverse. Several branches of the phylogenetic trees contain both B. cereus and B. thuringiensis strains.


311

12.3

Virulence factors of Bacillus cereus

Two distinct types of food poisoning have been linked to Bacillus cereus, an emetic poisoning and a diarrheic poisoning. Emetic poisoning is characterised by vomiting, associated with diarrhoea in approximately a third of the cases, and occurs within 1–5 h of ingestion of contaminated foods (Kramer and Gilbert, 1989). Diarrhoeal poisoning is characterised by a watery diarrhoea associated with abdominal pain and occurs within 8–16 h of ingestion of the contaminated food (Granum and Lund, 1997). Food poisoning caused by B. cereus is generally mild. However, bloody diarrhoea leading to some fatal cases has been described (Lund et al., 2000), as well as emetic poisoning leading to fatal liver failure (Mahler et al., 1997).

12.3.1

Emetic poisoning

The emetic toxin cereulide The emetic toxic agent was originally tested on primates (Kramer and Gilbert, 1989). It was attributed to a heat-stable factor, produced in the food and still active after the destruction of the bacteria. In the 1990s, the emetic factor was purified by high-performance liquid chromatography and characterised by mass spectrometry (Agata et al., 1994, 1995a; Shinagawa et al., 1995) as the small cyclic peptide cereulide: [D-O-Leu-D-Ala-L-O-Val-L-Val]3 The minimal dose causing emesis was estimated on the monkey Suncus murinus at 8 µg/kg. The structure and mode of action of cereulide are close to those of valinomycin, an ionophore of potassium (Mikkola et al., 1999). Cereulide is probably produced non-ribosomically by a cyclic peptide synthetase, as with other cyclic peptides of bacterial origin. Emetic symptoms occur through stimulation of the vagus afferent nerve by the toxin (Kramer and Gilbert, 1989). Cereulide toxicity on hepathocytes was experimentally confirmed by injecting cereulide in mice (Yokoyama et al., 1999), supporting the report of a fatal case of liver failure associated with B. cereus emetic poisoning (Mahler et al., 1997). Emetic B. cereus toxin should therefore no longer be considered as a cause of mild food poisoning. Acute poisoning may have a serious effect on human health. Cereulide is an extremely stable, highly lipophylic toxin that might persist in the human body, and it is toxic on various lines of human cells (Jääskeläinen et al., 2003). Investigations on emetic poisoning have been hampered by the lack of rapid methods to detect and quantify cereulide. The discovery that the emetic factor was toxic to mitochondria of Hep-2 cell cultures, causing swelling of mitochondria and leading to a vacuolisation of the cells, provided an easier way to detect emetic toxin (Sakurai et al., 1994). The emetic toxin presumably acts by uncoupling oxidative phosphorylations (Sakurai et al., 1994), disrupting energy production in


312 mitochondria. This feature was used to design an easier and more rapid bioassay based on the mobility of sperm cells (Andersson et al., 1998b). Mobility of sperm cells requires a high energy production by mitochondria. Sperm cells are therefore particularly sensitive to cereulide. This rapid bioassay was validated against the high-performance liquid chromatography-mass spectroscopy (HPLC-MS) analysis (Häggblom et al., 2002). The heat resistance and lipophilic nature of cereulide permit a fairly rapid and specific extraction in organic solvents. Cereulide production in foods Cereulide is not easily destroyed by heat treatments. For instance, it can resist 90 min at 126 °C (ICMSF, 1996) and is also resistant to acid conditions. Cereulide will therefore not be eliminated from foods in which it has been produced. When emetic strains of B. cereus were grown in shaken, liquid laboratory media incubated at room temperature, cereulide production was observed during the stationary phase, with a population of B. cereus of 108 CFU/ml (Häggblom et al., 2002). Concentration of toxin reached 10–25 µg/ml. In boiled rice incubated at 20 and 30 °C (Finlay et al., 2002a) and in various kinds of cooked rice, noodles, pasta and mashed potatoes (Agata et al., 2002) inoculated and incubated at 30 °C, cereulide was also detected for populations of emetic B. cereus of 107–108 CFU/g. Cereulide concentrations varied from 0.01 to 0.32 µg/g (Agata et al., 2002). Whether these foods could withstand such high B. cereus growth without showing spoilage was not discussed by the authors. In contrast, cereulide was not detected (< 0.005 µg/g) in eggs and meat products inoculated with the same emetic strains (Agata et al., 2002). Emetic strains of B. cereus also produced cereulide in milk. However, in milk incubated aerobically, emetic toxin production was ten-fold higher in shaken culture than in static culture. No toxin was detected whenever milk was incubated anaerobically (Finlay et al., 2002b). Similar results were obtained in laboratory liquid medium by Häggblom et al. (2002). Over seven emetic strains tested in milk, and three strains tested in rice, cereulide production was higher at 12 and 15 °C than at 20 and 30 °C. No toxin was detected at temperatures above 37 °C (Finlay et al., 2000, 2002a). It is also worth noting that at 12 and 15 °C, emetic toxin was detected for a lower B. cereus population than at 20 or 30 °C (i.e. 106 instead of 108 CFU/ml). In this respect, foods not properly refrigerated at 12–15 °C might be more hazardous than foods not refrigerated at all. Not enough work has been published to draw firm conclusions. Nevertheless it is clear that conditions permitting cereulide production are narrower than conditions permitting growth. Amount of cereulide in foods and emetic poisoning In a recent case of emetic poisoning caused by a pasta dish, the content of cereulide was approximately 1.6 µg/g of food (Jaaskelainen et al., 2003). From the amount of food consumed by the patients, the authors estimated the toxic dose for humans to be equal to or lower than 8 µg/kg body weight, similar to the dose found for


313 Suncus murinus monkey (Shinagawa et al., 1995). Amounts of cereulide found in 13 foods implicated in emetic poisoning, mostly rice and pasta dishes, ranged from 0.01 to 1.3 µg/g (Agata et al., 2002). Unless foods containing the lowest amounts of cereulide reported were erroneously attributed to emetic poisoning, these results indicate that some individuals are presumably more sensitive to cereulide than others.

12.3.2

Diarrheic poisoning

Diarrhoeal toxins Several proteins produced by B. cereus exhibit a cytotoxic activity on Caco2 human epithelial cell lines. Enterotoxic activity was demonstrated for some of them on ileal loop test. An extensive description of the various potential enterotoxins of B. cereus, and of the chronology of their discovery, can be found in Granum and Baird-Parker (2000). Among these proteins, firm evidence of their involvement in diarrhoeal poisoning was found for two enterotoxins, haemolysin BL (HBL) (Beecher and McMillan, 1991; Beecher and Wong, 2000) and non-haemolytic enterotoxin (NHE) (Lund and Granum, 1996, 1997). In addition, a cytotoxin (CytK) was recently isolated and characterised by Lund et al. (2000), from a B. cereus strain involved in foodborne poisoning that did not produce the enterotoxins HBL and NHE. Two other enterotoxins, BceT and EntFM, have been proposed as potential diarrhoeal factors of B. cereus (Agata et al., 1995b; Asano et al., 1997). Since then, several published works have reported their incidence among strains of B. cereus. However, enterotoxic activity of EntFM had not been demonstrated and BceT turned out to be an artefact (Hansen et al., 2003). The major features of HBL, NHE and CytK are summarised in Table 12.1. The respective roles of these enterotoxins and cytotoxins in the development of diarrhoea are not known. NHE and CytK have been respectively identified in B. cereus strains involved in food poisoning, and as producing none of the other enterotoxins (Lund and Granum, 1996; Lund et al., 2000). Both NHE and CytK are therefore presumably able to cause diarrhoea on their own. No such evidence exists for HBL enterotoxin; no strains involved in diarrhoeal poisoning and producing only HBL have been characterised so far (Guinebretière et al., 2002). Enterotoxins and diarrhoeal poisoning None of the three components of enterotoxin HBL had enterotoxic activity alone (Table 12.1). B with L1 showed some toxicity, but the three components were necessary to obtain the highest activity. A combination of 5 µg of each component was sufficient to cause significant symptoms on rabbit ileal loop, and 25 µg was necessary to achieve the maximum enterotoxic activity (Beecher et al., 1995). It was proposed that B component could bind to the cell and that L1 and L2 would cause lysis (Beecher and McMillan, 1991). An alternative mechanism proposed that the three components bind independently and constitute the lytic complex (Beecher and Wong, 1997). Injecting antibodies after injection of the toxin


314 Table 12.1

Bacillus cereus toxins involved in diarrhoeal poisoning

Toxin name Haemolysin BL (HBL)

Non-haemolytic enterotoxin (NHE)

Cytotoxin K (CytK)

Genes/proteins hblC/L2 hblD/L1 hblA/B nheA/A nheB/B nheC/C cytK/CytK

Molecular weight (kDa) 46 38 37 45 39 37 34

prevented symptom development in the ileal loop test, indicating that binding of the toxic components to cells might be weak (Kramer and Gilbert, 1989). The three components of NHE are also necessary for cytotoxic activity (Lund and Granum, 1996). Inhibition of protein synthesis of Vero cells of 50 % was obtained for 20– 30 ng of each component of NHE (Lund and Granum, 1996). CytK belongs to the family of β-barrel channel-forming toxin (Lund et al., 2000), similar to Clostridium perfringens β-toxin and might cause necrotic enteritis according to Lund et al. (2000). The toxin presumably forms oligomers and creates pores in cell membranes (Hardy et al., 2001). The amount of purified CytK toxin necessary to cause 50 % protein synthesis inhibition in Caco2 cells was 16 ng for 5 × 104 cells (Hardy et al., 2001). However, Fagerlund et al. (2004) observed that CytK toxins from six other B. cereus strains had a cytotoxic activity approximately 80 % lower. Bacillus cereus can produce its enterotoxins in foods (van Netten et al., 1990). However, these proteins are sensitive to low pH and to digestive proteases. Poisoning presumably occurs after production of enterotoxins in the small intestine by ingested cells of B. cereus (Granum and Lund, 1997). This assumption is also in accordance with the incubation time of 8–16 h for diarrhoeal poisoning (Kramer and Gilbert, 1989). The dose–response relation for B. cereus diarrhoeal poisoning is not accurately known. Granum and Baird-Parker (2000) estimated the number of cells ingested by patients to be around 105–108 CFU in most cases. Becker et al. (1994) reported seven diarrhoeal poisoning incidents, representing 78 cases in which the number of ingested B. cereus was presumably between 103 and 105 CFU. Numbers of B. cereus found in foods implicated in some foodborne diarrhoeal poisoning are presented in Table 12.2. However, the respective importance of spores and vegetative cells has never been reported in epidemiological investigation of foodborne cases. It is not known whether diarrhoeal foodborne poisoning occurs mainly through ingestion of spores or of vegetative cells, or if both forms are equally infectious. Spores of B. cereus would presumably survive gastric conditions better than vegetative cells (Clavel et al., 2004). For some strains, adhesion of B. cereus spores to intestinal epithelial cells was proposed as an additional virulence mechanism that could increase the severity of diarrhoeal poisoning (Andersson et al., 1998a). B. cereus spores would germinate, grow and produce toxins in closer contact to the intestinal epithelium, and for a longer period than with non-adhering strains.


315 Table 12.2

Examples of Bacillus cereus foodborne poisoning

Food categories (number Type of poisoning CFU B. cereus in of persons affected) the food Cod fish (4)

Diarrhoea

4 × 105 g–1

References Van Netten et al. (1990) Ripabelli et al. (2000) Penman et al. (1996)

102 g–1 chicken, 106 g–1 rice Diarrhoea and > 100 g–1 (refrigerator vomiting not working, food not heated sufficiently, poor restaurant hygiene) Cakes in banquets Diarrhoea > 102 g–1 foods (same Chelardi et al. (95 and 78) strain in the foods, in (2002) the confectioner’s kitchen and in patients’ faeces) Vegetable purée (44) Bloody diarrhoea 3 × 105 g–1 Lund et al. (6 people died) (2000) Potato salad with Diarrhoea 103 g–1 mayonnaise (food Gaulin et al. mayonnaise in a mishandled during (2002) banquet (25) preparation and consumption) Granum et al. Stew (17) Diarrhoea (3 104–105 per serving of persons hospital- incriminated food (1995) ised for up to 3 weeks) incriminated food Salad sprouts (4) Vomiting and 105 to 107 g–1 Portnoy et al. diarrhoea (1976) Orange juice from Vomiting and 100 ml–1 Talarmin et al. concentrate (43) diarrhoea (1993) Onion powder (18) Diarrhoea and ND Jackson et al. vomiting (mixed (1995) Norwalk virus, B. cereus and B.thuringiensis outbreak) Various foods from a Diarrhoea and/or 3–6 × 106 g–1 of foods Pena Gonzalez meal (77) vomiting (foods not properly (1998) refrigerated) Pasteurised milk (280) Nausea, vomiting 4 × 105 g–1 Van Netten et (complication in one al. (1990) patient with gastric ulcer) * Spaghetti with pesto (2) Diarrhoea and ND (B. cereus producing Mahler et al. vomiting (1 died emetic toxin isolated (1997) from fulminant from food residues. Food liver failure) left several hours at room temperature) * Chinese noodles (50) Vomiting and 6 × 107 g–1 cooked Takabe and diarrhoea (1 died noodles Oya (1976) from heart failure) * Fried rice (4) Vomiting > 106 g–1 fried rice Grein (2001) Chicken and rice meal (300) Quiche (79)

Diarrhoea


316 Table 12.2 cont’d Food categories (number Type of poisoning CFU B. cereus in of persons affected) the food * Cooked and fried rice (14)

Vomiting and diarrhoea

* Meat substitute dish (7) Vomiting * Restaurant meal with cooked rice (5) * Rice dishes, spaghetti and noodles (14 outbreaks from 1974 to 1999 in Japan) * Home-made pasta dish (2)

> 106 g–1 chicken fried rice (food not refrigerated) 102 g–1 meat substitute

References Khodr et al. (1993) Ripabelli et al. (2000) Ripabelli et al. (2000) Agata et al. (2002)

Vomiting

6×103 g–1 cooked rice

Vomiting

(1280 to 10 ng emetic toxin g–1 implicated foods)

Vomiting

7×106 g–1 (1500– Jääskeläinen et 3000 ng emetic toxin g–1 al. (2003) implicated foods

ND, not determined. * Evidence for emetic poisoning (short incubation period, typical symptoms).

12.4

The spores of Bacillus cereus

12.4.1 Adhesion of Bacillus cereus spores Bacillus cereus spores can adhere very strongly to the surface of food processing equipment (Andersson et al., 1995). Spores of B. cereus were found to be ten times more adherent and ten times more resistant to cleaning procedures on various inert surfaces than Bacillus subtilis spores (Faille et al., 2002). The high adhesion of B. cereus spores is presumably due to their high hydrophobicity (Andersson and Ronner, 1998). Among several B. cereus strains, strains with the strongest adhesion were those producing the most hydrophobic spores. Germination reduced adhesion of B. cereus spores. Spores of B. cereus have long appendages, the role of which in spore adhesion is unclear. Removing appendages from spores did not always reduce adhesion (Klavenes et al., 2002). The role of appendages seemed to vary with the strains and with the conditions under which spores had adhered to the surface. For instance, for some strains, removing appendages reduced adhesion under static conditions but not under fluid flow conditions. 12.4.2 Germination of Bacillus cereus spores Germination represents the passage of the dormant and resistant spores to active vegetative cells. Germination of bacterial spores is triggered by small molecules named ‘germinants’. The amino acid L-alanine is a fairly universal germinant for several Bacillus and Clostridium species. In contrast, germination of B. cereus spores is more efficiently triggered by nucleosides, and particularly inosine


317 (Senesi et al., 1991; Clements and Moir, 1998). Protein receptors of germinants are usually coded by three genes organised in operons. Similarly, in B. cereus, several operons coding for receptor proteins have been identified. The proteins GerIA, GerIB and GerIC, coded by the operon gerI, were identified as the receptor of inosine (Clements and Moir, 1998). However, another receptor, coded by the operon gerQ, is also involved (Barlass et al., 2002). The reason why two receptors are needed for germination in response to a single germinant is not known. Spores of B. cereus also germinate in response to L-alanine, although less efficiently than in response to inosine. The operon gerI, together with another operon ger L, act in synergy as the receptor of L-alanine. A new operon, gerP, consisting of six genes, has been discovered in B. cereus. The absence of one of the proteins GerP drastically reduces spore germination in response to L-alanine or inosine (Behravan et al., 2000). It was proposed that GerP proteins could help transfer germinants towards their receptors. This discovery is particularly interesting as receptors of germinants are presumably localised in the inner membrane of the spore. Mutation in the gerN gene stopped germination of B. cereus spores in response to inosine (but not to alanine) at an early stage (Thackray et al., 2001). GerN is presumably an antiport specifically associated to the inosine receptor and would create an accumulation of positive ions inside the spore (Southworth et al., 2001). How these various steps interact is still not known. However, investigations in B. cereus provided new perspectives for a global scheme for spore germination. Transfer of the germinant to the receptors (GerI and Ger Q) in the inner membrane might occur through GerP. Ion transport (GerN) might then be activated and, through an unknown signal, lytic enzymes located in the spore cortex would be activated, leading to cortex hydrolysis and germination. At least two types of cortex-lytic enzymes are required during the germination process to degrade the cortex: a spore cortex-lytic enzyme (encoded by the sleB gene) and a cortical fragment-lytic enzyme (encoded by the sleL gene). Both activities were first described and then extensively studied in B. cereus (for review see Makino and Moriyama, 2002). The exosporium is the outermost layer of B. cereus spores. This structure, which makes initial contact with the host, is absent in B. subtilis spores. Recent studies reported the identification of structural proteins of the exosporium and showed that two enzymes, alanine racemase and nucleoside hydrolase, are tightly absorbed to this layer. Alanine racemase converts L-alanine to D-alanine, a competitive inhibitor of germination, and inosine could be degraded by the nucleoside hydrolase. These two enzymes may be involved in the control of spore germination in B. cereus (Todd et al, 2003).

12.5

Ecology and epidemiology of Bacillus cereus

12.5.1 Foods implicated in B. cereus foodborne poisoning The incidence of B. cereus foodborne poisoning in developed countries varies to a large extent (Granum and Baird-Parker, 2000): around 33 % of bacterial foodborne


318 poisoning cases in Norway from 1988 to 1993; 8.5 % in the Netherlands in 1991; only 1–2 % in North America. In France, B. cereus is the fourth most common bacterial agent to cause foodborne poisoning (Haeghbaert et al., 2001, 2002a,b). In 2001, it represented 4.6 % of foodborne poisoning cases of known origin, and 4.5 % of B. cereus foodborne poisoning cases required hospitalisation (Haeghbaert et al., 2002a). Bacillus cereus foodborne poisoning is frequently mild and is therefore presumably underreported. In addition, notification of sporadic cases is not mandatory in Europe and the USA. Our knowledge on the epidemiology of B. cereus is certainly very partial. However, examples of documented cases illustrate that a wide range of foods can be a source of B. cereus poisoning (Table 12.2). Emetic poisoning was mostly attributed to rice and pasta dishes and, to a lesser extent, to dairy products. A broader range of foods have been implicated in diarrhoeal poisoning: sprouted seeds, orange juice from a canteen vending machine, vegetable purées, stews and various recipe dishes. Apart from the very atypical cases linked to sprouted seeds and orange juice, foods implicated in both emetic and diarrhoeal poisoning have been cooked or heated. A storage time without refrigeration between preparation of foods and consumption is frequently suspected as, for instance, emetic cases caused by fried rice prepared with rice cooked in advance and not refrigerated (Kramer and Gilbert, 1989). Heat treatment presumably kills non-spore-forming bacteria, giving way to the development of B. cereus spores.

12.5.2 Incidence of B. cereus in foods Bacillus cereus have been isolated in almost all categories of foodstuffs (Table 12.3). Raw fruits and vegetables, raw herbs, dry foods, raw milk and processed foods before storage usually contain fewer than 100 spores/g. However, presence of more than 1000 spores/g in medicinal plants has been reported (Martins et al., 2001). Bacillus cereus can grow in foods with pH higher than 4.5–4.7, depending on the growth media (Valero et al., 2000), and in foods with water activity (aw) higher than 0.92. Levels of B. cereus found in foods that support its growth depend mostly on storage temperature and storage duration, which are in turn functions of the rate of spoilage development. For instance, at the onset of spoilage, cooked vegetable dishes pasteurised in their final package contained up to 105–106 CFU/g when stored at 10 °C and fewer than 30 CFU/g when stored at 4 °C (Choma et al., 2000; Guinebretière et al., 2000). In contrast, B. cereus population was always lower than 100 CFU/g in cooked vegetable dishes packaged after heat treatment under high care conditions, whatever the storage temperature (Choma et al., 2000). In the former case, heat treatment had eliminated all non-spore-forming bacteria which permitted a long shelf-life and, presumably, reduced competition against B. cereus. In the latter case, contamination with non-spore-forming bacteria occurred during packaging, which caused rapid spoilage, but shelf-life was too short to permit B. cereus development. Baby foods deserve specific attention. Around 100


319 Table 12.3

Examples of incidence of B. cereus in various foods

Food categories

Percentage of positive Numbers of B. cereus samples (limit of in positive samples detection CFU g–1) (CFU g–1 or ml–1)

References

Spices

42 % (102)

Herbs and spices

100 % (102)

Van Netten et al. (1990) te Giffel et al. (1996a) AmodioCocchieri et al. (1998)

most samples < 104 few samples > 104 102 to 106

Read-to-eat chilled 6–21 % (all 103 to 105 foods in self-service categories positive: restaurants vegetables, rice, pasta, meat and fish) Fresh vegetables 0–100 % (10) 102 to 8 × 103 Vegetable salads Cooked chilled foods unstored stored at 4 ºC until use by date stored at 10 ºC until use by date Flour Liquid egg yolk pasteurised Bakery product

2 % (102)

< 103

0 % (10) 0 % (10)

< 10 < 10

0–100 %

104 to 106

55 % (102)

103

24 % (102)

< 103

90 % (102)

103 to 104

Pasteurised milk after 8 % (102) storage at 7 ºC until ‘best before’ date Pasteurised milk after 56 % (10) 8 days at 7 ºC Milk powder

27 % (10)

Powdered infant formulae

75 % (0.04)

From < 103 to > 105 From 103 to 3 × 105 4 spores g–1 (mean), up to 40 spores g–1 0.04 to 1 MPN g–1

Valero et al. (2002) Van Netten et al. (1990) Choma et al. (2000)

te Giffel et al. (1996a) Van Netten et al. (1990) te Giffel et al. (1996a) Van Netten et al. (1990) Larsen and Jorgensen (1997) Van Netten et al. (1990) Harmon and Kautter (1991)

MPN, most probable number.

spores/g of B. cereus were reported in some samples of dry baby foods formulae and 105 CFU/g were reached after 7–9 h at 27 °C (Becker et al., 1994).

12.5.3 Reservoirs and dissemination along the food production chain Soil can contain between 103 and 105 spores of B. cereus per gram (Christiansson et al., 1999; Guinebretière et al., 2003; te Giffel et al., 1995). Development of some strains of B. cereus and of some strains of the very close species B. thuringiensis were observed in the rhizosphere of plants and in the gut of earthworms (Halverson


320 et al., 1993; Hendriksen and Hansen, 2002). The climate presumably influences the structure of the B. cereus population in soil, psychrotrophic strains being more frequent in soil from cold regions (von Stetten et al., 1999). Development of rapid molecular typing methods with high resolution for B. cereus permitted an investigation of the contamination routes for foods. Soil appeared to be the primary source of contamination of milk and cooked chilled foods containing vegetables (Christiansson et al., 1999; Guinebretière and Nguyenthe, 2003), which is consistent with the report that B. cereus incidence was lower in milk from in-house dairy farms (Slaghuis et al., 1997). Additional sources of contamination have been identified along the food processing chain. Before processing, raw milk can be contaminated by B. cereus strains that persist in milk silo tanks (Svensson et al., 2004). Contamination of pasteurised and powdered milk by B. cereus strains persisting in pasteurising and drying equipment was also suspected (Eneroth et al., 2001; Svensson et al., 1999; te Giffel et al., 1996b). In the work from Svensson et al. (2004), some strains of B. cereus persisted in silo tanks from different dairy plants disseminated all over Sweden, indicating that widely distributed B. cereus might be selected by their ability to persist in processing equipment. In complex foods prepared from various raw materials, dry ingredients such as starch and dairy proteins, used as texturing agent in food processing, were identified as an important source of contamination (Guinebretière and Nguyen-the, 2003). Spores of B. cereus were also found in paper mill industries and in packaging materials (Pirttijarvi et al., 2000) which could represent an additional route for contamination of foods. The telluric origin of B. cereus and the various possible ways of secondary contamination makes unlikely its elimination from the food processing chain. However, indication that the bacterium could persist in food storage or food processing equipment stresses the need to develop hygienic measures efficient against B. cereus spores to prevent build-up of contamination during food processing.

12.5.4 Diversity of Bacillus cereus Bacillus cereus is present in foods and in the environment as a mixture of diverse strains. Typing of B. cereus isolates from soil using random amplification of polymorphic DNA (RAPD) discriminated 44 different strains among 60 isolates from the same sample (Christiansson et al., 1999) in Sweden. Similarly, 13 strains were discriminated among 35 isolates in a soil sample in France (Guinebretière and Nguyen-the, 2003). Results of the same order of magnitude were reported for raw milk and raw vegetables (Svensson et al., 1999; Guinebretière and Nguyen-the, 2003). Processing of raw milk into powdered milk reduced numbers of B. cereus types by 50 % (te Giffel et al., 1996b). Owing to this diversity of B. cereus, it is important to determine what hazard is represented by the various strains found in foodstuffs. Diversity of virulence features among B. cereus Incidence of emetic B. cereus strains in foods has so far not been estimated.


321 However, B. cereus strains involved in emetic poisoning appear to be a very homogeneous group. Unlike most B. cereus, they do not hydrolyse starch, they do not use mannose or salicine, and they belong to only three serotypes, among which serotype H1 prevails (Pirttijarvi et al., 1999; Raevuori et al., 1977). On the basis of their ribotypes, Pirttijarvi et al. (1999) proposed that emetic B. cereus strains from various geographical origins, and isolated at various dates, shared a clonal relationship. Therefore, incidence of starch negative B. cereus could provide an estimate of emetic B. cereus. Starch negative B. cereus represented, at most, 2–11 % of strains isolated from dairy products and from dairy farms (te Giffel et al., 1995). Starch negative strains represented only 4 % of B. cereus from various origins (Logan and Berkeley, 1984). We can assume that incidence of emetic B. cereus is below these figures as all starch negative B. cereus are not necessarily emetic. Production of enterotoxins HBL and NHE by B. cereus isolates from various origins had been tested using the immunological tests commercialised by Oxoid (BCET-RPLA®) and Tecra (BDE-VIA®). The BCET-RPLA® detects L2 component of HBL whereas BDE-VIA® detects NHEA (Beecher and Wong, 1994; Granum and Lund, 1997). In dairy products, HBL was detected in culture filtrates from 51 to 85 % of B. cereus strains and NHE was detected in 85–100 % (Beattie and Williams, 1999; Granum et al., 1993; Griffiths and Phillips, 1990). Among B. cereus strains from spices, legumes (Rusul and Yaacob, 1995) and from processed vegetables (Choma et al., 2000), 95–100 % of strains produced NHE and 54–95 % produced HBL. No results are available for CytK as no routine detection tests are available. Detection of the operons coding for the toxins NHE and HBL and of the gene coding for CytK by polymerase chain reaction (PCR) amplification, and confirmation by Southern blot hybridisation, showed that among 96 strains from various origins, only 3 strains did not carry nhe, 22 did not carry hbl and 49 did not carry cytK (Guinebretière et al., 2002). All B. cereus strains seem to carry at least one enterotoxin gene or operon and could potentially be able to cause diarrhoea. However, amounts of HBL and NHE detected in culture filtrates from B. cereus strains isolated from foods are very diverse (Beattie and Williams, 1999; in’t Veld et al., 2001). For instance, the production index for HBL and NHE among strains carrying the toxin genes varied from not detectable to the highest scale of the BCET-RPLA® and BDE-VIA® tests (Guinebretière et al., 2002). Similarly, the global enterotoxicity of culture filtrates, measured on human Caco2 epithelial cell lines, varied from a factor of 1 to 80 (Choma et al., 2000). Not all strains of B. cereus from foods produce the same amount of enterotoxins or have the same cytotoxicity. Comparison of strains from foods with strains implicated in foodborne poisoning (Guinebretière et al., 2002) revealed that all strains from foodborne poisoning cases produced high levels of NHE and/or HBL. In contrast, a majority of strains isolated from foods were weak producers of toxins (80 % of strains for NHE, 70 % for HBL), although they had the corresponding genes. Level of expression, rather than presence, of enterotoxin genes might be an indicator of virulence. Diversity of HBL proteins among several B. cereus strains was reported by Schoeni and Wong (1999), without any indication of possible variations in toxic


322 activity. Lund and Granum (1997) compared the cytotoxic activity of NHE and HBL complexes purified from three B. cereus strains and found that one strain produced complexes less toxic than those from the two other strains. Recently, Fagerlund et al. (2004) proposed that two forms of CytK toxin should be distinguished: CytK1 described in a strain involved in fatal foodborne poisoning (Lund et al., 2000), and CytK2 found in six other strains, which was much less cytotoxic than CytK1. The toxins produced by B. cereus may have diverse structures and activities on human cells, making the characterisation of its virulence even more difficult. The mere detection of toxin genes is certainly not sufficient. Presence of enterotoxins among related Bacillus species The presence of hbl and nhe operons and production of the toxins by some strains of Bacillus circulans, Bacillus pasteurii, and Bacillus amyloliquefaciens, all three Bacillus species outside B. cereus sensu lato, has been reported (Phelps and McKillip, 2002). Within B. cereus sensu lato, strains of B. weihenstephanensis, B. mycoides and B. thuringiensis produced HBL and NHE enterotoxins (Pruss et al., 1999; Stenfors et al., 2002). Similarly, enterotoxin operons nhe and hbl were detected among 74 B. thuringiensis strains with the same proportion as described for B. cereus (Gaviria Rivera et al., 2000). The authors found that culture supernatants of most strains were as cytotoxic as B. cereus strains that have been implicated in foodborne poisoning. Diversity of B. cereus for growth and survival ability Minimum growth temperatures reported for B. cereus ranged from above 10 °C to 4 °C (Nguyen-the and Carlin, 2003). Strains able to grow at 4 °C were found in various foods (Anderson Borge et al., 2001; Dufrenne et al., 1994; Francis et al., 1998; te Giffel et al., 1996a; van Netten et al., 1990). However, such strains are a minority (Nguyen-the and Carlin, 2003). Refrigerated storage of foods might not totally prevent B. cereus development, but it would certainly reduce the probability for B. cereus to reach high numbers. Lechner et al. (1998) proposed to classify psychrotrophic B. cereus as a new species, B. weihenstephanensis, characterised by specific sequences in 16S rDNA (von Stetten et al., 1998) and specific sequences in the gene of cold shock protein A, cspA (Francis et al., 1998). Therefore, refrigeration of foods might reduce the diversity of B. cereus population able to grow and might even select another species. However, Anderson Borge et al. (2001) isolated B. cereus strains able to grow below 7 °C that did not have the cspA specific sequence and that might be true B. cereus. Maximum growth temperature of B. cereus ranged from between 37 and 42 °C, for the less heattolerant strains, to above 48 °C (Anderson Borge et al., 2001), with some reports of B. cereus growing at over 50 °C (Nguyen-the and Carlin, 2003). Heat resistance in humid conditions reported for B. cereus spores varies to a very large extent (Table 12.4). Comparison of spore heat resistance obtained in different studies is difficult because conditions to produce spores, heating and recovery media have a significant impact on values of heat resistance (Gonzalez et al., 1999). However, spores of many strains would clearly survive several minutes at


323 Table 12.4 Examples of heat resistance of B. cereus spores, expressed as time for one decimal reduction of the initial number of spores (D) Origin of strains

Number of Heating strains temperature tested (ºC)

Milk1

6

Various dairy products2 Rice3

25 6

Rice4 Various foods5 Cooked vegetables6 Canned vegetables7 Foodborne diarrhoeal cases8

13 12 52 2 6

95 100 100 92 100 95 90 90 129.4 100

D (min) Mean

Minimum Maximum

2.0 0.8 3.5 22 4.8 2.8 Not calculated Not calculated Not calculated 6.7

1.8 0.7 2.0 16 4.2 1.5 2.2 0.7 0.19 0.6

2.8 1.5 5.4 36 6.3 6.0 > 100 5.9 0.28 27

1, Janstova and Lukasova (2001); 2, Wong et al. (1988); 3, Chung and Sun (1986); 4, Parry and Gilbert (1980); 5, Dufrenne et al. (1994); 6, Choma et al. (2000); 7, Bradshaw et al. (1975); 8, Rajkowski and Mikolajcik (1987).

Table 12.5 Examples of heat resistance of B. cereus spores, expressed as time for one decimal reduction in spore numbers at 90 ºC (D90 °C), for mesophilic and psychrotrophic strains Reference

Minimal growth temperatures (°C)

Dufrenne et al. (1995) Choma et al. (2000)

5–7 9–11 > 11 5 and < 10 > 10

Number of strains tested

D90°C (min) Median

6 8 14 6 28 18

5.8 8.5 29.5 1.1 1.3 3.8

Minimum Maximum 5.0 4.6 4.8 0.8 0.7 0.9

9.1 13.9 > 100 1.4 3.2 5.9

100 °C, and would therefore not be eliminated by normal cooking of foods. In the few works that compared spore heat resistance and minimum growth temperature on the same set of B. cereus strains (Table 12.5), strains able to grow at low temperatures produced spores with the lowest heat resistance. To prevent B. cereus development in refrigerated foods, moderate heat treatment might be sufficient.

12.6 Future trends The major difficulty in assessing the risk that B. cereus represents for consumers’ safety lies in the diversity in the behaviour and the virulence of strains, associated with a lack of clear taxonomy. Important questions to answer would be whether the psychrotrophic B. weihenstephanensis and the insect pathogen B. thuringiensis are


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13 Staphylococcus aureus as a food pathogen: the staphylococcal enterotoxins and stress response systems J. Gustafson, New Mexico State University, USA and B. Wilkinson, Illinois State University, USA

13.1

Introduction

Staphylococcus aureus is one of the most common agents implicated in food poisoning worldwide (Holmberg and Blake, 1984; Oda, 1998; Wieneke et al., 1993). It has been estimated that 185,000 cases of staphylococcal food poisoning (SFP) occur each year in the USA, the vast majority of which go unreported (Mead et al., 1999), costing 1.5 billion dollars in lost productivity and medical expenses (Todd, 1989). SFP results from the contamination of food with staphylococci that produce one of numerous thermostable enterotoxins, or the enterotoxins themselves. In addition to causing food poisoning, S. aureus causes integumental infections, is the leading cause of bacteremias and surgical wound infections and a common agent of endocarditis (Ing et al., 1997; Lowry, 1998). In 1995 the New York metropolitan area witnessed an estimated 13 550 S. aureus infections, which led to 1400 mortalities and cost an estimated 435.5 million dollars (Rubin et al., 1999). Indeed, coupled with the organism’s propensity to develop resistance to antibiotics, the financial cost of staphylococcal infections (Palumbi, 2001) and SFP seems almost impossible to calculate. In addition to S. aureus, there are a considerable number of other staphylococcal species. These are divided into coagulase-positive (S. aureus is coagulase-positive) and coagulase-negative species. Staphylococcus intermedius is coagulase-positive, can be isolated from dogs and has occasionally been associated with outbreaks


332 of SFP (Khambaty et al., 1994). Becker et al. (2001) surveyed veterinary and human isolates of S. intermedius and found that 11.3 % of the isolates were positive for the staphylococcal enterotoxin C gene (sec), and 30 isolates were confirmed positive for enterotoxin production. Thirteen additional species of coagulase-negative species were found to be non-enterotoxigenic. It should be noted that not all Staphylococcus–food interactions are ‘negative’ from a human standpoint. Coagulase-negative species, particularly Staphylococcus carnosus and Staphylococcus xylosus, are important in the production of fermented meat products such as sausages (Sondergaard and Stahnke, 2002). A clinical description of SFP can be quoted from Stephen D. Elek’s 1959 comprehensive treatise on the organism Staphylococcus pyogenes (S. aureus): The clinical course is characterisitic: symptoms generally appear in about three hours, occasionally one to six hours, after ingesting food containing enterotoxin. The incubation period depends not only on the amount of toxin consumed but also the susceptibility of the individual. Salivation is followed by nausea, vomiting, retching, abdominal cramps of varying severity, and diarrhoea. In very severe cases blood and mucus have been observed in the vomitus. …Marked prostration, headache, and sweating accompany severe attacks, and there may be fever or shock with subnormal temperature and lowered blood pressure. Death due to SFP is uncommon, but intoxication of particularly susceptible populations such as children and the elderly, can lead to mortalities (Holmberg and Blake, 1984; Pisu and Cavallazzi, 1951). Fluid replacement is critical for SFP therapy, but reducing abdominal cramping and diarrhea is also pursued. Administration of antibiotics is not indicated because SFP is mediated by preformed toxins and not by replicating organisms. Earlier experiments have demonstrated that humans who have received several subcutaneous injections of crude formalinized enterotoxin develop resistance to the effects of enterotoxins (Dolman, 1944). One of the first correlations of S. aureus with food poisoning was described by Denys (1894), when members of a family became ill after eating S. aureus contaminated meat obtained from a sick cow. Today, because of mass food production and distribution, single food sources contaminated with staphylocococci or their enterotoxins can reach thousands. A recent case in Japan clearly illustrates the threat that SFP poses to an unsuspecting public. From June until July 2000 a large outbreak of SFP involving 13 420 individuals occurred in the Kansai district in Japan (Asao et al., 2003). The source of intoxication was linked to low-fat milk and drink-type yogurts manufactured at an Osaka city factory. The main ingredient of the implicated products was powdered skim milk that came from a factory in Hokkaido. During production of 939 25-kg bags of powdered skim milk at the Hokkaido factory, operations were stopped for 3 h or longer due to a power outage, and processing was further delayed for 9 h. During this time viable bacterial counts in this product rose above in-house (< 9900 CFU/g) and Japanese Food Sanitation Law (< 50 000 CFU/g) standards. Some of these original 939 bags were then recombined with new powdered skim milk to make 750 bags. Some of the


333 reclaimed bags were estimated to contain 3.7 ng of S. aureus enterotoxin A (SEA) per gram of powdered skim milk and during this outbreak the per capita intake of SEA was estimated to be between 20 and 100 ng. As is common in outbreaks of SFP, no viable S. aureus were isolated from the SEA poisoned low-fat milk or powdered skim milk implicated. Furthermore, some of the low-fat milk products implicated in this outbreak were heated three times to 130 °C for 2–4 s, yet owing to the thermal stability of SEA, were still capable of causing intoxication. One can easily imagine today what an SFP outbreak would be like in a modern cramped airliner, even though only a handful of outbreaks on planes have been documented. For instance, in 1973 an SFP outbreak involving 247 passengers on three international flights was initiated by tainted custard dessert (Centers for Disease Control, 1973; Tauxe et al., 1987). In addition, it has been reported that one of the major causes of airline pilot incapacitations is gastrointestinal illness resulting from ‘food poisoning’ (Beers and Mohler, 1985). Staphylococcal enterotoxins (SE) also play a role in serious invasive staphylococcal disease, since in addition to eliciting gastrointestinal symptoms, SEs can act as superantigens and induce toxic shock (Banks et al., 2003; Ellis et al., 2003; Jarraud et al., 1999). In a post-9/11 world, the potential use of the staphylococcal enterotoxins in a terrorist attack needs to be fully understood by first responders, since the symptoms of enterotoxin inhalation would be markedly more serious than enterotoxin ingestion. The dose of aerosolized staphylococcal enterotoxin B (SEB) that incapacitates 50 % of the human population exposed is 0.4 ng/kg, while the lethal dose for 50 % of the humans exposed is estimated to be 20 ng/kg (Hursh et al., 1995). The lethal effects of the inhaled form of the SEB are probably due to its ability to act as a superantigen, as lymphoproliferation of T cells has been observed in rhesus monkeys following SEB inhalation (Mattix et al., 1995). Of nine humans who accidentally inhaled SEB, besides suffering from gastrointestinal symptoms, five individuals exhibited signs of inspiratory rales and dyspnea (shortness of breath) and seven experienced moderately intense chest pains (Ulrich et al., 1997). In addition, individuals exposed to SEB exhibit conjunctivitis with localized cutaneous swelling (Rusnak et al., 2004). SFP will continue to have a major impact on foodborne illness in the future. This chapter will discuss the inherent physiological and genetic aspects of the staphylococci that make them such effective foodborne pathogens. In-depth information on the staphylococcal enterotoxins and their detection, the staphylococcal stress systems, as well as suggestions to prevent staphylococci infection of foods will also be addressed.

13.2

Staphylococcal enterotoxins

SEs belong to a large family of staphylococcal and streptococcal exotoxins collectively referred to as the pyrogenic toxin superantigens (PTSAgs). Normal antigens stimulate 1 in 10 000 T cells, whereas PTSAgs can stimulate up to 20 % of all T cells. This non-specific polyclonal T-cell expansion results in a massive


334 pathological release of cytokines, which then induce the most severe symptoms of toxic shock. SEs induce polyclonal T-cell expansion by binding to both the major histocompatibility complex (MHC) class II molecules on target cells and T-cell receptors displaying specific β-chain variable domains (for review see McCormick et al., 2001). SEs have been classified as they were discovered as SEA through SEU (except SEF, SES and SET). Besides acting as superantigens, true SEs also exhibit emetic activity (Bergdoll and Schlievert, 1984; Bohach et al., 1990). SEA is the most common SE implicated in SFP outbreaks (Balaban and Rasooly, 2000), and the majority of all SFP outbreaks are probably caused by serotypes SEA to SEE (Bergdoll, 1983). In addition, outbreaks of SFP caused by strains of S. aureus strains harboring the enterotoxin genes seg to sej and ser have also been described (McLauchlin et al., 2000; Omoe et al., 2003). Many SEs have been identified on the basis of shared amino acid homology with known SEs. For instance, the newly described SEU was recently identified entirely on the basis of amino acid sequence homology to other SEs (Letertre et al., 2003). The more thoroughly described ser was recently cloned from a strain of S. aureus implicated in a SFP outbreak, that did not harbor sea through sei (Omoe et al., 2002, 2003). The authors of this work also demonstrated that: (i) ser is located on a plasmid adjacent to sej; (ii) the SFP outbreak strain actively produced SER in vitro; and (iii) purified SER stimulates T cells to produce elevated levels of cytokines inferring superantigenicity on SER. Furthermore, strains harboring ser also actively transcribe sej in vitro. This suggests that sej may as well have played a role in the SFP outbreak caused by strains harboring ser. Because of the cost of research involving monkeys, the authors were unable to assess the ability of SER to induce emesis. SEF was renamed toxic-shock syndrome toxin-1 (TSST-1) since it does not induce emesis in monkeys (Reiser et al., 1983). In addition, TSST-1 phylogenetically diverges on its own away from other PTSAgs (McCormick et al., 2001). SEI, SEK, SEL and SEQ also diverge into a single group based on amino acid sequence and do not, or poorly, induce the emetic response in monkeys (Orwin et al., 2002, 2003; for review see McCormick et al., 2001). Structurally, all SEs share a conserved twodomain topology, and it has been proposed that emetic SEs possess a cysteine loop structure that is involved with the emetic response (for reviews see Dinges et al., 2000; McCormick et al., 2001). The non-emetic or weakly emetic SEs do not appear to be capable of forming this structure (Munson et al., 1998; Orwin et al., 2002, 2003). Interestingly, based on amino acid sequence homologies, known emetic SEs capable of forming the cysteine loop resolve into at least two groups by phylogenetic analysis (McCormick et al., 2001), further complicating the relationship of SE protein structure and emesis. Using the house musk shrew model of SE-induced emesis, Hu et al. (2003) reported no correlation between SE evolutionary relatedness and emetic activity, although SEB-related SEs required high doses to elicit the emetic response. In addition, this study demonstrated that recombinant forms of SEA, SEB, SEC2, SED, SEE, SEG, SEH and SEI were all capable of inducing emesis in the house musk shrew model in a concentration-dependent manner. From a food safety perspective, two key aspects of SE protein biochemistry contribute to their ability to cause foodborne illness. Firstly, SEs exhibit a high


335 level of heat stability (Bergdoll, 1983) and environmental factors within food matrixes contribute to this heat stability (Schwabe et al., 1990). For example, SEA present within mushrooms retains biological activity after being heated to 121 °C for 28 min (Anderson et al., 1996). Secondly, SEs are tolerant of proteolytic cleavage by gastrointestinal proteases such as pepsin, trypsin, chymotrypsin and rennin (Bergdoll, 1983). This digestive resistance allows SEs to enter the gastrointestinal tract where they interact with suspected SE receptors initiating the symptoms of SFP. The combination of heat stability and protease resistance make SEs highly effective foodborne toxins. The emetic response initiation targets for SEs are believed to be located within the abdominal viscera and are thought to involve specific SE receptors (Sugiyama and Hayama, 1965). Perhaps the cysteine loop structure observed in emetic SEs is important for the binding of SEs to the emetic response receptors located within the abdominal viscera. Inflammatory mediators are released during SFP (Jett et al., 1990; Scheuber et al., 1987) and, recently, Lu et al. (2003) demonstrated that intracolonic instillation of SEB or SEA into mice leads to colonic inflammation. SE receptors have been identified on mast cells (Komisar et al., 1992; Reck et al., 1988), and it has been proposed that SE-mediated mast cell degranulation and the subsequent inflammation lead to the symptoms of SFP (Jett et al., 1990). In contrast to this theory, SEB ingestion leads to SFP symptoms in monkeys, but does not induce monkey mast cells to release inflammatory mediators (Alber et al., 1989). Intravenous administration of SEs produces vomiting and diarrhea in shorter times and at lower doses than when administered per os (Bergdoll, 1972). In addition, some SEs cross epithelial membranes (Hamad et al., 1997) and can appear in the blood of mice that have ingested these toxins. Evidence also demonstrates the presence of SE receptors, other than MHC class II molecules, on B cells, mast cells and colon carcinoma cells (Avery et al., 1994; Dohlsten et al., 1991; Herrmann et al., 1991). Furthermore, it seems reasonable to suggest that the superantigenicity of SEs also plays a role in the ability of these unique toxins to elicit the emetic response. Viewed collectively, this information implies that SE effects within and outside of the gastrointestinal tract may contribute to the symptomology of SFP. Regardless of the SE-mediated emetic initiating response, emesis is dependent on the medullary emetic center of the medulla oblongata, which is stimulated by nerve impulses carried on the vagus and sympathetic nerves (Sugiyama and Hayama, 1965). Another rather controversial finding is the association of toxigenic S. aureus strains and the pathogenesis of atopic dermatitis (for a review see Yarwood et al., 2000). Atopic dermatitis (AD) is a common disease in children that occurs in patients that produce high levels of serum IgE and demonstrate hypersensitivity to certain antigens (Horan et al., 1992). The rate of S. aureus colonization of patients with AD has recently been estimated to be an astounding 88–94 % (Breuer et al., 2002; Mempel et al., 2003). Of strains colonizing AD sufferers, one report indicated 71 % carried genes for at least one superantigen (TSST-1 and/or various SEs), although the vast majority of these strains carried only SE genes (Mempel et al., 2003).


336 13.2.1 The genetics and regulation of staphylococcal enterotoxins SE genes are located on the staphylococcal chromosome, plasmids and transposons and within phage and prophage genomes (for a review see Le Loir et al., 2003). In addition, a series of mobile genetic units containing SE genes referred to as staphylococcal pathogenicity islands (SaPIs) have been characterized (for review see Novick, 2003a). In a phage-dependent manner, the transduction of SaPI1 can be as high as 10–1 (phage 80α) or as low as 10–7 (phage φ11). During the phage lytic cycle, SaPI1 excises from the chromosome and is packaged into complete phage transducing particles at an extraordinarily high rate, in comparison to phage genomes. The extremely high frequency of phage 80α-mediated SaPI1 transfer differs considerably from conventional transduction (transduction frequencies of 10–5 to 10–7) and represents a unique mechanism for the transfer of SEs among staphylococci. Since numerous genetic elements are capable of horizontally transferring SEs, the background of staphylococci carrying these genes will continue to evolve. A timely study on 429 S. aureus blood (219) and anterior nasal nare (210) isolates has revealed that: (i) 73 % of these strains harbored at least one PSTAg gene and of these 62.9 % contained multiple PSTAg genes; (ii) 55 % of these strains carried a seg and sei combination; and (iii) sea and sec were carried by 15.9 % and 11.2 % of these isolates, respectively (Becker et al., 2003). This study demonstrated that the acquisition and maintenance of multiple PSTAgs (in particular SEs) is a ‘habitual’ feature of S. aureus. The carriage of multiple SE genes by strains of S. aureus has also been reported by other groups as well (Jarraud et al., 1999; McLauchlin et al., 2000; Omoe et al., 2002) and is in part attributed to the carriage of multiple SE genes on SaPIs. The genes seg and sei are carried on SaPIn3, which is probably why these genes are often reported to coexist within S. aureus. Now the question remains as to what benefit the presence of SE genes provides to S. aureus? Perhaps carriage of multiple PSTAgs allows for the selection of specific ecological niches by S. aureus within the human body. Becker et al. (2003) reported that the fixed gene combination of sed–sej is commonly identified among blood isolates, suggesting that the presence of this SE gene combination allows the organism to establish blood infections. A study by Banks et al. (2003) revealed that female genital tract isolates of S. aureus commonly harbor the combination seg and sei and demonstrated greater SEG and SEI antibody concentrations in healthy woman compared with men. The authors concluded that this SE combination, and/ or closely linked genes (perhaps on SaPIn3), may be important for the ability of S. aureus to colonize the female genital mucosa. The control of SE production in S. aureus is complex and involves numerous genetic loci. Thus far, individual SE promoters and phage promoter elements, global virulence regulatory operons/genes (e.g. agr, sarA, rot and sigB) and a cytoplasmic form of some SEs, have been implicated in the control of SE production in S. aureus (Fig. 13.1). The promoters for sea, seb and sed have been characterized (Borst and Betley, 1994; Mahmood and Khan, 1990; Tseng et al., 2004). Deletion between the transcriptional and translational start sites of the sea


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338 gene and mutagenesis with N-methyl-N-nitro-N-nitrosoguanidine have led to strains with increased SEA production (Borst and Betley, 1994; Friedman and Howard, 1971). The production of SEB is highly variable among various unrelated strains of S. aureus, not because of different seb upstream regulatory sequences, but rather because of alterations in various host factors (Compagnone-Post et al., 1991; see below). In addition to the SE gene promoters, Sumby and Waldor (2003) demonstrated that the lysogenic phage φSa3ms which carries sea, seg and sek can also provide unique mechanisms for up-regulating SE production. The addition of mitomycin C, leading to prophage induction, leads to the upregulation of all three SEs. The increase in sea expression results from read-through transcription of upstream latent phage promoters, while the majority of seg and sek transcripts initiate from the upstream phage repressor (cI) promoter, making cI, seg and sek a unique operon. One major locus controlling the production of SEs, in an SE gene-specific manner, is the quorum-sensing agr operon (for a review see Novick 2003b; Fig. 13.1). The agr operon produces two divergent transcripts, one encoding the quorum-sensing system AgrB, AgrD, AgrC and AgrA, and the other encoding a regulatory RNA, RNAIII. AgrA is a response regulator that when phosphorylated upregulates expression of the agr operon. AgrC is a sensor kinase that senses the levels of autoinducer in the environment. AgrB processes and exports AgrD, which then acts as an agr-autoinducing peptide. RNAIII encodes the delta hemolysin and the transcript itself acts as a regulatory signal on the Agr system and can induce the production of a number of SEs (Fig. 13.1). SEB, SEC and SED production is higher in S. aureus strains that have a wild-type agr operon compared with those with inactivated agr operons (Bayles and Iandolo, 1989; Gaskill and Khan, 1988; Regassa et al., 1991). The agr-reponsive element of sed has been mapped to within the minimal sed promoter (Tseng et al., 2004). The expression of some SEs has been shown to be independent of agr control (Orwin et al., 2001; Tremaine et al., 1993; Zhang et al., 1998; Fig. 13.1). The staphylococcal accessory regulator SarA (Cheung et al., 1992) also regulates the synthesis of SEs indirectly through its actions on the agr operon, or perhaps directly via interactions with specific SE gene promoters. SarA influences the expression of exoproteins by binding to specific DNA sequences within promoters of specific loci, including agr (Cheung et al., 1992; Chien et al., 1999; Sterba et al., 2003). The promoter region of sec includes a SarA binding site and it is likely that SarA modulates expression of sec independently of agr (Chien et al., 1999). SarA also clearly affects the expression of sed, independent of the agr operon (Tseng et al., 2004). Another member of the SarA transcriptional factor family, the repressor of toxin or rot locus, can inhibit the transcription of sea and sed (Tseng et al., 2004). In fact, the agr control over sed promoter activity is rotdependent (Tseng et al., 2004). Activation of the alternative sigma factor SigB also down-regulates synthesis of seb and sed (Tseng et al., 2004; Ziebandt et al., 2001). Additional SarA paralogues exist within S. aureus (Cheung and Zhang, 2002) and it is probable that at least some of these loci will also be found to control SE production. Futhermore, it has been shown that a cytoplasmic form of SEB acts as


339 a negative regulator of exoprotein production and as an autorepressor regulating the synthesis of itself (Vojtov et al., 2002). Interestingly, SEB is agr-regulated, while SEA, which lacks autorepressive activity, is not agr-controlled, suggesting that the inherent SEB regulatory property may be related to the larger regulatory network to which it belongs (Vojtov et al., 2002). In addition to complex genetic regulation, SEs are also regulated by extrinsic factors (for a review see Le Loir, 2003). Earlier studies on SE production in foods and laboratory media have been reviewed comprehensively by Smith et al. (1983). Factors that inhibit SE production include: low temperature; low water activity (aW) (Qi and Miller, 2000); low pH; low oxygen (Belay and Rasooly, 2002); and glucose addition or catabolite repression. Not surprisingly, the expression of agr is also down-regulated by salt, glucose and low pH (Cheung and Zhang, 2002). Therefore, we argue that many food matrixes can be altered to reduce the production of enterotoxins by contaminating staphylococci.

13.2.2 Detection of staphylococcal enterotoxins One of the major avenues of research in food microbiology has been to develop methods designed to detect the presence of staphylococci, SEs or SE genes in food items. Methods designed to detect staphylococci, SEs or SE genes in commonly mass-produced food matrixes should be simple, rapid and preferably inexpensive, since SFP is such a common foodborne disease. Since SEB is listed on the NIAID Biodefense Priority Pathogens, technologies designed to rapidly detect SEB with great sensitivity are required, and it is probable that these technologies will also be used to detect other SEs as well. While a range of media have been utilized for the detection and enumeration of S. aureus following SFP outbreaks and for routine food surveillance programs, Baird-Parker agar remains the media of choice (for review see Baird and Lee, 1995). Development of media and culture techniques aimed at recovering S. aureus sublethally-injured during food production (Onoue et al., 2002; Sandel et al., 2003) is an area that requires additional research to increase detection sensitivity. The US Department of Agriculture Food Safety and Inspection Service (USDAFSIS) has set a solution detection goal of 1 ng/ml for SE detection assays. Of the immunological-based methods designed to detect SEs, the most commonly employed is the enzyme-linked immunosorbent assay (ELISA), which can be rapid (1.5–24 h) and very sensitive (detects 0.1–1 ng/ml of SE) (for review see Su and Wong, 1997). Because of the high degree of homology among SEs, monoclonal antibodies manufactured to detect one SE cross-react with other SEs. For example, Meyer et al. (1984) reported on the development of a monoclonal antibody that cross-reacted with five SE serotypes. Perhaps qualitative ELISAs developed to detect the presence of SEs should be based on antibodies that react to an antigenic epitope common to all SEs. Nonetheless, diagnostic kits are available that can differentiate the toxins present (Su and Wong, 1997). Currently the USDA-FDA (Food and Drug Administration) employs a qualitative reverse passive latex agglutination test and a quantitative and qualitative biotin-strepavidin ELISA for


340 the detection of SEs. Both of these techniques require >24 h to carry out, and require significant sample preparation (Mageau, 1998). In addition to ELISA assays, other technologies are coming on line that are used to detect SEs. Several sensors based on surface plasmon resonance (SPR) have been developed with a detection limit for 1 ng/ml of SE in >30 min (Nedelkov et al., 2000). SPR is a label-free quantification method that utilizes an interaction of light photons with free electrons (surface plasmons) on a gold surface to quantify the amount or concentration alterations of a biomaterial (proteins, etc.) on a surface (Homola et al., 1999; Mullet et al., 2000). Therefore, affinity-captured enterotoxins analysed with SPR can be further analyzed by matrix-assisted laser desorption/ ionization time-of-flight (MALDI-TOF) mass spectrometry (Nedelkov and Nelson, 2003). Nedelkov and Nelson (2003) combined SPR and MALDI-TOF to produce a biomolecular interaction analysis mass spectroscopy protocol (BIAMS, Fig. 13.2) that can detect as little as 1 ng/ml of SEB from buffer or mushrooms. Theoretically, BIA-MS could be modified to identify all SEs via the mass spectrum analysis using an antibody that binds to a common SE epitope or by using multiple SE antibodies (Fig. 13.2). Perhaps BIA-MS could be used in centralized laboratories for the most definitive analysis of potential SE intoxicants from food matrixes, and could also be used to monitor food products for contamination of multiple bacterial toxins simultaneously. Recently, an array biosensor was designed that could detect SEB in concentrations as low as 0.5 ng/ml in multiple food matrixes in < 20 min with little pretreatment and no sample preconcentration (Shriver-Lake et al., 2003). This system is based on a waveguide with capture SEB-antibodies immobilized in stripes, a diode laser to generate evanescent illumination and collection optics. Samples applied are run perpendicular over patterned capture antibodies. This array biosensor has been designed for on-site analysis of multiple samples, weighs less than 12 lbs and is controlled by a laptop computer. Most assays are completed in 15 min, but improvement in sensitivity can be observed by increasing assay times. Like the BIA-MS discussed above, this system could also be modified to detect multiple SEs. Since there is a need for the detection of biological weapons in the field (e.g. SEB), this array biosensor has a unique position in application. In addition to detecting the SE directly, determining the presence of an SE gene(s) in food matrixes can indicate relevant staphylococci contamination. Numerous groups have developed polymerase chain reaction (PCR), multiplex PCR, and real-time PCR methodologies capable of detecting the presence of one or more SE genes (Klotz et al., 2003; Letertre et al., 2003; McLauchlin et al., 2000; Monday and Bohach, 1999; Nájera-Sánchez et al., 2003; Omoe et al., 2002; Sharma et al., 2000; Tamarapu et al., 2001). Sergeev et al. (2004) have combined PCR utilizing a set of degenerate primers designed to amplify a conserved sequence of 17 known SE genes and a SE gene oligonucleotide microarray. This combined method brings to bear the strength of both techniques: PCR amplication can detect target genes even if they are present in low concentrations; and the DNA–DNA hybridization on the array increases the specificity of the assay and allows parallel analysis of multiple SE sequences simultaneously. This technique


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Fig. 13.2 Two-analyte BIA-MS assay analyzing for toxic-shock syndrome toxin-1 (TSST-1, circles) and staphylococcal enterotoxin B (SEB, diamonds). Flow cells (FC) were derivatized with either anti-TSST-1 IgG (γ) in FC1 or a combination of anti-TSST-1 and anti-SEB (δ) IgG in FC2. Binding of anti-TSST-1 and anti-SEB to FC1 and FC2 was monitored with surface plasmon resonance (SPR) (for proteins, 1000 RU = 1 ng material per flow cell). A sample containing both TSST-1 and SEB (in a large excess of albumin carrier) was injected through both flow cells and TSST-1 and SEB binding was monitored with SPR (bottom right-hand corner). In the top of the figure, MALDI-TOF-MS spectra are determined directly from both flow cell eluents and demonstrates retention of TSST-1 in FC1, and both TSST-1 and SEB in FC2.


342 can also be used to begin to understand the complexities and epidemiology of SE gene acquisition by staphylococci and the disease state induced by certain gene combinations.

13.3 Growth of Staphylococcus aureus in the food environment The types of food associated with SFP vary widely from one country to another and include milk and cream, cream-filled pastries, butter, ham, cheeses, sausages, canned meats, salads, cooked meals and sandwich fillings (Le Loir et al., 2003). The typical chain of events leading to SFP is: (i) contamination of food with SEproducing S. aureus; (ii) exposure of said food to temperature and time that allows for relatively dense S. aureus growth (~107 CFU/g); (iii) production of enterotoxin(s); and (iv) ingestion of contaminated foods. Exposure to a temperature permitting growth commonly occurs due to a failure of refrigeration or to refrigerate, and is referred to as temperature abuse. Staphylococcus aureus has relatively complex nutritional requirements, requiring two or three B-group vitamins and 5–12 essential amino acids. The most commonly required amino acids for growth of S. aureus strains are arginine, valine, leucine, cysteine and proline (for review see Wilkinson, 1997). The physical environmental limits of the growth of S. aureus are not unusual. S. aureus is a facultative anaerobe that can grow over a wide range of temperatures (7– 48.5 °C) and pHs (4.2–9.3), with optimum temperatures of 30–37 °C and pH values of 7.0 –7.5 (Bergdoll, 1989; Schmitt et al., 1990). One unique aspect of S. aureus physiology is its ability to grow in foods of very low aw, generally accepted to be as low as 0.86 (Troller, 1986). We speculate that the predominant mode of bacterial growth in food is attached to surfaces in what is known as a biofilm. A biofilm is an accumulated mass of bacteria on a solid surface usually encased in an extracellular polysaccharide that they themselves synthesize. Biofilms can be found on any environmental surface in which sufficient moisture is present and their development is accelerated in flowing systems with adequate nutrients. The study of S. aureus biofilm has been an active area in recent years, although not specifically in food matrices. The role of cell surface molecules in biofilm development and maintenance has been emphasized by Gotz (2002), and Yarwood et al. (2004) have reported on the role of the virulence-regulating agr quorum-sensing system (see below) in the development of S. aureus biofilms. Beenken et al. (2004) compared gene expression in S. aureus biofilms with planktonic cultures and demonstrated that 48 genes were upregulated and 84 genes were down-regulated in biofilms. The authors of this study surmised that growth within a biofilm requires an adaptive response to cope with the low pH associated with an anaerobic biofilm lifestyle. Using a Tn917-lacZ S. aureus library, Lammars et al. (2000) isolated mutants demonstrating increased levels of β-galactosidase expression on milk-containing agar. Of these transposon mutants, eight had inserts in peptidoglycan and lysine


343 biosynthesis genes, five in genes involved in DNA biosynthesis, two in transcriptional regulators, and one in carbohydrate metabolism genes. Purine biosynthesis genes were also heavily induced in a milk-containing environment. Twelve of the insertions were in hypothetical genes, the functions of which are speculative.

13.4 Food processing and preservation: what microbes encounter Food preservation protocols are in part designed to kill or inhibit the growth of microbes and can be divided into physical and chemical methods. Physical methods of food preservation include dehydration, refrigeration and freezing, controlled atmosphere storage, modified atmosphere packaging, vacuum packaging, various forms of heat treatment, ultraviolet radiation, ionizing radiation and high hydrostatic pressure (Farkas, 1997). A large number of chemical compounds are used as food antimicrobials or preservatives, although propionates, sorbates, and benzoates are the most frequently utilized agents (Davidson, 1997). Multiple hurdle technology combines the addition of food antimicrobials and alterations of several environmental parameters at levels suboptimal for growth, in order to inhibit microbial contamination (Montville and Matthews, 2001). Therefore, in order to survive and grow, a lone population of S. aureus must mount an aggressive stress defense system when it finds itself in a food environment. Furthermore, bacteria can develop resistance to food antimicrobials employed to inhibit their growth. For instance, strains of S. aureus resistant to the action of parabens demonstrate altered membrane structure, which is thought to reduce the membrane absorption and thereby activity of these food antimicrobials (Bargiota et al., 1987).

13.5 The response of Staphylococcus aureus to particular types of stress While the stress response systems of S. aureus have been investigated (Clements and Foster, 1999; Wilkinson, 1997), these systems are far less understood than similar systems in Escherichia coli and Bacillus subtilis. In general, the activation of all bacterial stress response systems leads to global alterations in both the cell transcriptome and proteome. It has been proposed that multiple stresses impose an energy drain from microbes as they attempt to maintain homeostasis and grow (Montville and Matthews, 2001). Frequently, exposure of a microbe to one type of stress can provide cross-protection against another kind of stress (Leyer and Johnson, 1993; Pichereau et al., 2000). For instance, Shebuski et al. (2000) demonstrated that growth of S. aureus in low aw leads to cells with dramatically increased thermotolerance, and Noma and Hayakawa (2003) showed that preincubation of S. aureus at low temperatures protected cells from the effects of hydrostatic pressure. We speculate that microbes attempting to contaminate modern food environments must contend with multiple insults, which lead to the


344 induction of cross-protecting stress systems. What is known about the stress systems of S. aureus will be discussed in the following sections.

13.5.1 The SigB regulon Bacterial transcription is catalyzed by the multisubunit enzyme RNA polymerase. The core of RNA polymerase catalyzes elongation and termination of transcription but, in general, promoter recognition is the function of multiple sigma subunits. The sigma factor that carries out the transcription of the so-called housekeeping genes is SigA in S. aureus. Additional sigma factors, known as alternative sigma factors, help bacteria respond to environmental stresses by recognizing alternative sigma factor-specific promoters and transcribing the associated genes. SigH is a newly recognized S. aureus sigma factor involved in the transcription of DNA competence factors (Morikawa et al., 2003). A third sigma factor in S. aureus, SigB, is intimately involved in the general stress response of this organism and influences: the expression of virulence factors and global virulence regulators; carotenoid production; biofilm formation; and resistance to cell-wall-active antibiotics, ultraviolet radiation acid and hydrogen peroxide (Bischoff et al., 2004; Chan et al., 1998). Gertz et al. (2000) compared the protein profiles of a sigB– mutant and its parent strain by two-dimensional gel electrophoresis and revealed that 27 proteins were under the control of SigB. Several of the proteins were involved in NADH generation and membrane transport, and a ClpC gene homologue was also controlled by SigB. Clp proteins have been shown to act as both chaperones and proteases and are important in bacterial stress responses. The use of sensitive microarray-based analysis indicated that 198 genes are stimulated and 53 are repressed by sigB, demonstrating the global effect of sigB on gene regulation (Bischoff et al., 2004). The genes controlled by sigB were involved in a wide variety of cellular functions and, in general, adhesins tended to be upregulated, while toxins and other exoproteins were down-regulated. Furthermore, an alkaline shock protein is also up-regulated by the presence of sigB.

13.5.2 Heat and cold response Exposure of bacteria to low and high temperatures has profound effects on all aspects of microbial cell structure and function. The structural integrity of macromolecules, macromolecular assemblies, protein synthesis, membrane fluidity and membrane transport are all affected. Many heat and other stress-induced proteins are molecular chaperones or proteases. The major S. aureus heat shock proteins (Hsps) GroEL, GroES and DnaK are expressed upon exposure to heat and other stresses and after interaction of the organism with epithelial cells in vivo (Qoronfleh et al., 1998). Chastanet et al. (2003) have described a novel, dual mode of regulation of the dnaK and groESL operons in S. aureus by the CtsR and HrcA repressors. The cold shock response refers to changes occurring in the cell in response to a sudden decrease in temperature. Cold acclimation refers to longer-term alterations


345 in the cell in response to the re-establishment of growth at a low temperature. Cold shock proteins (Csp) are low molecular weight proteins that are heavily expressed in response to cold shock in a wide range of bacteria (Phadtare et al., 2000). These proteins are believed to have RNA chaperone activity that enables the ribosome to restart protein synthesis previously arrested by cold shock. The cspA gene of S. aureus was identified in an unusual context (Katzif et al., 2003). In the search for a Tn551-insertion mutant that was resistant to the human antimicrobial peptide cathepsin G, a mutant with a Tn551 insertion just upstream of the cspA gene start codon was isolated. The Tn551::cspA mutant and a separate cspA deletion mutant had a decreased capacity to respond to cold shock. Loss of CspA also resulted in loss of pigment production and a change in the level of at least 14 proteins. Ten Csps were induced upon downshift of S. aureus cultures from 37 to 10 °C, eight of which had molecular masses in the 9.8–14.5 kDa range. A 12.1 kDa protein was identified by N-terminal sequencing as a B. subtilis CspC homologue (D. O. Bayles and B. J.Wilkinson, unpublished observations). Even less is known about the physiology of S. aureus growing at low temperatures, i.e. cold-acclimated cells. Cells growing at 25 °C showed modest changes in the proportions of their different fatty acids, but carotenoid content was stimulated markedly (Joyce et al., 1970). Various compatible solutes such as glycine-betaine and carnitine, which act as osmoprotectants, i.e. they stimulate growth in media of high osmotic strength, act as cryoprotectants, and stimulate growth at low temperature in bacteria closely related to S. aureus (Bayles and Wilkinson, 2000). We have also shown that osmoprotectants stimulate the growth of S. aureus at low temperatures (A. Muhtaiyan and B. J.Wilkinson, unpublished observations), and therefore the presence of osmoprotectants in food may also stimulate S. aureus growth at low temperatures.

13.5.3 Acid stress The bacterial response to low pH is comprehensive, involving both constitutive and inducible responses, including removal of protons, alkalination of the environment, cell envelope composition changes, and production of stress proteins and associated transcriptional regulators. This topic in Gram-positive bacteria has been reviewed by Cotter and Hill (2003). Abee and Wouters (1999) have described acid stress in the context of food microbiology as the combined effect of low pH and weak organic acids, such as acetate, propionate and lactate, either present in food through fermentation or added to it as preservatives. S. aureus is killed at pH 2, but is protected from killing by pre-exposure to pH 4 via a sigB-dependent mechanism (Chan et al., 1998). Adaptation of S. aureus to acid also induces sodA which encodes the major superoxide dismutase, and sodA mutants demonstrate reduced acid tolerance (Clements et al., 1999). These findings suggest that acid stress of S. aureus also results in oxidative stress. Lithgow et al. (2004) also demonstrated a role for cysteine synthase in the acid tolerance mechanism of S. aureus. Furthermore, S. aureus growing in biofilms express the urease and arginine deiminase pathways which produce alkaline compounds and neutralize acids (Beenken et al.,


346 2004). As stated previously, it has been hypothesized that biofilm bacteria must contend with a low pH environment. Clearly, S. aureus possesses mechanisms to respond to acid insult.

13.5.4 Response to low availability of water The availability of water in a food has been measured by the aw of the food: P aw = — Po where P = vapor pressure of water in food and Po = vapor pressure of pure water at the same temperature. It should be noted that although the term aw is widely used in food science, it has its limitations, particularly on low moisture systems, as discussed by Stewart et al. (2002). Reduction of food aw by adding high concentrations of osmotically active solutes such as salt or sugars is a time-hallowed method of food preservation. Sodium chloride and sucrose are impermeant solutes that impose an osmotic stress on bacteria. Growth in the presence of high concentrations of NaCl also results in extensive alterations in gene and protein expression in S. aureus, many of which are not obviously related to osmoregulation (Vijaranakul et al., 1997). Bacteria respond to osmotic stress by elevating their internal concentrations of compatible solutes, either by accumulation of the solutes from the medium, or by biosynthesis. A compatible solute is one that has little or no inhibitory effect on cell metabolism, structure or growth at high concentrations. There has been a renaissance of interest in S. aureus osmoregulation since 1990 (for review see Wilkinson, 1997). The cellular level of K+ is high in S. aureus (> 1 M), and does not change much upon osmotic stress, in contrast to several other bacterial species. Various compatible solutes such as glycine-betaine, proline, choline, taurine, proline-betaine and carnitine act as osmoprotectants and accumulate via various transport systems in osmotically stressed S. aureus (Graham and Wilkinson, 1992; Vilhelmsson and Miller, 2002). Interestingly, sigB is involved with the up-regulation of a glycine-betaine transporter, suggesting a role for sigB in the response to osmotic alterations (Bischoff et al., 2004). The ability of osmoprotectants to protect bacterial viability is dependent on the substance used to reduce aw. For instance, when the permeant humectant glycerol was used to lower medium aw, growth of S. aureus was not enhanced by osmoprotectants (Vilhelmusson and Miller, 2002). Interestingly, Qi and Miller (2000) demonstrated that SEB production was more sensitive to low aw compared with SEA production. Furthermore the osmoprotectant proline, but not other osmoprotectants, was able to significantly increase SEB production at low aw. One wonders if osmoprotectants are present in various foods that protect S. aureus from low aw and increase the capacity of this organism to produce enterotoxins. aw is particularly relevant in the case of intermediate moisture foods. Such foods are semi-moist and compounds such as glycerol, sorbitol, salt and organic acids are included to bind


347 water and reduce the aw of the food. These foods, which do not require refrigeration, have attained considerable attention given the foreign policy pursued by the USA in the post-9/11 world. Vora et al. (2003) reported on the survival of S. aureus in three ready-to-eat meals with intermediate moisture rations: beefsteak, pH 5.03, aw 0.732; bread, pH 4.88, aw 0.861; and chicken pockets, pH 4.97, aw 0.853. Survival of the initial inocula was found to be highly variable and inocula size did not influence the survival or inactivation of S. aureus in these food items.

13.5.5 Hydrostatic pressure Application of high or ultra-high hydrostatic pressure (300–1000 MPa) is an emerging food preservation technology that kills or inhibits microbial growth (Farkas, 1997). Hydrostatic pressure treatment can be applied at moderate, ambient or lower temperatures, and treated foods retain many desirable characteristics of appearance, nutrients and flavor. In bacteria, high hydrostatic pressure fractures membrane and ribosome structure, disrupts non-covalent bonds (Abee and Wouters, 1999; Farkas, 1997) and can result in the production of Hsps and Csps as well as other proteins (Welch et al., 1993). S. aureus appears to be more resistant to the action of hydrostatic pressure than other bacteria (Alpas et al., 2000). Alpas et al. (2003) also reported on differences in hydrostatic pressure resistance between two S. aureus strains. Therefore a mechanism of hydrostatic pressure resistance based on genetic diversity exists in S. aureus.

13.6

Prevention of staphylococcal food poisoning

Clearly, common sense food preparation, such as appropriate cooking and holding temperatures and refrigeration, play important roles in the prevention of SFP outbreaks. Smith et al. (1983) called for the ‘optimization of anti-staphylococcal activity by manipulation of multiple environmental and nutritional parameters.’ It would appear that application of this theory akin to the multiple hurdle approach should create conditions in foods that result in staphylococcal death, arrest growth, and prevent SE production. Personal hygiene, such as the appropriate use of barrier techniques and hand-disinfection during food production, is also important. Besides these measures, increased efforts to detect staphylococci, SEs or SE genes is also required for the identification of contaminated food items during and after food production. While some effort has gone into modeling the growth of S. aureus in foods and laboratory media with varying physical and chemical environmental parameters (Stewart et al., 2002, 2003; Vora et al., 2003), the use and design of predictive models to assess the threat of staphylococci need to be further elucidated and applied. Certainly, the carriage of staphylococci is a major factor in SFP outbreaks. It has been estimated that 20–50 % of the population carry S. aureus in their nasal nares (Bergdoll, 1989; Kluytmans et al., 1997), a high percentage of which are enterotoxigenic (Bergdoll, 1989). Reservoirs of staphylococci causing foodborne


348 disease have been linked to food handlers involved with the preparation of the implicated foods (Eisenberg, 1975; Jones et al., 2002; Suzuki et al., 1999). Indeed, nose and hand carriage of enterotoxigenic strains has been reported to occur in flight catering employees (Hatakka et al., 2000). In fact, the nasal mucosa of some food handlers can be colonized with multiple strains of S. aureus, which may further complicate identification of the reservoir of the pathogen during SFP outbreaks (Acco et al., 2003). In addition, the presence of S. aureus in food or the carriage of said organisms by food handlers has also been implicated in outbreaks of severe systemic infections (Kluytmans et al., 1995). Clearance of S. aureus nasal carriage can significantly reduce the risk of developing S. aureus infection (Kluytmans et al., 1996; Yano et al., 2000; Yu et al., 1986). While perhaps overly optimistic, a combination of active S. aureus carriage-screening and eradication among individuals involved with food production should reduce the number of SFP outbreaks. One method to clear carriage is the application of a mupirocin cream to the nasal passages; however, mupirocinresistant strains of S. aureus are emerging (Chaves et al., 2004). Lysostaphin is a potent peptidoglycan hydrolase (glycylglycine endopeptidase) commonly used by staphylococcologists to degrade the thick peptidoglycan armor of these organisms for the isolation of genetic material. A recombinant lysostaphin cream formulated for eradication of S. aureus nasal colonization is currently in clinical trials by Biosynexus Inc. (Gaithersburg, MD, USA) (J. F. Kokai-Kun, personal communication). In the phase I clinical trials, the lysostaphin formulation has been shown to be safe and well tolerated, and efficacy studies with this formulation of lysostaphin are proceeding. Although lysostaphin resistance can occur in staphylococci (Climo et al., 1998), it has been shown that such strains do not appear in an animal model of lysostaphin eradication of nasal-carriage (Kokai-Kun et al., 2003).

13.7 Future trends The staphylococci will always present a unique threat to the food industry and human populations around the world. As with all foodborne disease, novel technologies to kill or prevent growth of staphylococci and inhibit SE production are required that pose no threat to a population already wary of appropriate food preparation technologies and additives. Researchers have come a long way in understanding the pathogenesis of SFP and mechanisms by which S. aureus can respond to stress within a food environment. Nonetheless, a major factor that seems to lessen our ability to prevent SFP outbreaks is not fully understanding our capable adversary. On the other hand, one only needs to scan recent literature on S. aureus to quickly conclude that all the molecular tools and protocols are present for intensive studies on the inherent genetic and physiological response and nature of the staphylococcal cell growing in a food environment. For instance, in the context of food safety, it would seem that more knowledge of the refrigerated cell is needed, analogous to the knowledge that was developed for the stationary phase cell in the past decade or so (Kolter


349 et al., 1993). What is the molecular composition and metabolic state of the refrigerated cell, and what dynamic processes occur when the temperature rises sufficiently to permit growth (temperature abuse)? Clearly, it is also desirable to know more about gene expression required for growth of S. aureus in unique food environments which could be investigated with DNA microarray technology or via selective transcriptional analysis protocols. It is also important to take into account the effect of all the unique environmental stimuli in food matrices that contribute to the production of SEs. It is impossible to predict what the ~3000 FDA-approved food additives (http://vm.cfsan.fda.gov/ dms/eafus.html) will have on the expression of enterotoxins, but perhaps one can mitigate enterotoxin production significantly at concentrations deemed safe for food consumption. We have also discussed the stress system cross-protective activities provided by stimulation of single S. aureus stress systems. What is the common thread that ties these systems together? Can further research on these pathways help in developing technology aimed at preventing outbreaks of SFP or provide targets for the production of novel antimicrobials? SFP is a self-limiting disease which requires mostly supportive treatment, but in today’s world where the inhalation of weaponized SEs is possible, we now require SE vaccines, functional SE antitoxins and other immunological therapeutic approaches to prevent SE superantigenic effects. This research may also increase treatment options for SFP and probably invasive staphylococcal disease as well, and be used to prevent SFP. While the superantigen mechanism of SEs seems to be well characterized, more research is also required in order to understand how SEs elicit the emetic response, in particular the identification of the pertinent cells involved, SE receptors and initiating emetic event(s). Can we prevent SFP after the intoxicating last meal? Lastly, since the cost of the monkey model for SE emetic studies is inhibitory to SE research, perhaps new animal models for SE-induced emesis should be further investigated.

13.8

Acknowledgements

We would like to acknowledge Christopher T. D. Price (University of Louisville, School of Medicine) for work throughout this chapter and especially for his work on Fig. 13.1, and Jacqueline Marek for administrative duties throughout the course of this work. We would like to thank Dobrin Nedelkov (Intrinsic Bioprobes Inc., Tempe, AZ) for Fig. 13.2 and for additional correspondence on BIA-MS. We also greatly appreciate correspondence with Lisa Shriver-Lake (Center for Bio/Molecular Science and Engineering, Naval Research Laboratory, Washington, DC) and John Kokai-Kun (Biosynexus Inc., Gaithersburg, MD). JEG would like to dedicate his contributions to this chapter to his wife Sofia, for so many years of understanding.


350

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358

14 Vibrio species: pathogenesis and stress response F. Reen and E. Boyd, University College Cork, Ireland

14.1

Introduction

14.1.1 Aim and scope of this chapter Foodborne diseases pose a significant public health problem that is not easily controlled and are a major cause of illness and death throughout the world. The application of modern genetics to the study of bacterial virulence has led to a significant increase in our knowledge of bacterial virulence mechanisms, not least that of the Vibrio species. Within the scope of this chapter, the adaptive responses of a number of Vibrio species to their environment and their host are examined. We discuss the enhanced capacity of the Vibrio spp., through gene acquisition, genome plasticity and evolution, to adapt to and survive in several environmental and hostspecific niches. We look at novel quorum-sensing circuits identified in several Vibrio spp., and we also describe several stress response systems that combine to greatly enhance the survival of these pathogens in an often hostile environment. The implications of this enhanced survival and durability for the food industry and vaccine development are discussed, as well as the ongoing effort to further understand these pathogens. We concentrate predominantly on the small subset of strains/species that are associated with human disease, including the cholera toxin-producing strains of Vibrio cholerae, which are responsible for epidemic/pandemic cholera, thermostable direct haemolysin-producing strains of Vibrio parahaemolyticus, and Vibrio vulnificus. Although the primary manifestation of infection with these strains is


359 Table 14.1 host

Vibrio spp. implicated in foodborne disease of humans, and their primary

Species

Host

Disease

V. cholerae O1/O139 V. cholerae non-O1/non-O139 V. vulnificus V. parahaemolyticus V. mimicus V. harveyi V. anguillarum V. salmonicida V. carchariae V. damsela V. alginolyticus

Human Human Eel, oyster Fish and shellfish Shellfish Shrimp Salmonids, shrimp Salmon Shark Damsel fish Seabream

Epidemic cholera Endemic cholera, diarrhoea, Vibriosis Vibriosis Vibriosis Penaeid/luminescent vibriosis Vibriosis Vibriosis Vibriosis Vibriosis Vibriosis

gastroenteritis, they can also result in wound or ear infections and (particularly for V. vulnificus) septicaemia in persons who have liver disease or are immunocompromised.1 In the USA, V. parahaemolyticus and V. vulnificus are the most common vibrios identified in seafood associated illness.2 Several other Vibrio species, such as V. damsela, V. alginilyticus, V. anguillarum and V. mimicus, are also capable of causing disease in humans, generally in an indirect manner through the consumption of contaminated fish produce (Table 14.1). 14.1.2 V. cholerae as an adapted host-specific pathogen Vibrio cholerae is the aetiological agent of the endemic and epidemic diarrhoeal disease cholera.3 Cholera toxin (CT), the main cause of the explosive diarrhoea associated with cholera, is found predominantly in isolates of V. cholerae belonging to the O1 and O139 serogroups.4,5 Strains belonging to other serogroups, collectively referred to as non-O1/non-O139, have been implicated in moderate to severe forms of gastroenteritis but generally do not encode CT.6–8 The other main virulence factor of V. cholerae is the toxin co-regulated pilus (TCP), an essential intestinal colonisation factor and, as the name implies, it is coordinately regulated along with cholera toxin.9 Both CT and TCP are encoded on mobile genetic elements CTXφ and VPI-1, respectively, which may explain their sporadic distribution among strains.10,11 Cholera has a short incubation period and its symptoms include abdominal pain, mild fever, headache and vomiting, as well as the classic symptom of diarrhoea. Although the incidence levels of cholera O1 disease in the developed world are relatively low, this pathogen still has a high fatality rate in immunocompromised individuals. Cholera was the first disease for which there was organised modern public health surveillance and reporting of levels of incidence across the world. It is one of the three diseases currently reportable under the International Health Regulations of 1969 and incidence of cholera is influenced by several factors including the changing microecology of V. cholerae, vulnerability of people through exposure to health risks, resistance to infection through immunity and/or nutritional status,


360 and environmental, socio-economic and behavioural changes.12,13 The 1990s bore witness to an unprecedented increase in the global burden of cholera, particularly approaching the millennium.12–15 However, annual figures released by the WHO represent only the tip of the iceberg, because the morbidity and mortality caused by V. cholerae are grossly underreported owing to surveillance difficulties and also for fear of economic and social consequences.16 This is particularly true during an epidemic, when not all cases are cultured because of the massive numbers that are affected. Furthermore, several cholera endemic countries, e.g. Bangladesh, are not included in the WHO report. In 1992, a new non-O1 strain was implicated in outbreaks of cholera in Bangladesh and India that have since been referred to as the 8th pandemic of cholera. It is now known that this O139 strain is a derivative of O1 El Tor and may have arisen through O-antigen transfer.17 El Tor strains have since re-established themselves as the main aetiological agent of cholera. It is as yet unclear as to what specifically determines the seasonal appearance of epidemic V. cholerae strains and outbreaks of cholera in regions where it is endemic. Environmental microbiology research has consistently confirmed the importance of V. cholerae’s preferred brackish (saline and alkaline) environments typical of coastal areas where, during inter-epidemic periods and when microenvironmental conditions are not favourable, V. cholerae will survive and multiply.18 A significant development in the study of this disease confirmed the existence of aquatic environmental reservoirs in which V. cholerae survives for long periods of time and from which a toxigenic form, under particular conditions of temperature, alkalinity and salinity, may emerge to support epidemic conditions.19,20 The ability of V. cholerae serogroup O1 to adaptively respond to environmental variations has led to the idea that this species can successfully occupy one or more ecological niches in a variety of aquatic habitats, and several studies have illustrated their ability to associate with a variety of such organisms including crustaceans, zooplankton, phytoplankton and algae.19,21,22 These associations are thought to prolong survival and enable the bacteria to gain nutrients from their aquatic hosts. It is possible that the physiology and structure of V. cholerae serogroup O1 in environmental reservoirs might differ in fundamental ways from these features of the organism in the intestine.23

14.1.3 Other Vibrio species and their role as pathogens and symbiotes Estuarine V. vulnificus is an invasive and rapidly fatal human pathogen, causing three distinct syndromes of infection: primary septicaemia through ingestion of raw or undercooked shellfish (primarily oysters), wound infection by exposure of a pre-existing wound to seaweed or shellfish, and gastroenteritis.24 Reduced stomach acidity and an impaired immune system allow V. vulnificus, ingested in raw seafood, to cross the intestinal mucosa, enter the blood stream and cause primary septicaemia. For those who develop septicaemia, only 40–60 % survive even with appropriate treatment, and tissue debridement and/or amputation is often required for treatment of wound infections. Unlike V. cholerae, V. vulnificus does not cause large outbreaks but, rather, severe and often fatal infections in


361 susceptible individuals who consume as few as 100 bacterial cells.25 A number of potential V. vulnificus virulence factors have been identified but these were also found associated with environmental isolates.26 These include many extracellular products that could help in adaptation to host and environmental changes, including a capsule, hydroxamate and phenolate siderophores, haemolysin, protease, hyaluronidase, elastase, chondroitin sulphatase, phospholipase and mucinase.27 Vibrio parahaemolyticus is a major causative agent of gastroenteritis, particularly in areas of high seafood consumption and is an emerging pathogen in North America.28,29 A characteristic of pathogenic strains is the production of a thermostable direct haemolysin (TDH) on Wagatsuma’s blood agar, resulting in the haemolytic Kanagawa phenomenon (KP).30,31 V. parahaemolyticus isolates also produce a type three secretion system (TTSS), unlike V. cholerae and V. vulnificus, which may explain differences in infection processes.32 V. parahaemolyticus has adapted for survival in several environments and displays multiple phenotypes. In addition to the swimmer-swarmer cell dimorphism, V. parahaemolyticus also displays a second adaptive mechanism whereby the organism switches between a translucent colony type and an opaque colony type, a phenomenon also seen in V. vulnificus.33 In contrast to V. vulnificus, infection with V. parahaemolyticus generally results in a self-limiting gastroenteritis with symptoms of nausea, diarrhoea and vomiting appearing 12–24 hours after consumption of contaminated seafood.25 The nascent light-emitting organ of newly hatched juveniles of the Hawaiian sepiolid squid Euprymna scolopes is specifically colonised by cells of V. fischeri that are obtained from the ambient seawater.34 Unlike the three aforementioned Vibrio species where the Vibrio–host interaction is pathogenic and ultimately detrimental to the survival of the host, V. fischeri enters into a symbiotic relationship with its host, Euprymna scolopes, whereby the bacterium provides luminescence for the developing squid and the host provides an ambient reservoir for survival of the bacterium. Vibrio alginolyticus and V. hollisae are halophilic vibrios and both have been isolated from immunocompromised individuals with gastroenteritis and septic infections.25 These marine pathogens are generally found in shellfish, oysters and coastal fish. V. fluvialis is occasionally associated with gastroenteritis following the ingestion of raw oysters and has been found in fish, shellfish and freshwater clams.25 V. mimicus, initially referred to as sucrose-negative V. cholerae non-O1, has been isolated from fish, oysters, shrimp sediments, plankton and the roots of aquatic plants and is mainly associated with gastroenteritis following the ingestion of raw seafood and, in particular, raw turtle eggs.35,36 As with most Vibrio spp., infections are most likely to occur in areas where water temperature remains high for most of the year; this explains the predominance of reported disease cases in equatorial regions and in the mid-Atlantic and Gulf Coast states of North America.

14.2 Quorum-sensing in Vibrio species Within the host the motile vibrios must evade the innate defence mechanisms,


362 penetrate the mucous layer covering the intestinal villi, adhere to and colonise the epithelial surface of the small intestine, assume a non-motile phase, replicate and cause disease by secreting numerous exoproteins at the site of infection.25 The complexity and instability of the host environment suggests that multiple factors (virulence and stress response genes) of various functions are required to facilitate host colonisation and dissemination. Many of these virulence and stress factors are coordinately regulated among the bacteria by a phenomenon known as quorumsensing. Quorum-sensing is a phosphorelay process involving pheromone-like autoinducers. Bacteria monitor their environment for the presence of other bacteria through quorum-sensing and thereby ensure that a sufficient number of bacteria are present to coordinate a virulence response that will overwhelm the host defences.37 Apart from signal accumulation, quorum-sensing is also thought to be governed by factors such as diffusion processes, signal stability and the presence of other cells using the same signal and may in fact serve as a means for sensing the diffusion potential of the surrounding environment rather than acting as censustaking machinery.38 The functions controlled by quorum-sensing are varied and reflect the needs of a particular species of bacteria inhabiting a given niche; these include bioluminescence, secretion of virulence factors, biofilm formation, sporulation, conjugation, pigment production, entry into stationary phase, nutrient limitation and stress response.39–43

14.2.1 AHL systems vs novel AI systems The Gram-negative quorum-sensing bacteria typically possess proteins homologous to the LuxI and LuxR proteins of Vibrio fischeri, the bacterium in which they were initially discovered (Fig. 14.1).44 The LuxI-type proteins catalyse the formation of a specific acyl-homoserine lactone (AHL) autoinducer that freely diffuses into and out of the cell and increases in concentration in proportion to cell population density. The LuxR-type proteins each bind a specific AHL autoinducer when the concentration of autoinducer (AI) reaches a threshold level and these LuxR-AHL complexes in turn activate transcription of target genes by recognising and binding specific DNA sequences at quorum-sensing regulated promoters.44–46 The model for quorum-sensing in several pathogenic Vibrio species is more elaborate than the luxI–luxR paradigm of V. fischeri, in that there are additional layers of gene control allowing the bacteria to integrate pieces of sensory information, which presumably confers plasticity to the genetic network (Fig. 14.2). The V. cholerae quorum-sensing circuit has at least three parallel systems that converge to control the virulence regulon, although the third of these systems has yet to be characterised.41 V. harveyi uses two parallel systems to regulate the expression of target genes, including those required for bioluminescence, siderophore production and colony morphology.47,48 A dual-channel quorum-sensing system has recently been identified in V. anguillarum.49 Two autoinducer synthases, LuxLM and LuxS, each catalyse the synthesis of the specific autoinducers HAI-1 and AI2 (a unique furanosyl borate diester), respectively (Fig. 14.1).50–52 Accumulation of


363

Fig. 14.1 (a) Structure of several acyl-homoserine lactone (AHL) autoinducers involved in quorum-sensing with special focus on Vibrio spp. The core structure of these speciesspecific autoinducers is essentially conserved as a C4-homoserine lactone, which is then further modified according to the species. (b) Synthesis of AI-2 from S-adenosyl methionine in V. harveyi.


364

Fig. 14.2 Quorum-sensing in Vibrio spp. Several non-AHL quorum-sending circuits are employed by a number of Vibrios to regulate a wide range of responses to environmental stimuli. These cascades converge to regulate the LuxO regulatory protein which in turn controls the physiological response. Only two of the systems have been characterised to date, with the third system remaining largely unknown although it is thought to elicit its response through LuxO. à, AHL system signal molecules; ∆, AI-2 signal molecules; H, histidine domain; D, aspartic acid domain.

these autoinducers leads to a density-dependent metabolic response, which is mediated by a species–specific transcriptional regulator identified as LuxR and HapR in V. harveyi and V. cholerae, respectively. V. vulnificus, V. parahaemolyticus and V. anguillarum have also been shown to possess the AI-2 system, with the identification of the luxR homologues smcR, opaR and VanT, respectively.53–56 The LuxR protein homologues are highly conserved and show the least amount of divergence in a tetR-type putative helix-turn-helix domain that is required for DNA-binding (Fig. 14.3). Indeed, the intergenic region and the transcription initiation sites are conserved between V. harveyi, V. cholerae and V. parahaemolyticus at the luxR, hapR and opaR loci, respectively.54,57 The V. fischeri LitR transcriptional regulator has a markedly different C-terminus from the homologues described above and this may be related to the uniquely symbiotic nature of its relationship with the host. Interestingly, tetR transcription factors act as repressor proteins, whereas the LuxR homologues appear to function as both activators and repressors.54,58


365 1

25

50

75

100

125

150

175

205

(a) (b) VhLuxR VpOpaR VvSmcR VaVanT VcHapR VfLitR

MD-SIAKRPRTRLSPLKRKQQLMEIALEVFARRGIGRGGHADIAEIAQVSVATVFNYFPT MD-SIAKRPRTRLSPLKRKQQLMEIALEVFARRGIGRGGHADIAEIAQVSVATVFNYFPT MD-SIAKRPRTRLSPLKRKQQLMEIALEVFARRGIGRGGHADIAEIAQVSVATVFNYFPT METSIEKRPRTRLSPQKRKLQLMEIALEVFATRGIGRGGHADIAEIAQVSVATVFNYFPT MDASIEKRPRTRLSPQKRKLQLMEIALEVFAKRGIGRGGHADIAEIAQVSVATVFNYFPT MD-TIQKRPRTRLSPEKRKEQLLDIAIEVFSQRGIGRGGHADIAEIAQVSVATVFNYFPT *: :* ********* *** **::**:***: ****************************

59 59 59 60 60 59

VhLuxR VpOpaR VvSmcR VaVanT VcHapR VfLitR

REDLVDEVLNHVVRQFSNFLSDNIDLDIHARENIANITNAMIELVSQDCHWLKVWFEWSA REDLVDEVLNHVVRQFSNFLSDNIDLDIHARENIANITNAMIELVSQDCHWLKVWFEWSA REDLVDEVLNHVVRQFSNFLSDNIDLDLHAKENIANITNAMIELVVQDNHWLKVWFEWSA REDLVDDVLTHVVRQFSNFLADNIDLDLHAKDNLTNITTKMISLVIEDCHWLKVWFEWSA REDLVDDVLNFVVRQYSNFLTDHIDLDLDVKTNLQTVCKEMVKLAMTDCHWLKVWFEWSA REDLVDDVLNKVENEFHQFINNSISLDLDVRSNLNTLLLNIIDSVQTGNKWIKVWFEWST ******:**. * .:: :*: : *.**:..: *: .: ::. . . :*:*******:

119 119 119 120 120 119


STRDEVWPLFVTTNRTNQLLVQNMFIKAIERGEVCDQHEPEHLANLFHGICYSIFVQANR STRDEVWPLFVSTNRTNQLLVQNMFIKAIERGEVCDQHDSEHLANLFHGICYSLFVQANR STRDEVWPLFVTTNRTNQLLVQNMFIKAIERGEVCDQHNPEDLANLFHGICYSLFVQANR STREEVWPLFVSTNRTNQLLVQNMFIKAIERGEVCDRHEPEHLATLFLGIFYSLFVQANR STRDEVWPLFVSTNRTNQLLIRNMFMKAMERGELCEKHDVDNMASLFHGIFYSIFLQVNR STRDEVWPLFLSTHSNTNQVIKTMFEEGIERNEVCNDHTPENLTKMLHGICYSVFIQANR ***:******::*: ..: :::.** :.:**.*:*: * :.::.:: ** **:*:*.**

179 179 179 180 180 179


SKSEAELTNLVSAYLDMLCIYNREHH FKGEAELKELVSAYLDMLCIYNREHTNNTAELSKLVSSYLDMLCIYKREHE IQDEASMGVLVKSYLSMLCIYKKDHLGEQEAVYKLADSYLNMLCIYKN--NSSSEEMEETANCFLNMLCIYK----

(b)

:

205 204 205 205 203 201

...:*.*****:

(c) Fig. 14.3 (a) Illustration of the domain structure of the TetR-family transcriptional regulators from the Conserved Domain Database (CDD). (b) ClustalW alignment of the deduced amino acid sequence of LuxR homologues isolated from several Vibrio species. Residues that are conserved in at least five of the six sequences are shaded. Numerous bacterial transcription regulatory proteins bind DNA via a helix-turn-helix (HTH) motif generally located in the initial third of the protein. (c) TetR folds into 10 α-helices with the three N-terminal α-helices of the repressor forming the DNA-binding domain: this structural motif encompasses an HTH fold with an inverse orientation compared with that of other DNA-binding proteins.


366 14.2.2 AphA regulation links quorum-sensing and pathogenesis in V. cholerae Vibrio cholerae possesses a virulence regulon of more than 20 genes involved in colonisation, toxin production and bacterial survival within the host.59,60 Unlike other bacterial species in which quorum-sensing activates virulence gene expression at high cell densities, quorum-sensing appears to co-ordinately repress ToxR-regulated virulence genes in V. cholerae through two parallel signal transduction cascades.59–61 The ToxR–ToxS and the TcpP–TcpH signalling circuits exert their regulatory control over virulence by influencing the expression of toxT, which in turn activates the transcription of a variety of genes required for virulence.59 TcpP and TcpH are encoded within the VPI-I, and their expression is dependent upon two unlinked regulators, AphA and the LysR-type regulator AphB.62,63 Both of these regulators are responsive to environmental stimuli through the action of the AI-2 quorum-sensing circuit. LuxO negatively controls tcpP expression indirectly through hapR, which is thought to exert its repressive effect on TcpP through repression of aphA by binding close to the site of transcription initiation in a mechanism that is as yet unclear.64 AphB regulation is less well understood although it has been shown to be independent of quorum-sensing. This may be a way of enabling signals from the environment to independently modulate the cooperative activities of AphA and AphB, thus providing additional levels of control for the regulation of a critical activation step in the virulence cascade.65 Several virulent strains of V. cholerae have defective quorum-sensing systems. El Tor N16961 and classical O395 possess a naturally occurring frame shift mutation in hapR, and other strains, such as classical CA401, have a naturally occurring point mutation in the aphA promoter that prevents HapR from binding even if it is functional.66 It has been proposed that the loss or down-regulation of HapR may be advantageous for survival or colonisation within the host.61 It is possible, therefore, that the presence of a non-functional binding site at the aphA promoter might provide an advantage to strains such as CA401 (which has a HapR binding site identical to that of O395 but is HapR+) by eliminating one branch of regulation by HapR (virulence) while leaving the other pathways that it regulates (such as protease production) intact.66 Aside from their role in the virulence cascade, several other physiological responses have been shown to be mediated either directly or indirectly by HapR homologues and a summary of these is presented in Table 14.2.

14.2.3 Integration of various environmental cues with quorum signals It is not yet clear how quorum signals and other environmental cues are integrated to regulate virulence in vivo. One possible model is that on initial (low bacterial cell density) colonisation of a host by V. cholerae, LuxO represses hapR and allows the expression of tcpP, resulting in the expression of the virulence factors in the ToxR regulon. When a high cell density is reached, autoinducer accumulates and LuxO no longer represses hapR expression.65 Subsequent production of HapR indirectly


367

Table 14.2

Transcriptional regulators of Vibrio spp. implicated in foodborne disease of humans

Species

Transcriptional regulator

Up-regulation

V. cholerae

HapR

pva cheYAW

Down-regulation

vps hapA aphA V. vulnificus

SmcR

vvp vvhA vps

V. parahaemolytics

OpaR

vps

V. anguillarum

VanT

V. harveyi

LuxR

serA empA hpdA, hgd vps luxCDABE proAC pscT, popB vps

V. fischeri

LitR


luxCDABE

Physiological process Penicillin amidase, nutrient scavenging Chemotaxis, motility Biofilm Metalloprotease Virulence Metalloprotease Cytolysin Biofilm Alkaline phosphatase, motility, fimbriae, Starvation adaption Biofilm Opacity, capsular polysaccharide Serine and glycine synthesis Metalloprotease Pigment production, tyrosine metabolism Biofilm Bioluminescence Metalloprotease Type III secretion system Biofilm Bioluminescence

368 represses tcp and ToxR regulon expression. Furthermore, at high cell density, HapR activates the expression of hapA, which encodes the HA protease.65 Protease expression might promote detachment of V. cholerae cells, and thus facilitate establishment of new infection foci elsewhere within the gastrointestinal tract or, alternatively, promote the exit of V. cholerae from the host. Repression of the TCP might also promote detachment of V. cholerae from the epithelium. It is important to note that negative regulation of the hapR homologue luxR by LuxO in V. harveyi has not been observed. Therefore, this variation in the regulatory circuit could be unique to V. cholerae.

14.2.4 Selective advantage of multiple quorum-sensing systems Why do several of the Vibrio species use multiple quorum-sensing systems to regulate the same set of target genes, when a single system is apparently sufficient for many other species of Vibrio and other Gram-negative bacteria? AI-1, like other AHL autoinducers, is species-specific and is therefore most likely used for intraspecies cell–cell communication. Homologues of the AHL systems found in V. harveyi and V. cholerae have yet to be found in other Vibrio species. Conversely, many diverse species of bacteria possess a conserved LuxR homologue and produce AI-2, suggesting that AI-2 may function in interspecies cell–cell communication.41,52,67,68 To date, AI-2 is the only species non-specific autoinducer identified in both marine and terrestrial bacteria.50,69

14.2.5 Signal-mediated cross-talk Bacteria often exist in their natural habitats as highly organised communities composed of multiple species. Successful association among these bacterial populations requires effective intra- and inter-species cross-talk.42 ‘V. angustum’ S14 produces extracellular signalling metabolites during carbon and energy starvation that play an important role in the expression of proteins crucial to the development of starvation and stress-resistant phenotypes. This highly organised development of starvation adaptation is prevented by the presence of a signal antagonist as is the expression of proteins induced upon carbon starvation.70,71 The signal antagonist, halogenated furanone-2 from the marine red alga Delisea pulchra, has structural similarities to the V. harveyi AI-2 molecule and possibly acts as a competitor of the AI-2 for its receptor. The presence of either V. vulnificus, V. cholerae or V. alginolyticus has been shown to overcome the antagonistic effect of furanone-2 on culturability during carbon starvation, and in fact to rescue the ‘V. angustum’ S14 cells.72 Further studies on the ability of Vibrio species to ‘cross-talk’ found that this phenomenon varied among species, suggesting that while some aspects of the signalling systems are conserved between species, there are specific features in each system, particularly with respect to the production and activity of the signal molecules.72


369

14.3

Biofilm formation and surface adhesion

Vibrios interact with various surfaces found in the environment in a process thought to be regulated by quorum-sensing to generate biofilms, which may promote survival.73–77 Growth and further colonisation of a surface result in the development of an adherent microbial community with the characteristic biofilm architecture of pillars of bacteria surrounded by water channels in a process thought to require exopolysaccharide synthesis.77–82 Biofilms are very prevalent in nature, and organisms within biofilms have been shown to be slow-growing because of cell density and limited access to nutrients, factors that may explain the high resistance of biofilm populations to antimicrobial agents.83,84 Quorum-sensing-deficient biofilms have lower colonisation capacities than those of wild-type biofilms, suggesting that quorum-sensing may promote cellular exit from the biofilm once the organisms have traversed the gastric acid barrier of the stomach.75 In contrast to other bacterial pathogens that induce biofilm formation at high cell density in the presence of quorum-sensing autoinducers, V. cholerae represses these behaviours at high cell density through regulation of the vps genes.85 The distinct environments occupied by this aquatic pathogen presumably include niches where cell–cell communication is crucial, as well as ones where loss of quorum-sensing via hapR mutation (as seen in several pathogenic V. cholerae strains) confers a selective advantage. Bacterial biofilms could represent a complex habitat where such differentiation occurs.74

14.4 Stress response mechanisms Bacteria in natural environments are constantly challenged by the need to adapt to changes in nutrient availability and stress conditions. A range of bacteria, including Vibrio spp., have been shown to elicit sophisticated intracellular reorganisation programmes in response to such changes. Indeed, the V. cholerae life cycle, which includes growth within an aquatic environment, oral ingestion by human hosts, passage through the low pH environment of the stomach, colonisation within the small intestine, and subsequent dissemination in the cholera stool back into its aquatic niche, presents many challenges to the invasive organism. Many bacteria react to environmental perturbations, such as both high and low temperatures, periods of nutrient starvation, osmotic changes and oxidative stress, by the production of stress proteins and sigma factors, which guarantee the continued viability of the bacteria under otherwise deleterious conditions.86 A summary of the various stress response systems and the genes and sigma factors that regulate these systems in V. cholerae is provided in Table 14.3.

14.4.1 The role of sigma factors in the environmental stress response In pathogenic bacterial species, the role of rpoS in the general stress response and virulence is varied. RpoS (σs) functions as a central regulator of as many as 25


370 Table 14.3

Stress responses and their transcriptional regulators from Vibrio cholerae

Stress

Regulatory genes

Sigma factor

Acid tolerance response Osmotolerance Heat Cold Oxidative stress Ethanol Nutrient deprivation Bile tolerance

toxR, ompU, cadA tcpR groEL dnaK

RpoS RpoS RpoH RpoH RpoS RpoE RpoS

ppGpp, csrS ompU, ompT

stationary-phase-responsive genes, as well as genes involved in adaptation to diverse types of stresses including exposure to H2O2, hyper-osmotic stress and nutrient deprivation.23,87–89 In respect of transcriptional regulation, the function of V. cholerae rpoS appears to be similar to that of the well-studied rpoS homologue of E. coli, which also regulates the transcription of multiple genes, some of which participate in the general stress response, are required for starvation-induced changes in cell shape, or confer an adaptive response to hyper-osmolarity.89–92 It is unclear whether RpoS is important for intestinal colonisation, and it is currently proposed that this sigma factor may serve to aid in surviving environmental stresses encountered in vivo and/or to aid in optimising the rapid growth phase that occurs subsequent to intestinal colonisation.23,91 RpoS has been shown to be under the control of two global regulatory proteins in E. coli, RelA and SpoT.93 A SpoT homologue, csrS, has been identified in Vibrio spp. strain S141, and the function encoded by this gene has been shown to be essential for the successful development of starvation and stress resistance.94 The intracellular signal molecule, guanosine tetraphosphate (ppGpp), which accumulates in response to nutritional deficiency, is required for the successful adaptation to starvation in bacteria. Its accumulation largely exerts control over the macromolecular synthesis that is appropriate for entry into starvation and nongrowth, and this process is regulated by RpoS.95 RpoS in V. harveyi does not appear to have a role in surviving oxidative or hyper-osmotic challenge, but it does function in surviving ethanol stress and persisting during the stationary phase of growth.96 In addition, V. harveyi RpoS is known to control the expression of various virulence genes and is involved in colonisation and adhesion to host tissue. An RpoS-deficient mutant of V. vulnificus is impaired in surviving various stresses and lacks proteolytic activities that are potentially important for virulence.97 RpoS also regulates the expression of V. vulnificus genes encoding possible virulence determinants that are responsible for the enormous tissue damage that occurs during infection.98,99 The sigma factor σ54, encoded by rpoN, is widely distributed among bacteria and regulates diverse functions. In addition to flagellar biogenesis and motility which appear to be regulated by σ54 in most vibrios, this sigma factor has also been reported to play a role in host colonisation, bioluminescence and the swimming-


371 swarming phenotype of V. cholerae, V. harveyi and V. parahaemolyticus, respectively.100–102 This alternate sigma factor also has multiple functions in the symbiote V. fischeri where it contributes to biofilm development, nitrogen assimilation and the regulation of bioluminescence.103 Related to its role in motility, σ54 plays an essential role in the establishment of symbiotic colonisation by V. fischeri.103 There is increasing evidence that extracytoplasmic function (ECF) sigma factors regulate stationary-phase gene expression in many bacterial species. Two such alternate sigma factors, σ32 (rpoH) and σE (rpoE) have been implicated in the response to environmental stress, and their activities are specifically increased by exposure to conditions such as high temperature or ethanol.104,105 Whereas σ32 responds to the presence of misfolded proteins in the cytoplasm and directs the transcription of a number of heat shock genes that function to assist in the folding or unfolding of proteins, σE controls a less well-defined extracytoplasmic response that is specifically induced by the generalised effectors heat and ethanol as well as by the accumulation of immature outer membrane precursors in the periplasmic space.106,107 With respect to virulence, σE has been shown to play a role in the survival of a number of pathogenic bacteria within their hosts.108

14.4.2 Acid stress response Many microorganisms possess an adaptive stress response that gives them the ability to survive exposure to extreme acidic environments and acid tolerance response (ATR) systems have been characterised in both V. cholerae and V. parahaemolyticus.109 This response is crucial to the survival of Vibrio spp. within the host when one considers its in vivo phenotype of being sensitive to even mildly acidic pH. The identification of V. cholerae genes such as cadA, which are induced during infection, has led to the finding that V. cholerae mounts a robust ATR when exposed to mild acid.110 The ATR has been best characterised in E. coli and Salmonella enterica serovar Typhimurium, in which it has been shown that exposure to sublethal pH induces the expression of numerous acid shock proteins (ASPs) that promote bacterial survival in subsequent extreme acid environments.111 Since V. parahaemolyticus is a particularly vulnerable species and highly susceptible to environmental stresses, such an ATR phenomenon could significantly enhance its protection against acid or other stresses and may well increase its survival rate within the human host or in other adverse environments.112 Furthermore, several lines of evidence strongly suggest that ATR may be a significant component in epidemic spread and virulence of V. cholerae.110,113 The response in V. cholerae is comprised of two overlapping branches that encompass inorganic acid (low pH) and organic acid (low pH plus short-chain fatty acids) challenges. In the small intestine, V. cholerae normally encounters organic acid challenge. Inorganic and organic ATRs require both up-regulation and downregulation of protein expression. In fact, the expression levels of as many as 110 protein species have been shown to be affected by exposure to organic acid.114 The lysine decarboxylase activity of CadA, which consumes lysine and a proton and produces carbon dioxide and cadaverine within the cytoplasm of the bacterial


372 cell, plays a major role in the maintenance of pH homeostasis upon exposure to inorganic and organic acid stress.113 In V. cholerae, acid-induced regulation of the cadBA operon is under the control of CadC, a new member of the ToxR-like family of transcriptional regulators.115 Independent of its effect on virulence gene regulation, ToxR is required for organic but not inorganic ATR. This requirement appears intricately linked to regulation of the outer membrane porins, OmpU and OmpT, as ompT expression is significantly repressed by acid stress, and ectopic expression of ompU in a toxR– strain is unable to completely alleviate the ATR defect.113

14.4.3 Bile tolerance OmpU has recently been shown to be important for survival of V. cholerae upon exposure to bile and other detergents that might be encountered by the bacterium during intestinal colonisation.116,117 The fact that ompU transcription levels are not altered upon exposure to organic acid suggests that the pathways and regulatory networks by which organic acid and bile stresses are received and then responded to are different.114 Bile stimulates the ToxR-mediated transcription of ompU, and it is thought that OmpU and OmpT provide a passageway for hydrophilic solutes through the outer membrane and demonstrate that bile might interfere with this traffic in OmpT-producing cells by functionally inhibiting the OmpT pore. The OmpU protein of V. fischeri appears to play a role in the normal process by which the bacterium initiates its colonisation of the nascent light-emitting organ of juvenile squids.34 In V. anguillarum, OmpU is not required for virulence, possibly owing to a second OMP also critical for resistance to bile.118

14.4.4 Oxidative stress Oxidative stress is another situation likely to be encountered in the marine environment, and a significant overlap exists in the global regulators utilised by a cell to withstand both starvation and oxidative stress.119,120 Bacteria adapt to nongrowth conditions primarily by defending against intra- and extracellular oxidative stresses. This is largely achieved by preventing the accumulation of oxidative species in the non-growing cell, exposure to UV irradiation, reactive chemical species, oxidative stress at the surfaces of higher organisms, and a series of oxidative stress conditions in bacterial high-density populations, including biofilms.69,121,122 A range of bacteria including Vibrio spp. have been shown to elicit sophisticated intracellular reorganisation programmes in response to such stimuli, leading to the development of multi-stress resistant cells capable of long-term survival as well as immediate recovery and outgrowth.123–126

14.4.5 Dormancy and the viable but non-culturable (VBNC) state There is increased understanding of how toxigenic isolates of V. cholerae O1 and other vibrios are able to enter a period of dormancy in unfavourable environ-


373 ments.19,127 Bacterial cells that encounter changes in concentrations of a particular nutrient usually compensate their metabolic activities by altering the expression of individual operons. If, in addition to nutrient depletion, the cells are subjected to reduction in temperature and/or elevation in salinity, several marine Vibrio species have been reported to rapidly go non-culturable but remain viable (VBNC) and potentially pathogenic.127–131 The organism encounters these conditions as it is disseminated back into the low temperatures of the aquatic environment after successful colonisation of the human host. V. cholerae has been shown to be able to survive temperatures approaching 15 ºC, but any further reduction in temperature resulted in complete absence of growth.132 V. vulnificus is only detected in large numbers when the seawater temperature rises above approximately 17 ºC. At temperatures lower than this, the bacterium enters the VBNC state and undergoes changes that potentially allow it to persist for extended periods.133 In order to withstand the deleterious consequences of cold shock, the expression of several cold shock proteins (Csps) is specifically induced to protect the cell from the low temperatures. The converse situation is just as detrimental to cell survival and thus many bacteria, including Vibrio spp., induce the heat shock response as a cellular safeguard and homeostatic reply to contend with stress induced destruction of proteins.134 Critical to this response are the heat shock proteins (Hsps) of which DnaK and GroEL are perhaps the best studied.104 The cold and heat shock response represents a vital adaptation system necessary for survival of V. cholerae in the harsh and changeable environment, which it has to endure during the course of its life cycle.

14.5 Risk assessment in food Two fundamental objectives of microbiological testing of food are essentially (i) to establish the absence of a human health hazard due to microbial contamination of food (food safety); and (ii) to define the quality standard of food (food quality). Identification of potential pathogens must also be followed up by definition of their pathogenicity and virulence characteristics and of critical factors for their transmission. Standard microbiological testing is a fundamental instrument in the analysis of food quality at all stages of production and, when performed by industrial-scale food producers, may guarantee a high standard of food quality and its progressive improvement.135

14.5.1 Sources of Vibrio contamination and means of infection Shellfish have often been suspected of spreading cholera, and several studies in coastal areas of the USA have served to reawaken the role of the non-human animal reservoir.136 The foods that cause greatest concern to importing countries are seafood and vegetables that may be consumed raw. The WHO believes that the best way to deal with food imports from cholera-affected areas is for importing countries to agree with food exporters on good hygienic practices that need to be followed during food handling and processing to prevent, eliminate or minimise


374 the risk of any potential contamination; and to set up arrangements to obtain assurance that these measures are adequately carried out.137 The risks for high-income consumers of contracting cholera from fish exported from such areas are minimal, particularly if the fish is well cooked. Even if not well cooked, a well-nourished fit and healthy individual in a wealthy part of the world remains blissfully unaware of the fact they have consumed V. cholerae. To succumb to cholera, they would have to consume a dose somewhere in the region of 108 organisms.138 However, it has been proposed that the human passage of V. cholerae enhances subsequent waterborne spread of the bacterium by creating a hyper-infectious bacterial state that is maintained after dissemination and effectively lowers the infectious dose in secondary individuals.139 Furthermore, some seafood enthusiasts prefer their oysters and clams ‘on the half shell’ (uncooked), and people in other countries, in particular Japan, enjoy the taste of raw fish. Such inadequately cooked fish and shellfish have caused a variety of foodborne disease outbreaks due to parasitic worms, viruses and bacteria. Those whose resistance is severely impaired can become seriously ill from much lower doses of V. cholerae. Should infection occur, the rapid response with correct rehydration and antibiotics that one can expect in a developed region of the world means that risk of death is negligible in those instances. The substantive risks of cholera infection are associated with the poorest of the world’s citizens who are likely to be at the core of an epidemic, when shifts in the disease’s ecology in the marine environment coincides with a shift in vulnerability. In severe cases, an effective antibiotic can reduce the volume and duration of diarrhoea and the period of Vibrio excretion. Tetracycline is the usual antibiotic of choice, but resistance to it is increasing. Other antibiotics that are effective when V. cholerae are sensitive to them include cotrimoxazole, erythromycin, doxycycline, chloramphenicol and furazolidone.137 However, the current epidemic strain of V. cholerae is multidrug resistant to several of these recommended antibiotics. Mass prophylaxis has been shown to be unsuccessful and its use is discouraged.15 Another worrying factor is the emergence of antimicrobial resistance in bacterial fish pathogens following use of antimicrobial agents in aquaculture.140

14.5.2 Water contamination and cholera outbreaks In coastal areas of many of the poorest parts of the world drinking water supply is infiltrated by seawater, creating more favourable conditions for V. cholerae due to the associated increases in pH and salinity. Surface water from ponds and rivers is used by some villages in poorer countries as drinking water for reasons of taste, convenience or a local belief that ‘quality’ water is ‘natural’, i.e. not chemically treated.141,142 Surface water has been linked with transmission of cholera since the pioneering work of Snow in 1854.143

14.5.3 Rapid detection and monitoring of cholera incidence There is a need to more widely develop monitoring systems linked to environ-


375 mental health management that can effectively track the status of Vibrio contamination from environmental reservoirs to human host and its route back to the aquatic environment. Development of effective strategies for prevention, detection and treatment of foodborne diseases has relied and will continue to rely to a significant extent on the tools of molecular biology. Numerous advances have been made in understanding the molecular basis of virulence mechanisms and toxin biosynthesis in organisms that routinely contaminate food and feed. Several procedures have recently been developed for detection of specific pathogenic Vibrio spp. based on detection of DNA coding for factors associated with virulence. Gene probes specific for the cytotoxin haemolysin produced by V. vulnificus and the thermostable haemolysin produced by V. parahaemolyticus have been successfully employed in identifying these two species in shellfish.144,145 Gonzalez and co-workers have recently coupled a multiplex polymerase chain reaction (PCR) and a DNA microarray to construct a sensitive and specific assay suitable for the simultaneous detection of important marine fish pathogens including Vibrio spp.146 Environmental health monitoring of risk of diarrhoeal disease in developing countries generally relies on faecal coliform counts in water supply, though in many parts of these regions there is only sporadic monitoring. Where this is systematic, it is a reasonable broad indicator of poor-quality environments where cholera outbreaks are likely. However, there is also a need for the more specific development of systematic information gathering on the status of freestanding vibrios, or vibrios associated with other organisms. This should also provide detailed information on where and when changing risks associated with shifts in toxigeneity and virulence are occurring, thus contributing to a more detailed indication of shifts in Vibrio ecology that is necessary for a more thorough understanding of the real and perceived risks to vulnerable people.13

14.5.4 Prevention of incidence of Vibrio-related disease Methods to reduce pathogen numbers and prevent their growth in seafood intended for human consumption include refrigeration, relaying, cooking, irradiation and harvest during cooler months, along with good food-handling practices (Table 14.4). Vibrio spp. adhere strongly to the shellfish digestive tract and cannot be successfully removed by rinsing the shellfish or by depuration. Precautions should also be taken to prevent an increase in numbers of vibrios between harvest and consumption. V. cholerae can survive on a variety of foodstuffs for up to five days at ambient temperature and up to ten days at 5–10 °C. The organism can also survive freezing. Typically, shellfish harvested in Gulf Coast waters are stored on the deck at ambient temperatures (as high as 33 ºC in July) until the ship returns to shore. Refrigeration of freshly harvested shellfish would prevent this increase in numbers of vibrios and also decrease the viability of the cells already present.147,148 Vibrio cholerae is sensitive to acidity and drying, and commercially prepared acidic (pH 4.5 or less) or dried foods are therefore without risk. Gamma irradiation and temperatures above 70 °C also destroy the Vibrio in foods processed by these


376 Table 14.4

Handling seafood and prevention of bacterial contamination

The manufacturer • Post-harvest refrigeration prevents propagation of most Vibrio spp. • Vibrio spp. are sensitive to acidity and drying, and this should be taken into account when processing seafoods. Gamma irradiation is yet another method that can be used to reduce the risk of potential Vibrio contamination. • Implementation of ‘hurdle technology’ employing a combination of several techniques can drastically reduce the incidence of Vibrio disease while maintaining the quality of the processed food. A sample of the techniques employed would include: • low and high temperatures; • low water activity; • acidity; • low redox potential; • preservatives; • ultra-high pressure. The consumer • Always look for food that is well packaged and where there is no appearance of damage or tear. • Refrigerate seafood and keep from contact with cooked or ready-to-eat foods to prevent cross-contamination. • Store in airtight containers or wrap in cling-film and store in the lower section of the refrigerator on a tray or plate to prevent leakage. • Where possible, ensure that food is thoroughly cooked.

methods, according to the standards of the Codex Alimentarius. Indeed, lowering the pH during consumption of shellfish and seafood has been shown to inhibit transmission of vibrios. Some evidence for the effectiveness of this strategy is provided by Mujica et al.149 who maintain that promotion of the consumption of toronja drink, which has a pH of 4.1, may be a useful cholera prevention strategy for the Amazon region of Peru, a theme that has also been upheld by Anand.150

14.5.5 Minimal processing and a reappraisal of disease risk assessment The consumers’ increasing demand for high-quality foods that retain their natural flavour, colour and texture and contain fewer additives such as preservatives, has led to the introduction of ready-to-eat convenience foods preserved by mild technologies. In fact, one of the most important recent developments in the food industry has been the development of minimal processing technologies designed to limit the impact of processing on nutritional and sensory quality and to preserve food without the use of synthetic additives. Refrigeration is the main mild preservation technique that these perishable food products rely on after production. The difficulties faced in maintaining sufficiently low temperatures throughout the chain of production, processing, transport and storage prior to consumption, make the use of additional control measures a prerequisite when dealing with minimally processed food products. In response to this requirement, new technologies have been developed that can


377 be used in tandem with age-old methods to control the growth of spoilage and pathogenic bacteria. This has become known as ‘hurdle technology’.151 Each preservative step places another hurdle in the way of bacterial growth and the accumulative effect of these hurdles should prevent bacterial propagation. As previously discussed, the bacteria can mount a response to several external stresses, but when a combination of stresses is faced, this often proves too much for the bacterial population present in the food. The most important commonly used hurdles are high temperature, low temperature, low water activity, acidity, low redox potential and preservatives.152

14.6 Future trends The properties that make one pathogenic clone of V. cholerae more evolutionarily fit than another are not yet clearly understood. Recent studies have identified diverse environmental strains that show pathogenic potential, but these strains do not cause epidemics of cholera.153–155 The application of microarray-based comparative genomic analysis may expand our understanding of the phylogenetic relationships between pathogenic and non-pathogenic strains of V. cholerae, and lead to the discovery of new genes that might be involved directly or indirectly in the evolution of pathogenic clones.155–157 Paradoxically, besides virulence genes and genetic elements mediating their transfer, the single most important contributor to the evolution of pathogenic V. cholerae is the human host itself, which supports the selective enrichment of pathogenic strains from an immensely diverse mixture of environmental Vibrio strains. Vaccination against cholera is a powerful and feasible disease prevention strategy, because recovery from infection results in long-term protective immunity.158,159 Two strategies that are currently being used to develop oral vaccines against cholera are (i) vaccines based on a combination of killed whole V. cholerae cells with CTB and (ii) vaccines based on live, attenuated, CT-defective V. cholerae strains, thereby mimicking natural infection.160 Compared with the killed-whole-cell vaccine, a live oral attenuated vaccine offers great promise for preventing cholera.159,161 Several engineered live attenuated oral vaccine candidates have been developed, including CVD101, CVD103, CVD103-HgR, Peru-14 and Peru-15, and some have been used in clinical trials.158,159,162–164 Most candidates are derived from wild toxigenic strains and were constructed by deleting the CT gene besides other potential virulence genes, such as the haemolysin gene and the haemagglutinin/protease gene, and adding the ctxB gene to depress the reactogenicity and to elicit antibacterial and antitoxic immune responses.158,159,162–165 However, these results must be qualified by the discovery of ctxAB, as part of the lysogenic phage CTXφ genome, whereby it was shown that CTXφ may transfer this enterotoxin gene from a toxigenic strain to a non-toxigenic strain, thus raising safety concerns over the use of those genetically engineered live oral vaccines.10,166 Live vaccine may potentially regain virulence by acquiring the enterotoxin gene


378 horizontally from toxigenic strains in the host intestine or in the environment, particularly when they are used in epidemic areas where wild toxigenic strains may exist.167 Introduction of the rstR of an El Tor strain Bin-43 into IEM101 to protect the vaccine from CTXETφ infection resulted in high anti-CTB and vibriocidal antibody responses in animal tests and conferred full protection against challenge with at least 4 µg of CT and four strains of wild-type V. cholerae.167 This strain is proposed as a potential vaccine candidate by virtue of several features, two of which are immunity to CTXETφ infection and the capacity to colonise the small intestine.167 The elucidation of acid survival systems in intestinal pathogens is particularly important for gaining insight into systems that may serve as key targets for therapeutics or vaccines. Key questions that remain to be answered include the identification of additional key regulators of ATR, gaining a more thorough understanding of how bacteria actually ‘sense’ changes in pH and discovering the functions of the plethora of putative ‘effectors’ of unknown functions that mediate acid resistance. A more complete understanding of the role of putative toxins and other virulence determinants in the pathogenesis of the non-cholera Vibrio infections will be a major goal that will need to be achieved before rational policies regarding the risk assessment of vibrio-contaminated food can be addressed. The lack of well-characterised genotypic markers for the various Vibrio spp. has hindered the development of modern tests that can distinguish potentially pathogenic vibrios from non-pathogenic environmental strains. The social and economic cost of bacterial food contamination is a significant burden on communities and their health systems and is of serious concern to the food industry. In the USA, diseases caused by the major pathogens alone are estimated to cost up to US$35 billion annually (1997) in medical costs and lost productivity. The re-emergence of cholera in Peru in 1991 resulted in the loss of US$500 million in fish and fishery product exports that year.166

14.7


Bacterial contamination of foods is a worldwide problem and there are a plethora of agencies concerned with prevention, detection and treatment of food-related illnesses. Guidelines are provided for the correct processing of foods in such a way as to eliminate the risk of disease from particular pathogens. The WHO plays a ‘public health advocacy’ role in food safety and carries this role primarily through the provision of objective information on known and emerging problems (http:// www.euro.who.int/foodsafety). As part of the EU integrated approach to food safety, member states aim to ensure a high level of food safety within the EU through coherent and adequate monitoring, while ensuring the effective functioning of the internal market (http://europa.eu.int/comm/food/index_en.htm). The latter part of this statement is important in that any viable risk assessment strategy has to protect the interests of the food industry, while at the same time drawing up


379 guidelines to protect consumers. Food safety in the USA comes under the remit of the FDA, and this agency provides several useful sources of information on pathogenic foodborne bacteria, such as the Bad Bug Book, which can be accessed directly from the FDA website (http://vm.cfsan.fda.gov).

14.8

Acknowledgements

We thank the Department of Microbiology, UCC, National University of Ireland, Cork for continued support. Owing to space constraints, literature citations have been limited, in some cases, to recent relevant reviews. Therefore, we apologise to those authors whose important work has not been included or cited. Research in EFB’s laboratory is supported by Enterprise Ireland basic research grants and a Higher Education Authority PRTLI-3 grant.

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Part III Pathogen resistance and adaptation to particular stresses

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15 Understanding pathogen survival and resistance in the food chain S. Brul and J. Wells, University of Amsterdam, The Netherlands and J. Ueckert, Unilever, The Netherlands

15.1

Introduction

Food manufacturing by large, often multinational companies is key to the production of both fresh produce and processed foods. The latter shifts more and more to products that have a fresh-like connotation and thus the chill chain receives growing interest. The production of food is currently regulated in many ways, not least by the industry itself (see e.g. Huggett, 2001). Checking production systems and lines for the presence of unwanted microorganisms is done but not any longer on a routine basis. In general food safety is now guaranteed via the application of the Hazard Analysis and Critical Control Points (HACCP) principles. This is a systematic approach to the identification, assessment and control of hazards in a particular food operation. It aims to identify problems before they occur and to establish measures for their control at stages in production that are critical to ensuring the safety of food. In this way control against foodborne pathogens becomes proactive rather than reactive, since remedial action is taken before any problems manifest themselves. The HACCP system is outlined in Table 15.1 (CAC, 2001). Risk assessment and predictive modelling are increasingly used to establish critical limits for each critical control point (CCP). For preservation processes, socalled process criteria have to be established, and where preservative agents are added to food products, product (formulation) criteria are needed. The applications of the HACCP principles in food manufacturing and quantitative microbiological


392 Table 15.1

The principles of the HACCP system

Principle

Activity

1 Conduct a hazard analysis

List all potential hazards associated with each step, conduct a hazard analysis, and consider any measures to control identified hazards Determine (CCPs)

2 Determine the critical control points (CCPs) 3 Establish critical limit(s) 4 Establish a system to monitor control of the CCP 5 Establish corrective actions

6 Establish verification procedures

7 Establish documentation and record keeping

Establish critical limits for each CCP Establish a system of monitoring for each CCP Establish the corrective action to be taken when monitoring indicates that a particular CCP is not under control Establish procedures for verification to confirm that the HACCP system is working effectively Establish documentation concerning all procedures and records appropriate to these principles and their application

Source: CAC (2001).

risk assessment have been discussed extensively elsewhere (see e.g. Mortlock et al., 1999; Kvenberg and Schwalm, 2000; Nauta, 2002). The current challenge is to assess at relevant points in the food chain the absolute numbers and physiological characteristics of unwanted microorganisms, in particular, their (preservation) stress resistance. Food production starts with the use of a large variety of raw materials ranging from fresh ingredients such as various fresh vegetables to significantly processed raw materials (e.g. herbs and spices). Microbial human pathogens may be present in significant amounts on fresh raw materials (Chasseignaux et al., 2003; Duffy, 2003). These pathogenic microorganisms may survive and multiply post-harvest and thus present a food safety hazard. This is particularly relevant, for instance, for Salmonella species, certain pathogenic strains of Escherichia coli such as O157:H7 and many Campylobacter jejuni isolates (see e.g. Panisello et al., 2000; Zhao et al., 2001). For these types of microorganisms, very low numbers have repeatedly been indicated as risk factors in causing severe (intestinal) health issues (see e.g. Rodrigues et al., 2001). This is particularly the case with the fresh food chain where no processing occurs upon harvesting of the food products (see e.g. Knudsen et al., 2001). It is, however, also relevant for very mildly processed foods such as certain dairy products and certain uncooked fermented meat products (see e.g. Levine et al., 2001; Wiedmann, 2003). The stresses that microorganisms have to overcome in order to survive and multiply in the food chain are diverse. They range from establishing themselves in


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(a) (a)

(b)

(b)

(c)

(c)

(d) (d)

(e)

(e) Campylobacter jejuni

Fig. 15.1 A schematic representation of the food chain and points where pathogens, here exemplified by Campylobacter jejuni, need to be controlled: (a) the farm, (b) the meat processing plant, (c) the supply chain, (d) the retailer and (e) in the home of the consumer.

environments with high salts and high in fatty acid/bile, e.g. the intestinal tract of animals, to surviving high-temperature treatment and the addition of agents that lower water activity (aw) or pH, or interfere with cellular metabolism. A significant number of food preservatives, in particular weak organic acids, are known to cause such stresses. Figure 15.1 outlines the situation for C. jejuni, which is endemic but nonpathogenic in chicken. At the same time, highly virulent strains for man are the predominant cause of gastrointestinal disease in Western countries.

15.2 Stresses encountered in animal hosts Of the millions of illnesses caused each year by foodborne pathogens, many are caused by bacteria that have reservoirs in healthy food animals, otherwise known as foodborne zoonoses (Wells and Bennik, 2003; Mead et al., 1999). For example, Salmonella, Escherichia coli O157:H7, Campylobacter and Yersinia enterocolitica are all zoonotic pathogens that have emerged as major causes of foodborne illness since the 1980s. Some of these have only recently been shown to be predominantly foodborne. E. coli O157:H7, for example, was first identified in an outbreak in 1982 resulting from consumption of contaminated hamburgers and was then shown to originate from healthy cattle (Riley et al., 1983). Campylobacter was recognised as a cause of gastrointestinal disease only in the 1970s when veterinary diagnostic methods were used to test specimens from humans with acute enteritis


394 (Dekeyser et al., 1972). Similarly, Vibrio vulnificus was identified as a foodborne pathogen in late 1979 when isolated from the bloodstream of individuals with chronic liver disease who had consumed raw oysters (Blake et al., 1979). Unlike many other zoonoses the foodborne pathogens mentioned above do not cause illness in the animal host from which they can spread to humans. C. jejuni, for example, can reach up to 1010 colony-forming units per gram in the caecal contents of apparently healthy chickens. Commensalism or absence of pathology in zoonotic farm animals makes detection of the pathogen more difficult, thus increasing the chance of it spreading throughout the animal reservoir. Commensal behaviour may also allow the pathogen to colonise animal hosts in high numbers, increasing the risk to humans as in the case of C. jejuni. Furthermore, the pathogen may have a lifelong association with the animal host as in the case of chickens with ovarian infections with Salmonella enterica serovar Enteritidis or a calf carrying E. coli O157:H7. A better understanding of how pathogens persist in animal reservoirs and the factors affecting their transmission is therefore critical to future strategies for long-term prevention.

15.2.1 Innate defence mechanisms In the gastrointestinal tract of animal hosts the enteric pathogens have to cope with various natural or constitutive defence mechanisms that have evolved to prevent microbial infections (Sarker and Gyr, 1992). For example, the mucous layer covering the mucosal epithelium forms a barrier that can limit access of microbes to receptors present on host cells. Peristalsis and mechanical flushing can also remove microbes that become trapped within the mucous layer. Some pathogens, e.g. Campylobacter, possess flagella that are used to swim through viscous mucus and reach the epithelial cell surface, making it essential for colonisation in chickens and virulence in animal models (Ketley, 1997). Epithelial cells may also contribute to the protection against pathogens as they undergo continuous cell renewal, which facilitates the removal of damaged cells (Kraehenbuhl et al., 1997). Colonisation of the mucosal tissues is a strategy often used by commensals and pathogens to overcome these constitutive defence mechanisms and persist in the host. To achieve this, many bacteria have developed tissue adherence mechanisms involving specific adhesins that bind to receptors on the cell surface, typically carbohydrate or peptide sequences (Taylor, 1991). Adhesion mechanisms can determine the host specificity of a pathogen as in the case of different fimbrial type strains of enterotoxigenic E. coli that bind to species-specific carbohydrate receptors in calves, piglets and humans (Robins-Browne and Hartland, 2002). The secretion of gastric acid by the stomach also plays an important role in protecting animals against pathogens ingested in food or water (Peterson et al., 1989). This is reflected in the fact that hypochlorhydria predisposes to intestinal bacterial and parasitic infections (Cook, 1985). Stomach pH can be as high as 6.0 after consumption of large amounts of food, but the mean is approximately pH 2.0 (Texter et al., 1968). Enteropathogens such as Salmonella and E. coli have


395 developed adaptive mechanisms for surviving transient periods of severe acid stress. In Salmonella enterica, for example, the acid tolerance response (ATR) is orchestrated by the induction of around 50 acid shock proteins that protect the cell against acid (pH 3.0) and other environmental stresses (Audia et al., 2001). This involves modulating the activities of several regulatory proteins including RpoS, Fur, PhoP and OmpR in response to pH stress (Bang et al., 2002). In Salmonella it was also shown that the physiological changes that occur in response to acid stress also differ in the stationary and logarithmic phases of growth. E. coli has three dedicated acid resistance systems not present in Salmonella that allow this organism to survive acid stress (pH 2.0).

15.2.2 Immunological defence mechanisms The mucosal immune system is specialised in two seemingly opposing functions. It has to mount an effective immune response to invading pathogens while maintaining tolerance (low levels of immunological responsiveness) to harmless antigens from food and commensal bacteria. This is especially challenging in the gut, which encounters more antigens than any other part of the body. Loss of tolerance to the normal microflora, for example in inflammatory bowel disease, results in uncontrolled inflammatory responses and damage to the mucosal barrier (Mowat, 2003). To a large extent invasiveness of pathogens and routes of antigen uptake and recognition of pathogen ‘danger’ signals probably dictate the inflammatory response to pathogenic bacteria by the immune system. In contrast, interactions between nonimmune and immune cells and non-invasive bacteria at mucosal surfaces favour the production of IgA antibodies and systemic tolerance. IgA is synthesised in the lamina propria and then secreted across the epithelium in a dimeric form bound to the secretory component (SIgA). SIgA acts as a protective coating for the mucosal surface protecting against microbial adherence, colonisation and the effects of microbial toxins (Kraehenbuhl and Neutra, 1992). Although SIgA does not kill bacteria in the lumen of the gut, it can inhibit growth and restrict colonisation. One way in which bacteria can avoid the immune responses of the host is to produce IgA proteases that cleave IgA at the hinge region detaching the Fc region. Some enteric bacteria also avoid the inhibitory effects of antibodies by undergoing antigenic variation, for example by switching fimbrial type. Among Gram-negative pathogens including E. coli and Salmonella, the O-polysaccharide of lipopolysaccharide (LPS) is the basis for antigen variation. Similarly there are multiple serotypes of the flagella antigens in both Salmonella and E. coli. Antigenic variation ensures the existence of multiple serotypes of the bacterium so that it is afforded multiple opportunities to bypass a mounting immune response. Campylobacter jejuni can also vary the structure of its lipo-oligosaccharide (LOS) outer core using a variety of genetic mechanisms, including phase variation of hypervariable genes (Gilbert et al., 2002; discussed below). Several serotypes of LOS are also sialylated in C. jejuni and this confers serum resistance and lowers immunogenicity (Guerry et al., 2000). Sialylation of LOS results in molecular mimicry of host gangliosides such that immunological tolerance to the host antigen


396 prevents effective immune responses from being elicited. Antigen mimicry is a strategy frequently used by pathogens to depress the host’s adaptive immune defences and is probably also associated with the post-translational sialylation of flagella and N-linked glycosylation of outer-membrane proteins in C. jejuni (Thibault et al., 2001; Wacker et al., 2002). Antigen mimicry by pathogens can also break immunological tolerance, leading to autoimmune disease. For example, infection with certain LOS serotypes of C. jejuni is linked to the generation of neurological complications such as Guillain Barré syndrome and Miller Fisher syndrome in humans (Nachamkin et al., 1998). The prevailing theory behind the origin of these neuropathies is that the C. jejuni ganglioside mimics induce autoimmunity against gangliosides found on neurones, resulting in damage to the nervous tissue. In addition to LOS, C. jejuni also produces a high molecular weight polysaccharide encoded by a cluster of putative capsule biosynthetic genes that were discovered by genome sequencing (Parkhill et al., 2000). The capsule is now known to be a key determinant of the existing antibody typing schemes and is likely to play a role in avoiding adaptive immune responses and facilitating persistence in animal hosts (Dorrell et al., 2001; Pearson et al., 2003; Karlyshev et al., 2000). Recent work on the comparative genome analysis of C. jejuni revealed the existence of seven major plasticity regions (PR) in the genome, three (PR4, 5 and 6) of which contain genes involved in the production and modification of antigenic surface structures. These regions are hypervariable, indicating that antigenic diversity is an important characteristic of the lifestyle of Campylobacter and that animal hosts may indeed be a driving force for the emergence of new antigenic variants (Pearson et al., 2003). Another striking discovery made from the sequencing of the C. jejuni genome was the identification of 25 hypervariable genes containing homopolymer pG:C. Variation in the length of the homopolymer tracts was observed among different clones selected for genome sequencing, indicating that the hypervariable genes have potential to undergo a high rate of slipped-strand mis-pairing (Parkhill et al., 2000). Many of the hypervariable genes are found in loci involved in the production or modification of surface structures such as the flagellum, LOS and capsule, suggesting that they play a role in antigenic variation. This is reminiscent of the phase variation used by other pathogens such as Neisserria gonorrhoeae to modulate expression of surface expressed virulence factors during infection (Belkum et al., 1998; Moxon et al., 1994). Hypervariable genes have not been found in the genome of E. coli or Salmonella foodborne pathogens, although some generally conserved chromosomal regions are more divergent than others (e.g. fimbrial biosynthetic operons), perhaps as a consequence of positive selection in the host environment. Alternatively, increased mutation rates at some loci or differential retention of paralogous genes during strain evolution may also account for the presence of these hypervariable genes.

15.2.3 Microbial antagonism Microbial antagonism refers to the stresses afforded by an intact normal flora in a


397 healthy animal. There are three main ways in which the resident flora provides antagonism against non-indigenous species. The normal flora is highly adapted to the environment of the host and competes for nutrients and colonisation sites on the mucosal surfaces. Certain species of the normal flora may also produce natural biocins that kill or inhibit the growth of other species. A variety of the end-products or metabolites of the normal flora can also inhibit other microorganisms, e.g. lactate, propionate and peroxides. Long-term recognition of the importance of the microbiota in the ‘healthy gut’ has fuelled more recent interest in the use of probiotics as preventative and therapeutic agents in gastrointestinal disease. Several studies have now shown that certain probiotic lactic acid bacteria are capable of conferring protection against enteric pathogens in both animal and human studies (reviewed by Servin and Coconnier, 2003). Often this is associated with enhanced humoral and cellular immune responses to the pathogen, resulting in protective effects of increased microbe-specific intestinal IgA as discussed in the preceding section (Shu et al., 2000, 2001; Shu and Gill, 2002). Increased concentration of organic acids and lowered pH in the intestine has also been correlated with increased resistance of mice to Salmonella enterica serovar Typhimurium following administration of Bifidobacteria and transgalactosylated oligosaccharides as a prebiotic (Asahara et al., 2001). More recently, probiotic interference with pathogen adhesion and invasion in addition to enhanced barrier function of the epithelium have been shown to protect epithelial cells from infection with enteroinvasive E. coli (Resta-Lenert and Barrett, 2003).

15.2.4 Iron stress Iron is an essential micronutrient for all living organisms and its acquisition from the environment is vital to bacteria as it plays an essential role in cellular metabolism. At physiological pH in the presence of oxygen, iron is found in its ferric state (Fe3+) as an insoluble hydroxide. Within animal tissues, the majority of Fe3+ is tightly bound to high-affinity iron binding and transport proteins such as haemoglobin, transferrin, lactoferrin and ferritin, providing a non-specific defence system against microbial infection. Lactoferrin is the iron chelator, found in the mucosal secretions, that scavenges free iron to the extent that only a minor fraction of free iron is available. As iron restriction presents a major obstacle to colonisation of the intestine which is fundamental to both commensalism and pathogenesis, bacteria have evolved high-affinity iron scavenging and uptake systems. However, accumulation of iron in the cell can be deleterious owing to iron catalysis of the Fenton reaction, leading to the formation of hydroxyl radicals within the cell (Touati, 2000). Thus iron uptake and assimilation need to be tightly controlled in order to avoid such oxidative stress-mediated damage to cellular proteins, lipids and DNA. Bacteria also utilise iron storage proteins to store up reserves in case of iron depletion. In many bacteria, the negative regulator Fur (ferric uptake regulator) controls expression of iron transport and storage systems. Fur was first described in Salmonella Typhimurium and extensively studied in several Gram-negative


398 Low iron

High iron

Fe2+

Fe2+ Fe2+ Fe2+

Fe Fur regulated gene

Promoter ON

Fe2+

2+

Fur regulated gene

Promoter OFF

Fig. 15.2 Simplified model of negative regulation by the ferric uptake regulator Fur. Binding of the Fur repressor to a Fur box motif within a promoter is dependent on Fe2+ as a cofactor. Thus iron depletion in the cell leads to de-repression and transcriptional activation.

foodborne pathogens including E. coli C. jejuni and Vibrio vulnificus (Ernst et al., 1978; Hantke, 1984; Earhart, 1996; van Vliet et al., 2002; Litwin and Calderwood, 1993a). The detection of an iron-limited niche also serves as a stimulus for some pathogenic bacteria to express other virulence determinants required for growth and survival in vivo (Litwin and Calderwood, 1993b). In some Gram-positive bacteria and C. jejuni, iron levels are linked to oxidative stress responses via a Fur-like iron responsive regulator, PerR (Bsat et al., 1998; Horsburgh et al., 2001). The Fur protein acts as a transcriptional repressor by binding to DNA sequences called fur-boxes in the promoter regions of iron-regulated genes. Binding of the Fur repressor is dependent on Fe2+ as a cofactor, so under depressed iron conditions, the promoters become active (Fig 15.2). However, the typical Fur– promoter interaction may differ for some promoters, resulting in positive expression under increasing iron concentration (Delany et al., 2001) In salmonellae expression of acid tolerance, response is positively controlled by Fur, as is sodB expression in E. coli (Dubrac and Touati, 2000). The iron uptake systems of enteric foodborne pathogens reviewed elsewhere (Andrews et al., 2003; Braun and Braun, 2002) indicate that there are two general strategies for iron uptake. One strategy deploys the production and uptake of siderophores and the direct utilisation of iron compounds derived from the host such as transferrin, lactoferrin or haem-containing molecules. Siderophores are ferric chelators that are secreted and then transported back into the cell via membrane-associated transport systems. In Gram-negative bacteria, the Fe3+ transport systems for siderophores and host iron compounds usually consist of an outer membrane (OM) receptor, with iron compound OM transport energised by a TonB ExbB ExbD complex, a periplasmic binding protein and an inner membrane (IM) ABC transporter. In contrast to E. coli and Salmonella, C. jejuni produces few, if any, siderophores but is able to transport extracellular ferrichrome and enterochelin chelators produced by other bacteria in addition to other iron compounds found in the host (van Vliet et al., 2002).


399

15.3

Food preservation strategies

In previous paragraphs microbial pathogens in the animal host, predominantly including survival strategies, were discussed. We indicated the kinds of stress that microbes encounter and have to deal with in order to persist. In the following sections, we will discuss those stresses that microbial pathogens encounter and have to deal with as persistent colonisers of (parts of) the processed food chain. We will first outline and discuss the kinds of stress normally linked with both classical and innovative preservation strategies.

15.3.1 Classical preservation treatments and ingredient formulations (e.g. heat, aw, pH, fermentation) Classically, foods are preserved through variation in the composition of ingredients and the application of a number of physical stresses to the product. These classical methods are all still in use today and include drying, heating, freezing, acidification (perhaps through fermentation) and chemical preservation (discussed in Brul et al., 2002a, 2003; Gould, 2000). Drying (desiccation, dehydration) is a very important technology in many foods. Proper drying allows hardly any microbial growth in raw materials and finished products. Survival of pathogens in a dormant state (e.g. bacterial endospores) is nonetheless possible. Some of these dormant pathogens can be induced to germinate and thus become noxious upon re-hydration of the food. In general, bacterial pathogens are inhibited in their growth and do not germinate at low water activity (i.e aw values at or below 0.9; see e.g. Tienungoon et al., 2000 and following discussion). This is in contrast to certain fungal cells that are able to still survive and grow at aw values of 0.8 and even to aw values of 0.65 in the case of some xerotolerant and xerophilic fungal species (Cuppers et al., 1997). Thermal treatment of foods can obviously increase shelf-life by pasteurising or sterilising food. Indeed a pasteurisation treatment is sufficient for the inactivation of most non-spore-forming bacterial pathogens. However, great care should be taken to avoid the use of sublethal temperature treatments as it increases the risk of bacterial survival in a quiescent state and possible formation of a biofilm. This may constitute a permanent source of infection in a food manufacturing operation (see e.g. Brul et al., 2003; Bower and Daeschel, 1999). The inactivation and/or prevention of outgrowth after thermal treatment of bacterial spores poses a greater problem. It is well established that significant variations in spore thermal resistance exists among spore-forming bacteria (see e.g. Ingram, 1969). Next to full inactivation, heat-induced spore damage and consequent inhibition of spore germination may be sufficient to prevent outgrowth in the finished product. In order to perform such treatments in a knowledge-based way, insight is needed into spore-repair mechanisms and resistance against compounds in the media that may prevent subsequent vegetative growth (discussed in e.g. Brul et al., 2002b; Keijser et al., in preparation). Clearly freezing is a technology that needs the full supply chain to be in-line with


400 the food manufacturing operation itself. It is crucial that the temperature is maintained at the required level to avoid temporary thawing of the food and contamination due to the rupture of, for example, packaging material. While, generally, freezing microorganisms does not lead to substantial inactivation, freezing may lead to some inactivation owing to ice-crystal formation in membranes of cells and subsequent membrane rupture and leakage of cellular constituents. Currently, research aimed at low-temperature treatment of microbes including high-pressure-induced freeze–thawing is being pursued as an alternative processing technology (see e.g. Shen et al., 2005). As a rule of thumb, many foodborne pathogens will not grow at pH values below 4. Thus acidification can be an effective means to control unwanted growth of, for example, Salmonella spp., Escherichia coli strain variants O157:H7, and non O157 enterohaemorrhagic strains, Campylobacter jejuni, etc. Also spores will not germinate at these pH values and thus, for example, Bacillus cereus will not grow to high numbers where toxin production is likely to occur. However, cells may be able to survive acid shock. Indeed studies have been reported where cells survived exposure to pH values as low as 2–3 (see e.g. reviews by Hecker and Volker, 2001 with respect to, potential, toxin-producing bacilli, and by Merrell and Camilli, 2002 with respect to gastrointestinal infectious pathogens). Subsequently, if conditions are such that the pH increases to 4 and higher, such cells may repair any incurred damage and then may start to multiply. Generally when cells encounter organic acids with a low acid pK value, lowering the pH to that value with this acid is much more effective than doing so with a strong acid. The principle for this is that such organic acids diffuse into cells in their undissociated form and in the cell, where the pH is high, may release the proton(s), thus effectively acidifying the cytosol (see e.g. Marquis et al., 2003). Organic acids may easily be introduced into the food by fermentation with lactic acid producing bacteria, e.g. during production of yoghurt, or by acetic acid producing bacteria during the production of vinegar (see also e.g. Vinderola et al.,, 2002). Fermentation may also introduce other antimicrobial compounds, a prime example being ethanol (discussed in Tyopponen et al., 2003).

15.3.2 Novel preservation technologies The consumer demand for healthy minimally processed foods is high and on the rise. Thus the industry is constantly seeking to improve its processing technology in order to guarantee food safety with a minimal risk of microbial and chemical spoilage, while ensuring product quality and wholesomeness (Gould, 2000, 2001). High hydrostatic pressure is a technology that has been under investigation in that context for a great many years in various laboratories of food processing companies. It is applied for commercial food products at pressure levels up to 700 MPa and has been proven to inactivate vegetative organisms (Smelt et al., 2002) as well as spores (discussed in a review by Raso and Barbosa-Canovas, 2003; see Oh and Moon, 2003 for a practical example). As may be appreciated


401 from these papers, inactivation of spores by high pressure proceeds, however, only after significant preheating to temperatures as high as 90 °C. Thus, in fact the application of high pressure is more to ensure rapid and instantaneous heating to 121 °C through adiabatic pressure-induced heating, than to affect the integrity of the bacterial endospore. Recently Iwahashi et al. (2003) reported on the use of genome-wide micro-array-based information to categorise the types of stress inflicted upon microbial cells by high-pressure treatment. For that analysis, they used bakers’ yeast and applied a low-level pressure for an extended time period and, for a shorter time, higher-level pressure treatment. Their conclusion is that stresses that denature proteins and high-pressure-induced stresses on cells are similar, implying that cells sense high pressure mostly at the level of a perturbation of (membrane?) protein structure. Ohmic heating (Joule heating, electrical-resistance heating, electroconductive heating) is achieved by passing electric currents through foods which are placed between electrodes. For inductive heating, electric currents are induced within food materials through oscillating electro-magnetic fields generated by electric coils. With both processes, rapid – and in many cases uniform – heating of liquids and particulates can be achieved. Limited information is currently available regarding industrial applications of the processes. Microwave and radio frequency heating use electromagnetic waves of given frequencies to generate heat. Owing to difficulties in achieving uniformity of heating, industrial preservation processes have not yet been consistently successful. See Gould (2000) and Brul et al. (2003) and references therein for further discussion. High-intensity pulsed electric fields involving the subjection of foods placed between electrodes to pulses of high voltage have been demonstrated to effectively and controllably permeabilise (reversible or irreversible) biological membranes (Anon, 2001). Pilot-scale equipment is available in Europe as well as in the USA. The impact of pulsed electric fields on vegetative microorganisms has recently been shown in a few experimental practical examples by Wouters et al. (2001), Garcia et al. (2003) and Alvarez et al. (2003a) (see also Raso and BarbosaCanovas, 2003). Ultrasound energy is generated by sound waves of 20 kHz and above. Ultrasound has a wide range of applications in medicine and biotechnology with limited applications regarding the inactivation of microorganisms. Selective inactivation of various microorganisms has been accomplished recently, and the work reported by Alvarez et al. (2003b) even describes a synergy with a lowered water activity. Finally, in many applications, food manufacturers also make use of ultra violet (UV) light to inactivate microorganisms, and gamma irradiation of selected foods is used in the USA (see for an example of the response of foodborne pathogens to UV-energy, e.g. Yaun et al., 2003; see for studies using gamma irradiation, e.g. Rajkowski et al., 2003). Some of these non-thermal (or, rather, sometimes low thermal) techniques are already used commercially or are very close to commercial application. All of the


402 techniques discussed have their own specific applications. For example, pulsed electric field technology is only suitable for the treatment of pumpable liquid products, and treatment with UV light is suitable only for surface decontamination and for treatment of fluids with a high transparency. In summary, microorganisms, including the group of bacterial pathogens, are subjected to a variety of stresses upon application of these novel food processing systems. These range from thermal stresses similar to classical heat treatment to stresses inflicting in particular ways upon secondary and tertiary levels of protein organisation.

15.3.3 Preservatives The addition of chemicals to preserve foods is a very common practice. Probably the best described class of preservatives, and thus stress, that microbes encounter during food manufacturing are the weak organic acids. These include acetic, lactic, benzoic and sorbic acid. The molecules inhibit the outgrowth of both bacterial and fungal cells (Hsiao and Siebert, 1999; Brul and Coote, 1999). Sorbic acid is also reported to inhibit the germination and outgrowth of bacterial spores (see Blocher and Busta, 1985 for the original observation and also Sofos and Busta, 1993 for more background reading). As explained above, in solution, weak acid preservatives exist in a pH-dependent equilibrium between the undissociated and dissociated state. These weak acid ‘chemical’ preservatives have optimal inhibitory activity at low pH because this favours the uncharged, undissociated state of the molecule (see above). Another antimicrobial compound that may be added to foods or generated endogenously is hydrogen peroxide. Many bacteria, both Gram-negatives and Gram-positives including important pathogens, are inhibited by the lactoperoxidase system (see e.g. McLay et al., 2002). The system requires hydrogen peroxide and thiocyanate. It is thus mainly active against organisms producing H2O2. Alternatively, H2O2 may also be added to the foods that are to be preserved. Under suitable experimental conditions, the reaction generates a short-lived singlet oxygen species that is extremely biocidal (see Banks et al., 1986 and, for more recent mechanistic studies, Cavalcante et al., 2002). In addition, incomplete reduction of molecular oxygen may lead to the generation of the superoxide radical. Together with H2O2 and trace quantities of metal ions, the superoxide radical may react to form, in the Fenton reaction, the highly biocidal hydroxyl radical.

15.3.4 Natural antimicrobials, e.g. allylisothiocyanate (AITC) and nisin Examples of naturally occurring antimicrobial compounds include the use of antimicrobial flavours and fragrances, certain peptides from plant (crop) and microbial origin, and microbial wall lytic enzymes (see e.g. Brul and Coote, 1999; Cleveland et al., 2001; O’Rourke, 2002). Naturally occurring herbs and spices are among the prime natural antimicrobials.


403 These compounds are often membrane active through a significant hydrophobic surface area of the compound. In addition, naturally occurring antimicrobial peptides are often amphipathic and form hydrophilic pores in membranes, thus provoking leakage of cellular constituents and cell death (Brul and Coote, 1999). Cells may respond to these peptides in various ways. For Salmonella Typhimurium, regulation of virulence gene expression by cationic antimicrobial peptides had been described (Bader et al., 2003). The formation of membrane pores delineated by amphipathic peptides is concentration dependent and is thought to be dependent on the existence of a membrane potential in the target cells. Cells subjected to treatment with natural antimicrobials thus are exposed to membrane perturbation in some cases, depending on the type and level of stress, and even to membrane leakage and individual cell death. Combinations of naturally occurring antimicrobials (in particular small organic biomolecules such as various flavours) have often been proposed as the way forward (see e.g. Leistner, 2000). Such combinations should minimise adverse organoleptic effects of the individual components while maximising their (combined) antimicrobial effect through synergistic action on the microbes (e.g. Ueckert et al., 1998). In order to apply such a combination treatment, a better understanding of the physiology and molecular cell biology of foodborne pathogens would be most useful. In this way the continued ensuring of food safety, i.e. the absence of increased virulence in strains occurring in the food chain, will be facilitated tremendously while applying innovative preservation concepts (discussed in Brul et al., 2002a,b).

15.3.5 Food structure Finally, the structure of foods is a ‘stress’ used in the processing industry. In particular the manufacturing of spreads, mostly water-in-oil emulsions, is heavily based on this principle. Here the ‘structuring‘ of the product, leading to the confinement of microorganisms to the water droplets, ensures that bacteria cannot reach high numbers in the total product. This is based on the assumption that the water droplets are smaller than a critical size and that there is no or hardly any coalescence of droplets taking place. This type of product structuring is very efficient in combination with a pasteurisation treatment of the water-phase of spreads in preventing high levels of bacterial growth, including growth of major pathogens (see e.g. Guentert and Linton, 2003). However, it is less intrinsically robust against fungal spoilage (see e.g. van Zijl and Klapwijk, 2000). Verrips and co-workers have discussed and reviewed the microbiological stability of water-inoil emulsions as an example of how compartmentalisation can be used effectively in food preservation, and its effects have been modelled extensively in the past (see e.g. Verrips, 1989). The main conclusions are still valid, though here and there slightly amended (see e.g. ter Steeg et al., 2001). Other types of direct product structuring do play a role in mediating the propensity of cells to grow simply by limiting diffusion of end-products away from a growing colony.


404

15.4

Microbial stress responses to food preservation regimes

15.4.1 Mechanisms of stress resistance There are intrinsic factors that play a pivotal role in determining whether microorganisms are a priori sensitive or resistant to a given (food preservation) stress. Such factors are, for instance, the presence of the outer membrane in Gram-negative bacteria which makes them generally less susceptible to hydrophobic antimicrobial compounds (Savage, 2001). Also the membrane composition per se has a prime influence on stress sensitivity. A good example here is the fact that certain acid-resistant streptococci and Listeria monocytogenes strains have a pronounced different fatty acid membrane composition compared to sensitive bacteria (Begley et al., 2002). In particular, the increased levels of C14:0 and C16:0 as well as the decreased levels of C18:0 seem to be a distinguishing factor for acid-resistant versus acid-sensitive L. monocytogenes cells.

15.4.2 Adaptation at the gene-expression level in vegetative cells Microorganisms also need to be able to defend themselves against the changing environmental challenges if they are to be successful persistent colonisers of a given environment. They are generally quite well equipped for this. Special sigma factors direct the RNA-polymerase to the transcription of stress-related genes in Gram-positive bacteria such as L. monocytogenes, Bacilli and Staphylococci. In Gram-negative pathogens such as Salmonella enteritidis, Escherichia coli and Campylobacter jejuni strains, the RpoS/sigma S system has this function (see e.g. Hecker and Volker, 2001; Brul et al., 2003; Wells and Bennik, 2003). As the paradigm of stress response regulation, the sigma B system in the nonpathogenic Gram-positive species Bacillus subtilis is often described. This system acts as a master ‘general stress response’ regulator upon which many specific responses build. The growth-limiting stresses can be divided in two: an energy stress, such as carbon or phosphate limitation, and an environmental stress, such as ethanol, acid, heat or salt shock. Activation of sigma B induces the expression of 150–200 general stress proteins (see Fig. 15.3), many of which have unknown functions. These newly synthesised proteins will then help the cell to cope with the exposed stress and generally also a myriad of other stresses. Corroborating this, deletion of sigma B leaves cells with a much increased sensitivity against heat (assayed at 54 °C), ethanol (9 %), salt (10 %) and acid (pH 4.3). In addition such cells are more sensitive to freezing, desiccation and glucose/phosphate exhaustion (Hecker and Volker, 2001; van de Guchte et al., 2002). In contrast to the function of the sigma B-induced proteins, much is already known about the activation of the general stress response (Fig. 15.4). The signal transduction pathway leading to the activation of sigma B consists of two branches: one for sensing energy stress (such as carbon, oxygen or phosphorus limitation) and one for sensing environmental stress (such as acid, ethanol, heat or salt shock). The RbsW protein (for regulator of sigma B) has an anti-sigma factor function. Thus RbsW prevents untimely activation of sigma B, i.e. during logarithmic


405

Fig. 15.3 Induction of the general stress response induces the synthesis of 150–200 proteins that are involved in the adaptation and resistance to various kinds of stresses. Also proteins are synthesised that are involved in the regulation of σB itself, next to proteins with still unknown functions. Figure taken from Brul et al. (2003).

growth in rich media. RsbV acts as an anti-anti-sigma factor preventing in turn the formation of the RsbW–sigma B complex. RsbV activity is itself modulated through phosphatases RsbP (Vijay et al., 2000) and RsbU (Voelker et al., 1995). It is thought that the PAS domain of RsbP plays a crucial role in sensing energy stress (Vijay et al., 2000) next to the alpha/beta hydrolase RsbQ (Brody et al., 2001; not indicated in Fig. 15.4). The activation of the environmental stress response pathway involves the RsbU regulator, the RsbS protein prone to regulation through phosphorylation, the RsbR (kinase activator), RsbT (kinase) and the RsbX (phosphatase) next to a number of Y genes. Possibly the latter form part of a newly defined chill-induced sigma B activation pathway (Brigulla et al., 2003). The exact means by which energy and environmental stress signals enter the signal transduction pathway are at present still not known. Definition of the sigma B regulon and its function are currently also topics of extensive study in Bacillus cereus, a true toxigenic spore former (van Schaik et al., 2004). In Escherichia coli similar studies have been performed with respect to RpoS functioning. It was shown that entry into stationary phase is an induction signal for the RpoS (general stress) response but that such a response is already induced in logarithmically growing cells when these are cultured in minimal medium (Tao et al., 1999). Identification of the signal for RpoS activation produced under these conditions remains to be elucidated. It is known that RpoS-regulated promoters are also positively influenced by pppGpp (guanosine pentaphosphate) and ppGpp (guanosine tetraphosphate) products of the so-called bacterial stringent response (Gentry et al., 1993). This stress response is triggered during nutritional stress when free tRNA at the A-site of the 50S ribosome induces ribosome bound RelA to start synthesising guanosine phosphate compounds. Chatterji and Ojha (2001) recently discussed the role of the stringent response in starvation and stress response signalling and RpoS-mediated transcriptional regulation. The sigma S protein encoded by the RpoS gene is also responsible in E. coli for


406

Fig. 15.4 Model of the signalling pathway for σB activation. The pathway consists of two branches: the energy stress (a) and the environmental stress (b) pathway. (Taken from Akbar et al., 2001.)

the increased synthesis of trehalose upon exposure of cells to dessication stress (Welsh and Herbert, 1999). Trehalose is well known for its protective effect on yeast cells exposed to dessication and thermal stress (de Virgilio et al., 1994; Sano et al., 1999; Mensonides et al., 2005). The mechanisms by which the compound exerts its effect are not fully understood but are presumed to be multiple and based on the relative inertness of the compound and its tendency to form a glassy state upon drying (Welsh and Herbert, 1999; discussed also in Pikal et al., 2004).


407 15.4.3 Heterogeneity in response The response of microorganisms to their environment was for many years thought to be, in a given clonal population, reasonably homogeneous. However, there is a large body of evidence that underlines the fact that this is mostly not the case. For example, upon entry into carbon starvation, not all Bacillus cells in a clonal population will start sporulating (Hecker and Volker, 2001). Signalling the onset of sporulation is extremely intricate as illustrated in the schemes shown in Fig. 15.5

Fig. 15.5 Phospho-relay signalling involved in the initiation of sporulation. The main interactions that occur within and associated with the phospho-relay in Bacillus subtilis are illustrated; for further explanation, see text. Arrows indicate activation, and barred lines indicate repression. Phosphorylated forms of proteins are indicated by ~P. Unknown regulatory mechanisms are indicated with a question mark (taken from Philips and Strauch, 2002).


408

DA CF

R2 R1 G en re

Events

fl u e nc ce es or

R3 Red

PI

e scenc fluore

Fig. 15.6 Resistance distributions of clonal Lactobacillus plantarum population treated with nisin at 0.5 µg ml–1, analysed by flow cytometry. Enzymatic activity is indicated by area R1, membrane damage by area R3. Sub-populations of three regions have been sorted on MRS agar (de Man et al., 1960) to visualise survival. Region 1 indicates active cells without damage, region 2 compromised cells and region 3 dead, membrane-damaged cells without enzymatic activity.

(Philips and Strauch, 2002). The high level of signalling complexity in terms of numbers of regulatory compounds involved is presumed to play a significant role in defining levels of heterogeneity in sporulation development in an isogenic starting culture. Indeed recently, the group of Losick has even shown that Bacillus subtilis cell populations show an intriguing behaviour whereby those cells that have committed to the sporulation process actually inactivate, i.e. kill, the cells in the same population that have not made that commitment (Gonzalez-Pastor et al., 2003). Physiological variability of vegetative cells within clonal populations following stress or inactivation treatments can be caused by variability in mutational frequency, resistance distributions linked to the prokaryotic cell cycle and the individual physiological state (Davey and Kell, 1996). Assessing heterogeneity in distributions of physiological parameters within clonal bacterial populations in response to food preservation stresses using flow cytometry has been described in some detail by Ueckert et al. (1995, 1997). An example relating to individual cell responses to food preservation stresses is given in Fig. 15.6. What are possible


409 mechanisms that generate and ensure the persistence of heterogeneity in stress response by bacterial cells? In Escherichia coli it has been shown that in vegetative cells there is major heterogeneity in expression of proteins, coupled with stochastic effects (noise) relating to the position of the protein-encoding gene on the genome (Elowitz et al., 2002). Furthermore, in stationary phase cells, i.e. upon long-term starvation of E. coli, only 99 % of the cell population loses viability, leaving a considerable number of surviving bacteria when initial cell concentrations are high. In fact the surviving cells will feed on the nutrients released from dead cells and can remain viable for years. Mutation in cells present in such surviving populations ensures that fitness increases over time in the population (Finkel and Kolter, 1999). In general terms, the phenomenon whereby bacteria create advantageous mutations in nonproliferating cells has been known for some time and has been coined adaptive mutation (see e.g. Foster, 1999). The occurrence of adaptive mutations has recently been shown in the opportunistic pathogen Pseudomonas aeruginosa. An increased mutation rate goes hand in hand with increases in multi-drug resistance in cells exposed for prolonged periods to the antibiotic tetracycline (Alonso et al., 1999). In addition, in cystic fibrosis patients it has been shown that a significant amount of P. aeruginosa isolates are hypermutable strains – an important advantage in their adaptation to environmental stressors (Oliver et al., 2000). Clearly, the resulting pattern of heterogeneity in bacterial stress response in isogenic cultures is also highly relevant to the food industry where the chances of low numbers of microorganisms with certain unwanted physiological characteristics (preservation stress resistance, high virulence) being present in the food chain are of prime importance.

15.4.4 Development of bacterial spores as a survival stage Some Gram-positive bacteria are capable of generating highly stress-resistant endospores upon encountering severe environmental stress (McKane and Kandel, 1996). Such spores may manifest themselves as highly resistant against preservation treatments. In particular spores of the pathogenic species Clostridium botulinum are of major concern. Spores of the non-proteolytic variant of Clostridium may germinate at chill temperatures and give rise to vegetative growth of these toxigenic microorganisms (see e.g. Graham et al., 1997). Spores of the proteolytic form of C. botulinum are more heat resistant but can only germinate at higher storage temperatures (see e.g. Braconnier et al., 2003), i.e. a minimal heat treatment will not inactivate these spores but germination at chill is not possible. Care should be taken that such a minimal heat treatment does inactivate the nonproteolytic species since these can grow out at chill temperatures as stated. The toxin produced by C. botulinum species is heat labile but extremely toxic to people. It is estimated that less than 1 µg intake is lethal. Like, for example, tetanospasmin (the Clostridium tetani exotoxin), C. botulinum toxin is a metalloproteinase that acts on the presynaptic membranes at neuromuscular junctions (Montecucco et al., 1996). Once bound, it prevents correct signalling of acetylcholine at the synapse by


410 cleaving proteins involved in its release. For a recent detailed account of background and management of C. botulinum infections see Robinson and Nahata (2003). Clostridium perfringens is less dangerous but is nonetheless one of the ‘Big Five’ expressed in cases of food poisoning in Western countries (the others being Campylobacter jejuni, Bacillus cereus, Salmonella Enteritidis, Staphylococcus aureus) (Brynestad and Granum, 2002). C. perfringens causes food poisoning mainly through intake of high numbers of cells from, for example, foods not properly heat processed. Cells that survive passage of the stomach sporulate in the intestine, producing concomitantly enterotoxinmediating food poisoning. The enterotoxin is highly toxic to eukaryotic cells with concentrations as low as 1 ng/ml causing detectable cell damage (McClane, 2000). Fortunately, the enterotoxin is heat labile at temperatures higher than 55–60 °C and, in addition, food poisoning symptoms are mild because C. perfringens cells and unbound enterotoxin are flushed from the small intestine owing to the profuse diarrhoea (see for more extensive discussion also de Jong, 2003). While Clostridia are most important for bacterial food safety, not much is known with respect to the molecular mechanisms governing their spore formation. Such studies are much more prevalent in Bacilli. These aerobically growing microorganisms produce heat to even extremely heat-resistant endospores (Kort et al., 2005). Multiple signals influence the levels of SpoOA~P. These nutritional signals, environmental signals and signals integrating the cellular status (DNA damage, cell cyle) are received and integrated by five different kinases (KinAKinE) in a manner as yet to be elucidated (see e.g. Philips and Strauch, 2002). The integration of the signals does not lead in all cells in a population to the onset of sporulation. Presumably this is dependent on other factors such as the recently

Fig. 15.7 Heterogeneity in sporulation of a Bacillus subtilis culture. B. subtilis strain PspoIIA-gfp (reporter for progression of sporulation) was grown in sporulation medium which contained dehydrated nutrient broth (0.8 %), NaOH (0.5 mM), MgSO4 (1 mM), KCl (1 g/l), Ca(NO3) (1 mM, and MnCl2 (0.01 mM) in Erlenmeyer flasks at culture volumes of 50 ml. Fluorescence microscope images (2000× magnification) were taken from cells harvested at time points 2–4 h after initiation of sporulation which was triggered by a loss of available nutrients. Heterogeneity in expression of GFP from the spoIIA promoter can be observed. (Figure kindly provided by J.W. Veening; see also Veening et al., 2004 and Veening et al. in preparation.)


411 identified factors that govern ‘cellular cannibalism’ in Bacillus subtilis (GonzálezPastor et al., 2003; Engelberg-Kulka and Hazan, 2003). Heterogeneity in the sporulation process can be visualised with the use of fluorescent reporter proteins. Figure 15.7 illustrates this point using fluorescent reporters that Veening et al. have made for GFP fused to the SpoIIA protein (unpublished experiments presented in a poster, see also Veening et al., 2004).

15.5

Genomics-based detection in the food chain

Genomics technology allows for analysis of gene presence and gene utilisation (expression analysis) on a genome-wide scale. The fundamental insight gained enables, for example, a detailed analysis of many environment adaptation systems in microorganisms. This application of the genomics technique has been discussed extensively in the previous paragraphs of this chapter. Application of a geneexpression (or proteomics) fingerprint in screening for the occurrence of main pathogenic microoorganisms is now a real possibility. For Escherichia coli screening naturally occurring variants for the presence of genes or the lack of genes commonly found in K-12 has been done (Dobrindt et al., 2003). A technique commonly used for genotyping is the so-called amplified fragment length polymorphism (AFLP) technology. Subsequent to amplification and analysis of the resulting fragments using gel electrophoresis, the amplified fragments can be sequenced and thus assigned to certain genes and cellular functions. Upon analysing a sufficiently large sample of spoilage isolates for the occurrence of certain AFLP fragments, these can be statistically linked to the physiological (i.e. food spoilage) characteristics of the strains under study (Geornaras et al., 1999). This can be done using principal components analysis (PCA). The molecular biomarkers can subsequently be used to design thematic microarrays containing the DNA probes characteristic for the pathogenic strains under investigation. These arrays may be used in laboratory settings and form the basis for the development of thematic DNA chips to be used in practical tests for the presence of pathogens at identified sites in the food chain. In this context food product ingredients and sites in processing plants are key points of interest. Applications of new nucleic acid-based technologies for microbial community analyses in foods have recently been reviewed by the group at MATFORSK (Rudi et al., 2002)

15.5.1 Detection: whole genome analysis Macrorestriction analysis, pulsed field gel electrophoresis and genomic subtraction analysis are a few commonly known examples. Recently DNA array technology was assessed for this purpose (Al-Khaldi et al., 2002; Call et al., 2003). This has meanwhile proven to be a powerful technique to characterise genetic variation among various E. coli isolates. Various detection systems based on DNA detection are on the market (e.g. see systems marketed by Roche Diagnostics http:/ /www.roche-applied-science.com/molecular-food-safety-testing), and the reader


412 is advised to consult the Internet for further possibilities and McMeekin (2003) for an independent account of the available products. The use of DNA-based analysis systems seems to be particularly appropriate for the detection of spores of toxigenic bacterial spore formers such as Bacillus cereus.

15.5.2 Transcription and protein expression The use of expression (transcript)-based arrays in transcriptomics studies of pathogen behaviour is now a well-established technique in a laboratory setting. Indeed time-resolved transcriptome analysis reflects the versatility of many pathogens and highlights the organism’s immediate, continuous and truly genome-wide response to its (changing) environment (Conway and Schoolnik, 2003). Isolating RNA from environmental samples remains a major challenge. Recently various projects studying food and environmental samples have made significant progress in this field, however (discussed extensively in Cook, 2003). Also in the area of food production monitoring, a project that encompases TNO Food Research, Unilever Research, the University of Amsterdam and a start-up diagnostics company, Checkpoints, focuses partially on the development of extraction procedures for the optimal isolation of RNA from (food) environmental samples. The main aim in these projects is (1) to be able to confirm that a certain (set of) gene(s) is actively used and (2) to be able to monitor the response of a microorganism to their abiotic/abiotic environment, including monitoring their response to preservation strategies (Oomes and Brul, 2004). The most promising isolation method currently in use would be the one where cells are first purified based on (antibody) affinity beads which are captured using magnetic separation. In this set-up large volumes of food sample can be analysed. This is accomplished through the use of standard homogenisation procedures coupled to prolonged recirculation of the homogenate over the affinity beads. After subsequent washing steps, the captured cells/spores are eluted and ready for further analysis using all available culturing and molecular techniques.

15.6 Future trends The current trend in food science and the monitoring of pathogen behaviour is that analysis times need to be shortened while sensitivity and reliability need to be maintained and if possible even enhanced. The current state-of-the art of understanding pathogen behaviour reflects our ever-increasing knowledge and insight in the ‘parts list’ of (micro)biological systems. This is of course a first condition to be able to further unlock the ‘black-box’ of cellular behaviour under various environmental conditions. Subsequently it may be expected that in future settings a link will be developed between this increasing biological information and developments in micro-nanotechnology that will allow for the developments of measurement and control systems that can be used in practical settings in the food industry to optimise the use of raw materials and come to optimal process settings.


413

15.7


A useful entry on the worldwide web regarding (functional) genomics and, in particular, the newly developing and highly relevant field of systems biology is http://www.systembiology.netsbnl. Further information is available on the website of institutes such as the UK Institute for Food Research (IFR), the Institute Pasteur (IP), the Dutch Institute for Applied Food Research (TNO-Food), the Dutch National Institute for Health and Environment (RIVM), the Wageningen Centre for Food Sciences (WCFS), the Swammerdam Institute for Life Sciences (SILS) and the Groningen Biomolecular and Biotechnology institute (GBB). For further information on Gram-negative bacterial pathogens, the IFR website is a good entry point, the Gram-positive pathogens being particularly well described on the WCFS website. Finally, SILS will also expand its activities in the coming years towards the Gram-negative pathogen Campylobacter jejuni. Information of a general nature on the topic can be obtained at the website of the Federation of European Societies of Microbiology (FEMS) and the American Society for Microbiology (ASM). The 2003 1st Congress for European Microbiologists featured many relevant topics. In 2004, the Food Micro congress in Slovenia was the meeting point for researchers and technologists active in the food industry (see http:// www.foodmicro2004.org for further information). Finally, for a comprehensive overview of how the microbiological insights (should) fit in the day-to-day practice of food processing, the websites of the European Federation for Food Sciences and Technology (Effost) and the US Institute for Food Technologists (IFT) should be consulted. In addition, Bruin and Jongen have recently written an extremely timely standard paper on the implications that genomics will have in food science and food safety (Bruin and Jongen, 2003). For a recent review on the genomics of foodborne pathogens and the potential impact this might have on future strategies for the control and eradication of foodborne disease, see Wells and Bennik (2003).

15.8

Acknowledgements

S. Brul would like to thank project collaborators of the Microbiological Control Department at the Unilever Research Laboratory in Vlaardingen, the Netherlands, as well as the current members of the Molecular Biology and Microbial Food Safety Chair at Amsterdam University for their support and innovative ideas that helped to shape this chapter. J. Wells wishes to thank Mark Reuter and Bruce Pearson for useful discussion and for help with the GeneSpring package. J. Wells also gratefully acknowledges the BBSRC UK for financial support for the Campylobacter microarray studies.


414

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16 Pathogen resistance and adaptation to heat stress V. Juneja, United States Department of Agriculture, USA and J. Novak, American Air Liquide, USA

16.1

Introduction

Heat treatment of foods is one of the oldest and most effective physical processing strategies for controlling foodborne pathogenic microorganisms. The effectiveness of heat in inactivating the microorganism of concern is dependent upon the severity of the treatment and the ability of the pathogen to withstand the treatment, recover from injury, proliferate, and withstand subsequent treatments that may occur. Effective injury repair can result in removal of damaged DNA and proteins followed by resynthesis of cellular constituents and repair of cellular membranes. Although injured microorganisms may be more susceptible to inactivation, they may also utilize mechanisms for increasing resistance to subsequent stresses. It is important that heat treatments ensure lethality without enabling enhanced resilience or cross-protection following inadequate inactivation attempts. Another consideration is the avoidance of excessive damage to the food product. Clearly, there is a narrow range of time/temperature treatments that can be applied to deliver maximal pathogen inactivation and minimal food product adulteration. There needs to be assurance that foodborne pathogens are killed during heating. Under certain conditions of food processing, microorganisms could become more heat resistant. Prior exposure to low heat may also render the organism more resistant to a subsequent heat treatment that would otherwise be lethal (Murano Mention of brand or firm name does not constitute an endorsement by the US Department of Agriculture over others of a similar nature not mentioned.


423 and Pierson, 1992; Lou and Yousef, 1996; Juneja et al., 1997). Heat shock proteins are synthesized concurrent with increased heat resistance. It is possible that heat shock proteins protect cells either by preventing the denaturation of normal proteins, or through the removal of heat-damaged proteins (Nguyen et al., 1989). ‘Heat-shocked’ cells require greater inactivation temperatures than ‘non-heatshocked cells’ to achieve the same level of lethality (Farber and Brown, 1990). The induced thermotolerance of microorganisms from heat shock is of concern to food processors that typically use temperatures below 65 ºC to process their products. Other conditions where the increased heat resistance of microorganisms could be problematic include products left on warming trays before final heating or a process failure. The additional heat adaptation of pathogens needs consideration to ensure required inactivation of microorganisms, leading to safer foods.

16.2

Predicting pathogen resistance

The heating times at a specific temperature are often increased to ensure the inactivation of high microbial populations in foods. Incorporated into this design is the normal expected microbial concentration for a given product. The heat resistance of bacteria has been historically described by two characteristic parameters, D- and z-values. In principle, the D-value is the time necessary to inactivate 90 % of the initial population of microorganisms present in a food at a specific temperature, whereas the z-value describes the temperature change necessary to result in a 90 % change in the D-value. Although universally accepted, these calculations deviate from the expected linear relationships for microorganisms fairly regularly, and especially for bacterial spores. It has long been accepted that when bacteria are killed by heat they die at a constant rate, i.e. via first-order kinetics (Stumbo, 1973; Tomlins and Ordal, 1976). This model of thermal inactivation forms the basis of calculations used in thermal processing and has served the food industry and regulatory agencies for decades. Unfortunately not all the microbial cells in a given population have identical heat exposure or resistance, and the improbable chance of a heat-sensitive target in one cell determining the death rate of the entire cell population fails (McKee and Gould, 1988). There have been significant deviations from predicted relationships observed by many researchers using different methodologies (Tomlins and Ordal, 1976; Pflug and Holcomb, 1983; Cole et al., 1993). Frequently a shoulder or lag period is observed when bacterial populations remain constant or a tailing slower death rate is observed when subpopulations of more resistant bacteria are present (Fig. 16.1). At present, there is no satisfactory explanation for the variability in thermal death kinetics. Some investigators have suggested that deviations from linear survival curves result from heterogeneous cell populations (Hansen and Riemann, 1963). Possible explanations for the ‘shoulder effect’ include poor heat transfer and the requirement for sufficient cellular injury before observed cell death leads to the expected first-order inactivation relationship. Other theories concentrate on


424

Fig. 16.1 Thermal inactivation of microorganisms. The straight line represents the traditional first-order kinetics of log number of survivors declining in a linear manner with time. The sigmoidal curve depicts a more realistic representation.

the need for multiple inactivation events or the activation of spores to germinate making them more susceptible to the lethal effects of heat. Clumping of small numbers of cells or spores may protect cells against thermal destruction (Stumbo, 1973; Hansen and Riemann, 1963). In response to environmental conditions or even physiological changes during its life cycle, an individual cell can have varying degrees of heat resistance (Cerf, 1977). Also, heat resistance can be acquired, as a result of sublethal heating, and lead to deviations from linearity in plotted survival curves. There have been numerous attempts to explain these deviations from the expected linear survival survivor curves (Casolari, 1988; Gould, 1989; Pflug, 1990; Whiting, 1995).

16.3

Factors influencing the development of resistance

An appropriate heat treatment designed to achieve a specified lethality of microorganisms is influenced by many factors, some of which can be attributed to the inherent resistance of microorganisms, while others are due to environmental influences. Examples of inherent resistance include the differences between species and the different strains or isolates of bacteria (assessed individually or as a mixture) and the differences between spores and vegetative cells. Environmental factors include those affecting the microorganisms during growth and formation of cells or spores (i.e. stage of growth, growth temperature, growth medium, previous exposure to stress) and those active during the heating of bacterial suspensions,


425 such as the composition of the heating menstruum, water activity (aw), pH, added preservatives, method of heating, and methodology used for recovery of survivors. The persistence of heat shock-induced thermotolerance appears to be a function of many factors including the heat shock temperature, the previous incubation temperature of the cell, and the metabolic state of the cell (Lindquist, 1986). Temperature fluctuations are a common occurrence in food processing environments, as well as during transportation, distribution, and storage or handling of foods in supermarkets or by consumers. Therefore, temperature can play a role in the food environment stresses to which bacterial cells will be exposed. Guidelines have been suggested to avoid environmental stresses so as not to enhance pathogen survival during thermal processing procedures (Juneja et al., 1997). There are time–temperature combinations that produce maximum thermotolerance following heat shock. Temperatures between 45 º and 50 ºC are optimal for development of the heat shock response in mesophilic bacteria (Lindquist, 1986). In one study, Listeria monocytogenes cells in sausage required 120 min at 48 ºC prior to final treatment at 64 ºC to exhibit a 2.4-fold increase in thermal resistance as compared to non-heat-shocked cells (Farber and Brown, 1990). Lower exposure times reduced the heat resistance effect. The authors also reported that the heat-shocked cells retained their increased heat resistance for 24 h after storage at 4 ºC. Mackey and Derrick (1986) increased the heat resistance of Salmonella Typhimurium (Salmonella enterica serovar Typhimurium) to a range of lethal temperatures (52–59 ºC) in tryptone soya broth by prior exposure of the cultures to sublethal heat shock at 48 ºC for 30 min. Greatest heat resistance was reached within 30 min of exposure and persisted for 10 h. A similar effect was demonstrated with S. Thompson when the organism was preheated at 48 ºC and then subjected to 54 or 60 ºC in tryptone soya broth, liquid whole egg, 10 % (w/v) or 40 % (w/v) reconstituted dried milk, or minced beef (Mackey and Derrick, 1987). Shenoy and Murano (1996) heat shocked Yersinia enterocolitica in brain–heart infusion broth at 45 ºC for 60 min and subsequently observed an increased number of survivors at 55 or 60 ºC when compared with non-heat-shocked cells. The thermotolerance of L. monocytogenes at 65 ºC increased with the duration of the heat shock for up to 120 min, regardless of the heat shock temperature from 40 to 46 ºC (Pagan et al., 1997). In contrast to these studies demonstrating a parallel increase in heat resistance with the increase in the time of heat shocking, Murano and Pierson (1992) heatshocked Escherichia coli O157:H7 cells in trypticase soy broth (TSB) at 30, 34, 42, or 45 ºC for 0, 5, 10, or 15 min and reported that heat shocking at 42 ºC for 5 min resulted in the greatest log number of survivors at 55 ºC compared with non-heat shocked controls. Linton et al. (1990) heat-shocked log phase cells of L. monocytogenes Scott A in TSB supplemented with 0.6 % yeast extract (TSYE) at 40, 44, and 48 ºC for 3, 10, and 20 min, followed by heating at 55 ºC for 50 min. The optimum heat shock condition for increasing subsequent heat resistance was 48 ºC for 10 min where heat resistance at 55 ºC increased 2.3-fold in non-selective agar (TSYE) and 1.6-fold in selective agar (McBride Listeria). Cells that were


426 heat-shocked at 48 ºC for 10 min were consistently more resistant to subsequent heating at 50, 55, 60, and 65 ºC than non-heat-shocked cells. Juneja et al. (1997) used a submerged-coil heating apparatus to determine the effect of prior heat on E. coli O157:H7, inoculated in a model beef gravy, and the persistence of the thermotolerance at 4, 15, and 28 ºC after heat shock. When beef gravy samples inoculated with a four strain cocktail of E. coli O157:H7 were subjected to sublethal heating at 46 ºC for 15–30 min, followed by cooking to a final internal temperature of 60 ºC, the organism survived 1.56-fold longer than non-heat-shocked cells. In this study, a linear decline in the log number of survivors with time was observed. The induction of thermotolerance by heat shock was maintained for at least 48 h at 4, 15, or 28 ºC. However, when a similar study was conducted in bags of ground beef, heated using a water bath, the primary thermotolerant response of E. coli O157:H7 switched to non-linear inactivation kinetics, resulting in the presence of a shoulder (Juneja et al., 1997). Unlike the beef gravy, it was interesting to note that E. coli O157:H7 cells in beef lost their thermotolerance after 14 h at 4 ºC and after 24 h in beef held at 15 or 28 ºC. Listeria monocytogenes suspended in tryptic phosphate broth (TPB), heatshocked at 46 ºC for 30 min, and held at 4, 10, or 30 ºC before heating at 58 ºC, resulted in thermotolerance being maintained for a longer time at 4 and 10 ºC compared with cells stored at 30 ºC (Jorgensen et al., 1996). Even after 48 h, cells grown and held at 4 ºC after the heat shock were two-fold more heat resistant than non-heat-shocked cells grown at 4 ºC. These findings have important implications for the survival of pathogens in pre-cooked foods which are then stored at refrigeration temperatures. Bunning et al. (1990) heat-shocked stationary phase cells of L. monocytogenes grown at 35 ºC (control), at 42, 48, and 52 ºC for 5–60 min prior to heating at 57.8 ºC. Although heat shocking at 42–48 ºC for 5–60 min consistently increased D-values at 57.8 ºC by 1.1 to 1.4-fold, these data were not statistically different from non-heat-shocked cells. When similar experiments were conducted with Salmonella Typhimurium, D-values increased by 1.1 to 3.0-fold and were significantly different than non-heat-shocked cells. When L. monocytogenes cells were held at 42 ºC, thermotolerance remained at a maximum level for at least 4 h. However, in preheated cells incubated at 35 ºC, the increased thermal tolerance lasted less than 1 h. A given atmosphere combined with heat stress can further increase the heat resistance of E. coli O157:H7. In a study by Murano and Pierson (1992), when log phase cells of E. coli O157:H7 grown in TSB at 30 ºC were subjected to heat shock at 42 ºC for 5, 10 or 15 min before final heating at 55 ºC, D-values increased by more than 2-fold for aerobically grown cells, and 1.5-fold when grown under anaerobic conditions. The D-values of anaerobically grown non-heat-shocked controls at 55 ºC were significantly higher than those of aerobically grown controls. Anaerobiosis is considered a form of stress to bacterial cells. Using a submerged coil heating apparatus set at 58 ºC, L. monocytogenes cells, grown at either 10 or 30 ºC, were shown to have no difference in thermotolerance, but were significantly (p < 0.001) more heat resistant (1.5-fold) than cells grown


427 at 4 ºC (Jorgensen et al., 1996). The heat shock-induced thermotolerance could be lost following a given growth period as a result of metabolic turnover. For example, cells grown at 4, 10 or 30 ºC showed the same amount of reduction when held at 30 ºC as a result of new protein synthesis and degradation following heat shock. The degree to which E. coli O157:H7 heat-shocked and non-heat-shocked cells are injured following a heating process and the ability of injured cells to repair themselves under aerobic and anaerobic conditions has been described by Murano and Pierson (1993). Bacteria encounter stress from both oxygen surplus and deprivation (Potter et al., 2000). Not surprisingly, the D-values of heat-shocked cells increased along with the numbers of injured cells as a result of heat shock (Murano and Pierson, 1993). When cells were recovered under anaerobic conditions, a higher recovery of injured cells was observed along with higher D-values as compared with cells recovered aerobically. This phenomenon was observed regardless of whether the cells were previously heat-shocked. A possible explanation includes the spontaneous formation of toxic oxygen radicals in aerobic media, which heated cells are unable to deactivate due to the heat inactivation of detoxifying enzymes such as catalase and superoxide dismutase (SOD). Since anaerobic storage is a practice that is prevalent in the food industry for shelf-life extension of processed meats, the microbiological safety of such foods should be of concern because of the enhanced recovery of injured pathogens following heat treatment. Linton et al. (1992) assessed the effect of recovery medium on the survival of heat-injured L. monocytogenes. The D-values at 55 ºC for heat-shocked (48 ºC for 10 min) log phase cells of L. monocytogenes Scott A were 2.1-fold higher than non-heat-shocked cells on non-selective agar (TSYE) incubated aerobically and similarly 2.2-fold higher for cells enumerated anaerobically on TSYE agar. On selective medium (McBride Listeria-ML), the values were 1.4-fold higher than those of non-heat-shocked cells. Interestingly, no growth was observed on ML agar incubated anaerobically. Fedio and Jackson (1989) exposed stationary-phase cells of L. monocytogenes Scott A to a preheating treatment of 48 ºC for 1 h in TSYE broth followed by heating at 60 ºC for 20 min. Preheating rendered the pathogen more resistant, and a 4-log10 higher number of cells were recovered as compared to non-heat-shocked cells regardless of the recovery medium (selective or non-selective). Increases in D-values (up to 22 % compared to the control) for Salmonella Enteritidis (Salmonella enterica serovar Enteritidis) following heat shock (42 ºC for 60 min) were reported by Xavier and Ingham (1997). This study suggested that: (i) short-term temperature abuse of foods containing S. Enteritidis may render the cells more resistant to subsequent heat treatments; (ii) anaerobic microenvironments may enhance survival of heat-stressed cells (i.e. increases in D-values up to 28 % compared with the aerobic value); and (iii) heat shock results in the overexpression of proteins that may be related to increased thermotolerance. Heat stress conditions may be encountered in minimally processed, cook–chill processed foods of extended durability. Slow heating rate/long come-up times and low heating temperatures employed in the production of sous-vide cooked foods


428 expose microbial cells to conditions similar to heat shock. Stephens et al. (1994) and Kim and Thayer (1996) have shown that slowly raising the cooking temperature enhanced the heat resistance of L. monocytogenes in broth and pork, respectively. Hansen and Knochel (1996) reported a difference between slow (0.3–0.6 ºC/min) and rapid (>10 ºC/min) heating and the heat resistance of L. monocytogenes in cooked beef at a pH of 6.2, but not less than pH 5.8. Tsuchido et al. (1974, 1982) observed increased thermotolerance of E. coli by raising the temperature of the cell suspension from 0 to 50 ºC at various rates prior to holding at 50 ºC. Also, Thompson et al. (1979) increased the thermotolerance of S. Typhimurium in beef under realistic conditions of constantly rising temperature. Subsequently, Mackey and Derrick (1987) reported that the heat resistance of S. Typhimurium, measured as survival following a final heating at 55 ºC for 25 min, increased progressively as cells were heated during linearly rising temperatures. In that study, cells were heated at a rate of 0.6 or 10 ºC/min from 20 to 55 ºC, and then subjected to a heat challenge at 55 ºC for 25 min. The authors reported that the extent of induced thermotolerance was inversely related to the rate of heating, i.e. the slower the temperature rise, the greater the increase in resistance. Quintavala and Campanini (1991) determined the heat resistance of L. monocytogenes 5S heated at 60, 63, and 66 ºC in a meat emulsion at a rate of 5 ºC/min resulted in Dvalues at least two-fold higher than cells of L. monocytogenes in meat, exposed to instantaneous heating. Cellular targets for heat damage are ribosomes, nucleic acids, enzymes and/or proteins (Abee and Wouters, 1999). Mild heat treatment can also lead to modifications of the cell membrane by increasing the saturation and length of the fatty acids needed to maintain optimal fluidity of the membrane and activity of intrinsic proteins (Russell and Fukanaga, 1990; Russell et al., 1995). In a study on the physiological state of cell membranes from Gram-negative bacteria, the total saturated fatty acids (SFA) and total unsaturated fatty acids (UFA) were highly influenced by temperature (Dubois-Brissonnet et al., 2000). When the temperature was increased from 15 to 40 ºC, SFA increased from 25 to 39 %, whereas UFA decreased from 66.5 to 51 % (Dubois-Brissonnet et al., 2000). Stress-adapted bacteria are capable of resisting similar (homologous) or different (heterologous) stresses. Termed ‘cross-protection’, exposure to one stress is capable of altering resistance to another stress and is mediated by the rpoS gene. Wang and Doyle (1998) reported that sublethal heat treatment of E. coli O157:H7 cells substantially increased their tolerance to acidity. In contrast, although heat resistance increased in bacterial spores produced at higher temperatures (Condon et al., 1992; Sedlak et al., 1993), lactic acid through a lowering of pH was effective in reducing the heat resistance of the spores produced at higher temperatures (Palop et al., 1996). Lou and Yousef (1997) examined the effect of sublethal heat (45 ºC for 1 h) on the resistance of exponential phase cultures of L. monocytogenes to certain environmental stresses and found that this greatly increased resistance of the pathogen to normally lethal doses of hydrogen peroxide, ethanol and NaCl. As a consequence of stress-induced cross-protection, Lou and Yousef (1997) concluded that certain stresses might counterbalance the benefits of multiple stress


429 hurdles. In L. monocytogenes, stress induced by heat treatment did not lead to acid tolerance, but cells induced by low pH did become more resistant to heat, salt concentration and antimicrobial peptides (Hill and Gahan, 2000). Different cross-protection adaptations of specific pathogens must be taken into account when assessing the microbial safety of minimal food processing technologies alone or in combination. Komatsu et al. (1990) showed that exposure of yeast cells to a heat shock conferred protection against freezing in liquid nitrogen. Additionally, it was found that carbon starvation in E. coli elicited an essential need for DnaK expression in order to acquire heat and oxidation resistance (Rockabrand et al., 1998). Further research is needed to fully understand the levels and types of stresses necessary to elicit universal or specific adaptations among foodborne pathogens. In response to environmental conditions or even physiological changes during its life cycle, an individual cell can have varying degrees of heat resistance (Cerf, 1977). The heat resistance can be acquired as a result of sublethal heating and lead to deviations from linearity in plotted survival curves. There have been numerous attempts to explain these deviations from the expected linear survivor curves (Casolari, 1988; Gould, 1989; Pflug, 1990; Whiting, 1995).

16.4

Targets of heat damage

Heat is believed to be uniformly distributed in a cell, resulting in damage to only the most sensitive molecules within that cell (Moats, 1971). Potential targets of heat damage have been implicated with associations to various pathogen viabilities. These include proteins, enzymes and cellular membranes, as well as nucleic acids (Marquis et al., 1994). Even mild heat is known to play a key role in DNA damage and hydrolysis. Purine bases were first shown to be liberated from DNA at elevated temperatures (80–95 ºC) followed by depyrimidation of cytosine and thymine bases at approximately 5 % the rate of depurination (Lindahl and Nyberg, 1972; Lindahl and Karlstrom, 1973). Apurinic sites led to greater than 95 % chain breakage over control DNA (Lindahl and Andersson, 1972). The DNA chain breaks were found at the 3' side of the apurinic sugar moiety between the sugar and and the phosphate residue (Lindahl and Andersson, 1972). The chain breakage was also considered a significant mechanism of heat-induced lethality in bacterial spores (Grecz and Bruszer, 1981). In addition to depurination and hydrolysis of phosphodiester bonds, cytosine deamination was also present, although DNA thermodegradation was inhibited by physiological salt concentrations of 50–1000 mM KCl and 5–10 mM MgCl2 (Marguet and Forterre, 1994). However, abasic sites were spontaneously generated under physiological conditions by hydrolysis of the N-glycosylic bond (Kubo et al., 1992). More recently it was found that covalently closed circular plasmid DNA can initially withstand denaturation at temperatures of 95–107 ºC, but subsequent depurination events eventually lead to DNA strand breakage followed by denaturation (Masters et al., 1998). Spores generally have heat resistance mechanisms that extend heat tolerances


430 above vegetative cells. DNA repair mechanisms have been shown to have a significant influence on heat resistance (Hanlin et al., 1985). Heat resistance of spores is attributed in part to restricting the mobility of heat-labile components of the spore core such as proteins and DNA (Gombas, 1983). Previously, it was assumed that increases in sporulation temperature increased heat resistance as a result of spore mineralization and dehydration stabilizing internal spore components (Beaman and Gerhardt, 1986; Palop et al., 1999). Dehydration of the spore core has been recently shown to measurably alter spore components, resulting in the increased resistance to heat (Novak et al., 2003). Melting of spore DNA may occur at temperatures above 90 ºC and is reflected in differential calorimetric scans of spores as an endothermic peak at 90–91 ºC (Belliveau et al., 1992; Teixeira et al., 1997). In contrast, spore DNA is further protected by small acid soluble proteins (SASPs) that bind tightly and specifically to the A form of DNA, reducing the rate of depurination in vitro by at least 20-fold (Fairhead et al., 1993; Lindahl, 1993; Setlow, 1994, 1995). The SASPs are also effective protectants against DNA damage from dessication, oxidation and UV irradiation (Setlow, 1995). The SASPs protect DNA so well against damage that cell death may be correlated with damage of molecules other than DNA (Setlow, 1995). A SASP-like protein in Clostridium perfringens has been observed to cross-react immunologically with antiserum raised against Bacillus subtilis SspC (Novak et al., 2001). Additional evidence in support of the role of DNA in thermoresistance was found in an observed correlation between spore DNA content and elevated heat resistance (Belliveau et al., 1990). Dipicolinic acid (DPA) or calcium-DPA complexes with DNA were also correlated with spore heat resistance (Lindsay and Murrell, 1985). Whatever the DNA protective mechanism involved, DNA stability is vital to thermoresistance in a microorganism. An increasing amount of evidence suggests that ribosome damage and degradation is the cause of cell death following thermal stress (Lee and Goepfert, 1975; McCoy and Ordal, 1979). Ribosome denaturation occurs in the same temperature region as thermal inactivation. Numerous investigators have used differential scanning calorimetry (DSC) to examine thermal transitions as indicators of potential sites of cellular injury (Anderson et al., 1991; Teixeira et al., 1997; Novak et al., 2001). The DSC is an effective technique in measuring changes in protein denaturation temperatures with corresponding changes in denaturation enthalpy (Kijowski and Mast, 1988). It was suggested that half the enthalpy of ribosome denaturation is associated with protein denaturation (Mackey et al., 1991). Allwood and Russell (1967) observed a direct correlation between loss of RNA and heat-induced loss of viability of Staphylococcus aureus at temperatures up to 50 ºC. Magnesium is known to have a stabilizing effect on ribosomes. In a study involving mild heating of Staph. aureus, Hoa et al. (1980) reported that heating results in membrane damage, leading to the loss of Mg2+ ions and destabilization of the ribosomes. Depletion of Mg2+ leads to 70S ribosome dissociation into 30S and 50S subunits, ribonuclease inactivation, and destruction of 30S subunits (Hurst, 1984; Hurst and Hughes, 1978, 1981). Earlier studies including a number of bacterial species showed that the 30S ribosomal subunit is


431 specifically destroyed during heat treatment, while the 50S ribosomal subunit appears to be stable, and that 16S rRNA is the prime target of degradation in the heat-injured cells, while 23S rRNA appears to be unaffected (Rosenthal and Iandolo, 1970). Miller and Ordal (1972) examined the rRNA profiles of cells at various times during heat injury at 47 ºC. The degradation of rRNA and ribosomal subunits occurs differently during heat injury; the 16S and 30S subunits are affected more readily following heating for 5 min, and the 23S and 50S subunits are degraded more slowly, disappearing after 30 min of heat treatment. Stephens and Jones (1993) proposed that the protection of the 30S subunit is a critical mechanism for increased thermotolerance. In their study, the osmotic and heat shock-induced increased thermotolerance response of L. monocytogenes was concurrent with increased thermal stability of the 30S ribosomal subunit, as measured by DSC. The authors proposed that the stabilization of the subunits occurred through cellular dehydration, leading to an increase in the internal solute concentration, including Mg2+ ions, which may contribute to tighter coupled particles of the 30S subunits. Tolker-Nielsen and Molin (1996) reported that heat lethality of Salmonella Typhimurium coincides with a significant reduction in the cellular content of 16S ribosomal RNA, thereby suggesting that the degradation of ribosomal RNA is a direct cause of cell death. This conclusion is based on the findings of carbon-starved and magnesium-supplemented cells, which survive heat treatment much better and which also maintain stable levels of ribosomal RNA. Proteins and enzymes are also considered to be potential sites responsible for heat lethality. It has been postulated that water that is in close contact with the proteins inside the cell could be a factor determining the cell’s inactivation. As the cell is heated, water molecules begin to vibrate, and this vibration causes the disulfide and hydrogen bonds in the surrounding proteins to weaken and break, altering the final three-dimensional configuration and possibly preventing the protein from functioning (Earnshaw et al., 1995). The crucial protein that is the rate-limiting, primary target in heat killing is unknown, but the current belief is that membrane proteins may be denatured by heat initially because of peripheral locations followed by the denaturation of crucial proteins within ribosomes (Belliveau et al., 1992). There is evidence that catalase and SOD may be sensitive to heating. These enzymes detoxify oxygen radicals like superoxide and hydrogen peroxide, which form spontaneously in the presence of oxygen and, if undisturbed, can result in death of cells as a result of lipid peroxidation and membrane damage (Kellogg and Fridovich, 1975). A recent study identified a heat-induced 22 kDa protein, rubrerythrin, from Cl. perfringens, that may play a role as a scavenger of oxygen radicals under stressful conditions (Novak et al., 2001). Warth (1980) observed a range of sensitivities for spore enzymes and concluded that the enzymes in extracts of spores were inactivated at temperatures ranging from 24 to 46 ºC lower than those needed to inactivate the same enzymes within intact spores. Membrane-bound ATPase has been associated with heat resistance/sensitivity of microorganisms. Coote et al. (1991) suggested that ATPases are essential for the basal heat resistance of the cell to cope with elevated temperatures. Nonetheless,


432 thermotolerance induced by sublethal heating is a mechanism independent of ATPase activity. Flowers and Adams (1976) suggested that the cell membrane is the site of thermal injury of spores subjected to mild or sublethal heating; membrane damage consequently increases sensitivity to environmental stresses. When spores are lethally heated, damage to the membrane permeability barrier results in the release of intracellular constituents and there is a temperature-dependent progressive loss of calcium and dipicolinic acid (DPA) (Brown and Melling, 1973; Hunnell and Ordal, 1961; Rode and Foster, 1960). The death of spores proceeds faster than the release of DPA (Belliveau et al., 1992). When vegetative cells are heated, there is a rapid efflux of ions, amino acids, and low molecular weight nucleic acid components, thereby suggesting that interference with the semipermeability of membranes is a common consequence of heating (Tomlins and Ordal, 1976).

16.5

Strategies to counter pathogen resistance

For strategies to effectively control growth and survival of microorganisms, they must overcome homeostatic mechanisms that the microorganisms have evolved to resist stress (Gould et al., 1995). Food handling conditions should be optimized for maximum microbial lethality during cooking. For example, storage of foods at low temperatures may affect the response of pathogens to subsequent stresses; E. coli O157:H7 is resistant to freezing in ground beef (Pandhye and Doyle, 1992) and chicken meat (Conner and Hall, 1996). Additionally, the heat resistance of E. coli O157:H7 in a nutrient medium and in ground beef patties was reported to be influenced by storage and holding temperatures (Jackson et al., 1996). Cultures stored frozen (–18 ºC) had greater heat resistance than those stored under refrigeration (3 ºC) or at 15 ºC, perhaps due to physiological changes within the bacterial cell as a result of freezing (Jackson et al., 1996). Another study (Katsui et al., 1982) showed that the exposure of E. coli to 0 ºC before heating significantly increased the heat sensitivity of the exposed cells. Juneja et al. (1997) reported that the heat resistance of E. coli O157:H7 inoculated in ground beef was not altered after storage at 4 ºC for 48 h. It is generally accepted that the heat shock response and exhibition of increased thermotolerance is rapidly lost upon chilling and rewarming of cells. Arguably, Williams and Ingham (1997) refuted the hypothesis that short-term temperature abuse significantly increased the heat resistance of E. coli O157:H7 in ground beef. Growth of microorganisms is generally inhibited at pressures in the range of 20 –130 MPa, whereas higher pressures between 130 and 800 MPa could result in cell death (Abee and Wouters, 1999). Both high pressure and high temperature destabilize the quarternary structure of proteins (Jaenicke, 1981). An effective strategy for control of foodborne pathogens may include high hydrostatic pressures in combination with heat treatments. It is presumed that an increased proportion of dissociated ribosomal subunits as a result of high-pressure treatment could induce a sigma-32 factor-dependent heat-shock response (Craig and Gross,


433 1991). Alternatively, high pressure may affect the phosphorylation state or ATPase activity of the heat shock protein, DnaK, which in turn could also modulate the heat shock response (McCarty and Walker, 1991). The food industry requires a better understanding of the kinetics and mechanism of pressure inactivation before adoption of pressure-based preservation processes. E. coli can acquire high levels of resistance to pressure killing by spontaneous mutation (Hengge-Aronis, 1993). The authors used alternating cycles of exposure to high pressure and outgrowth of surviving populations to select for highly pressure-resistant mutants of E. coli MG1655. Three barotolerant mutants (LMM1010, LMM1020, and LMM1030) were isolated independently by using outgrowth temperatures of 30, 37 and 42 ºC. Survival of these mutants after pressure treatment for 15 min at ambient temperature was 40–85 % at 220 MPa and 0.5–1.5 % at 800 MPa, while survival of the parent strain decreased from 15 % at 220 MPa to 2 × 10–8 % at 700 MPa. Two of the three mutants (LMM1020 and LMM1030) also exhibited higher heat resistance, expressed as increased D-values at 58 and 60 ºC, and lower z-values than those for the parent strain. Interestingly, the ability of the mutants to grow at moderately elevated pressure (50 MPa) was reduced at temperatures above 37 ºC, suggesting that resistance to pressure inactivation in these mutants is unrelated to barotolerant growth. The generation of increased pressure-resistant mutants questions the safety of high-pressure food processing, and may have significant implications for the successful application of high-pressure processing in food preservation. Spore-formers are known to exhibit enhanced pressure resistance as well; therefore, it is recommended that highpressure technologies be used in combination with other treatments to be truly effective (Bower and Daeschel, 1999). Significant reductions in Bacillus anthracis spores were recently obtained using 500 MPa pressure and held at 75 ºC (CleryBarraud et al., 2004). Jorgensen et al. (1995) used the submerged coil heating apparatus to determine the effect of osmotic up-shock and down-shock, and osmotic adaptation using different levels of NaCl on the corresponding changes in thermotolerance of L. monocytogenes. Subjecting cells to an osmotic down-shift (1.5 to 0.09 moles/ml) caused a rapid loss of thermotolerance, rendering cells 10-fold more heat sensitive than cells grown and heated in TPB containing 1.5 moles/ml NaCl. Subjecting cells grown in media containing 0.9 moles/ml NaCl to a short osmotic up-shock in media containing 0.5, 1.0 or 1.5 mol/ml NaCl resulted in an 1.3, 2.5 and 8-fold increase in thermotolerance, respectively. When cells were adapted to high salinities, an additional two- to threefold increase in thermotolerance occurred compared with cells subjected to an osmotic up-shock at the equivalent level of NaCl. Thus, varying degrees of physical dehydration would lead to enhanced thermotolerance of the foodborne pathogen. The increased thermotolerance observed during the extended exposure to high salinities might be associated with the degree to which the cells have undergone deplasmolysis and accumulated compatible solutes, i.e. the concentration and composition of intracellular solutes. According to Piper (1993), increased thermotolerance could be a result of increased structurization of the intracellular water. This mechanism could be linked with the enhanced


434 thermostability of ribosomal contents known to occur by both osmotic dehydration and heat shock in L. monocytogenes (Stephen and Jones, 1993). Survival of L. monocytogenes in low-temperature environments and high salt concentrations is attributed to the accumulation of the osmoprotectants glycine betaine and carnitine (Sleator et al., 2001). Low-temperature growth requires, in addition to membrane fluidity, mechanisms for regulating the uptake or synthesis of solutes and the maintenance of macromolecular structural integrity of ribosomes and other components important for gene expression and metabolism (Wouters et al., 2000). Proteins (7 kDa) produced in response to temperature downshock or a sudden decrease in temperature are known as cold-shock proteins (CSPs). In a study by Miller and Eblen (1997), the submerged coil heating apparatus was used to determine whether L. monocytogenes cells are more vulnerable to heating after a cold shock. In the model system, cultures were cold-shocked by a temperature down-shift from 37 ºC to 15 ºC or 0 ºC for 0, 1 and 3 h. Cold-shocked and control samples were then evaluated for thermal resistance at 60 ºC. The results indicated that the cells grown at 37 ºC to stationary phase, cold-shocked at 0 ºC for 3 h, then heated at 60 ºC, exhibited lower D-values than control cells that were not cold shocked. The decrease in D-values at 60 ºC ranged from 25 to 40 % for two L. monocytogenes strains and a strain of L. innocua. In a second experimental series by Miller and Eblen (1997), the effect of cold shock on thermal resistance (D60-values) of cells grown at 37 ºC to either lag, exponential or stationary phase was determined. Stationary cells were over 50 % more thermally resistant (D60 = 1.27 min), than lag and exponential cells, which had D60-values of 0.83 and 0.79, respectively. When these cells were cold-shocked at 15 ºC or 0 ºC prior to heating at 60 ºC, D-values were lowered by 42 %, 30 % and 8 % compared with non-shocked controls for stationary, lag and exponential cells, respectively. The authors pointed out that the maximum effect was in stationary-phase cells, which would most likely be expected to be present in contaminated foods. Some physical preservation treatments may be best when applied in combination with other technologies. Ultraviolet irradiation (254 nm) can cause cumulative damage to microbial DNA (Bower and Daeschel, 1999). Sublethal UV irradiation leads to the induction of numerous proteins as well as increased protection against heat (Duwat et al., 2000). Although the methodology is effective in decreasing cell numbers, it does not result in complete sterilization and, therefore, cannot be recommended as a definitive process to sanitize foods by itself (Bower and Daeschel, 1999). Ionizing radiation is known to damage microbial DNA. At temperatures above freezing, cellular inactivation by DNA disruption and production of hydroxyl radicals occurs (Buchanan et al., 1999). At freezing temperatures, DNA damage was the cause of irradiation inactivation and not cellular membrane disruption (Kim and Thayer, 1996). Detrimental effects of ionizing radiation on food products include oxidative rancidity of lipids, which can be prevented by vacuum packaging, and a loss of some minor vitamin components (Farkas, 1987). Although bacterial spores are more resistant, synergistic effects of gamma


435 irradiation and heat may be used to control spore-formers since the heat sensitization of irradiated spores is not readily repaired (Gombas and Gomez, 1978). Unfortunately, as with other food preservation methods, there is some indication of an acid (low pH)-induced cross-protection against gamma radiation sensitivity in enterohaemorrhagic E. coli (Buchanan et al., 1999). It is important to be mindful that the limitations of radiation that may be applied to a particular product are also determined by the organoleptic changes that occur (Grant and Patterson, 1991).

16.6 Future trends The use of heat for the inactivation of microorganisms is the most common process in food preservation today. Heat treatment designed to achieve a specific lethality for foodborne pathogens is one of the fundamentally important strategies used to ensure the microbiological safety of thermally processed foods. Heat resistance of microorganisms can vary depending on the species and strain of bacteria, food composition, physiological stage of microbial cells or spores, and recovery conditions (type of media, temperature, atmosphere, and time of incubation) for the detection of survivors. Food characteristics leading to increased thermal resistance of an organism include water activity and the presence of carbohydrates, lipids, proteins, salt, etc. Heat resistance of spores is attributed primarily to thermal adaptation, mineralization and dehydration. Alterations in membrane fatty acid profile results in an altered response to subsequent heat treatment. Potential targets of heat damage include nucleic acids, proteins and cellular membranes. Quantitative knowledge of the factors in food systems that interact and influence the inactivation kinetics are required to accurately estimate how a particular pathogen is likely to behave in a specific food. There is a need for a better understanding of how the interactions among preservation variables can be used for predicting the safety of minimally processed, ready-to-eat foods. The effects and interactions of temperature, pH, sodium chloride content and sodium pyrophosphate concentration are among the variables that researchers have considered when attempting to assess the heat-inactivation kinetics of foodborne pathogens. Incorporation of these multiple barriers increased the sensitivity of cells/spores to heat, thereby reducing heat requirements and ensuring the safety of ready-to-eat food products. The future of thermal death determination of bacteria will probably rely on predictive thermal inactivation kinetics modeling. Complex multifactorial experiments and analyses to quantify the effects and interactions of additional intrinsic and extrinsic factors and development of ‘enhanced’ predictive models are warranted to ensure the microbiological safety of thermally processed foods. In view of the continued interest in minimally processed foods, it would be logical to define a specific lethality at low temperatures. It would be useful to determine the possible effects of injury to vegetative cells and spores that may result from mild heat treatments and the factors in foods that influence the recovery of cells and or spores heated at these low temperatures. In conclusion, future research should focus on


436 conducting dynamic pasteurization (low-temperature, long cooking-time) studies to assess the integrated lethality of cooking and develop integrated predictive models for pathogens for the thermal inactivation, injury, repair and behavior in ready-to-eat meats, including those packaged in modified atmospheres.

16.7


Information on expected growth characteristics of foodborne pathogens with respect to food environment variables and holding temperatures can be obtained using the US Department of Agriculture’s (USDA) pathogen modeling program, accessed at http://www.arserrc.gov/mfs/pmparameters.htm. Recommended safe cooking temperatures can be accessed from the Illinois Department of Public Health at http://www.idph.state.il.us/about/fdd/safecooktemp.htm. The Bad Bug Book brings together facts regarding food, foodborne pathogenic microorganisms and natural toxins from the Centers for Disease Control and Prevention (CDC), the USDA Food Safety Inspection Service (FSIS), the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) at http://vm.cfsan.fda.gov/ mow/intro.html. The USDA Economic Research Service (ERS) foodborne illness economic cost calculator can be accessed at http://www.ers.usda.gov/data/foodborneillness. Disease facts from the CDC’s Morbidity and Mortality Weekly Report can be accessed at http://www.cdc.gov/MMWR. The responsibility for heat inactivation of foodborne pathogens in foods ultimately lies with the educated consumer. Adequate guidelines are available for the proper cooking, cooling and storage of food products to enable the avoidance of most cases of foodborne illness today.

16.8 References ABEE, T. AND WOUTERS, J.A. (1999) ‘Microbial stress response in minimal processing’. Int. J. Food Microbiol. 50, 65–91. ALLWOOD, M.C. AND RUSSELL, A.D. (1967) ‘Mechanism of thermal injury in Staphylococcus aureus’. Appl. Microbiol. 15, 1266–9. ANDERSON, W.A., HEDGES, N.D., JONES, M.V. AND COLE, M.B. (1991) ‘Thermal inactivation of Listeria monocytogenes studied by differential scanning calorimetry’. J. Gen. Microbiol. 137, 1419–24. BEAMAN, T.C. AND GERHARDT, P. (1986) ‘Heat resistance of bacterial spores correlated with protoplast dehydration, mineralization, and thermal adaptation’. Appl. Environ. Microbiol. 52, 1242–6. BELLIVEAU, B.H., BEAMAN, T.C. AND GERHARDT, P. (1990) ‘Heat resistance correlated with DNA content in Bacillus megaterium spores’. Appl. Environ. Microbiol. 56, 2919– 21. BELLIVEAU, B.H., BEAMAN, T.C., PANKRATZ, S. AND GERHARDT, P. (1992) ‘Heat killing of bacterial spores analyzed by differential scanning calorimetry’. J. Bacteriol. 174, 4463–74. BOWER, C.K. AND DAESCHEL, M.A. (1999) ‘Resistance responses of microorganisms in food environments’. Int. J. Food Microbiol. 50, 33–44.


437 BROWN, M.R. AND MELLING, W.J. (1973) ‘Release of dipicolinic acid and calcium and activation of Bacillus stearothermophilus as a function of time, temperature, and pH’. J. Pharm. Pharmacol. 25, 478. BUCHANAN, R.L., EDELSON, S.G. AND BOYD, G. (1999) ‘Effects of pH and acid resistance on the radiation resistance of enterohemorrhagic Escherichia coli’. J. Food Prot. 62, 219–28. BUNNING, V.K., CRAWFORD, R.G., TIERNEY, J.T. AND PEELER, J.T. (1990) ‘Thermotolerance of Listeria monocytogenes and Salmonella typhimurium after heat shock’. Appl. Environ Microbiol. 56, 3216–9. CASOLARI, A. (1988) ‘Microbial death’. In: Bazin M.J. and Prosser J.I., eds. Physiological Models in Microbiology, Vol. 2. Boca Raton, FL, CRC Press, Inc., pp. 1–44. CERF, O. (1977) ‘Tailing of survival curves of bacterial spores – a review. J. Appl. Bacteriol. 42, 1–19. CLERY-BARRAUD, C., GAUBERT, A., MASSON, P. AND VIDAL, P. (2004) ‘Combined effects of high hydrostatic pressure and temperature for inactivation of Bacillus anthracis spores’. Appl. Environ. Microbiol. 70, 635–7. COLE, M.B., DAVIES, KW., MUNRO, G., HOLYOAK, C.D. AND KILSBY, DC (1993) ‘A vitalistic model to describe the thermal inactivation of Listeria monocytogenes’. J. Indust. Microbiol. 12, 232–9. CONDON, S., BAYARTE, M. AND SALA, F.J. (1992) ‘Influence of the sporulation temperature upon the heat resistance of Bacillus subtilis’. J. Appl. Bacteriol. 73, 251–6. CONNER, D.E. AND HALL, G.S. (1996) ‘Temperature and food additives affect growth and survival of Escherichia coli O157:H7 in poultry meat’. Dairy Food Environ. Sanit. 16, 150–3. COOTE, P.J., COLE, M.B. AND JONES, M.V. (1991) ‘Induction of increased thermotolerance in Saccharomyces cerevisiae may be triggered by a mechanism involving intracellular pH’. J. Gen. Microbiol. 137, 1701–8. CRAIG, E.A. AND GROSS, C.A. (1991) ‘Is hsp70 the cellular thermometer?’ Trends Biochem. Sci. 16, 135–140. DUBOIS-BRISSONNET, F., MALGRANGE, C., GUERIN-MECHIN, L., HEYD, B. AND LEVEAU, J.Y. (2000) ‘Effect of temperature and physiological state on the fatty acid composition of Pseudomonas aeruginosa’. Int. J. Food Microbiol. 55, 79–81. DUWAT, P., CESSELIN, B., SOURICE, S. AND GRUSS, A. (2000) ‘Lactococcus lactis, a bacterial model for stress responses and survival’. Int. J. Food Microbiol. 55, 83–6. EARNSHAW, R.G., APPLEYARD, J. AND HURST, R.M. (1995) ‘Understanding physical inactivation processes: combined preservation opportunities using heat, ultrasound and pressure’. Int. J. Food Microbiol. 28, 197–219. FAIRHEAD, H., SETLOW, B. AND SETLOW, P. (1993) ‘Prevention of DNA damage in spores and in vitro by small, acid-soluble proteins from Bacillus species’. J. Bacteriol. 175, 1367–74. FARBER, J.M. AND BROWN, B.E. (1990) ‘Effect of prior heat shock on heat resistance of Listeria monocytogenes in meat’. Appl. Environ. Microbiol. 56, 1584–7. FARKAS, J. (1987) ‘Decontamination, including parasite control, of dried, chilled and frozen foods by irradiation’. Acta Alimentaria 16, 351. FEDIO, W.M. AND JACKSON, H. (1989) ‘Effect of tempering on the heat resistance of L. monocytogenes’. Lett. Appl. Microbiol. 9, 157–60. FLOWERS, R.S., AND ADAMS, D.M. (1976) ‘Spore membrane(s) as the site of damage within heated Clostridium perfringens spores’. J. Bacteriol. 125, 429–34. GOMBAS, D.E. (1983) ‘Bacterial spore resistance to heat’. Food Technol. 37(11), 105–10. GOMBAS, D.E. AND GOMEZ, R.F. (1978) ‘Sensitization of Clostridium perfringens spores to heat by gamma radiation’. Appl. Environ. Microbiol. 36, 403–7. GOULD, G.W. (1989) ‘Heat-induced injury and inactivation’. In: Gould, G.W., ed. Mechanisms of Action of Food Preservation Procedures. New York: Elsevier Science Publishers, Ltd, pp. 11–42.


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442

17 Pathogen resistance and adaptation to emerging technologies G. Gould, University of Leeds, UK

17.1 Introduction The majority of techniques that are employed, singly or in combinations (‘hurdle technologies’; Leistner and Gould, 2002), to preserve foods and to ensure their safety, act by slowing down or by completely inhibiting microbial multiplication (Gould, 2000). These techniques include reduction in temperature (chill and frozen storage); reduction in water activity (aw; by drying, curing, conserving); reduction in pH (by the addition of acids or by fermentation); removal of oxygen (in vacuum or modified atmosphere (MA) packs); addition of carbon dioxide (in MA packs); addition of preservatives (e.g. inorganic ones (sulphite, nitrite), organic ones (propionate, sorbate, benzoate, parabens), bacteriocins (nisin) and antimycotics (natamycin)); and control of the microstructure in water-in-oil emulsion foods. In contrast to these inhibitory procedures, few currently employed technologies act primarily by inactivating microorganisms in foods. While heating remains overwhelmingly the most utilised and effective inactivation technique, it has become clear that it would be valuable with respect to food safety to have alternative inactivation technologies; new, non-thermal, procedures that target the elimination of spoilage and food-poisoning microorganisms from the most often contaminated foods. This is particularly so because the lapses of hygiene that will always occur in the home or in the food service establishment, etc., would be of little consequence if the organisms of greatest concern were not present in the first place. Inactivation techniques are therefore generally preferable to inhibitory ones, and it is encouraging that most of the new and ‘emerging’ technologies act in this way.


443 The most efficacious of the new technologies include gamma and electron beam irradiation, application of high hydrostatic pressure and high-voltage electric field gradient pulses (‘electroporation’). High-intensity laser or non-coherent light pulses are applicable to clear liquids and to food and packaging surfaces. The efficacy of some other techniques, including combined mild heating with a small pressure rise and ultrasonication (‘manothermosonication’; Sala et al., 1995) and pulsed high magnetic fields (Hoffman, 1985), remain to be established. Although effective in inactivating food spoilage and poisoning microorganisms, some aspects of the new technologies raise safety issues that must be addressed as their use expands.

17.2 Ionising irradiation Exposure of foods to ionising radiation in order to extend shelf-life and to improve microbiological safety is hardly a new technology, although its wide application is still now only slowly emerging. It was first proposed a century ago and, at ‘pasteurisation’ doses up to 10 kGy, is now legal in more than 40 countries around the world (Patterson and Loaharanu, 2000). The 10 kGy limit derived from reports of Joint FAO/IAEA/WHO Expert Committees on the Wholesomeness of Irradiated Foods (WHO, 1977, 1981), which concluded that the ‘irradiation of any food commodity up to an overall average dose of 10 kGy presents no toxicological hazard – and introduces no special nutritional or microbiological problems’. A subsequent Joint FAO/IAEA/WHO Study Group considered High Dose Irradiation: Wholesomeness of Food Irradiated with Doses Above 10 kGy (WHO, 1999), and further concluded that ‘food irradiated to any dose appropriate to achieve the intended technological objective is both safe to consume and nutritionally adequate’. It is therefore recommended now that sterilising doses, usually within the range 30–50 kGy depending on food type and constituents, should be permitted. The wide variety of target foods include those given low doses, often below 1 kGy, to prevent sprouting (e.g. potatoes, onions) and higher ‘pasteurising’ doses to inactivate parasites, fungi and vegetative spoilage and food poisoning bacteria (e.g. fruit, poultry, other meats, fish, pulses, dried vegetables, herbs and spices; see country-by-country unconditional and conditional clearances listed by Patterson and Loaharanu, 2000). Vegetative forms of food poisoning microorganisms are satisfactorily eliminated from most foods by doses within the 3–10 kGy range, with the exception of some dried foods, such as dried vegetables, herbs and spices, in which radiation resistance is enhanced by the low aw, and for which higher doses may be necessary (Patterson, 2000). Reported D10-values of food poisoning microorganisms in moist foods depend on food type and on the conditions of irradiation, in particular the presence of oxygen and the temperature. Species of Campylobacter, Aeromonas and Yersinia are relatively sensitive, with D10-values below 0.2 kGy, whereas some salmonellae and Listeria monocytogenes are particularly resistant, with


444 Table 17.1

Radiation resistance of vegetative microorganisms in foods

Organism

Food

D10-value (kGy)

Reference

Aeromonas hydrophila Bacillus cereus

Fish in air Roast beef in air

0.14–0.19 0.17

Campylobacter jejuni Yersina enterocolitica

Pork in vacuum Pork in CO2/N2

0.19 0.18–0.19

Thayer (1995) Grant and Patterson (1992) Collins et al. (1996) Grant and Patterson (1992) Thayer and Boyd (1992) Thayer and Boyd (1992) Patterson (1988). Grant and Patterson (1992)

Escherichia coli O157:H7 Chicken in vacuum 0.27 Staphylococcus aureus

Chicken in vacuum 0.26–0.36

Salmonella Typhimurium Listeria monocytogenes

Chicken in air Pork in air

0.44–0.50 0.57–0.65

D10-values above 0.6 kGy in some foods. Typical resistances are listed in Table 17.1, and illustrate why recommended doses for elimination of vegetative pathogens, commonly ranging from 3 to 7 kGy for different foods, will deliver more than a 6 log reduction in numbers of most vegetative pathogens. This is equivalent, for comparison, to US Department of Agriculture (USDA) requirements for a 107fold increase (7D process) to inactivate salmonellae in cooked meat, and 5D for Escherichia coli O157:H7 in fermented sausage, and 6 or 7D heat processes to ensure safety with respect to Listeria monocytogenes in mildly heated, in-pack pasteurised, chill-stored foods (Mossel and Struijk, 1991). Spore forms of microorganisms are more resistant than vegetative cells so that higher doses are needed if commercial sterility is required. It is usually necessary that sterilisation by irradiation be preceded by mild heating, in order to inactivate endogenous enzymes such as proteases and lipases that would otherwise cause loss of organoleptic quality during prolonged storage. Irradiation of foods in the frozen state helps to minimise free radical-induced quality loss reactions, though slightly raises radiation resistance. Spores of proteolytic strains of Clostridium botulinum are the most radiation-tolerant of the pathogenic bacteria. Consequently, for low acid products, sterilisation processes must deliver doses sufficient to match the level of safety, with respect to C. botulinum spores, that is internationally accepted as necessary for thermally processed foods. This is that the probability of growth from a spore in a food should be reduced by a factor of at least 1012-fold (12D; Stumbo, 1973; Pflug and Gould, 2000). Depending on the food type and conditions of irradiation, the doses necessary to achieve this range from about 30 to 50 kGy. For instance, the dose requirement is lower for reduced aw, salt- and nitrite-containing cured products such as hams and bacon, and higher for foods containing no additional inhibitory components, as illustrated in the examples given in Table 17.2. Guidelines for the production of safe radiation-sterilised foods are therefore well established, though still little used, around the world. Viruses, having smaller genomes, and therefore constituting smaller nucleic


445 Table 17.2 Gamma irradiation dose delivering 1012-fold inactivation of Clostridium botulinum spores in various foods* Foodstuff

12D value (kGy)

Reference

Bacon Ham Beef Chicken Pork

26.5–28.7 31.4 41.2 42.7 43.7

Anellis (1975) Anellis et al. (1979b) Anellis et al. (1979a) Anellis (1977) Anellis (1977)

*Irradiated at –30 ºC except bacon, at 5–25 ºC.

Table 17.3 Virus

Gamma radiation resistance of some viruses that may contaminate food Food

Cocksackie

Water (0.5 °C) Water (–90 °C) Beef (0.5 °C) Beef (–30 °C) Foot and mouth Animal tissues (wet) Animal tissues (dry) Polio Oysters Fish fillets

D10-value (kGy) 1.4 5.3 7.6 6.8 4.8 6.3 4.0 3.0

Reference Sullivan (1973) Sullivan (1973) Sullivan (1973) Sullivan (1973) Massa (1966) Massa (1966) DiGorilamo et al. (1972) Heidelbaugh & Giron (1969)

acid targets for reaction with radiation-derived free radicals or for disruption by direct hits, are generally more radiation tolerant that bacteria. Reported D10 values range from about 1.4 to over 7 kGy, and are very dependent on food type and temperature of irradiation (Table 17.3). However, the risk of their survival in irradiation-sterilised foods is minimised because they are also inactivated by the pasteurisation heat treatment that is normally given to inactivate food enzymes. Most of the food poisoning parasites borne by fish, crustaceans and snails are relatively radiation-sensitive, and are controllable even by doses below 1 kGy, e.g. Clonorchis and Opistorchis (fish liver flukes); Cysticercus (beef and pork tapeworms); Taenia (beef tape worms); Toxoplasma (meat, poultry); Trichinella (pork nematode) (Wilkinson and Gould, 1996). In contrast, for Angiostrongylus (shellfish, molluscs) the minimum effective dose is ca. 2 kGy, and for Anisakis spp. (fish nematodes) is up to 10 kGy. Prions, the causative agents of scrapie in sheep, bovine spongiform encephalopathy (BSE) in cattle and variant CJD in people, contain no nucleic acids, and consequently are highly radiation tolerant and likely to survive doses employed to sterilise foods. Preformed bacterial and fungal toxins in foods are generally much more radiation-resistant than the microorganisms which formed them. For example, C. botulinum type E toxin had a D10-value of about 21 kGy (Skulberg, 1965) when irradiated in bacteriological medium. Staphylococcus aureus enterotoxin A had a D10-value as high as 200 kGy, depending on the menstruum (Modi et al., 1990). The resistance of toxins therefore represents a potential risk if badly stored or


446 highly contaminated foods or raw materials are irradiated. Guidelines for food irradiation therefore emphasise the need to utilise only high quality raw materials. Since ionising radiation can cause mutations, concern has been expressed that mutations in microorganisms in irradiated foods might lead to a safety risk, e.g. by raising their pathogenicity. However, the literature suggests that this is of no concern (Ingram and Farkas, 1977; Patterson and Loaharanu, 2000). Indeed, mutants are generally greatly disadvantaged compared with their wild types, are poor competitors and become more sensitive to environmental factors such as heat, drying and preservatives (Diehl, 1990). Despite the evident safety of the process, its effectiveness and the wealth of information supporting its practical application, food irradiation has been very slow to expand, mainly because of the suspicions of consumers. Promotion for the eradication of specific high-profile pathogens, such as E. coli O157:H7 from meat, particularly in the USA, may encourage wider use. Non-ionising radiation, high-intensity UV-rich visible, laser and non-coherent light pulse technologies have been developed for the decontamination of water and air, the surfaces of packaging materials and some foods, e.g. baked goods (Dunn et al., 1995). Important possibilities were mentioned by Dunn et al. (1997), including the effective eradication of salmonellae from the surfaces of shell eggs. Applications have also been pursued in the pharmaceutical industries (Ohlsson and Bengtsson, 2002).

17.3

High-pressure processing

In comparison with irradiation, far less work has been undertaken on the microbiological safety of the other new and emerging technologies, of which high pressure is currently the most advanced. Although a newly applied technology, the effects of high hydrostatic pressure on microorganisms in foods were first researched more than 100 years ago (Hite, 1899). The first products marketed, in Japan in the 1980s, were acidic foods, including jams, fruit juices and sauces. Now juices and smoothies (yoghurt-based fruit drinks) are sold in Europe, guacamole has been a very successful product in the USA, and non-acid products have appeared. These include vacuum-packed sliced ham in Spain and the USA, and whole meal kits containing cooked meats, vegetables and sauce components in the USA. Pressuretreated oysters have been a great success, with valuably enhanced safety resulting from the elimination of Vibrio parahaemolyticus and V. vulnificus, and with useful pressure-induced loosening of the adductor muscle, so that the normally troublesome shucking is unnecessary (He et al., 2002; Calik et al., 2002; Lopez-Caballero et al., 2000). The range of pressure-treated products is expected to continue to expand (Hendrickx and Knorr, 2001; Smelt, 1998). High pressure will inactivate all vegetative and spore forms of bacteria and fungi if the applied pressure is high enough. However, the resistance of spores is such that it remains technically difficult to use pressures sufficiently high to sterilise foods commercially, though work to this end is proceeding (Heinz and


447 Knorr, 2001). Applications so far therefore have targeted the elimination of vegetative microorganisms, with ‘pressure pasteurisation’ processes. For acid food products, safety is assured by the low pH, because any surviving pathogenic spores will be unable to outgrow. However, for the more recently introduced near-neutral pH products, the possibility of surviving spores growing during storage must be addressed. Low-temperature chill storage will prevent this occurring, but any degree of temperature abuse could introduce a hazard. With respect to safety, the pressures within the range from about 300 to about 600 MPa, which are usually applied for a few minutes at ambient temperatures, greatly reduce the numbers of key vegetative pathogens in foods. However, there are wide variations in the sensitivities of different species and strains. Pressure tolerance may increase following prior mild heating (Iwahashi et al., 1991; Smelt, 1998), and following survival of a sublethal pressure treatment (e.g. of E. coli O157: Benito et al., 1999; Lado and Yousef, 2002). Cycles of pressure treatment of E. coli, followed by growth of survivors, led to a population exhibiting increased pressure tolerance (Hauben et al., 1996), through the generation of resistant mutants (Hauben et al., 1997b). On the other hand, high pressure may transiently sensitise otherwise resistant cells to antimicrobial agents such as nisin and lysozyme (Hauben et al., 1996). There are also often large effects of food constituents on pressure tolerance, and the kinetics of inactivation are more complex than those we are used to for heat. This all makes evaluation and assurance of safety more difficult than for, e.g., heat pasteurisation, where such effects do occur, but to a much smaller extent. Table 17.4 lists some reported log reductions in CFU of vegetative pathogens that can be achieved by practicably usable pressures, and indicates that the 106-fold reductions that would normally be targeted are readily delivered. However, there is need for caution because of the variabilities mentioned above. Some of the large variations in strain sensitivities are illustrated in Table 17.5 for L. monocytogenes. Patterson et al. (1995) showed that log kills varied enormously, e.g. from as high as 3.1 to as low as just 0.2 for the same pressure treatment of different strains. Table 17.4

Pressure sensitivity of vegetative pathogens

Microorganism Vibrio parahaemolyticus Aeromonas hydrophila Yersinia entercolitica Campylobacter jejuni Salmonella Typhimurium Listeria monocytogenes Escherichia coli O157:H7 Staphylococcus aureus

Pressure (MPa)*

Log reductions

170 300 300 300 300 340 600 700 600

>6 >6 >6 6 6 6 2.5 5 2.1

*Pressure applied for 10–30 min at ambient temperature. Adapted from Patterson et al. (1995) and Smelt et al. (2001).


448 Table 17.5 Variability in pressure tolerance of strains, specific food effects, and nonlinear inactivation kinetics of vegetative pathogens Log kill after pressurisation for 5 min

15 min

30 min

L. monocytogenes: 375 MPa, 20 °C, phosphate-buffered saline Strain NCTC 11994 0.2 1.6 Scott A 0.3 4 Chicken isolate 3.1 6

3 4.5 7

E. coli O157:H7: 700 MPa, 20 °C Medium P-buffered saline Chicken meat UHT milk

7 4.5 1.4

3.8 1.4 0.9

6.2 3.2 1.2

Y. enterocolitica: 300 MPa, 200 °C, phosphate-buffered saline 4 6

6.3

Adapted from Patterson et al. (1995).

Table 17.5 also illustrates the large protective effects of some foods, such as UHT milk for E. coli O157:H7, which are not yet fully explained, though calcium ions have been shown to protect E. coli from inactivation by high pressures (Hauben et al., 1997a). Patterson et al. (1995) also showed that log-linear inactivation kinetics was seldom followed, often with long tails on survivor curves. Their data, summarised in Table 17.5, dramatically illustrate the important effect of the plateauing of the survivor curve of pressure-treated Y. enterocolitica. New pressure processing developments will have to take account of these variabilities that are associated with the process, if microbiological safety problems are to be avoided, and on a food-by-food basis until comprehensive predictive models are developed. In contrast to vegetative cells, bacterial spores are intrinsically extremely pressure tolerant, shown nearly 100 years ago to survive exposure to pressures in excess of 1200 MPa (Larson et al., 1918). However, it was shown later that, surprisingly, under certain conditions spore inactivation was more rapid and complete at lower rather than at higher pressures (Clouston and Wills, 1969; Sale et al., 1970). This peculiar phenomenon was explained when it was found that inactivation of spore in the lower pressure region occurred in two stages (Clouston and Wills, 1969; Gould and Sale, 1970). First, pressure caused spores to germinate, then pressure (or temperature if high enough) inactivated the germinated forms. Also, throughout the whole pressure range, there was a strong synergy with heat. These findings led to investigations of the combined use of pressure with raised temperature (Sale et al., 1970) and with low dose irradiation (Wills, 1974) to achieve higher levels of spore inactivation, and to the demonstration that repetitive pressurisation was more effective than continuous application (Furukawa et al., 2003; Hayakawa et al., 1994a,b). The strong pressure–heat synergism has been confirmed for a wide range of species, although the effectiveness of the combination


449 Table 17.6

General pattern of high-pressure effects on microorganisms

Low pressure

Medium pressure

High pressure

Little effect on vegetative cells

Lethal for vegetative cells, with great variability

Rapidly lethal for vegetative cells

Synergistic with natural spore germinants, with great strain–strain variability and ‘poor’ kinetics

Causes spore germination directly, with great strain– strain differences in sensitivity and kinetics

Directly kills spores, without intervening germination At high temperatures, heat is the major cause of spore death

varies greatly for the different types of spores (Murrell and Wills, 1977; Kimagasa et al., 1992; Kowalski et al., 1992; Seyerderholm and Knorr, 1992; Hayakawa et al., 1994a). The general pattern of responses of spores to pressure is summarised, and contrasted with the responses of vegetative cells, in Table 17.6. At low pressures (even well below 100 MPa for the most sensitive types of spore) pressure acts by amplifying the effects of normal spore germinants. At medium pressures (300–600 MPa or so) pressure directly causes germination without the need for synergism with germinants. However, attempts to employ pressure germination, plus mild heating to inactivate the germinated forms so as to sterilise foods, have proved to be disappointing. Failure has resulted from the wide variation of pressure sensitivity of different spores, but more so from the kinetics of pressure-induced germination which, like those of normal nutrient-induced germination (Gould, 1970), are non-log-linear, showing extensive tailing so that the large levels of inactivation necessary for safe sterilisation processes can not be achieved (Heinz and Knorr, 1998). At still higher pressures, however, spores are inactivated directly, without the need for prior germination, This difference of mechanism was demonstrated very clearly by analysis of the chemical and morphological changes occurring in high pressure-inactivated spores (Heinz and Knorr, 1998, 2001), which differed from those typical of normal germination. Paidhungat et al. (2002) used well-characterised germination-defective mutants of Bacillus subtilis to illustrate clearly the gemination pathway-dependence of pressure-induced germination in the lowpressure region, and the bypassing of this pathway when higher pressures were applied. At these higher, directly inactivating, pressures, there is a very pronounced synergism with heat, so that high pressure–temperature combination treatments have become attractive targets for food sterilisation. Possibly attractive processes would employ high pressures essentially to reduce the heat resistance of spores, so that sterilisation could be achieved at lower temperatures than in conventional thermal processing, with consequently less damage to product quality (Rovere et al., 1998), or to deal with particularly troublesome types of spore contamination. For example, Lee et al. (2002) reported useful inactivation of spores of Alicyclobacillus acidoterrestris in apple juice by moderate pressure–temperature combinations. A further potential advantage is that the adiabatic heating and cooling that accompany rises and falls in pressure


450 Table 17.7

Inactivation of spores of pathogens by pressure–temperature combinations

Organism

Pressure/temperature/time

Log Reference inactivation

Bacillus cereus

690 MPa/60 °C/1 min 700 MPa/70 °C/5 min Clostridium botulinum E 827 MPa/40 °C/10 min 227 MPa/50 °C/5 min Clostridium botulinum A 800 Mpa/88 °C/9 min Clostridium botulinum A 827 Mpa/75 °C/20 min

8 5 5 5 3 3

Raso et al. (1998) Rovere et al. (1998) Reddy et al. (1999) Reddy et al. (1999) Rovere et al. (1998) Reddy et al. (2003)

may be used to improve heating profiles, avoiding long ‘come up’ and ‘cool down’ stages, thus minimising quality-damaging ‘over-processing’. The extent of the synergism of pressure with temperature for spore inactivation reduces as the temperature is raised (Rovere et al., 1999), so that the potential benefits are less at conventional thermal processing temperatures at which, for example, process times could otherwise be reduced, than at lower temperatures. But at these lower temperatures, the use of high pressure may deliver substantial benefits over conventional processing, especially for foods that are particularly heat-sensitive and of sufficiently high value to bear the high investment costs of pressure processing systems (Heinz and Knorr, 2001). With respect to safety, it will be necessary that any combined process matches the safety delivered by conventional thermal processing, i.e. a minimum 12D process for destruction of spores of proteolytic strains of C. botulinum in low acid, high aw foods, and also that spores of other food poisoning microorganisms, such as B. cereus and C. perfringens, are satisfactorily controlled. The reported effect of some combination processes on spores of B. cereus and C. botulinum listed in Table 17.7 illustrate that temperatures well above 90 °C, in combination with relatively high pressures, will be needed in order to achieve a sufficient spore kill and degree of safety assurance. Pressure-tolerance studies of viruses so far reported show some wide variabilities. Some viruses seem to be very pressure-sensitive. For example, a pressure of 450 MPa for 5 min at an initial temperature of 21 °C reduced plaque-forming units of hepatitis A virus by a factor of at least 107, and 275 MPa was similarly effective against feline calcivirus, a Norwalk virus substitute (Kingsley et al., 2002). A pressure of 300 MPa for 2 min at 25 °C reduced rotavirus levels by about 108-fold (Khadre and Yousef, 2002). In contrast, poliovirus was hardly affected by treatment at 600 MPa for 5 min (Kingsley et al., 2002).

17.4

High-voltage pulsed electric fields

While the use of electric fields to heat foods is well established, e.g. by electrical resistance or ‘ohmic’ heating (Fryer, 1995) and by microwave heating (Mullin, 1995), the use of high-voltage electric pulses to bring about essentially nonthermal inactivation of microorganisms has been explored more recently (Castro et


451 al., 1993; Barbosa-Canovas et al., 1999a). At lower, non-lethal voltage gradients, the technique is also well established as the basis for ‘electroporation’, by which genetic material can be exchanged between protoplasted cells of microorganisms, plants and animals (Neumann et al., 1989). A method was patented for inactivating microorganisms with an electric field by Doevenspeck (1960). Later studies demonstrated the inactivation of bacteria and yeasts, and the lysis of protoplasts and erythrocytes (Sale and Hamilton, 1967, 1968). Even later studies concentrated on evaluating the effects of electrical variables (Hulsheger et al., 1981, 1983), and on the effects of environmental variables (Mizuno and Hori, 1988; Jayaram et al., 1992). Commercially practicable machines have been developed and patented (Dunn et al., 1988). The lethal effects result from the permeabilisation of the cell membrane that occurs when the voltage gradient is sufficiently high to overcome its intrinsic resistance (Hamilton and Sale, 1967). Breakdown occurs when the potential difference across the membrane exceeds about one volt (Chernomordik, 1987; Glaser et al., 1988). Massive leakage of cell contents then occurs, leading to death (Tsong, 1991). Electric fields may be delivered as oscillating, bipolar, exponentially decaying or square wave pulses. Bipolar pulses are more lethal than monopolar ones because, it is presumed, rapid reversal in the direction of movement of charged molecules causes greater damage to cell membranes. Bipolar pulses generate less electrolysis in the material being treated, which may be advantageous for organoleptic reasons, and possibly also for toxicological and nutritional reasons, and they are energy efficient (Qin et al., 1994). It is generally most economic to raise the field strength as high as possible while reducing the duration of the pulses, without reducing pulse energy (Grahl et al., 1992). On the other hand, the use of very high field strengths demands more complex engineering and equipment (Zhang et al., 1994). As a result of these competing requirements, modern pulse field devices employ field strengths from about 20 up to about 70 kV/cm, with pulse durations between approximately 1 and 5 µs. Repetition rates are typically between 1 and up to about 30 s at the higher voltages in order to minimise rises in temperature. Treatment chambers may operate batchwise or continuously. The earliest versions were not fully enclosed and so were limited to voltage gradients of about 25 kV/cm because this is the approximate breakdown voltage of air (Sale and Hamilton, 1967; Dunn and Pearlman, 1987). Enclosure and design improvements led to devices that could deliver over 40 kV/cm (Grahl et al., 1992, Zhang et al., 1994). These devices were useful for laboratory studies to optimise design variables for efficient killing of microorganisms. Continuous operation is essential for cost-effective commercial applications, able to treat liquids and liquids containing particulates, and a number of such systems, mostly designed around coaxial cylindrical electrodes, have been developed (Boulart, 1983; Hoffman and Evans, 1986; Dunn and Pearlman, 1987; Sato and Kawata, 1991; Bushnell et al., 1993; Qin et al., 1994; Sitzmann, 1995; Barbosa-Canovas et al., 1999a,b). Pulsed field inactivation has been reported for a wide range of food poisoning and spoilage microorganisms, including E. coli, Salmonella Typhimurium,


452 Salmonella Dublin, Streptococcus thermophilus, Lactobacillus brevis, Pseudomonas fragi, Klebsiella pneumoniae, Staph. aureus, L. monocytogenes, Saccharomyces cerevisiae and Candida albicans (Barbosa-Canovas et al., 1999a,b). Bacterial spores are far more tolerant than vegetative cells, perhaps because of the thick coats and cortex layers that surround the central protoplast, and because of the relatively dehydrated state and probable low electrical conductivity of the protoplast itself. However, at high voltage gradients, spore inactivation has been observed. Marquez et al., (1997) reviewed previous positive reports, and also reported their own new findings, in which log inactivations of about 3.5 and 5 were obtained for spores of B. subtilis and B. cereus, respectively, with 30 or 50 pulses of 50 kV/cm. Inactivated spores showed visible holes in their coats, or substantial swelling, in the electron microscope. While, therefore, some inactivation of spores has been observed, they, and also yeast ascospores (Mertens and Knorr, 1992), are far more resistant than vegetative cells (Sale and Hamilton, 1967; BarbosaCanovas, 1999a), and so are not at present realistic targets for pulsed field inactivation in foods such as to achieve sterility. With respect to safety, there are a number of food-related intrinsic and extrinsic factors that influence the efficacy of the electric treatments. Inactivation increases greatly with rise in temperature, e.g. for E. coli (Qin et al., 1994) and for L. brevis (Jayaram et al., 1992). Importantly, low ionic strength greatly favours inactivation (Table 17.8). Reduction in pH increased inactivation, e.g. it was doubled for E. coli by reducing the pH of skimmed milk from 6.8 to 5.7. Log phase cells were more sensitive than stationary phase ones. Application of electric pulse treatments to a number of liquid foods has indicated that useful ‘cold pasteurisation’ inactivation of vegetative bacteria and yeasts can be achieved (Barbosa-Canovas et al., 1999a). For example, treatment of apple juice at temperatures below 30 °C with fewer than ten pulses in a continuous treatment chamber brought about a more than 106-fold reduction in numbers of Sacch. cerevisiae at a voltage gradient of 35 kV/cm; 22 kV/cm caused about a 102-fold inactivation (Qin et al., 1994). Inactivation of E. coli is illustrated in Table 17.8. Bendicho et al. (2002) summarised attempts to treat milk and milk ultrafiltrate,

Table 17.8 Inactivation of Escherichia coli by pulsed electric fields: medium effects and non-log-linear kinetics Treatment: 55 kV/cm; 20 pulses Skim milk + KCl at Survivors Treatment: 35 kV/cm; 2 µs pulses No. of pulses Survivors (%) Expected if log-linear

2 20 20

Data adapted from: Qin et al. (1994) and Zhang et al. (1995).


0.03 M 0.02

0.17 M 0.3 5 2 0.3

8 0.8 0.007

453 reporting more than 6 and 9 log inactivation of E. coli with 50 pulses of 60 kV/cm and 80 pulses of 70 kV/cm, respectively. It is important regarding safety that studies of inactivation rates under different conditions have generally indicated kinetics that, on the basis of log survivor vs treatment time, or vs number of pulses, show long tails (Table 17.8). Approximately straight lines are seen for log survivors vs log treatment time, or vs log number of pulses (Zhang et al., 1995). Consequently, very large log reductions may be difficult to obtain under practical and economically viable conditions. Some potentially useful synergies have been described. For example, electroporated cells of E. coli, L. monocytogenes and Salmonella Typhimurium became much more sensitive than untreated cells to the bacteriocins nisin and pediocin (Kalchayanand et al., 1994).

17.5

Conclusions

The use of physical techniques to inactivate microorganisms without the application of heat, or with the use of less heat than would otherwise be necessary, is attractive from the point of view of product quality, and the most promising new and emerging techniques reviewed aim to do this. Three facts limit their potential at present. First, bacterial spores remain most tolerant to the techniques so that, with the exception of irradiation, sterilisation on a commercial basis, as opposed to pasteurisation, is not yet possible. Second, food components influence efficacy of pressure and electroporation far more than we are accustomed to, e.g. with heat and, to some extent, with irradiation. Third, the kinetics of inactivation are different from those resulting from heating, so that a careful new approach to product safety will be needed if the application of the techniques continues to expand. Most would agree that, at the very least, ‘equivalence’ with established safe technologies must be assured, particularly as the successfully expanding use of high pressure is applied to foods with higher pHs and awvalues, which are less intrinsically safe (or ‘fail safe’) than the low pH foods first commercialised. Ensurance of equivalence is not straightforward. For example, the main pathogens targeted by heat pasteurisation of milk are Mycobacterium and Brucella species and Coxiella burnetii; for liquid egg, salmonellae; for sous vide and similarly processed foods, the spores of non-proteolytic strains of C. botulinum. The new pasteurisation processes should deliver at least equivalent safety factors against these organisms. In addition, however, it must be accepted that other vegetative pathogens may show a higher degree of resistance to the alternative methods of processing than do the organisms mentioned above; for example, L. monocytogenes and verotoxin-producing strains of E. coli may need particular consideration. It would be necessary to determine the relevant pathogen that showed the highest resistance to the proposed type of treatment, and to specify the reduction in viable numbers that should be achieved, e.g. 104, 105, 106 for a particular category of food. For commercial sterilisation of low acid–high aw foods, the same safety factor that is used in heat processing, i.e. at least a 1012-fold


454 reduction of viable spores of C. botulinum, would be needed. However, it should be considered that spores of some strains of other pathogenic spore-formers, e.g. C. perfringens, B. cereus, B. subtilis and B. licheniformis, may show greater resistance than C. botulinum to a new process, and therefore require a more severe treatment. Stewart et al. (2002) recently emphasised, with respect to the new technologies, that ‘Obstacles to commercialisation include the lack of systematic inactivation kinetic data, the interpretation of non-linear death kinetics and the need to establish equivalent control measures for non-thermal treatments in comparison with traditional heat processes’. They recommended following the guidelines given by the International Commission on Microbiological Specifications for Foods (ICMSF, 2001a,b). Maintenance of a good safety ‘track record’ for new technologies will be very important if they are to continue to expand. A major failure could set back development by many years.

17.6


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17.7 References ANELLIS A (1975), ‘Low temperature irradiation of beef and methods for evaluation of a radappertized process’, Appl Microbiol 30, 811–20. ANELLIS A (1977), ‘Cryogenic gamma irradiation of prototype pork and chicken and antagonistic effect between Clostridium botulinum types A and B’, Appl Environ Microbiol 34, 631–47. ANELLIS A, ROWLEY D B AND ROSS E W (1979a), ‘Microbiological safety of radappertized beef’, J Food Protect 42, 927–32. ANELLIS A, SHATTUCK E, LATT T, SONGSPASERTCHAI S, ROWLEY D B AND ROSS EW JR (1979b), ‘Gamma irradiation at –30+/–10° of low level nitrite/nitrate ham’, in Barker A N, Wolf L J, Ellar D J, Dring G J and Gould G W, Spore Research 1997, London, Academic, 631–48. BARBOSA-CANOVAS G V, POTHAKAMURY U R, GONGORA-NIETO M M AND SWANSON B G (1999a), Preservation of food with pulsed electric fields, New York, Academic.


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457 KIMAGASA H, TAKAO K, FUKUMOTO K AND ISHIHARA M (1992), ‘Changes in tea components during processing and preservation of tea extracts by hydrostatic pressure sterilisation’, Nippon nog kaichi 66, 707–712. KINGSLEY D H, HOOVER D G, PAPAFRAGKOU E AND RICHARDS G P (2002), ‘Inactivation of hepatitis A virus and calicivirus by high hydrostatic pressure’, J Food Prot 65, 1605– 9. KOWALSKI E, LUDWIG H AND TAUCHE B (1992), ‘Hydrostatic pressure to sterilise foods 1. application to pepper (Piper nigrum L)’, Deutsche lebens runds 88, 74–5. LADO B H AND YOUSEF A E (2002), Alternative food-preservation technologies: efficacy and mechanisms’, Mic Infect 4, 433–40. LARSON W P, HARTZEL T B AND DIEHL H S (1918), ‘The effect of high pressure on bacteria’, J Infect Dis 22, 271–9. LEE S-Y, DOUGHERTY R H AND KANG D-H (2002), ‘Inhibitory effects of high pressure and heat on Alicyclobacillus acidoterrestris spores in apple juice’, Appl Environ Microbiol 68, 4158–4161. LEISTNER L AND GOULD G W (2002), Hurdle technologies: combination treatments for food stability, safety and quality. New York, Kluwer/Plenum. LOPEZ-CABALLERO M E, PEREZ-MATEOS M, MONTERO P AND BORDERIAS A J (2000), ‘Oyster preservation by high-pressure treatment,’ J Food Prot 63, 196–201. MARQUEZ V O, MITTAL G S AND GRIFFETHS M W (1997), ‘Destruction and inhibition of bacterial spores by high voltage pulsed electric field’, J Food Sci 62, 399–401/9. MASSA D (1966), ‘Radiation inactivation of foot and mouth disease virus in the blood, lymphatic glands and bone marrow of the carcasses of infective animals’, in Food irradiation, Vienna, IAEA (STI/PUB/127), 329–46. MERTENS B AND KNORR D (1992), ‘Development of nonthermal processes for food preservation’, Food Technol 46, 124–33. MIZUNO A AND HORI Y (1988), ‘Destruction of living cells by pulsed high voltage applications, IEEE Trans Indust Appl 24, 387–95. MODI N K, ROSE S A AND TRANTER H S (1990), ‘The effect of irradiation and temperature on the immunological activity of staphylococcal enterotoxin A’, Int J Food Microbiol 11, 85–92. MOSSEL D A A AND STRUIJK C B (1991), ‘Public health implications of refrigerated pasteurised (“sous-vide”) foods’, Int J Food Microbiol 13, 187–206. MULLIN J (1995), ‘Microwave processing’, in Gould G W, New methods of food preservation, Glasgow, Blackie, 112–34. MURRELL W G AND WILLS P A (1977), ‘Initiation of Bacillus spore germination by hydrostatic pressure: effect of temperature’, J Bact 129 1272–80. NEUMANN E, SOWERS A E AND JORDAN C A eds (1989), Electroporation and electrofusion in cell biology, New York, Plenum. OHLSSON T AND BENGTSSON N (2002), ‘Minimal processing of foods with non-thermal methods’, in Ohlsson T and Bentsson N, Minimal processing technologies in the food industry, Cambridge, Woodhead, 34–60. PAIDHUNGAT M, SETLOW B, DANIELS W B, HOOVER D, PAPAFRAGKOU E AND SETLOW P (2002), ‘Mechanisms of induction of germination of Bacillus subtilis spores by high pressure’, Appl Environ Microbiol 68, 3172–5. PATTERSON M F (1998), ‘Sensitivity of bacteria to irradiation on poultry meat under various atmospheres’, Lett Appl Microbiol 7, 55–8. PATTERSON M F (2000), ‘High pressure processing of foods’, in Robertson R K, Batt C A and Patel P D, Encyclopedia of food microbiology, London, Academic, 1059–65. PATTERSON M F AND LOAHARANU P (2000), ‘Irradiation’, in Lund B M, Baird-Parker A C and Gould G W, The microbiological safety and quality of food, Gaithersburg, Aspen, 65–100. PATTERSON M F, QUINN M, SIMPSON R, DOYLE M P, BEUCHAT L R, MONTVILLE T J AND GILMOUR A (1995), The sensitivity of vegetative pathogens to high hydrostatic pressure treatment in phosphate buffered saline and foods’, J Food Prot 58, 524–9.


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460

18 Pathogen resistance and adaptation to natural antimicrobials P. Davidson, T. Taylor and L. Santiago, University of Tennessee, USA

18.1

Introduction

Naturally occurring food antimicrobials include compounds from microbial, plant and animal sources. They are, for the most part, proposed only for use in foods. A few, such as nisin, natamycin, lactoferrin and lysozyme, are approved by regulatory agencies in some countries for application to foods (Davidson, 2001). The function of food antimicrobials is to prolong shelf-life and preserve quality through inhibition of spoilage microorganisms and as interventions for inhibition or inactivation of pathogenic microorganisms. Generally, food antimicrobials are primary or adjunct contributors to a combination of inhibitors and inhibitory conditions (e.g. low pH, low temperature) sometimes termed ‘hurdle technology’ (Leistner and Gorris 1995; Leistner, 2000) or multiple interventions.

18.2

Types of natural antimicrobials by source

18.2.1 Animal sources Chitosan, (1-4)-2-amino-2-deoxy-beta-D-glucan, is a natural constituent of fungal cell walls and is produced commercially from chitin, a by-product of shellfish processing. Chitosan is actually a series of polymers with different ratios of glucosamine and N-acetyl glucosamine. Chitosan inhibits growth of foodborne molds, yeasts and bacteria including Aspergillus flavus, Byssochlamys, Botrytis


461 cinerea, Mucor racemosus, Rhizopus stolonifer, Saccharomyces cerevisiae, and Zygosaccharomyces bailii and Escherichia coli, Lactobacillus fructivorans, Listeria monocytogenes, Salmonella, Staphylococcus aureus and Yersinia enterocolitica (Papineau et al., 1991; Sudarshan et al., 1992; Wang, 1992; Roller and Covill, 2000). Reported minimum inhibitory concentrations for both bacteria and yeasts vary widely depending on polymer characteristics and pH, temperature and presence of interfering substances such as proteins and fats (Sudarshan et al., 1992; Chen et al., 1998; Roller and Covill, 1999; Tsai and Su, 1999; Rhoades and Roller, 2000; Tsai et al., 2000). Chitosan may directly affect the microbial cell by interaction with the anionic cell wall polysaccharides or components of the cytoplasmic membrane resulting in altered permeability or prevention of transport (Fang et al., 1994; Tsai and Su, 1999). In milk and colostrum, the primary iron-binding protein is lactoferrin. Alone, lactoferrin is inhibitory to a number of microorganisms including Bacillus subtilis, B. stearothermophilus, L. monocytogenes, Micrococcus species, E. coli and Klebsiella species (Oram and Reiter, 1968; Korhonen, 1978; Reiter, 1978; Mandel and Ellison, 1985; Payne et al., 1990). According to Payne et al. (1994), the compound has no activity against Salmonella enterica serovar Typhimurium or Pseudomonas fluorescens. Since it is cationic, lactoferrin may increase the outer membrane permeability to hydrophobic compounds, including other antimicrobials. Naidu and Bidlack (1998) reported that lactoferrin blocks adhesion of microorganisms to mucosal surfaces, inhibits expression of fimbria and other colonizing factors of enteric pathogens, such as E. coli, and interferes with lipopolysaccharides of Gram-negative bacteria. A modification of pure lactoferrin called ‘activated lactoferrin’ (ALF) was developed by Naidu et al. (2003). They reported that ALF was effective in controlling the adhesion of E. coli O157:H7 to beef tissue, and Salmonella Typhimurium or Campylobacter jejuni to poultry broiler skin. In the USA, lactoferrin has been approved by regulatory agencies when used as an antimicrobial spray on beef carcasses, subprimals and finished cuts (Taylor et al., 2004). Hydrolyzed lactoferrin (HLF) is produced by acid-pepsin hydrolysis of bovine lactoferrin (Bellamy et al., 1992). Jones et al. (1994) reported that the compound was inhibitory to Shigella, Salmonella, Y. enterocolitica, E. coli O157:H7, Staph. aureus, L. monocytogenes and Candida. In contrast, while HLF was effective against L. monocytogenes, E. coli and Salmonella in peptone yeast extract glucose broth, it had little or no activity in more complex media, including trypticase soy broth (TSB) and UHT milk (Facon and Skura, 1996; Branen and Davidson, 2000; Murdock and Matthews, 2002). The addition of ethylenediaminetetraacetic acid (EDTA) enhanced the activity of HLF in TSB, indicating that decreased activity of HLF may be due, in part, to excess cations in the medium (Branen and Davidson, 2000). Venkitanarayanan et al. (1999) found that, while lactoferricin B reduced viable E. coli O157:H7 in 1 % peptone, it was much less effective as an antimicrobial in ground beef. Lactoperoxidase is an enzyme that occurs in raw milk, colostrum, saliva and other biological secretions. This enzyme reacts with thiocyanate (SCN–) in the presence of hydrogen peroxide and forms antimicrobial compounds. This is


462 termed the lactoperoxidase system. This system is generally more effective against Gram-negative bacteria than Gram-positive bacteria (Björck, 1978). However, the lactoperoxidase system does inhibit both Gram-positive and Gram-negative foodborne pathogens and spoilage microorganisms including C. jejuni, L. monocytogenes, Pseudomonas aeruginosa, Salmonella, Staph. aureus, and Yersinia enterocolitica in microbiological media and in foods (Beumer et al., 1985; Siragusa and Johnson, 1989; Kamau et al., 1990; Kennedy et al., 2000; Elliot et al., 2004). Lysozyme is an enzyme that catalyzes hydrolysis of the β-1,4 glycosidic bonds between N-acetylmuramic acid and N-acetylglucosamine of the peptidoglycan in bacterial cell walls. It is present in avian eggs, mammalian milk, tears and other secretions, insects and fish. In hypotonic solutions, the enzyme causes lysis of bacterial cells (Masschalck and Michiels, 2003; Cagri et al., 2004). The enzyme is most active against Gram-positive bacteria because the peptidoglycan of the cell wall is more exposed. It inhibits the foodborne bacteria Bacillus stearothermophilus, Clostridium botulinum, C. thermosaccharolyticum (Thermoanaerobacterium thermosaccharolyticum), C. tyrobutyricum, L. monocytogenes and Staph. aureus (Hughey and Johnson, 1987; Hughey et al., 1989). Lysozyme is less effective against Gram-negative bacteria owing to reduced peptidoglycan content (5–10 %) and the presence of the outer membrane of lipopolysaccharide (LPS) and lipoprotein (Wilkins and Board, 1989). Gram-negative cell susceptibility can be increased by combination with chelators (e.g. EDTA) that bind Ca2+ or Mg2+ which are essential for maintaining integrity of the LPS layer. Another method for increasing activity of the compound is to modify the structural function of lysozyme. Touch et al. (2003) reduced the disulfide bonds in lysozyme and treated with cysteine and glutathione. This resulted in a more flexible lysozyme molecule with an increased surface hydrophobicity. Reduced lysozyme displays increased antimicrobial activity because it is better able to bind to lipopolysaccharides and permeabilize the outer membrane (Touch et al., 2003).

18.2.2 Plant sources Onion (Allium cepa) and garlic (Allium sativum) have been shown to inhibit growth and toxin production of many microorganisms including B. cereus, C. botulinum type A, E. coli, Lactobacillus plantarum, Salmonella, Shigella and Staph. aureus, and the fungi Aspergillus flavus, A. parasiticus, Candida albicans, and species of Cryptococcus, Rhodotorula, Saccharomyces, Torulopsis and Trichosporon (Saleem and Al-Delaimy, 1982; Conner and Beuchat, 1984; Beuchat, 1994; González-Fandos et al., 1994). Cavallito and Bailey (1944) identified the antimicrobial component of garlic as allicin (diallyl thiosulfinate; thio-2-propene-1sulfinic acid-5-allyl ester). Allicin is formed by the action of the enzyme, allinase, on the substrate alliin [S-(2-propenyl)-L-cysteine sulfoxide]. The reaction only occurs when cells of the garlic are disrupted, releasing the enzyme to act on the substrate. A similar reaction occurs in onion except the substrate is [S-(1-propenyl)-L-cysteine sulfoxide] and one of the major products is thiopropanal-S-oxide.


463 The products responsible for antimicrobial activity are also apparently responsible for flavor of onions and garlic. The mechanism of action of allicin is probably the inhibition of sulfhydryl-containing enzymes (Beuchat, 1994). Hydroxycinnamic acids include caffeic, p-coumaric, ferulic and sinapic acids. These phenolic compounds are found in plants and plant foods. They frequently occur as esters and less often as glucosides (Ho, 1992). Ferulic and p-coumaric acids have been shown to be inhibitory to B. cereus, Lactobacillus collinoides, L. brevis and Staph. aureus and the yeast Saccharomyces cerevisiae (Baranowski et al., 1980; Herald and Davidson, 1983; Stead, 1993). These two compounds were much less effective against the Gram-negative bacteria P. fluorescens and E. coli (Herald and Davidson, 1983). In contrast, alkyl esters of hydroxycinnamic acids were effective inhibitors of the growth of P. fluorescens (Baranowski and Nagel, 1983). Chipley and Uraih (1980) found that ferulic acid inhibited aflatoxin B1 and G1 production by Aspergillus flavus and A. parasiticus by up to 75 %. Isothiocyanates (R–N=C=S) are derivatives from glucosinolates in cells of plants of the Cruciferae or mustard family (cabbage, kohlrabi, Brussels sprouts, cauliflower, broccoli, kale, horseradish, mustard, turnips, rutabaga). These compounds are formed from the action of the enzyme myrosinase on the glucosinolates when the plant tissue is injured or mechanically disrupted. In addition to the allyl side group (allyl isothiocyanate, AIT), other isothiocyanate side groups include ethyl, methyl, benzyl and phenyl. Isothiocyanates are inhibitory at low concentrations to fungi, yeasts and bacteria in the vapor phase and in liquid media (Isshiki et al., 1992; Mari et al., 1993). Inhibition against bacteria varies but generally Grampositive bacteria are less sensitive to AIT than Gram-negative bacteria (Delaquis and Mazza, 1995; Delaquis and Sholberg, 1997). Ward et al. (1998) applied horseradish essential oil distillate (ca. 90 % AIT) to the headspace of cooked roast beef inoculated with E. coli O157:H7, Lactobacillus sake, L. monocytogenes, Salmonella Typhimurium, Staph. aureus and Serratia grimeseii. AIT at 20 µl/l of air inhibited the pathogens and spoilage microorganisms on the beef. Delaquis et al. (1999) showed that 20 µl horseradish essential oil per liter of air inhibited Pseudomonas and Enterobacteriaceae on pre-cooked roast beef slices stored for 28 days at 4 °C. The mechanism by which isothiocyanates inhibit cells may be due to inhibition of enzymes by direct reaction with disulfide bonds or through thiocyanate (SCN–) anion reaction to inactivate sulfhydryl enzymes (Delaquis and Mazza, 1995). Spices and their essential oils have varying degrees of antimicrobial activity. Among the spices, cloves, cinnamon, oregano, thyme, sage, rosemary, basil and vanillin have the strongest antimicrobial activity. The major antimicrobial components of clove (Syzygium aromaticum) and cinnamon (Cinnamomum zeylanicum) essential oils are eugenol (2-methoxy-4-(2-propenyl)-phenol)) and cinnamic aldehyde (3-phenyl-2-propenal), respectively. Cinnamon and clove essential oils or their components are inhibitory against C. jejuni, E. coli, Salmonella Enteritidis, L. monocytogenes, Staph. aureus, Aspergillus and Penicillium (Azzouz and Bullerman, 1982; Smith-Palmer et al., 1998; Vazquez et al., 2001; Weissinger et al., 2001; López-Malo et al., 2002). The antimicrobial activity of oregano


464 (Origanum vulgare) and thyme (Thymus vulgaris) has been attributed to the terpenes carvacrol and thymol, respectively. Both compounds have inhibitory activity against Bacillus subtilis, B. cereus, C. jejuni, E. coli, L. monocytogenes, Lactobacillus plantarum, Micrococcus sp., Pediococcus cerevisiae, Pseudomonas aeruginosa, Proteus species, Salmonella Enteritidis, Staph. aureus, Vibrio parahaemolyticus, and A. parasiticus (Moleyar and Narasimham, 1992; Firouzi et al., 1998; Smith-Palmer et al., 1998; Davidson and Naidu, 2000; Ultee and Smid, 2001; Valero and Salmeron, 2002). Ultee et al. (2000) and Ultee and Smid (2001) determined that carvacrol in combination with cymene (methyl-isopropylbenzene) and soy sauce inhibited B. cereus growth in rice, and carvacrol alone inhibited toxin production by the microorganism in soup. Ultee et al. (1999, 2002) determined that carvacrol depletes intracellular ATP, reduces the pH gradient across the cytoplasmic membrane and collapses the proton motive force (PMF) of B. cereus, leading to eventual cell death. Rosemary (Rosmarinus officinalis) contains primarily borneol (endo-1,7,7trimethylbicyclo[2.2.1] heptan-2-ol) along with pinene, camphene and camphor, while sage (Salvia officinalis) contains thujone ((4-methyl-1-(1methylethyl)bicyclo[3.1.0]-hexan-3-one)). At 2 % in growth medium, sage and rosemary were more active against Gram-positive than Gram-negative bacterial strains (Shelef et al., 1980). Smith-Palmer et al. (1998) demonstrated that rosemary (0.02–0.05 %) and sage (0.02–0.075 %) were inhibitory to L. monocytogenes and Staph. aureus but not to Gram-negative bacteria. Sweet basil (Ocimum basilicum) essential oil has limited antimicrobial activity, with linalool and methyl chavicol the primary antimicrobial agents. Basil essential oil is active against certain fungi but has little activity against most bacteria (Lachowicz et al., 1998; Wan et al., 1998). Vanillin (4-hydroxy-3-methoxybenzaldehyde) is a major constituent of vanilla beans, the fruit of an orchid (Vanilla planifola, Vanilla pompona or Vanilla tahitensis). Vanillin is most active against molds and non-lactic Grampositive bacteria (Jay and Rivers, 1984; López-Malo et al., 1995; Cerrutti and Alzamora, 1996). Delaquis et al. (2002) demonstrated that oil of cilantro (leaves of Coriandrum sativum L.) were effective in inhibiting the growth of L. monocytogenes. The inhibitory activity was attributed to the presence of alcohols and aldehydes (C6–C10).

18.2.3 Microbial sources Natamycin (C33H47NO13; 665.7 Da) or pimaricin is a polyene macrolide antibiotic that is an antifungal agent. It was first isolated from Streptomyces natalensis, a microorganism found in soil from Natal, South Africa (Anon., 1991). Natamycin is active against nearly all molds and yeasts, but has little or no effect on bacteria or viruses. Bacteriocins are polypeptides of varying length produced by microorganisms for the purpose of protecting the producing organism against microbial competitors and pathogens. Bacteriocin taxonomy has been reviewed by Klaenhammer (1988), Pitt and Gaston (1995), Ennahar et al. (2000), Riley and Wertz (2002a,b) and Riley et al. (2003). Though other classes exist, the colicins, synthesized by


465 E. coli, are, to date, the most widely studied of the bacteriocin classes produced by Gram-negative bacteria. Colicins are proteins that bind specific target cell receptor molecules (Riley and Wertz, 2002a). They are produced in the cells and then released into the environment following cell membrane lysis. Colicins are proteins and hence they differ from bacteriocins produced by most Gram-positive bacteria in size. Colicins can range from 449 to 629 amino acid residues, while most Grampositive bacteriocins are small polypeptides of 30–50 amino acids (Riley and Wertz, 2002b). Bacteriocins produced by Gram-positive bacteria have also been widely studied. In addition to size, they differ from the colicins in that their release is not necessarily a cell-lysis mediated event. Gram-positive bacteria have developed various transport systems to export the peptide out of the cell once synthesized (Tagg et al., 1976; Gouaux, 1997). The Gram-positive lactic acid bacteria (LAB) produce a wide array of bacteriocins (Klaenhammer, 1988; 1993). One of those, nisin, belongs to the class I-a group of lantibiotics, larger in size then their I-b counterparts and lytic through membrane permeabilization (Delves-Broughton, 1990; Delves-Broughton et al., 1996). Class II bacteriocins function to permeabilize and porulate target membranes but are larger then the lantibiotics and share some conserved amino acid residue sequences (Klaenhammer, 1993; Riley and Wertz, 2002a). Class III bacteriocins are larger polypeptides and heat-labile, and class IV peptides require binding to specific carbohydrate or lipid moieties for activity (Barefoot and Klaenhammer, 1984; Bruno and Montville, 1993). Nisin is a 34 amino acid peptide produced by a strain of the dairy starter culture, Lactococcus lactis ssp. lactis. Nisin has a narrow spectrum inhibiting only Grampositive bacteria, including Alicyclobacillus, Bacillus cereus, Brochothrix thermosphacta, C. botulinum, C. sporogenes, Desulfotomaculum, Enterococcus, Lactobacillus, Leuconostoc, L. monocytogenes, Micrococcus, Pediococccus, Sporolactobacillus and Staphylococcus (Thomas et al., 2000). Against bacterial spores, nisin is sporostatic rather than sporicidal (Delves-Broughton et al., 1996). Nisin does not generally inhibit Gram-negative bacteria, yeasts or molds. The spectrum of activity of nisin can be expanded to include Gram-negative bacteria when it is used in combination with chelating agents (e.g. EDTA), heat or freezing (Delves-Broughton and Gasson, 1994; Carneiro de Melo et al., 1998). The presence of food components, such as lipids and protein, influences nisin activity (Scott and Taylor, 1981a,b). The primary mechanism of nisin is believed to be the formation of pores in the cytoplasmic membrane that result in depletion of proton motive force and loss of cellular ions, amino acids and ATP (Crandall and Montville, 1998). Based upon target microorganisms, nisin application falls into one of three categories: (1) prevention of spoilage by spore-forming bacteria, (2) prevention of spoilage by lactic acid bacteria and related microorganisms, or (3) inactivation or inhibition of Gram-positive pathogenic bacteria, e.g. B. cereus, C. botulinum or L. monocytogenes (Thomas et al., 2000). Examples of class II bacteriocins with potential for use in foods include pediocins, sakacin, leucocin, lactacin and lactococcin (Ennahar et al., 2000). Many of these compounds could potentially be used as food antimicrobials but, at the


466 present time, few are approved by international regulatory agencies to be added to foods in their purified form. One approach to using these compounds has been to grow bacteriocin-producing starter cultures in a medium such as whey, non-fat dry milk or dextrose. The fermentation medium is then pasteurized and spray-dried, which kills the starter culture but retains the active antimicrobial. Bacteriocin mechanisms of action have been reviewed in detail (Driessen et al., 1995; Venema et al., 1995; Rodriguez, 1996; Matsuzaki, 1998; Shand and Price, 1998; Breukink and de Kruijff, 1999; Ennahar et al., 2000; McAuliffe et al., 2001; Hechard and Sahl, 2002; Sharma et al., 2002).

18.3 Potential resistance responses by pathogens to natural antimicrobials Resistance responses of microorganisms to antimicrobials may be intrinsic, apparent or acquired. Intrinsic or innate resistance is a chromosomally controlled property naturally associated with a microorganism. Differences in resistance to antimicrobials among types, genera, species and strains of microorganisms under identical environmental conditions and concentrations are, most likely, controlled by innate resistance. Mechanisms of innate resistance may include barriers to prevent entry of the antimicrobial (e.g. outer membrane of Gram-negative bacteria, teichoic acids of Gram-positive bacteria), efflux (i.e. mechanisms to pump compounds out of the cell), lack of a biochemical target for attachment or inactivation, or enzymatic inactivation of antimicrobials (Bower and Daeschel, 1999). For certain types of antimicrobials, adaptation to an antimicrobial may occur. This increased resistance may be demonstrated in the laboratory by exposing a microorganism to a stepwise increase in concentration of a compound. However, this resistance is often unstable and the microorganism may revert to the sensitive phenotype when grown in an antimicrobial-free medium. This has been termed ‘back-mutation’ (Russell, 1991). Adaptation or temporary resistance probably plays a large or the major resistance role to naturally occurring food antimicrobials. In these instances, stress responses probably confer temporary resistance to the target microorganism. Related to intrinsic or innate resistance is what could be termed ‘apparent’ resistance (Davidson and Harrison, 2002). Apparent resistance to antimicrobials has its parallel in thermal resistance. Just as pre-exposure conditions, suspending medium and post-exposure conditions influence apparent heat resistance of a microorganism, so can they influence apparent resistance to antimicrobials. Apparent resistance is influenced by such things as exposure conditions, e.g. environmental conditions of use, or interaction of antimicrobials with components of the suspension medium or food product (Davidson and Harrison, 2002). Acquired resistance results either from genetic changes in the microbial cell through mutation or by acquisition of genetic material via plasmids or transposons containing integron sequences (Russell, 1991; Russell and Chopra, 1996; Roe and Pillai, 2003). Acquired resistance to naturally occurring food preservative antimicrobials is much less studied than that for therapeutic antibiotics.


467

18.4

Factors influencing development of resistance

18.4.1 Microbial factors Application of naturally occurring food antimicrobials against food spoilage or pathogenic microorganisms results in inhibition or inactivation depending upon the type and concentration of the compound utilized. Therefore, microorganisms are exposed to both lethal and sublethal stresses. Bacteria have evolved several strategies to survive bacteriostatic or potentially bactericidal stresses. For example, endospore formation is a survival strategy for Bacillus and Clostridium species. For species unable to form endospores, other significant physiological changes may enhance the ability of the microorganism to survive the stress. Regardless of the strategy, genetic regulatory modification is involved. Sigma (σ) factors, common regulatory factors, are frequently involved in enhanced resistance to stress. Sigma factors produced in response to a stress bind to core RNA polymerase to confer different promoter specificities. These changes lead to the production of stress proteins, which function to protect the cell. For example, RpoS is a regulatory factor required for transcriptional activation of a large number of genes required for tolerance to environmental stresses including growth phase dependent acid tolerance.

18.4.2 Compound related factors Therapeutic antibiotics generally have specific target sites in microbial cells and, consequently, significant potential for development of mutations and potential acquired resistance. In contrast, naturally occurring food antimicrobials are generally non-selective in their inhibition, and resistance to these compounds is primarily caused by innate or intrinsic factors (Russell et al., 1997). While acquired resistance must be a concern in utilization of naturally occurring food antimicrobials, such resistance may be rare. Natural antimicrobials from animal sources Studies on potential resistance to the lactoperoxidase system have involved interaction with acid adaptation. Ravishankar and Harrison (1999) exposed acidadapted L. monocytogenes to an activated lactoperoxidase system. They found survival rates were similar for the acid-adapted and non-adapted cells at pH 4.5 in the presence of an activated system, indicating no cross-protection. In contrast, Leyer and Johnson (1993) reported increased resistance to an activated lactoperoxidase system with acid-adapted Salmonella Typhimurium when tested in a laboratory culture medium. There are several possible reasons for the differences between the studies: (1) lactoperoxidase system activity can vary depending on the test medium, (2) a greater degree of acid tolerance could be exhibited by Salmonella compared with Listeria or (3), the lactoperoxidase system may be less effective against Salmonella. Lactoferrin is a chelator that inhibits some microorganisms by binding iron. However, some bacteria may be resistant to lactoferrin because they adapt to low


468 iron environments by producing siderophores such as phenolates and hydroxamates (Ekstrand, 1994; Farnaud and Evans, 2003). This includes P. fluorescens, Enterococcus faecalis and Bifidobacterium bifidum, which are highly resistant to this protein (Cagri et al., 2004). Gram-negative bacteria have an outer membrane that restricts penetration of lysozyme to the cell wall. However, the protective effects of the outer membrane can be voided if the electrostatic repulsive effects of the phospholipid head groups are neutralized, and stabilizing multivalent cations are removed from the outer membrane via chelators. This effect has been demonstrated for Gram-negative bacteria exposed to chelators and nisin (Ellison and Giehl, 1991; Stevens et al., 1991, 1992; Payne et al., 1994; Facon and Skura, 1996; Ocana-Morgner and Dankert, 2001; Branen and Davidson, 2004). The LPS portion of the outer membrane in Gram-negative bacteria has also been demonstrated to function to restrict passage of lysozyme. Tamaki and Matsuhashi (1973) observed that E. coli mutants with an incomplete LPS (glucose residue-negative) were sensitive to lysozyme. These studies point to the importance of the outer membrane and LPS as protective barriers for Gram-negative bacteria against lysozyme. In some instances, it has been shown that development of resistance to one antimicrobial polypeptide may confer cross-protective resistance to lysozyme. Mantovani and Russell (2001) observed that Streptococcus bovis cells gained lysozyme resistance (4 mg/ml lysozyme) after adaptation to nisin by modification of LPS lipoteichoic acids. Gram-positive bacterial sporeformers are highly resistant to lysozyme in the spore stage via a spore coat and constituent proteins. Bacillus anthracis, the causative agent of anthrax, was determined to be resistant to lysozyme up to 1 mg/ ml in the presence of EDTA (Kim et al., 2004). Following incubation of B. subtilis spores with 250 µg/ml lysozyme for 10 min, viable spores were reduced by 3 log in B. subtilis mutants that lacked spore coat protein-encoding gene (yjcC) (Kuwana et al., 2003). Some bacterial species have instrinsic resistance mechanisms to lysozyme. Escherichia coli is known to encode a lysozyme-binding protein, effectively inactivating the enzymatic activity (Monchois et al., 2001; Deckers et al., 2004). When the ykfE gene (recently renamed ivy due to its ability to encode protein that inactivates vertebrate-source lysozyme) was deleted from an E. coli strain, subsequent cell inactivation by lysozyme was increased over wild-type cells from ca. 74 to 84 % (Deckers et al., 2004). Bacteria have other means of resisting the enzymatic activity of lysozyme other than binding proteins. Many species of bacteria are able to post-translationally modify the constituents of their peptidoglycan so as to be resistant to cleavage by lysozyme (Zipperle et al., 1984; Clarke and Dupont, 1992). Acetylation at C6 (O-acetyl) and the addition of de-N-acetyl groups to the peptidoglycan backbones have both been shown to be widespread among Gram-positive species and have also been shown to influence resistance of the cell to the enzyme (Zipperle et al., 1984; Clarke and Dupont, 1992; Masschalck and Michiels, 2003; Weidenmaier et al., 2003). Sigma factors that regulate the stress response mechanisms of microorganisms may influence resistance to


469 lysozyme. Listeria monocytogenes was shown to contain a signal transduction system (CesRK) that, upon exposure to lysozyme, provided antibiotic resistance to the cells (Kallipolitis et al., 2003). Sigma E has also been shown to dramatically increase the resistance of bacteria to lysozyme. Streptomyces coelicolor mutants containing a σE deletion were shown to be up to 50 times more sensitive to lysozyme than were wild-type cells (Paget et al., 1999a,b). Resistance to lysozyme may be acquired. Bacillus subtilis mutants lacking bacilysin-production ability were demonstrated to be between 200–300 times less resistant to lysozyme versus wild-type cells. Furthermore, following transduction of a bacilysin-encoding DNA fragment, mutants retained/acquired resistance to lysozyme equivalent to wild-type cell resistance (Ozcengiz and Alaeddinoglu, 1991). Natural antimicrobials from plant sources Bacillus cereus exposed to non-lethal concentrations of carvacrol, a component of the essential oils of oregano and thyme, demonstrated resistance to the normally bactericidal compound (Ultee et al., 2000). Resistant cells had decreased fluidity in the cell membrane and changes in the phospholipid and fatty acid composition of the cell membrane. Resistance to carvacrol, however, apparently does not confer resistance to other membrane-active compounds. Pol et al. (2001) adapted cells of B. cereus to carvacrol by growing them in 0.3 mM of the compound. Adapted cells were more sensitive to a subsequent nisin exposure than cells that were not adapted. In a similar study to that of Ultee et al. (2000), E. coli with increased resistance to thymol and eugenol (essential oils found in thyme and cloves, respectively) were created using gradient plates (Walsh et al., 2003). Essential oil resistant strains were more resistant to the antibiotic chloramphenicol. Resistance to the essential oil components was not stable, indicating adaptation (Walsh et al., 2003). Methicillin-resistant and sensitive S. aureus were found to be sensitive to oregano essential oil and its components, carvacrol and eugenol (Nostro et al., 2004). Koga et al. (1999) reported that certain strains of Vibrio parahaemolyticus are more resistant to basil and sage essential oils than the parent strain. In contrast, Ohno et al. (2003) passed Helicobacter pylori through ten transfers of lemongrass essential oil without any increase in resistance. Rickard et al. (2004) exposed E. coli SPC105 to aqueous and ethanolic extracts of nine different spices to determine both growth inhibition and induction of the chromosomal multiple antibiotic resistance (mar) operon. Ethanolic extracts of all nine spices inhibited growth of the microorganism to various extents, and cinnamon, tarragon, dill, garlic, cayenne pepper and paprika induced the mar operon. The major mechanism of the Mar phenotype is increased efflux. The essential oil of the Australian tea tree (Melaleuca alternifolia), or tea tree oil (TTO), is inhibitory to several foodborne microorganisms. Gustafson et al. (2001) found that mutants of E. coli AG100 exhibiting the Mar phenotype were slightly more resistant to TTO than the parent strain. Mechanisms for resistance to TTO by Pseudomonas aeruginosa are related to barrier properties of the outer membrane as well as efflux capabilities (Longbottom et al., 2004).


470 Natural antimicrobials from microbial sources Most of the attention on acquired resistance to natural antimicrobials has been focused on microbially-derived antimicrobials. The probable reason for this is the similarity that some of these compounds have with therapeutic antibiotics regarding form and/or ability to kill target cells. It has been suggested that use of microbially-derived antimicrobials in foods could result in acquired resistance to the compounds themselves or possibly cross-resistance to medically important antibiotics. However, in contrast to therapeutic antibiotics, microbially derived antimicrobials generally have a much narrower activity spectrum, i.e. affecting limited types of target microorganisms, and often have different mechanisms. Natamycin has no medical uses and is used primarily as an antifungal agent on cheese. De Boer and Stolk-Horsthuis (1977) investigated the potential for development of resistance to natamycin among fungi. They reported no evidence of resistant fungi in cheese warehouses where natamycin was used for periods of up to several years. They also attempted to induce tolerance in 26 strains of fungi by transferring each culture 25–31 times in media containing concentrations of natamycin equal to and greater than the minimum inhibitory concentration (MIC). The MIC increased in only 8 of 26 strains by a maximum of 4 µg/ml following multiple transfers. They concluded that lack of increased resistance among fungi was caused by the lethal (as opposed to static) activity of the compound along with its instability over time. Nisin has a relatively narrow spectrum, affecting primarily vegetative cells and spores of Gram-positive bacteria. The peptide alone generally does not inhibit Gram-negative bacteria, yeasts or molds. The mechanism of antimicrobial action of nisin against vegetative cells includes binding to the anionic phospholipids of the cell membrane, insertion into the membrane, and resultant pore formation. Disruption of the cytoplasmic membrane causes efflux of intracellular components and eventual depletion of the proton motive force (Crandall and Montville, 1998). Microorganisms exhibiting resistance to nisin may inactivate the peptide via enzymes or alter membrane susceptibility (Cleveland et al., 2001). Streptococcus thermophilus, Lactobacillus plantarum and certain Bacillus species that produce the enzyme nisinase, neutralize the antimicrobial activity of the peptide (Hurst and Hoover, 1993). Spontaneous nisin-resistant mutants, including L. monocytogenes, C. botulinum, Bacillus species and Staph. aureus can occur via exposure of wildtype strains to nisin or transfer of strains in media containing increasing concentrations of nisin (Harris et al., 1991; Ming and Daeschel, 1993; Mazzotta et al., 1997; Cleveland et al., 2001). Listeria monocytogenes resistant mutants may occur at a rate of 1 in 106 to 108 (Harris et al., 1991; Ming and Daeschel, 1993) or even lower (Schillinger et al., 1998). These are stable mutants. Crandall and Montville (1998) observed that nisin-resistant (NisR) strains of L. monocytogenes had altered phospholipid composition, including decreased anionic phospholipid and increased phosphatidylethanolamine in the cell membrane, resulting in a decreased net negative charge which could hinder binding of cationic compounds such as nisin. In addition, the cell membranes of NisR strains exhibited increased long-chain fatty acids and reduced ratios of C15/C17 fatty acids, suggesting


471 reduced fluidity and stabilization caused by reduced effect on PMF (Ming and Daeschel, 1993; Mazzotta et al., 1997). These and other changes suggest an alteration of the cytoplasmic membrane to prevent access by nisin. A second means of bacterial resistance to antimicrobial peptides involves crossprotection. For example, resistance to nisin may be gained through adaptation to other bacteriocins. AS-48, an antimicrobial peptide produced by Enterococcus faecalis, was shown to be very active against L. monocytogenes cells not adapted to the peptide (Mendoza et al., 1999). Adaptation to the peptide conferred resistance to nisin at a level sufficient to completely inhibit wild-type cells (Mendoza et al., 1999). Likewise, in L. monocytogenes cells found to be resistant to nisin, researchers determined similar resistance capabilities of the microorganisms to other class IIa bacteriocins similar in structure and mode of action to nisin (Gravesen et al., 2004). With regard to cross-protection to bacteriocins afforded through adaptation to environmental stresses (pH, salt, heat), there is not a large database of accumulated information to date (Ganzle et al., 1999a,b,c; Bouttefroy and Milliere, 2000). Further, studies published to date disagree on the potential for cross-protection against nisin after adaptation to other environmental stressors. For example, researchers determined that L. monocytogenes sensitivity to nisin increased with increases in salt and acid (Thomas and Wimpenny, 1996; Ganzle et al., 1999c; Bouttefroy and Milliere, 2000). However, researchers were not able to show that heat or cold adaptation affected the activity of nisin on target cells (Thomas and Wimpenny, 1996; DeMartinis et al., 1997; Bouttefroy and Milliere, 2000). Listeria monocytogenes nisin-resistant cells have been shown to be as resistant to heat as wild-type cells and, if resistant cells were exposed to nisin with subsequent heating, their sensitivity increased significantly (Modi et al., 2000). In contrast, there are reports that acid-adaptation does aid in the resistance of Grampositive pathogens to nisin. In a study involving acid adaptation at pH 5.5 and bacteriocin sensitivity, van Schaik et al. (1999) found that acid-adapted L. monocytogenes was more resistant to nisin and lacticin 3147. The difference in resistance between the acid adapted and non-adapted cells was more noticeable with nisin than with lacticin 3147. As the experiment was done in microbiological medium (tryptic soy broth supplemented with 0.6 % yeast extract), whether this change in resistance would occur in a food system is uncertain. Acquired resistance is predominantly gained via gene transfer through conjugative and non-conjugative plasmid and transposon movement between microbes. Klaenhammer and Sanozky (1985) reported a plasmid in L. lactis capable of conjugal transfer that could induce nisin resistance as well as bacteriophage resistance. Pediocin immunity was reported to be located and transferred among Pediococcus acidilactici variants by a plasmid (pSMB74). Resistance to the bacteriocin was reported to be acquired by cells not containing the plasmid that were grown in the presence of the bacteriocin (Noerlis and Ray, 1994). Davies et al. (1996) described L. monocytogenes cells with acquired resistance to nisin that, upon loss of their cell wall, quickly lost acquired resistance. The lipoprotein encoded by nisI in L. lactis was recently hypothesized to act as a nisin-intercepting protein (Stein et al., 2003). Plasmids containing the nisI and nisFEG genes were


472 inserted in a B. subtilis strain known to be sensitive to nisin. Upon plasmid insertion, B. subtilis cells were three-fold more tolerant to nisin than wild-type cells. Gravesen, et al. (2001, 2004) determined via mutagenesis studies that a three gene system found in L. monocytogenes aided the organism in resisting the action of nisin by shielding Lipid II, a key component of Gram-positive peptidoglycan layers and target molecule for nisin (Hsu et al., 2002; van Heusden et al., 2002). Cotter and Guinane (2002) determined sensitivity to nisin in L. monocytogenes was controlled by the LisRK signal transduction system. These researchers showed in one strain of L. monocytogenes that removal of the genes responsible for this system removed sensitivity to nisin as indicated by lag times that were much shorter than the parent strain (Cotter and Guinane, 2002). It was concluded that the mutation did not allow certain membrane alterations from taking place that would normally give greater protection against other compounds (e.g. ethanol, acid) but would make the cell more susceptible to nisin. Robichon et al. (1997) described the loss of resistance to mesentericin Y105 by L. monocytogenes via the loss of a sigma factor (σ54). Dalet et al. (2001) reported that the loss of σ54 resulted in the loss of sensitivity of Enterococcus faecalis and L. monocytogenes to various II-a bacteriocins (mesentericin, pediocin, enterocin) but did not affect sensitivity to nisin, a class I-a peptide. Recently published studies on B. subtilis indicate that the organism possesses sigma factors that assist in altering the membrane phospholipid content and structure specifically for nisin resistance (Cao and Helmann, 2004). B. subtilis mutants in which specific σX was deleted lost ability to express multiple genes used in the encoding proteins for cell wall synthesis and defense (Cao and Helmann, 2004). These genes encoded proteins that functioned to bind penicillin, a β-lactam type antibiotic, esterify the amino acid alanine, and synthesize phosphatidylethanolamine. All these aided the cells in resistance to cationic peptides, such as nisin, by decreasing negative charge on the cell membrane, a key for nisin attraction and attachment (Cao and Helmann, 2004).

18.5

Predicting pathogen resistance

Microbial foodborne pathogens could become resistant to naturally occurring compounds directly or indirectly. In predicting the potential for direct development of resistance to naturally occurring antimicrobials, one needs to examine the routes of resistance development to therapeutic antibiotics to determine if potential parallels exist. For example, for medically important antibiotics, pathogenic microorganisms may be exposed repeatedly to sublethal concentrations of the compounds in animals, humans and the environment. There really is no such parallel for naturally occurring antimicrobials. It is possible that microorganisms may be exposed to certain compounds in the environment, but there are many types of naturally occurring antimicrobials from a large number of sources, and many have different mechanisms. Should naturally occurring compounds be utilized extensively in food processing systems, would there be chances for repeated


473 exposure? Unless food containing antimicrobials are returned to the processing plant and incorporated into new products, the likelihood of re-exposure is remote. The greatest potential for resistance among the naturally occurring antimicrobials lies in the microbially-derived compounds or the bacteriocins. However, an important question concerning the potential for microorganisms to acquire resistance to bacteriocins is whether they have a natural advantage over non-resistant strains in food systems. Mazzotta et al. (2000) demonstrated that nisin-resistant strains of L. monocytogenes and C. botulinum were not as resistant as wild-type strains to other traditional food antimicrobials including sodium chloride, sodium nitrite and potassium sorbate. Dykes and Hastings (1998) observed that leucocinand sakacin-resistant L. monocytogenes B73 had a more reduced growth rate in BHI broth without bacteriocin than with bacteriocin-sensitive strains. In addition, NisR strains failed to compete with bacteriocin-sensitive strains when grown in mixed populations, even at 1:1. The researchers concluded that the bacteriocinresistant phenotype of L. monocytogenes B73 was not likely to become stable in natural populations. Gravesen et al. (2002) also observed that pediocin-resistant L. monocytogenes frequently exhibited a reduced growth rate and extended lag phase in a microbiological broth medium compared to wild-type cells. However, nisinresistant L. monocytogenes strains had fewer and less pronounced growth rate reductions. Interestingly, pediocin- and nisin-resistant strains were no more stress susceptible (pH, salt, low temperature) and grew equally well in a model sausage system as the parent strains. Mazzotta and Montville (1999) demonstrated that nisin-resistant C. botulinum 169B spores have similar heat resistance patterns as wild-type spores. Therefore, acquired resistance to a single bacteriocin by a microorganism does not appear to automatically confer resistance to other antimicrobials or preservative treatments or any natural selection advantage for a population in the absence of the inhibitor. This may be true for other resistance adaptations as well. Another route to development of resistance to a particular compound is indirect. There has been much research on the effect of stress factors (e.g. heat, cold, starvation, low pH/organic acids, antimicrobials) on developed resistance of microorganisms to subsequent stresses. For example, it has been demonstrated that foodborne pathogens may develop a tolerance or adaptation to organic acids or bacteriocins following prior exposure to low pH. Developed resistance is termed, in the case of acids, tolerance, adaptation, or habituation depending upon how the microorganism is exposed to the stress and the physiological conditions that lead to enhanced survival (Foster, 1995; Buchanan and Edelson, 1999). While this increased resistance could potentially be a problem in application of naturally occurring antimicrobials for controlling pathogens, it has not been demonstrated that this is a problem in actual food processing systems (Davidson and Harrison, 2002).

18.6

Strategies for overcoming resistance

While not highly likely, naturally occurring antimicrobial-resistant pathogenic


474 microorganisms may arise if a food is preserved exclusively by a single antimicrobial. To overcome this potential hazard, combinations of antimicrobials or combinations of antimicrobials with other preservation methods (hurdle technology) should be utilized (Leistner and Gorris, 1995; Mulet-Powell et al., 1998; Schillinger et al., 1998; Leistner, 2000). In theory, combinations of naturally occurring antimicrobials could be successfully applied if the mechanism of action of the compounds was different. For example, combining of bacteriocins has been shown to increase inhibition of target pathogens (Crandall and Montville, 1998; Schillinger et al., 1998, 2001; Vignolo et al., 2000). However, even this strategy must be validated for each combination. For example, Crandall and Montville (1998) demonstrated that L. monocytogenes ATCC 700302 was both nisin- and pediocin-resistant. However, cross-resistance among bacteriocins is variable. Rasch and Knøchel (1998) found no cross-resistance between nisin- and pediocinresistant strains of L. monocytogenes but did find pediocin and bavaricin cross-resistance. The best strategy for overcoming resistance is simply to avoid situations in which resistance could arise. Therefore, repeated exposure of foodborne pathogens to sublethal concentrations of naturally occurring antimicrobials should be avoided. In the food processing industry, this probably means that outdated or leftover product containing antimicrobials should be discarded rather than being reused or reworked.

18.7


DAVIDSON P M, SOFOS J N AND BRANEN A L (2005) Antimicrobials in Foods, 3rd Ed. CRC Press, Boca Raton, FL. DAVIDSON P M (2001) ‘Chemical preservatives and natural antimicrobial compounds’, Food Microbiology: Fundamentals and Frontiers. Doyle MP, Beuchat LR and Montville TJ. American Society for Microbiology, Washington, DC. DAVIDSON P M AND HARRISON M A (2002) ‘Resistance and adaptation to food antimicrobials, sanitizers, and other process controls’, Food Technol 56, 69–78. DILLON V M AND BOARD R G (1994) Natural Antimicrobial Systems and Food Preservation, CAB Intl, Wallingford, UK. LÓPEZ-MALO A, ALZAMORA S M AND GUERRERO S (2000) ‘Natural antimicrobials from plants’, in Alzamora SM, Tapia MS and López-Malo A, Minimally Processed Fruits and Vegetables, Aspen Publ., Gaithersburg, MD. NAIDU A S (2000) Natural Food Antimicrobial Systems, CRC Press, Boca Raton, FL.

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19 Pathogen resistance and adaptation to disinfectants and sanitisers A. van Asselt and M. te Giffel, NIZO food research, The Netherlands

19.1 Introduction: type of disinfection In the food industry worldwide, millions of tonnes of safe and healthy food are produced every year by many people using a large amount of equipment. In producing food, the equipment used gets soiled by both product and microorganisms. In order to avoid recontamination of the fresh product due to fouled surfaces, each piece of equipment or processing line needs to be cleaned and disinfected at regular intervals. Therefore, cleaning and disinfection are important unit operations that are carried out in each food factory on a regular basis. Within the dairy industry, for example, cleaning and disinfection is carried out on a daily basis, sometimes several times a day. For condiments, the frequency differs per batch of product; however the equipment is cleaned and disinfected usually after 8–16 hours operation. In the beverage industry, owing to the acid character of fruit juices and soft drinks, cleaning and disinfection is applied after 60–100 hours of production. Disinfection is defined as the treatment of surfaces/equipment using physical or chemical means such that the amount of microorganisms present is reduced to an acceptable level (Krop, 1990; Donhauser et al., 1991). Prior to disinfecting, cleaning of the surface is necessary to remove organic compounds adhered to the surface. Without proper cleaning, disinfection is useless, as remaining product will inactivate the disinfecting agent and microorganisms present will survive the disinfecting treatment. In practice, 90–95 % of the microorganisms present are removed by an efficient cleaning protocol (Krop, 1990). Disinfection reduces the


485 level of remaining microorganisms. This means that, in general, a disinfected surface/piece of equipment is not sterile and implies that disinfection is not equal to sterilisation where viable microorganisms can no longer be detected. Disinfection can be performed by using physical (steam, ultraviolet, irradiation) or chemical methods. In general, physical methods are preferred as they are very reliable and leave no residues. However, physical methods cannot always be applied owing to restrictions such as temperature, safety of personnel or design of the equipment. In those cases, chemical disinfectants are used (Krop, 1990). In this chapter, the mode of action of the main disinfectants, the behaviour/response of pathogenic bacteria towards chemical disinfectants and some future developments are discussed. The effect of physical methods is not discussed.

19.2

Types of disinfectant and their mode of action

A wide range of disinfectants are available, which can be divided into the following groups (see also Table 19.1):

• • • • • • •

halogen-releasing agents (HRAs); quaternary ammonium compounds (QACs); peroxygens; alcohols; aldehydes; (bis)phenols; biguanides.

Each of the different groups has its own applications within the food industry and has its own restrictions in use. For application, it is important to realise what the proposed effect of a disinfectant is on a target organism and what possible protection mechanisms are present within the organism. In following paragraphs, the different compounds, their mode of action and their applications are discussed. Table 19.1

Disinfectants and their modes of action

Biocide

Mode of action

Target

Halogen-releasing agents Quaternary ammonium compounds (QACs) Peroxygens Alcohols (ethanol) Aldehydes (Bis)phenols

Halogenation/oxidation Electrostatic (ionic) interaction Oxidation Protein denaturation Alkylation reaction Penetration/partition phospholipid bilayer Electrostatic (ionic) interaction

Nucleic acids, proteins Cell surface, enzymes, proteins Lipids, proteins, DNA Plasma membrane Cell wall Phospholipid bilayer

Biguanides


Cytoplasmic membrane (bacteria)/plasma membrane (yeasts)

486 19.2.1 Halogen-releasing agents Chlorine-based compounds are the most frequently applied halogen-releasing agents (HRAs). They include sodium hypochlorite, chlorine dioxide, and the Nchloro compounds such as sodium dichloroisocyanurate (NaDCC). A very cheap and frequently applied formulation is an aqueous solution of sodium hypochlorite, producing hypochlorous acid (HClO) (Krop, 1990, McDonnell and Russell, 1999). HClO is the active component and results in the inactivation of all types of microorganisms such as bacteria, viruses and spores (Sofos and Busta, 1999). Another applied form of chlorine is chlorine dioxide (ClO2). It is synthesised by the reaction of chlorine and sodium hypochlorite. However, chlorine dioxide is much more unstable than a standard hypochlorous solution and decomposes chlorine into gas at temperatures higher than 30 °C when exposed to light (Beuchat, 1998). This can lead to dangerous situations as high concentrations of chlorine gas are explosive (Speek, 2002, Codex, 2003). However, when the solution is kept cool and protected from light, the disinfectant can be kept stable at concentrations of up to 10 g/l (Erco Worldwide, 2004). Mode of action of hypochlorous acid Although the exact mode of action is not known, the main disinfecting effect of chlorine is caused by oxidative activity. In particular, nucleic acids and proteins are destroyed, resulting in irreversible changes and disruption of DNA–protein synthesis (Krop, 1990). The mechanism of killing of spores differs due to their thick proteinaceous coat. Therefore higher concentrations are needed than for inactivation of vegetative cells. Young and Setlow (2003) concluded that hypochlorite affects spore germination possibly because of the severe damage to the spore’s inner membrane. For spore suspensions, Young and Setlow (2003) showed that a concentration of 50 mg/l during 10 min at room temperature is sufficient to achieve 4 decimal reductions of Bacillus subtilis spores. A concentration of 50 mg/l resulted in 1 decimal reduction of B. cereus spores after 1.5 min (Wang et al., 1973). These results show that the minimal inhibitory concentration can vary per species. Mode of action of chlorine dioxide Chlorine dioxide (ClO2), if applied properly, appears to be 2.5 times more oxidative than sodium hypochlorite (Speek, 2002; Rodgers et al., 2004), and is effective against bacteria, viruses and spores (Hoxey and Thomas, 1999). The action of chlorine dioxide involves disruption of the cell’s protein synthesis and membrane permeability control mechanism. It produces no harmful by-products as trihalomethanes, nor does it react with ammonia. After treatment with chlorine dioxide, spores of B. subtilis can undergo the initial steps in spore germination but the process stops owing to membrane damage (Young and Setlow, 2003). An aqueous chlorine dioxide treatment of alfalfa seeds inoculated with Escherichia coli for 10 min at a concentration of 25 mg/l resulted in approximately 1 log reduction of the microorganism (Singh et al., 2003). Compared with standard chlorine solutions (sodium hypochlorite), a concentration of 3 mg/l chlorine


487 dioxide has the same inactivating effect on E. coli O157:H7 and Listeria monocytogenes as 200 mg/l of chlorine when applied for decontamination of fruit surfaces (Rodgers et al., 2004). Iodine Iodine is widely used for sanitising food processing equipment and surfaces. Iodine is less reactive than chlorine and is less affected by the presence of organic matter but has disadvantages such as staining human skin and plastic parts of equipment, and also has a relatively higher price than chlorine (Krop, 1990; Hugo and Russell, 1999). Solutions of 15 % active chlorine are commercially available for € 0.20–0.30 per kg whereas a 6 % solution of iodine in 70 % ethanol costs approximately € 400 per kg (Boom Chemicals). Iodine is applied in three possible formulations: ethanol–iodine, aqueous iodine solutions and iodophores. The iodophores are most frequently applied and have high solubility in water, produce no vapour (below 50 ºC), are less corrosive to stainless steel than chlorinecontaining solutions, and are generally effective against Gram-negative and Gram-positive vegetative cells, yeasts, moulds and viruses (Bernstein, 1990; Beuchat, 1998). Bacterial spores (B. cereus, B. subtilis and Clostridium botulinum type) are more resistant to iodophors (D-values are 10–100 times higher) and higher concentrations are necessary to achieve inactivation. Mode of action of iodine Similar to chlorine, the exact mode of action of iodine is not known. Iodine penetrates microorganisms and attacks specific groups of proteins, nucleotides and fatty acids in a way comparable to chlorine (McDonnell and Russell, 1999). The effective concentration of iodine is approximately 100 mg/l, which is as effective as 300 mg/l of chlorine (Krop, 1990).

19.2.2 Quaternary ammonium compounds QACs can be divided in two main subgroups (Reuter, 1998; Mohr and Duggal, 1997):

• tri-alkylbenzyl-ammonium compounds (e.g. benzalkonium chloride); • tetra-alkyl-ammonium compounds (e.g. didecyldimethyl-ammonium chloride). QACs combine antimicrobial properties with surface active properties and are therefore useful for hard surface cleaning and deodorisation (McDonnell and Russell, 1999). Compared with chlorine, they are more expensive but have the advantage of having residual action. QACs remain active on surfaces for approximately one day (e.g. fish industry) and therefore discourage further bacterial growth (Tatterson and Windsor, 2001). This adherence to the surface also has disadvantages. Removing the disinfectant from the surface by flushing with water becomes difficult, resulting in possible residues in the product (Kraemer, 1998). In general QACs are effective against vegetative bacteria but have greatest effectiveness against Gram-positive bacteria. Yeast and moulds can be inactivated


488 Table 19.2 agents

Efficacy of quaternary ammonium compounds on different infectious

Infectious agent

Efficacy

Bacteria Gram-positive Effective Gram-negative Effective

Comments

Reference Russell (1995)

Minimum inhibitory concentration (MIC) higher than Gram-positive Spores Ineffective Sporostatic Russell (1990) Viruses Quinn and Markey Lipid Effective (1999) Small non-lipid Ineffective Non-lipid Limited Mycobacteria Ineffective Russell (1996) Yeast/moulds Effective Moulds more resistant Russell (1999c)

to some extent but higher concentrations are necessary (Krop, 1990; Bernstein, 1990) (see Table 19.2). The pH range where QACs are most effective is between pH 6 and 10 (Beuchat, 1998), which limits its applicability in acid environments. Mode of action The principal actions of QACs are lowering of surface tension, inactivation of enzymes and denaturation of cell-proteins. As a result of adsorption of QACs onto the microorganism’s surface, the cell’s permeability is changed dramatically. This results in leakage of intracellular low-molecular compounds, degradation of proteins and nucleic acids, and cell wall lysis by autolytic enzymes (McDonnell and Russell, 1999). The concentration applied depends on the types of microorganism present in the product, the processing system or the environment. Concentrations typically used are in the range between 150 and 250 mg/l of active quaternary ammonium (QA) (Bernstein, 1990; Beuchat, 1998). Allerberger and Dierich (1988) showed a bactericidal effect on E. coli at a concentration of 100 mg/l. Low concentrations (0.0005 % w/v = 5 mg/l) of benzalkonium chloride are sporostatic, inhibiting outgrowth but not germination. QACs are not sporicidal (Russell, 1990).

19.2.3 Peroxygens Hydrogen peroxide and peracetic acid are the main representatives of the group of peroxygens. Hydrogen peroxide is widely applied within the food industry and is commercially available in concentrations varying between 3 and 90 % w/v, with 35 % routinely used in the food industry (McDonnell et al., 2002). It is applied for sterilising packaging material prior to filling (Mohr and Duggal, 1997), contact lenses, and the surface of fruit and vegetables. Hydrogen peroxide is both bactericidal and sporicidal (Hugo and Russell, 1999); in general, a concentration of 6 % is bactericidal. Peroxygens are generally more active against Gram-positive


489 bacteria than Gram-negative bacteria (Russell, 1990; McDonnell and Russell, 1999). To achieve a sporicidal effect, concentrations between 10 and 30 % are necessary. Peracetic acid is commercially available in 15 % solutions as a mixture of water, hydrogen peroxide and acetic acid and acts faster than hydrogen peroxide. It has a broad spectrum of efficacy against viruses, bacteria, yeast and spores (Bernstein, 1990). Compared with hydrogen peroxide, the activity of peracetic acid is hardly influenced by organic matter (Russell, 1990; McDonnell and Russell, 1999). Disadvantages are that peroxygens corrode on tools and equipment and are aggressive to, for example, human tissues (Reuter, 1998). However, the development and use of anticorrosives has reduced this concern (Marquis et al., 1995) Mode of action The mode of action of peroxygens is based on free-radical oxidation (e.g. hydroxyl radicals) of essential cell components such as lipids, proteins and DNA (McDonnell and Russell, 1999). Peracetic acid not only attacks the proteins in the cell wall but also migrates into the cell and disrupts inner cell components as well (Donhauser et al., 1991).

19.2.4 Alcohols The most widely used alcohols for disinfection are ethyl alcohol (ethanol, alcohol), isopropyl alcohol (isopropanol, propane-2-ol) and n-propanol, the latter particularly in Europe (Mohr and Duggal, 1997; McDonnell and Russell, 1999). In food production areas, alcohols are particularly used for the decontamination of hard surfaces of equipment (e.g. filling machines). The most effective concentration is between 60 and 70 % v/v (Mohr and Duggal, 1997). The concentrations to achieve reduction of growth or complete inactivation are higher than for chorine solutions or organic acids. Alcohols are quick reacting, have a broad spectrum of antimicrobial activity and inhibit growth of vegetative bacteria, viruses and fungi. Spores are fairly resistant to the effects of alcohol; however, a combination of 70 % v/v concentration with temperatures up to 65 ºC results in inactivation of spores, for example B. subtilis spores (Setlow et al., 2002). Compared with other disinfectants, the concentrations applied are much higher (50–100 times) and in fact alcohols are only effective if used directly as the substance itself, instead of a lowconcentration solution. This property makes alcohol more expensive to use than chlorine and QACs, and therefore it is not frequently applied on a large, industrial scale but is used mostly for applications such as small, difficult-to-reach spots in equipment, temperature probes and quick wipe-downs of working surfaces and scales. Mode of action The general mode of action for inactivation of microorganisms by alcohols is by denaturation of proteins (Schlegel, 1993), with the primary site of action being the cell (plasma) membrane. As a result of deterioration of the plasma membrane, the


490 cell wall starts to leak essential cell components such as ions (Ca2+) and low molecular weight solutes such as peptides and amino acids. Therefore, the mode of action and its effect on the metabolism of the microorganism depends very much on the concentration. Moulds and actinomycetes are most susceptible to alcohols and are inhibited at 4 % (v/v) whereas most bacteria can still grow at this concentration (Kalathenos and Russell, 2003). Application of 5.5 % (v/v) shows a bacteriostatic effect on E. coli, but in order to kill this microorganism concentrations of 22.2 % or higher are necessary (Allerberger and Dierich, 1988). Yeasts are able to grow at higher alcohol concentrations (8–12 % v/v) which is not surprising since they are responsible for the production of beer and wine (Saccharomyces cerevisiae). Concerning the inactivation of spores it appeared that in some way they were affected by the ethanol. Setlow and co-workers (2002) showed that the spore coat can be permeabilised. Consequently, ethanol in combination with other components or with high temperature (> 65 °C) is more effective than ethanol itself.

19.2.5 Aldehydes Two aldehyde compounds are mainly used for disinfecting i.e., glutaraldehyde and formaldehyde. Aldehydes are active against a wide range of bacteria, viruses, moulds and spores, and are easily removed from surfaces and are (bio) degradable (Mohr and Duggal, 1997). However, the activity of aldehydes is very easily influenced by remaining (protein) fouling which implies sufficient cleaning prior to disinfecting. From a toxicological point of view, aldehydes do not cause problems for humans when used within the prescribed concentrations (Mohr and Duggal, 1997). On the other hand, it is thought that formaldehyde can have mutagenic effects (McDonnell and Russell, 1999). Mode of action The mode of action of glutaraldehyde involves a strong association with the outer layers of bacterial cells (Denyer and Stewart, 1998; McDonnell and Russell, 1999). The cell’s chemical reaction with glutaraldehyde results in metabolic and replicative inhibition (Denyer and Stewart, 1998). The way formaldehyde reacts is most probably the same. Concerning processing conditions, an alkali environment is more favourable than an acid environment as more reactive sites will be formed on the cell surface. Applied concentrations vary between 0.08 and 1.6 % (w/w) for inactivating E. coli. For a sporicidal effect a solution of 2 % is normally sufficient.

19.2.6 Bisphenols Bisphenols are in fact hydroxy halogenated derivatives of diphenyl methane, diphenyl ether and diphenyl sulphide, and are active against bacteria, fungi and algae. Triclosan and hexachlorophene are the most widely used (McDonnell and Russell, 1999). Triclosan, a derivative of diphenyl ether, is known as an ingredient in some medicated soaps, hand-cleansing gels and toothpastes and is effective


491 against staphylococci (Hugo and Russell, 1999). It is currently applied as an antimicrobial layer in packaging material (Chung et al., 2003; Vermeiren et al., 2002) and conveyer belts (Stekelenburg and Hartog, 2002). Unfortunately, depending on the impurity of the starting material, triclosan can contain concentrations of dioxin and dibenzofurans, both substances are highly toxic to humans (Quantex Laboratories, 2001). Therefore, it is of great importance that the origin and way of production is known prior to application in food production areas. Hexachlorophene has been used in soaps as well; in 1972 it was restricted in use by the US Food and Drug Administration (FDA) to levels less than 0.1 %. Nowadays application as surgical scrubber in the case of certain infections is permitted (Spectrum Laboratories). Mode of action The exact mode of action is unknown so far but it is suggested that triclosan affects the cytoplasmic membrane. However, current research shows that triclosan inhibits one specific enzyme of the fatty acid synthesis of E. coli. This increases the risk of resistance against triclosan as one mutation of a gene can result in a decreased efficacy of the disinfectant (Sixma, 2001). Hexachlorophene affects bacteria by inducing leakage, causing protoplast lysis and inhibiting respiration.

19.2.7 Biguanides The group of biguanides is represented by chlorhexidine, alexidine and polymeric biguanides (Hugo and Russell, 1999; McDonnell and Russell, 1999). Chlorhexidine is probably the most widely applied biocide in hand washing and oral products such as mouth wash, mouth spray and throat-lozenges (Sixma, 2001) and is bacteriostatic at concentrations of 0.0001 mg/l as well as bactericidal at concentrations of 0.002 mg/l (Russell, 1991). Chlorhexidine has a broad spectrum of activity, is pH-dependent (higher efficacy at alkaline rather than acid pH), and its efficacy is greatly reduced by the presence of organic matter. High concentrations of chlorhexidine cause coagulation of intracellular constituents (Russell, 1990; McDonnell and Russell, 1999). Chlorhexidine is sporicidal only at elevated temperatures (> 0.005 mg/l at 70° C) and is in general more sporostatic; it has little effect on the germination of the spore but does not prevent the outgrowth of the spore (Gorman et al., 1987; Russell, 1991). Alexidine and the polymeric biguanides are used only on a small scale. The polymeric biguanides are used in particular by the food industry and also for the disinfection of swimming pools. An example is poly(hexamethylene biguanide) hydrochloride (PHMB), the main active ingredient of Vantocil, which is widely used in the food industry, hospitals, nursing homes and consumer households (Avecia, 2004). Mode of action In principle, chlorhexidine attacks the outer cell layer but not sufficiently to induce lysis or cell death. However, after crossing the cell wall it damages the cytoplasmic membrane (bacteria) or plasma membrane (yeast) (McDonnell and Russell, 1999).


492 Table 19.3

Summary: disinfecting agents

Biocide

Application

Bactericidal

Sporicidal

Comments

Halogen-releasing agents (HARs)

50–250 mg/l

> 10 mg/l

> 50 mg/l

Quaternary 150–250 mg/l ammonium compounds (QACs) Peroxygens 3–90 %

> 100 mg/l

No

>6%

10–30 %

Alcohols (ethanol)

20–70 % (w/v)

> 22 % (w/v)

60–70 % (w/v)

Chlorine cheap Iodine expensive Influenced by organic substances Residual action (approx 1 day) Neutral, non-aggresive More effective as mixture with acetic acid Not for large industrial application

Aldehydes Bisphenols Biguanides (chlorhexidine)

0.8–16 mg/l 2–20 mg/kg > 150 mg/l

< 10 mg/l > 10 mg/l 1–60 mg/l

20 mg/l None –

Applied in hand washing and oral products

Polymeric biguanide appears to have a non-specific mode of attack against cell membranes resulting in quick cell death.

19.2.8 Summary The different effective concentrations for the biocides are summarised in Table 19.3. It is obvious that the appropriate disinfectant has to be chosen according to the type of application or type and metabolic state of the microorganism.

19.3

Strategies for optimisation of cleaning and disinfection

Resistance development as a result of cleaning and disinfection is not (yet) a matter of major concern for the food industry. However, the food industry (and also the pharmaceutical industry) has to realise that the current processes of cleaning and disinfecting need to be carried out properly in order to avoid development of resistance. Even a short-term exposure to sublethal concentrations of QACs causes cellular changes to L. monocytogenes (Lunden et al., 2003). In addition, recirculation of product in the process chain (re-work) implies a possible risk as (remaining) microorganisms are exposed a second time to a cleaning and disinfecting step. This might induce the development of resistant mutants of the spoilage microorganisms. For the application of cleaning and disinfecting agents the following issues are important:

• Use of appropriate product. • Application of correct processing conditions.


493

• Influence of neutralising components. • Monitoring. 19.3.1 Use of the appropriate product Application of the right type of agent is important to achieve the desired chemical effect. With respect to disinfectants it is necessary that a product with the proper spectrum of activity is chosen. For example, to inactivate spores, the application of alcohols or QACs is useless as those agents are not sporicidal (Russell, 1990). Another point is that some solutions (e.g. chlorine solutions) act very aggressively towards metal surfaces and polymer seals. This results in corrosion of the materials, providing bacteria with places where they are able to survive cleaning and disinfecting procedures (Kraemer, 1998) 19.3.2 Application of the right cleaning/disinfecting conditions The combination of concentration, mechanical action, time and temperature is of major importance for efficient cleaning and disinfection. The applied concentration should not be higher or lower than the advised concentration. Excessively high concentrations can lead to insolubility and increased corrosiveness. Concerning protein fouling, it is known that too high concentrations of alkaline ( > 0.5 %) result in polymerisation of the protein and form a rubbery layer (Bird, 1994; Jeurnink and Brinkman, 1994). These kinds of rubbery layers obstruct and prevent the penetration of cleaning and disinfecting solution into the fouling, resulting in a decreased fouling removal rate (Jeurnink et al., 1996). Concerning starch fouling the concentrations of alkaline needed vary between 9 and 20 % (w/v) (Bird, 1994), which is quite different from those needed for dairy processes. Therefore, the applied concentration depends on the type of fouling. An increase in temperature results in an increased efficiency. However, for dairy processes at temperatures above 80 °C the opposite effect can be achieved as proteins coagulate, resulting in an increase of fouling instead of a decrease. In addition, for all processes, cleaning at temperatures above 80 °C results in higher energy consumption without extra cleaning benefit and can lead to damage to the equipment (corrosion). An optimal working temperature therefore is around 70 °C. In combination with the 0.5 % alkaline solution (for dairy environments) this is sufficient to inactivate any vegetative pathogenic microorganism (Jeurnink et al., 1996). In the case of membrane systems even lower temperatures (40–60 °C) are advised because of the rather vulnerable composition of the membranes and its modules (Shorrock et al., 1998). Contact time is the third most important parameter of disinfection processes. The longer the contact time, the greater the number of microorganisms that are inactivated. In most cases, there is a direct link between contact time and concentration. There are various models predicting the inactivation of a disinfectant, but not all of them are easy to use (e.g. there are too many unknown parameters). In general the simple Chick-Watson (1908) log-linear model is used mostly (Lambert and Johnston, 2000; Cho et al., 2003; Kamase et al., 2003).


494 N Log —1 = –kCnt No Where

N1 = number of surviving microorganisms N0 = initial number of microorganisms k = disinfection rate constant C = disinfectant concentration n = dilution coefficient t = contact time The dilution coefficient (n) differs per type of disinfectant. For example, for QACs n = 1, which implies that by halving the concentration the contact time (t) is doubled. For ethanol n = 10, which implicates an efficiency reduction by a factor of 210 ( = 1024) when halving the concentration (Krop, 1990). The effect of mechanical action is obvious; the more mechanical energy is put into the removal of the fouling, the more efficiently the fouling will be removed; thus a more efficient cleaning process is obtained (Gibson et al., 1999). However, there is a limit, as too much mechanical action (e.g. by using metal scrubbing devices) may cause damage to the cleaned object/surface. The final effect depends on the right combination of the conditions discussed. However, it remains possible to select for different combinations of conditions as long as ‘the sum’ of the conditions will be the same, e.g. a reduction of the concentration can be compensated for by an increase in time or mechanical action (Krop, 1990).

19.3.3 Influence of neutralising components Prior to disinfecting, the equipment or surface to be treated should not contain any components that can inactivate the disinfectant. Organic matter (e.g. food residues, milk, stone, blood) are well known for their neutralising effect. In general these organic materials interfere by reacting with the biocide, leaving a reduced concentration of antimicrobial agent for attack on microorganisms. In addition to organic materials, surface active agents and metal ions can act as interfering substrate (Russell, 1999a).

19.3.4 Monitoring As shown, many characteristics concerning the application of disinfectants and the inactivation of microorganisms are known. However, knowing does not guarantee appropriate control of the process. Thorough analysis of available data is necessary for making the right decision with regard to type of disinfectant, process conditions and required effect. Monitoring devices to analyse cleaning and disinfection processes, and databases containing inactivation kinetics of relevant microorganisms in combination with predictive knowledge can be a great help in optimising relevant processes. With regard to monitoring, OPTICIP, a monitoring device to make and optimise


495

Before 5 Calcium(g/l) Turbidity 4 Current cleaning time 3 Possible reduction

Alkaline cleaning

Acid cleaning

2

1

0

0

1

2

3

4 Time

5

6

7

8

After 5 Calcium(g/l) Turbidity

Improved cleaning time 4

3

Acid cleaning

Alkaline cleaning

2

1

0

0

1

2

3

4 Time

5

6

7

8

Fig. 19.1 OPTICIP, a monitoring device to make and optimise CIP-cleaning.


496 cleaning in place (CIP) procedures, can be applied (van Asselt and te Giffel, 2002). A typical cleaning procedure of an evaporator before and after optimisation is shown in Fig. 19.1. The system monitors the removal of organic and inorganic fouling off-line in combination with the in-line measurement of parameters such as temperature, flow, conductivity and valve settings. The turbidity of the cleaning solution is a measure for the removal of organic fouling. The calcium concentration is a measure for the removal of inorganic fouling. Conductivity measurement is used for separation of the various cleaning phases and gives an indication of the concentration of the cleaning solution used. Sharp slopes between subsequent phases indicate that rinsing and cleaning phases are properly separated (van Asselt et al., 2002). More simplified systems are also available. Johnson-Diversey introduced ‘Shurlogger’ a real-time CIP monitoring system based on flow, temperature and conductance (Dodd, 2003). However, the fouling removal is not taken into account. Therefore, this system gives a less detailed analysis compared with OPTICIP. A quick monitoring device is the application of ATP as measurement for remaining microorganisms and/or organic substances. The principle is based on the fact that every organic cell contains ATP as energy carrier. The reaction of the enzyme luciferase with ATP results in the emission of light that can be measured by a specific light-measuring device. The more light is emitted, the more ATP was present and the more the surface or liquid was contaminated with microorganisms or organic matter. It is even possible to differentiate between microbial and organic ATP. A disadvantage of this method is that the detection limit is relatively high. The minimum concentration of microorganisms is approximately 103–104 CFU/ml (Moore et al., 2001) before this method becomes reliable, whereas this amount is already crossing the limit of levels of contamination. Thus, measuring ATP is suitable for a quick inventory of the cleanliness of equipment or rinsing water. The method is not applicable to determine the antimicrobial activity of disinfecting agents. A different way to optimise cleaning and disinfecting processing uses a combination of databases and predictive modelling. NIZO Premia™ is an example that combines research knowledge with predictive modelling. It is a software platform that is used for optimisation of product properties or process performance. For example, fouling is mainly caused by denaturation of proteins and precipitation of minerals. The denaturation process of β-lactoglobulin (an important whey protein) can be described as a consecutive set of reactions (de Jong, 1996). This knowledge can be used to predict the fouling behaviour in heat exchangers of different dairy type products. By predicting the amount of fouling produced, the optimum running time for heat exchangers can be determined. In addition, the composition of the fouling layer is known, which makes it possible to choose the right cleaning procedures (cleaning agents, temperature, etc.). After optimisation with NIZO Premia, it appeared possible to reduce the amount of fouling by 50–80 %, resulting in longer running times and higher process efficiency (de Jong et al., 2002b). Another possibility is to use predictive modelling for the design of new


497 processing lines, making the effects concerning fouling and product properties visible. A typical example is the development of a new type of evaporator at a Dutch dairy company where the use of NIZO Premia resulted in 70 % less energy use compared with standard designed evaporators (Vissers et al., 2002). A third option is the use of predictive modelling to assess the opportunities and risks of the formation of biofilms. Biofilms are layers of bacteria and their extracellular products. Biofilms can cause problems in the food industry since bacteria in them are less affected by cleaning and disinfecting agents. They can also cause pre-mature spoilage of products at the beginning of the production process. The process of biofilm formation (adherence, growth and release) can be quantified using predictive models (de Jong et al., 2002a,b; den Aantrekker et al., 2003) These models can be used either for preventing the formation of biofilms or for defining the right strategy for removal of the biofilm. Thus, predictive modelling is a powerful tool to analyse and optimise critical processes within the food industry.

19.4 Types of pathogen response to disinfectants When applying chemical disinfectants in a process or on process equipment, it is important to know how microorganisms/pathogens may respond. Like every other organism, microorganisms protect themselves against all kind of influences from the environment. Some of the protection mechanisms are intrinsic (natural property) but others are acquired (mutation or acquisition of plasmids) during the evolution of the organism. A general overview of relevant disinfectants and their mode of action is given in Table 19.1. The possible modes of action of the applied disinfecting treatments and pathogen responses, as discussed in the previous section, are discussed below. 19.4.1 Target area of disinfectant The target comprises four areas:

• Cell membrane and its outer layers. Breaking down results in quick cell death/ inactivation of the microorganism (Todar, 2001).

• Damage to enzymes + important metabolic processes; some heavy metals (e.g. copper, silver, mercury) act as poisons to enzymes. Added as salts or organic combinations, they bind to SH groups of enzymes and cause changes in the structure (tertiary and quaternary) of these proteins (Schlegel, 1993). • Affecting the synthesis of proteins in the target organism results in growth prohibition (Schlegel, 1993; Todar, 2001). • Inhibition of DNA synthesis or breakage of the DNA strands resulting in the blockage of cell growth (McDonnell and Russell, 1999). 19.4.2 Pathogen response Adding disinfectants will result in increased stress on the bacteria and their metabolism. In principle, they have three ways of responding to disinfectants:


498

• Alteration of the target. • Reduction of target access. • Inactivation of the disinfectant. As disinfectants have in principle a broad spectrum of activity, it is not likely that the alteration of the target will work. The two other mechanisms seem to be possible, and a combination of resistance mechanisms is also a possibility (Chapman, 1998). The fact that microorganisms show this kind of behaviour is caused by either intrinsic or acquired resistance (Russell, 1995; McDonnell and Russell, 1999).

19.4.3 Intrinsic resistance This type of resistance is defined as a natural chromosomally controlled property of a bacterial cell to circumvent the action of a disinfectant. This kind of resistance is demonstrated particularly by Gram-negative bacteria and bacterial spores (Russell, 1991; McDonnell and Russell, 1999). Bacterial spores, the genera Bacillus and Clostridium in particular, are the most resistant, e.g. C. perfringens and B. cereus (Russell, 1995). The exact mechanism of sporicidal action is not fully understood; however, as the primal target area of biocides lies within the spore it is expected that, owing to the different layers of the spore, the penetration of biocides is limited.

19.4.4 Acquired resistance Acquired, non-plasmid-encoded resistance occurs when bacteria are exposed to gradually increasing concentrations of a certain biocide. Acquired, plasmidencoded resistance is in most cases some form of resistance against metal-based biocides (silver, copper or mercury) (Chapman, 1998; McDonnell and Russell, 1999; Russell, 1999b). However, a recent study investigated the resistance of Salmonella against hypochlorous acid (concentrations up to 28 mg/l) and indicated an emerging problem for the food industry (Mokgatla et al., 2002). Normally, a chlorine concentration of 10 mg/l is sufficient to inactivate vegetative, nonspore-forming microorganisms (Krop, 1990). This type of resistance might be caused by remaining organic substances (partly) inactivating the chlorine solution. It does show that, when applying certain disinfectants, it is important to apply correct concentrations of disinfectant in combination with a clean surface in order to achieve efficient inactivation of microorganisms. Therefore, this kind of resistance appears to be unstable and could also be considered as pseudo-resistance (Heinzel, 1998). Pseudo-resistance occurs when bacteria appear to be resistant to a certain kind of biocide but, when placed in a biocide-free environment, the resistance disappears. A few reasons are known to cause this apparent resistance:

• Use of an inefficient product (i.e. disinfectant with limited spectrum of activity). • Incorrect use of the disinfectant (not according to the conditions recommended by the supplier).


499

• Insufficient contact (time) with the surface to be treated. • Insufficient availability of the reactive agent. It is obvious that these reasons may lead to survival of bacteria. Although it is not considered to be microbial resistance, it is probably the most widely spread form of perceived resistance (Heinzel, 1998). In addition, it is even thought possible that some microorganisms are able to use the intended disinfectant as a source of energy and, instead of being inactivated, they start to grow.

19.5

Predicting microbial resistance

Predicting pathogen resistance against current disinfectants would be very useful for application in food factories and hospitals. Compared with antibiotics, the mode of action of preservatives/disinfectants is less well understood. Antibiotics normally have one specific group or subgroup of bacteria as target microorganisms, whereas disinfectants attack bacteria in general (Russell, 1991). Therefore, it is rather difficult to determine the exact effect on microorganisms beforehand. However, the mechanisms of action of disinfectants are becoming increasingly clear, allowing the effect they have on microorganisms to be predicted. Whether microorganisms will survive disinfection in practice depends on more than one factor. At least 15 factors appear to influence the possible resistance of a microbial strain (Baquero et al., 1998). A real model to predict the effect of a single disinfectant in an unspecified environment might therefore be difficult to realise. Thus, testing under practical conditions remains necessary to determine the effect of a certain disinfectant. Although not every detail is known, it is possible to determine whether a disinfectant will be effective based on the following information:

• • • •

Type of bacteria – metabolic state. Revival of injured cells/ biodiversity of microorganisms. Influence of remaining organic matter/biofilms. Processing conditions: temperature, pH.

19.5.1 Type of microorganisms – metabolic state The metabolic state of microorganisms is important in determining the possible effect of the disinfectant. With regard to vegetative cells, Gram-negative microorganisms appear more resistant than Gram-positive microorganisms owing to the composition of the cell wall. The cell wall of Gram-positives contains fewer lipids than that of Gram-negatives (Russell, 1999a). Bacterial spores are highly resistant to chemical and physical agents, due mainly to the spore coat and spore cortex (Bloomfield and Arthur, 1994; Setlow et al., 2002). For chemical agents, sporicidal concentrations are in most cases ten times (or more) higher than bactericidal concentrations (Russell, 1990). In the case of phenols, organic acids, QACs, biguanides, organomercurials (e.g. methyl-Hg, ethyl-Hg) and alcohols used at high concentrations, the agents have no sporicidal effect (Russell, 1990).


500 19.5.2 Revival of injured cells Another aspect is the difference in cell damage after treatment with disinfectants or physical agents. This implies that a certain number of cells can revive. However, this revival does not strictly indicate a resistance mechanism but is due to the statistical variance of the protection systems of the microorganism. It is important to realise that sensitivity also varies within the defined species, and resistance is defined as the tolerance of a disinfectant that exceeds the natural variance (Russell, 1991; Heinzel, 1998).

19.5.3 Processing conditions: pH, temperature and concentration The pH is an important factor as it can modify the practical application of the disinfectant used (Russell, 1991). For example, for chlorine, the pH needs to be in the range between 5 and 8 in order to be effective as hypochlorous acid. Below pH 5, chlorine gas is produced and, above pH 8, ClO– is produced; these are, apart from the acute toxicity of chlorine gas, not active as disinfectant (Krop, 1990). Similar effects are known for other disinfectants. Therefore, it is imperative to know the pH of the environment to predict whether a disinfectant will be active. In addition to pH (as discussed earlier), processing parameters such as temperature, concentration and application time are important factors concerning (pseudo)resistance of microorganisms. For example, when the temperature of a solution of formaldehyde is increased by 10 °C, the effectiveness is increased between three and five times (Krop, 1990; Russell, 1999a). Concerning concentration, when the concentration of the applied disinfectant is too low, the disinfectant is only bacteriostatic instead of bactericidal. This implies that as soon as the disinfectant is used up, the bacteria start growing again. For example, chlorhexidine is bacteriostatic at 0.0001 mg/l and bactericidal at 0.002 mg/l (Russell, 1990). When concentrations are too high, the disinfectant will act faster, but the question is whether that is strictly necessary. When this is not the case, it will only cost money and may be dangerous for the environment (in terms of personnel and equipment).

19.5.4 Residual organic matter/biofilms The guideline for cleaning and disinfection is that disinfection can be effective only when the equipment or surface is properly cleaned prior to the disinfection (Krop, 1990). Any remaining organic matter will inactivate the disinfectant and microorganisms will not be affected (Kraemer, 1998). A second reason is that organic compounds act as a protective layer for the microorganisms. This is also the case when microorganisms have formed a biofilm where, as a result of nutrient limitation, a reduced growth rate makes the specific microorganisms less susceptible to disinfectants (Brown and Gilbert, 1993; Luppens, 2002). The fact that microorganisms can form biofilms, implicating a change in their growth characteristics, can also result in resistance against disinfectants for the following reasons (Brown and Gilbert, 1993):


501

• Exclusion/influencing of the disinfectant by the glycocalyx (a slimy layer surrounding the cell).

• Chemical reaction of the glycocalyx with disinfecting agents. • Limited availability of key nutrients results in decreased growth rate. • The attachment to surfaces causes depressing of genes associated with sessile (directly to the substrate) existence which coincidentally affects antimicrobial susceptibility. These effects can be regarded as pseudo-resistance, as the effect will end as soon as the biofilm no longer exists.

19.6 Future trends As microorganisms evolve and adapt to disinfecting strategies, the development of more effective cleaning and disinfecting strategies and new tools to monitor the efficiency of these strategies will continue. The following trends can be distinguished.

19.6.1 Disinfecting agents – total service The producers of disinfectants are continuously working on new formulations and new active components and new total service concepts to serve their customers. Ecolab, a producer of cleaning and sanitising solutions (www.ecolab.com), offers a complete farm-to-fork approach concerning the food safety of the products of their customers, called ECO-SHIELD. Other suppliers such as Johnson Diversey and Alconox offer the same kind of total service concepts. By offering these kinds of products, a great responsibility lies with the manufacturers of disinfectants to prescribe the right concentrations and procedures for application in order to avoid an increase in (pseudo)resistant pathogenic microorganisms in factory environments.

19.6.2 Incorporation of disinfectants Where possible, disinfectants become integrated with, for example, processing equipment, packaging material or sanitary devices (Stekelenburg and Hartog, 2002; Chung et al., 2003). The advantage is that growth of (pathogenic) microorganisms is continuously inhibited as long as the disinfectant remains active. Disadvantages are a decreased activity in time as a result of biological breakdown or uptake by the environment. Another issue is that there is a risk that personnel will become negligent with regard to factory hygiene, resulting in an unwanted change of attitude.

19.6.3 Objective monitoring tools Process monitoring will become more and more a matter of common sense. Currently, it is possible to monitor on-line physical and chemical parameters such


502 as flow, conductivity, pH, temperature, turbidity, concentration and pressure. Developments are ongoing for new sensors like Isfets (ion-selective transistors) used for specific ion-concentrations (van Asselt et al., 2002) or biosensors based on oxygen/yeast cells used for the determination of ethanol in beverages (Rotariu and Bala, 2003). For monitoring microorganisms, a range of in-line monitoring devices such as flow cytometry (e.g. Bactoscan) and ATP could be applicable. However, the main issue for these methods is the detection limit which is, in most cases, higher than 103 CFU/ml (http://www.foss.dk). This implies that the method is currently useful only as an emergency break and not as a monitoring device. It is expected that the accuracy of the methods will improve, but to what extent will depend on the demands from market and government. 19.6.4 Genomics A relatively new development in the study of microorganisms is genomics. Since the first microbial genome sequence was published in 1995, genomics has caused a revolution in the way people think about microorganisms. One of the main applications of genomics is industrial strain development, e.g. in order to provide a certain microorganism with a gene producing a specific flavour or functional property. Information available on the genome sequences may be used to determine the cell response on different stress situations such as high temperatures, high pressure, osmotic shock or disinfectants (Abee and Wouters, 1999; Wells and Bennik, 2003). Screening techniques (e.g. DNA microarray) enable the screening of large numbers of microorganisms for specific properties and selection of the microorganisms containing those properties. Concerning pathogenic microorganisms, a possible application could be the screening of pathogenicity or response towards disinfectant agents. This approach will, based on comparison between disinfectant-resistant versus disinfectant-sensitive strains, allow the determination of disinfectant efficacy or critical concentration.

19.7


EHEDG Guidelines and test methods: http://www.ehedg.org/f_guidelines.htm (6 August 2004) European Union Guidelines http://europa.eu.int/eur-lex/nl/search/search_lif.html (28 July 2004) European biocide guideline 98/8/EG: http://europa.eu.int/servlet/portail/Render Servlet?search=DocNumber&lg=nl&nb_docs=25&domain=Legislation&coll=& in_force=NO&an_doc=1998&nu_doc=8&type_doc=Directive (28 July 2004) United States – FDA Environmental Protection Agency: http://www.epa.gov (28 July 2004)


503 Pesticides: http://www.epa.gov/pesticides/factsheets/alpha_fs.htm (28 July 2004) FAO/WHO Codex alimentarius; Codex committee on food additives and contaminants: ‘Code of practice on the safe use of active chlorine’ (currently in preparation, at step 3 of 6).

19.8 References ABEE, T. AND WOUTERS, J. A. (1999) Microbial stress response in minimal processing, International Journal of Food Microbiology, 50, 65–91. ALLERBERGER, F. AND DIERICH, M. P. (1988) Effects of disinfectants on bacterial metabolism evaluated by microcalorimetric investigations, Zentralblatt fuer Bakteriologie, Mikrobiologie und Hygiene, 187, 166–79. AVECIA (2004) http://www.avecia.com/biocides/products/vantocil/vantocil_folder.pdf (30 March 2004). BAQUERO, F., NEGRI, M. C., MOROSINI, M. I. AND BLÁZQUEZ, J. (1998) Antibiotic-selective environments, Clinical Infectious Diseases, 27, S5–S11. BERNSTEIN, M. (1990) The chemistry of disinfectants, In Romney, A. J. D. CIP: Cleaning in Place, The Society of Dairy Technology, Huntingdon, pp. 30–40. BEUCHAT, L. R. (1998) Surface Decontamination of Fruits and Vegetables Eaten Raw: A Review, Food Safety Unit World Health Organization, Georgia, USA. BIRD, M. R. (1994) Cleaning agent concentration and temperature optima in the removal of food based deposition. In (Eds, Fryer, P. J., Hasting, A. P. M. and Jeurnink, T. J. M.) Fouling and Cleaning in Food Processing, European Commission, Jesus College, Cambridge, UK, p. 249. BLOOMFIELD, S. F. AND ARTHUR, M. (1994) Mechanisms of inactivation and resistance of spores to chemical biocides, Journal of Applied Bacteriology, 76, 91S–104S. BROWN, M. R. W. AND GILBERT, P. (1993) Sensitivity of biofilms to antimicrobial agents, Journal of Applied Bacteriology, 74, 87S–97S. CHAPMAN, J. S. (1998) Characterizing bacterial resistance to preservatives and disinfectants, International Biodeterioration & Biodegradation, 41, 241–5. CHO, M., CHUNG, H. AND YOON, J. (2003) Disinfection of water containing natural organic matter by using ozone-initiated radical reactions, Applied Environmental Microbiology, 69, 2284–91. CHUNG, D., PAPADAKIS, S. E. AND YAM, K. L. (2003) Evaluation of a polymer coating containing triclosan as the antimicrobial layer for packaging materials, International Journal of Food Science and Technology, 38, 165–9. CODEX (2003) Proposed draft code of practice on the safe use of active chlorine, Codex Alimentarius Commission, The Hague. DE JONG, P. (1996) Modelling and optimisation of thermal processes in the dairy industry, Delft University of Technology, Delft. DE JONG, P., TE GIFFEL, M. C. AND KIEZEBRINK, E. H. (2002a) Prediction of the adherence, growth and release of thermoresistant streptococci in production chains, International Journal of Microbiology, 74, 13–25. DE JONG, P., TE GIFFEL, M. C., STRAATSMA, H. AND VISSERS, M. M. M. (2002b) Reduction of fouling and contamination by predictive kinetic models, International Dairy Journal, 12, 285–92. DEN AANTREKKER, E. D., VERNOOIJ, W. W., REIJ, M. W., ZWIETERING, M. H., BEUMER, R. R., VAN SCHOTHORST, M. AND BOOM, R. M. (2003) A biofilm model for flowing systems in the food industry, Journal of Food Protection, 66, 1432–8.


504 DENYER, S. P. AND STEWART, G. S. A. B. (1998) Mechanisms of action of disinfectants, International Biodeterioration & Biodegradation, 41, 261–8. DODD, T. (2003) Cleaning records and CIP optimization, International Journal of Dairy Technology, 56, 247. DONHAUSER, S., WAGNER, D. AND GEIGER, E. (1991) Zur Wirkung von Desinfektionsmitteln in der Brauerei, Brauwelt, 131, 604, 606, 609, 612, 614, 616. ERCO WORLDWIDE (2004) http://www.clo2.com/factsheet/factindex.html (4 August 2004). GIBSON, H., TAYLOR, J. H., HALL, K. E. AND HOLAH, J. T. (1999) Effectiveness of cleaning techniques used in the food industry in terms of the removal of bacterial biofilms, Journal of Applied Microbiology, 87, 41–8. GORMAN, S. P., JONES, D. S. AND LOFTUS, A. M. (1987) The sporicidal activity and inactivation of chlorhexidine gluconate in aqueous and alcoholic solution, Journal of Applied Bacteriology, 63, 183–8. HEINZEL, M. (1998) Phenomena of biocide resistance in micro-organisms, International Biodeterioration & Biodegradation, 41, 225–34. HOXEY, E. V. AND THOMAS, N. (1999) Gaseous sterilization. In Russell, A. D., Hugo, W. B. and Ayliffe, G. A. J. Principles and Practice of Disinfection, Preservation and Sterilization, Blackwell Science Ltd, Oxford. HUGO, W. B. AND RUSSELL, A. D. (1999) Types of antimicrobial agents. In Russell, A. D., Hugo, W. B. and Ayliffe, G. A. J. Principles and Practice of Disinfection, Preservation and Sterilization, Blackwell Science Ltd, Oxford. JEURNINK, T. J. M. AND BRINKMAN, D. W. (1994) The cleaning of heat exchangers and evaporators after processing milk or whey, International Dairy Journal, 4, 347–68. JEURNINK, T. J. M., WALSTRA, P. AND DE KRUIF, C. G. (1996) Mechanisms of fouling in dairy processing, Netherlands Milk and Dairy Journal, 50, 407–26. KALATHENOS, P. AND RUSSELL, N. J. (2003) Ethanol as a food preservative. In Russell, N. J. and Gould, G. W. Food Preservatives, Kluwer Academic/Plenum Publishers, New York, pp. 196–217. KAMASE, Y., MURAKAMI, H., TAKAHASHI, R., TAKAOKA, R. AND NAKAMURA, Y. (2003) Development of medical disinfector using ozone, IHI Engineering Review, 36, 131–4. KRAEMER, J. (1998) Cleaning and disinfection, Mitteilungen aus dem Gebiete der Lebensmitteluntersuchung und Hygiene, 89, 14–20. KROP, J. J. P. (1990) Reiniging en Desinfectie, Bolsward, Agrarische Hogeschool Friesland. LAMBERT, R. J. W. AND JOHNSTON, M. D. (2000) Disinfection kinetics: a new hypothesis and model for the tailing of log-survivor/time curves, Journal of Applied Microbiology, 88, 907–13. LUNDEN, J., AUTIO, T., MARKKULA, A., HELLSTROM, S. AND KORKEALA, H. (2003) Adaptive and cross-adaptive responses of persistent and non-persistent Listeria monocytogenes strains to disinfectants, International Journal of Food Microbiology, 82(3), 265–72. LUPPENS, S. B. I. (2002) Suspensions or biofilms and other factors that affect disinfectant testing on pathogens, Wageningen University, Wageningen. MARQUIS, R. E., RUTHERFORD, G. C., FARACI, M. M. AND SHIN, S. Y. (1995) Sporicidal action of peracetic acid and protective effects of transition metal ions, Journal of Industrial Microbiology, 15, 486–92. MCDONNELL, G. AND RUSSELL, A. D. (1999) Antiseptics and disinfectants: activity, action, and resistance, Clinical Microbiology Reviews, 12, 147–79. MCDONNELL, G., GRIGNOL, G. AND ANTLOGA, K. (2002) Vapor phase hydrogen peroxide decontamination of food contact surfaces, Dairy, Food and Environmental Sanitation, 22, 868–73. MOHR, M. AND DUGGAL, S. (1997) Zielgerichte Sauberkeit; Teil 2, Lebensmitteltechnik, 29, 60–2. MOKGATLA, R. M., GOUWS, P. A. AND BROZEL, V. S. (2002) Mechanisms contributing to


505 hypochlorous acid resistance of a Salmonella isolate from a poultry-processing plant, Journal of Applied Microbiology, 92, 566–73. MOORE, G., GRIFFITH, C. AND FIELDING, L. (2001) A comparison of traditional and recently developed methods for monitoring surface hygiene within the food industry: a laboratory study, Dairy, Food and Environmental Sanitation, 21, 478–88. QUANTEX LABORATORIES (2001) http://www.quantexlabs.com/triclosan.htm. (17 August 2004). QUINN, P. J. AND MARKEY, B. K. (1999) Viricidal activity of biocides part B: activity against veterinary viruses. In Russell, A. D., Hugo, W. B. and Ayliffe, G. A. J. Principles and Practice of Disinfection, Preservation and Sterilization, Blackwell Science Ltd, Oxford. REUTER, G. (1998) Disinfection and hygiene in the field of food of animal origin, International Biodeterioration & Biodegradation, 41, 209–15. RODGERS, S. L., CASH, J. N., SIDDIQ, M. AND RYSER, E. T. (2004) A comparison of different chemical sanitizers for inactivating Escherichia coli O157:H7 and Listeria monocytogenes in solution and on apples, lettuce, strawberries, and cantaloupe, Journal of Food Protection, 67, 721–31. ROTARIU, L. AND BALA, C. (2003) New type of ethanol microbial biosensor based on a highly sensitive amperometric oxygen electrode and yeast cells, Analytical Letters, 36, 2459–71. RUSSELL, A. D. (1990) Bacterial spores and chemical sporicidal agents, Clinical Microbiology Reviews, 3, 99–119. RUSSELL, A. D. (1991) Mechanisms of bacterial resistance to non-antibiotics: food additives and food and pharmaceutical preservatives, Journal of Applied Bacteriology, 71, 191–201. RUSSELL, A. D. (1995) Mechanisms of bacterial resistance to biocides, International Biodeterioration & Biodegradation, 36, 247–65. RUSSELL, A. D. (1996) Activity of biocides against mycobacteria, Journal of Applied Bacteriology, 81, 87S–101S. RUSSELL, A. D. (1999a) Factors influencing the efficacy of antimicrobial agents. In Russell, A. D., Hugo, W. B. and Ayliffe, G. A. J. Principles and Practice of Disinfection, Preservation and Sterilization, Blackwell Science Ltd, Oxford. RUSSELL, A. D. (1999b) Bacterial resistance to disinfectants: present knowledge and future problems, Journal of Hospital Infection, 43 (Suppl. S), S57–S68. RUSSELL, A. D. (1999c) Antifungal activity of biocides. In Russell, A. D., Hugo, W. B. and Ayliffe, G. A. J. Principles and Practice of Disinfection, Preservation and Sterilization, Blackwell Science Ltd, Oxford. SCHLEGEL, H. G. (1993) General Microbiology, University Press, Cambridge. SETLOW, B., LOSHON, C. A., GENEST, P. C., COWAN, A. E., SETLOW, C. AND SETLOW, P. (2002) Mechanisms of killing spores of Bacillus subtilis by acid, alkali and ethanol, Journal of Applied Microbiology, 92, 362–75. SHORROCK, C. J., BIRD, M. R. AND HOWELL, J. A. (1998) Yeast deposit removal from polymeric microfiltration membrane. In (Eds, Wilson, D. I., Fryer, P. J. and Hasting, A. P. M.) Fouling and Cleaning in Food Processing ’98, Cambridge. SINGH, N., SINGH, R. K. AND BHUNIA, A. K. (2003) Sequential disinfection of Escherichia coli O157:H7 inoculated alfalfa seeds before and during sprouting using aqueous chlorine dioxide, ozonated water, and thyme essential oil, Lebensmittel Wissenschaft und Technologie, 36, 235–43. SIXMA, J. J. (2001) Disinfectants in Consumer Products, The Hague, Health Council of the Netherlands. SOFOS, J. N. AND BUSTA, F. F. (1999) Chemical food preservatives. In Russel, A. D., Hugo, W. B. and Ayliffe, G. A. J. Principles and Practice of Disinfection, Preservation and Sterilization, Blackwell Science Ltd, Oxford. SPECTRUM LABORATORIES, http://www.speclab.com/compound/c70304.htm. (17 August 2004).


506 SPEEK, A. J. (2002) Onderzoek naar het toepassen van decontaminatie- en desinfectiemiddelen in de groenten en fruit verwerkende industrie, Keuringsdienst van Waren Noordwest, Amsterdam. STEKELENBURG, F. K. AND HARTOG, B. J. (2002) Efficacy testing of antimicrobial agents, International Food Hygiene, 12, 5, 7. TATTERSON, I. N. AND WINDSOR, M. L. (2001) Cleaning in the Fish Industry, Aberdeen Torry Research Station. TODAR, K. (2001) The Control of Microbial Growth, University Wisconsin-Madison, Madison. VAN ASSELT, A. J. AND TE GIFFEL, M. C. T. (2002) Opti-Cip optimaliseert en valideert CIPreiniging, Voedingsmiddelentechnologie, 35, 84–5. VAN ASSELT, A. J., VAN HOUWELINGEN, G. AND TE GIFFEL, M. C. (2002) Monitoring system for improving cleaning efficiency of cleaning-in-place processes in dairy environments, Food and Bioproducts Processing, 80, 276–80. VERMEIREN, L., DEVLIEGHERE, F. AND DEBEVERE, J. (2002) Effectiveness on some recent antimicrobial packaging concepts, Food Additives and Contaminants, 19, 163–71. VISSERS, M. M. M., DE JONG, P. AND DE WOLFF, J. J. (2002) Nieuwe indampertechniek resulteert in 70 procent energiebesparing, Voedingsmiddelentechnologie, 35, 16. WANG, M. Y., COLLINS, E. B. AND LOBBEN, J. C. (1973) Destruction of psychrotrophic strains of Bacillus by chlorine, Journal of Dairy Science, 56, 1253–7. WELLS, J. M. AND BENNIK, M. H. J. (2003) Genomics of food borne bacterial pathogens, Nutrition Research Reviews, 16, 21–35. YOUNG, S. B. AND SETLOW, P. (2003) Mechanisms of killing of Bacillus subtilis spores by hypochlorite and chlorine dioxide, Journal of Applied Microbiology, 95, 54–67.


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20 Pathogen resistance and adaptation to low temperature J. Sutherland, London Metropolitan University, UK

20.1

Introduction

Temperature is an extrinsic environmental factor, which exercises a considerable degree of control over microbial growth responses and metabolism. Microorganisms can, at least to some extent, regulate their internal environment to modulate the effects of the external environment, e.g. in conditions of low pH, they deploy an acid tolerance response by utilising the proton pump mechanism to maintain intracellular pH. They also modulate the effects of a low water activity (aw) environment by accumulating compatible solutes in order to maintain intracellular water activity. However, microorganisms have no direct control over the temperature of their environment, i.e. they are unable to attain temperature homeostasis. This characteristic is of immense value to the food industry, since consistent, wellmaintained refrigeration is a highly important method for controlling growth of spoilage microorganisms and foodborne pathogens. Freezing of food also controls microbial growth in foods, but it is important to recognise that although refrigeration and freezing can cause injury to microorganisms, they do not necessarily destroy them, or any exoenzymes produced. If the temperature progressively declines to below 0 ºC, as well as cell membrane disruption (see below), the external environment of the microorganism may begin to freeze, resulting in reduced aw of the cell environment as a consequence of an increase in concentration of the salts in the medium surrounding the microbial cell. The microorganisms then initiate accumulation of compatible solutes to equalise the interior and external aw. This can assist in protecting some of the cellular


508 protein against damage attributable to ice formation, e.g. Pseudomonas syringae (a plant pathogen) can form ice-nucleation proteins in the outer-cell membrane, which regulates ice formation in the cells (Weber and Marahiel, 2003). While most foodborne pathogens do not grow at temperatures below 7 or 8 ºC, it is paradoxical that many can survive for lengthy periods at such temperatures, e.g. Salmonella spp. and Escherichia coli O157:H7 (Herbert and Sutherland, 2000). However, some foodborne pathogens are able to grow, albeit slowly, at refrigeration temperatures, e.g. Yersinia enterocolitica, Listeria monocytogenes, Aeromonas hydrophila, psychrotrophic Bacillus cereus and non-proteolytic Clostridium botulinum. The effects of temperature on microbial cells are diverse and include the following:

• It controls the ability of the microbial cells to grow, and determines the extent of growth.

• It affects the physiology and structure of the microbial cell. • It affects the metabolic functioning of the cell, including protein synthesis, as a consequence of expression of different genes.

• It may affect the virulence of pathogenic microorganisms. These characteristics will be considered in more detail.

20.2

Effect of temperature on microbial growth

20.2.1 Cardinal temperatures of microorganisms Microorganisms can be characterised on a basis of their minimum, maximum and optimum growth temperatures. These are described as the cardinal temperatures. The definitions proposed by Morita (1975) are those most generally accepted.

• Mesophile: Mesophilic organisms have an optimum growth temperature of 25– 40 ºC, a maximum of 40–50 ºC and a minimum of 5–25 ºC. This group includes most foodborne pathogens and some food spoilage organisms. Mesophilic organisms can also be psychrotrophic. • Psychrotroph: Psychrotrophic organisms have an optimum temperature for growth of greater than 15 ºC, a maximum of greater than 25 ºC and a minimum temperature which can be as low as 0 ºC. These organisms are frequently encountered in foods and can cause spoilage, e.g. Pseudomonas spp. and Brochothrix thermosphacta, or they can be foodborne pathogens, e.g. Y. enterocolitica, L. monocytogenes, A. hydrophila and psychrotrophic strains of B. cereus. These organisms are to some degree cold tolerant, but should not be confused with psychrophiles. • Psychrophile: Pyschrophilic organisms have an optimum growth temperature of below 15 ºC, a maximum of around 20 ºC, and a minimum of 0 ºC or lower. Such microorganisms are encountered in specific environments such as the polar regions and are not found in foods. Unlike psychrotrophs, low temperatures are essential for their growth and functioning.


509

• Thermophile: Thermophilic organisms have an optimum growth temperature of 45–65 ºC, a maximum of greater than 60 ºC and a minimum of 35–45 ºC. Such organisms can survive the canning process and may cause spoilage of canned goods, e.g. Clostridium thermosaccharolyticum, but none is known to be pathogenic. Other environmental conditions affect the cardinal temperatures. The temperatures quoted in Table 20.1 apply under otherwise optimal environmental conditions, i.e. at pH values of around 7.0 and water activities of non-inhibitory values greater than 0.99. When the environmental pH value and water activity are sub-optimal, the minimum temperature for growth will be increased. The cardinal temperatures also vary for different species of organism within a genus and even for different strains within a species. Table 20.1 shows the minimum growth temperatures for foodborne pathogens, in otherwise optimal environmental conditions. Campylobacter jejuni has an unusually compressed temperature range for growth, since it fails to grow below about 30 ºC and above 45 ºC and has a temperature optimum of 42–45 ºC (Doyle and Roman, 1981). In view of this temperature limitation, it is difficult to understand why C. jejuni is the primary cause of foodborne illness in the UK, since it will not grow in refrigerated foods or in foods stored at ambient temperature, unless it is unusually high.

20.2.2 Low temperature and the concept of metabolic exhaustion The ‘hurdle’ effect (Leistner, 2000) demonstrates the concept that inhibitory factors in foods, e.g. pH value, redox potential, low temperature and preservatives such as nitrite and carbon dioxide, can be regarded as hurdles that act in combination to preserve the food and maintain its safety. The hurdles can be raised or lowered, or even removed, according to the degree of preservation required, e.g. in fermented meats the pH value declines during fermentation by lactic acid bacteria, contributing to preservation and sensory acceptability, but it should not fall so low that it detracts from the flavour. In such products, the pH decreases to around 4.0– 4.5, inhibiting growth of Staphylococcus aureus, salmonellae and other foodborne pathogens and many spoilage organisms. While the product subsequently dries, the aw decreases to 0.90–0.93, which is inhibitory to the growth of spoilage microorganisms and foodborne pathogens (although not necessarily to Staphylococcus aureus). The combination of these highly inhibitory conditions obviates, or at least reduces, the requirement for chilling and, consequently, traditional fermented meats are sometimes stored at ambient temperatures. Microorganisms, including foodborne pathogens, grow more quickly at room temperature than under refrigeration, but they are also reported to die more rapidly, particularly in acidic environments, both in foods and in microbiological media. For example, numbers of salmonellae in egg mayonnaise decline more quickly when the mayonnaise is kept at room temperature than when it is refrigerated (Perales and Garcia, 1990).


510

Table 20.1

Minimum, optimum and maximum temperatures for growth of foodborne pathogenic microorganisms Minimum temperature (ºC)

A. hydrophila B. cereus: Psychrotrophic Mesophilic Campylobacter Cl. botulinum: Proteolytic Non-proteolytic Cl. perfringens E. coli (pathogenic) L. monocytogenes Salmonellae Shigella Staph. aureus V. cholerae V. parahaemolyticus Y. enterocolitica

Maximum temperature (ºC)

Reference

0–4

28–35

42–45

ICMSF (1996)

4–5 10 32

28–35 42–45 42–45

30–35 45 45

ICMSF (1996) ICMSF (1996) Doyle and Roman (1981)

12.5 3.0–3.3 12 7 0–4

35 30 43–47 35–40 30–37

50 45 50 46 45

Pierson and Reddy (1988)

5 7 7 10 5 –1

37 37 37 37 37 28–30

45 46 48 43 43 42

Adapted from Herbert and Sutherland (2000).


Optimum temperature (ºC)

ICMSF (1996) ICMSF (1996) Gill and Reichel (1989), Walker et al. (1990a) Simonsen et al. (1987) Fehlhaber (1981) Tatini (1973) ICMSF (1996) Beuchat (1973) Walker et al. (1990b)

511 This phenomenon also occurs with Escherichia coli O157:H7 in fermented meats and has raised concern that application of the hurdle concept to minimally processed foods may inhibit growth of foodborne pathogens, but would prolong their survival (Uyttendaele et al., 2001). This paradoxical behaviour is attributed to the phenomenon of ‘metabolic exhaustion’. In a hostile environment that does not allow growth, vegetative cells will survive or die. In a low-pH environment, the death process is accelerated and the nearer to the optimum growth temperature, the more rapidly the cells die. The cells metabolise vigorously to attempt to maintain the cellular systems and overcome the hurdle of low pH, and in doing so exhaust their energy resources and die (Leistner, 2000). However, at temperatures below the minimum for growth, even in conditions of low pH, the cells are not metabolising so actively and hence do not become metabolically exhausted as rapidly. Perales and Garcia (1990) recommend that to prevent salmonella infections from mayonnaise made from raw egg yolk, the mayonnaise should be stored at a warm temperature for some hours or days before refrigeration or consumption. This would take advantage of metabolic exhaustion to improve the safety of the product.

20.2.3 Effect of temperature on the kinetics of microbial growth For a microorganism growing in microbiological media or food, reduction of temperature has the effect of increasing lag and generation times and reducing growth rate. The lag time is considered to be an adaptation period during which the organism accustoms itself to new or modified environmental conditions. However, the situation regarding lag is complex and not fully understood (Robinson et al., 1998, 2001). It seems clear that the lag time depends not only on the environmental conditions but also on the previous history and physiological condition of the cells (Baranyi and Roberts, 1994). Consequently, lag can be considered to represent the amount of ‘work’ that has to be done by the cell to equip itself for growth in the new environment (Baranyi and Roberts, 1995). Growth rate, however, depends solely on the current environment and there is no historical ‘carry-over’. Increasing temperature increases the growth rate, but this is not a directly proportional relationship; the growth rate increases more rapidly as the temperature rises (Ross and Nichols, 1999). After the optimum temperature has been achieved, the growth rate decreases precipitously, since the raised temperature causes denaturation of the cellular proteins. In circumstances of progressive reduction of temperature, however, the rate of multiplication of cells decreases and finally ceases, whereupon the organisms enter a survival phase, which can be very prolonged. The precise temperature at which growth ceases is often difficult to define. Rather than a gradual decline of growth as the temperature approaches 0 ºC, it is suggested that there may be a specific threshold temperature below which multiplication of cells cannot occur (Ross and Nichols, 1999). The Arrhenius equation, originally developed to determine the effects of temperature on chemical reactions, can be applied to microorganisms; the rate of reaction increases with temperature (to the point of denaturation) and so does the rate of microbial growth:


512 k = Ae–E/RT or k = Aexp(–E/RT) where k = the rate constant for the reaction, R = the universal gas constant, T = temperature in degrees absolute, E = activation energy of the reaction and A = collision or frequency factor (a constant). The growth rate (r) of the organism can be substituted for k and the equation becomes: ln r = (–E/R)(1/T) + A This equation demonstrates an apparently linear relationship between the natural logarithm of growth rate and the reciprocal of the temperature in degrees Kelvin. The temperature range over which the relationship is linear depends on the temperature optimum and minimum of the organism. As the temperature approaches the minimum for growth, the slope of the graph tends to become vertical, i.e. there is a sharp cut-off at temperatures below which growth fails to occur. Similarly, as the temperature extends beyond the optimum to the maximum, the growth rate slows so that again the graph tends towards a vertical slope (Fig. 20.1). Detailed

Fig. 20.1 Growth rate of a mesophilic bacterium (Escherichia coli) as a function of temperature. The logarithm of the specific growth rate is plotted against 1/T (degrees Kelvin). Temperatures in ºC are included for comparison. The dashed extrapolation line indicates the linear response of the growth rate, which is constant in response to 1/T, and highlights the linear response in the mesophilic temperature range. (Adapted from Fuchs and Kroger, 1999.)


513 evaluation of the plots of bacterial growth rates may show that the apparently linear portion of the curve is actually slightly curved or multiphasic (Sorhaug, 1992). Nevertheless, such plots are valuable for comparing growth ranges of microorganisms. Ratkowsky et al. (1982) used a modification of the Arrhenius equation to demonstrate that if the square root of the growth rate was plotted against the absolute temperature, the relationship was linear between the optimum and minimum growth temperatures, although at temperatures beyond the optimum, the relationship was non-linear because of thermal denaturation of the cell components. A further modification by Ratkowsky et al. (1983) allowed growth rate predictions to be made over the entire biokinetic range of temperatures and resulted in the equation shown below: √r = b(T – Tmin) {1-exp[c(T – Tmax)]} where r = growth rate, T = temperature of prediction, Tmin = minimum growth temperature, Tmax = maximum growth temperature, b is a regression coefficient for temperatures below the optimum for growth and c is a regression coefficient for temperatures above the optimum. Cardinal temperature models such as the above have been exploited (Rosso et al., 1993, 1995) to predict growth rates of food-associated microorganisms.

20.3 Effect of low temperature on the structure, physiology and metabolism of bacterial cells Low temperatures induce changes in the structure of the microbial cell membrane and affect the metabolism of the cell in such a way as to promote continuing growth, or at least, survival. When the temperature drops to near or below the minimum for growth of the microorganism, physiological changes take place which represent attempts by the organism to maintain homeostasis. The metabolic responses are two-fold: 1. Maintenance of cell membrane structure and integrity. 2. Maintenance of protein production and enzymatic activity.

20.3.1 Cell membrane integrity: maintenance of homeoviscosity Fluidity of the bacterial cell membrane is essential for cellular function, and the process of homeoviscous adaptation enables cells to maintain membrane fluidity at low temperatures. The major component of the bacterial cell membrane is phospholipid, and these molecules are arranged as a bilayer (Fig. 20.2), with polar (hydrophilic) heads exposed at the surfaces inside and outside the cell, allowing interaction with the aqueous environment within and outside the cell. The fatty acid chains face inwards and, at normal temperatures, exist in a random, disordered state which maintains the fluidity of the membrane (Fig. 20.2(b)). When an abrupt


514

(a)

(b) Fig. 20.2 Illustration of the lipid bilayer structure in (a) the crystalline phase occurring at low temperatures and (b) the fluid (liquid) phase relevant to normal temperatures. Tm represents the transition phase in the membrane consequential to a temperature downshift (B→A). (Adapted from Ross and Nichols, 1999.)

temperature downshift occurs, the fatty acid chains become more ordered and crystalline in form, with a consequent decrease in fluidity of the membrane (Fig. 20.2(a)). This change of state is described as the lipid phase transition and the temperature at the midpoint of the transition is the transition temperature (Berry and Foegeding, 1997). The reduced fluidity of the membrane means that the lipid bilayer is no longer an effective barrier between the cell and the environment, which has adverse consequences for the microbial cell:

• In the crystalline state, the cell membranes become more fragile, with consequent leakage of the intracellular components across the bilayer. This migration of cell components may occur in two ways: firstly through minute cracks in the crystalline regions, resulting in leakage of the cell contents and secondly through formation of grain boundary effects. These are disordered areas at the interfaces between different parts of the crystalline regions, resulting in gaps which form during the lipid phase transition, allowing the escape of small molecules and ions from within the cell (Ross and Nichols, 1999). • The damaged membrane is no longer functional and cannot regulate normal physiological activities associated with healthy cell function, e.g. sugar, amino acid and ion transport across the membrane is disrupted, and membraneassociated oxidation and reduction enzymes are unable to operate.


515 In order to counteract these problems, microorganisms can modify their membrane characteristics to some extent, by adjusting the phospholipid fatty acid composition, allowing metabolic characteristics to continue during a temperature downshift. At normal growth temperatures for mesophilic microorganisms, the fatty acids in the cell membranes are generally 10–20 carbon atoms in length, mainly C16 (hexadecanoic fatty acids) and C18 (octadecanoic fatty acids). At low temperatures, more short-chain fatty acids with lower melting points are incorporated into the membrane, thus reducing the transition temperature. A greater proportion of unsaturated fatty acids are also incorporated into the membrane to maintain fluidity, replacing the longer chain and more saturated fatty acids associated with fluidity at higher temperatures. The double bonds in the unsaturated fatty acids prevent tight packing of the fatty acids into a crystalline form, which would occur with saturation of the fats. Branched chain fatty acids are frequently encountered in the cell membranes of psychrophilic microorganisms and promote fluidity of the membrane by preventing the close packing of the non-branched lipid chains. For example, in E. coli, the proportion of cis-vaccenic acid (C18–1) increases, accompanied by a decrease of palmitic acid (C16) as growth temperature declines, i.e. an unsaturated fatty acid replaces a saturated one. It appears that cis-vaccenic acid is essential for membrane fluidity and cannot be replaced by palmitoleic acid (C16–1). Initially, when E. coli cells are subjected to low temperature, palmitic acid is replaced by palmitoleic acid, which is then converted to cis-vaccenic acid by an enzyme, β-ketoacyl-ACP-synthase II (KAS II). KAS II is a constitutive enzyme but is regulated to become active at low temperatures (Ross and Nichols, 1999).

20.3.2 Metabolic changes at low temperatures Reduction of environmental temperature to one that is appreciably lower affects microorganisms in the environment by causing either cold adaptation or cold shock. Cold adaptation or acclimatisation as defined by D’Amico et al. (2002) is generally applicable only to psychrophiles, which are microorganisms that have become adapted to growth at low temperatures. The cell needs to make significant structural and physiological modifications, including synthesis of cold acclimatisation proteins (Caps) in order to contend with the reduction in biochemical reaction rates that occur as a consequence of the low temperature. However, Caps synthesis has been reported in some psychrotrophs (Hebraud and Potier, 1999). Cold shock, on the other hand, implies the situation where normally mesophilic or psychrotrophic microorganisms respond to a sudden significant decrease in temperature. This can result in changes to the cell membrane and synthesis of cold shock proteins (Csps), which protect the cell from the adverse environment (Goverede et al., 1998). These proteins are different from the heat shock proteins produced by microorganisms subjected to heat stress. Microorganisms regulate cellular metabolism as a response to lowered temperature in two ways. First, there may be a change in concentration of a particular enzyme, generally resulting from a change in the rate of synthesis of the enzyme, which modulates the rate and extent of metabolic activity within the cell. The rate


516 of enzyme-catalysed reactions depends on the temperature and the concentration of enzyme. Increases or decreases in temperature result in denaturation of the enzyme molecule by alteration of the tertiary and quarternary structures as a consequence of damage to the bonds that support the structure. This process is reversible so that if the temperature returns from the low to the normal range, a denatured enzyme can refold and resume normal functioning. The enzyme can also renew its structure to some degree after subjection to high temperature, but if the temperature is excessive, then the denaturation is irreversible. Second, structural changes induced by low temperatures may result in alteration or cessation of metabolic processes. As well as enzymes, other proteins (Csps) must be synthesised at low temperatures to allow microbial growth and/or maintenance. If Escherichia coli cells are transferred from an optimal growth temperature of 37 ºC to 10 ºC, gene products are induced, which can constitute a large proportion of the total cellular protein (Booth, 1999). Weber and Marahiel (2003) described three stages of cold shock when microorganisms are subjected to low temperatures:

• Stage I is effectively a lag or acclimatisation phase and is an initial shock response, immediately following an abrupt temperature downshift. This phase is transient but may last from one to several hours. During this time there is a substantial reduction in growth rate accompanied by reprogramming of protein synthesis. • Stage II is a recovery phase where the bacterial cells begin to multiply more rapidly and manifest additional changes in their protein profile compared with stage I. Cells in this phase are considered to have adapted to the low temperature environment. • Stage III is synonymous with entry of the cells to the stationary phase of growth when multiplication ceases. At this stage gene expression is again modified. Studies of Csps suggest that, despite immense diversity among microorganisms, this process is a general pattern and has been studied in many genera, including E. coli (Jones et al., 1987; Jones and Inouye, 1994), B. cereus (psychrotrophic strains; Mayr et al., 1996) and L monocytogenes (Bayles et al., 1996; WemekampKamphuis et al., 2002). It is supposed that the function of Csps is to protect the cell from low temperature. Chaperones are proteins that organise the folding of newly synthesised proteins. The Csps are thought to act as RNA chaperones, minimising the folding of RNA, thereby making it easier for the cell to translate the RNA message to synthesise protein (Bae et al., 1997). Csps in psychrotrophs such as Y. enterocolitica and L. monocytogenes differ from those of mesophiles in the following ways (Hebraud and Potier, 1999):

• The normal metabolic (‘housekeeping’) proteins of psychrotrophs are not repressed when the temperature is reduced; they are synthesised in the same way at optimal and low temperatures. • Cold shock proteins are manufactured by psychrotrophs in greater numbers the more the temperature is reduced.


517

• A second set of cold-induced proteins, cold acclimatisation proteins, are continually synthesised by psychrotrophs during storage at low temperatures, a characteristic also recorded by Barbaro et al. (2002) in a psychrotrophic Acinetobacter sp. Stress-induced changes in gene expression are directed towards maximising opportunities for survival of bacterial cells, and possibly restoring growth, with consequent metabolic changes. In the interests of promoting survival under all circumstances, it appears that expression of genes resulting from a particular type of stress may sometimes protect against other, unrelated, stressors, e.g. Leyer and Johnson (1993) reported that cells of Salmonella Typhimurium (Salmonella enterica serovar Typhimurium) adapted to mild acid stress were also protected from heat and osmotic stress, and Conner and Kotrola (1995) recorded that the presence of organic acids promoted the survival of E. coli O157:H7 at 4 ºC. A similar protective effect was noted in L. monocytogenes (Koutsoumanis et al., 2003). However, Koutsoumanis et al. also reported that the reverse was not observed, i.e. exposure of L. monocytogenes to low temperature stress failed to protect against acid stress. It is clear that there is considerable scope for further work in the area of cross-protection by stress-induced proteins.

20.4

Pathogenicity in relation to low temperature

Host-pathogen interactions include the concept of virulence of a microorganism. Virulence in infective foodborne pathogens is related to the ability of the pathogen to invade and multiply within the host cells, with subsequent release and possibly spread to other cells within the body of the host. Low and high temperatures can affect the regulation of virulence genes in pathogens. The effect of low temperature on subsequent virulence of foodborne pathogens is of interest to food microbiologists because of the importance of refrigeration as a means of preservation of perishable foods. Furthermore, there have been suggestions that virulence of some pathogens may be enhanced by growth at refrigeration temperature (Gray and Killinger, 1966; Kirov, 1997; Chan et al., 2001).

20.4.1 Effect of low temperature on virulence of psychrotolerant foodborne pathogens Yersinia enterocolitica Yersinia enterocolitica is capable of growth in foods stored at low temperatures and demonstrates temperature dependence in some of its phenotypic characteristics which are used as virulence markers, e.g. motility, calcium dependence and ability to take up Congo red dye. Colony morphology is also temperaturedependent (Varnam and Evans, 1991). The virulence factors of pathogenic strains of Y. enterocolitica are complex. Virulence in this organism is chromosomallyand plasmid-mediated (the pYV plasmid). The plasmid encodes for a group of


518 proteins (Yersinia outer membrane proteins; Yops), the expression of which are temperature, pH and calcium dependent and which have an important role in virulence. Incubation above 30 ºC and/or prolonged laboratory subculture can result in loss of the pYV plasmid and consequently of plasmid-mediated characteristics, including virulence. Li et al. (1998) reported that high concentrations of calcium in the growth medium stabilise the plasmid in vitro and prevent its loss at temperatures above 30 ºC. The situation with Y. enterocolitica is highly complex because some virulenceassociated factors are turned on at 26 ºC and off at 37 ºC, while for others the situation is reversed (Varnam and Evans, 1991). It is theorised that the organism prepares for invasion of mammalian cells while growing outside the body, i.e. at lower temperatures. Once the pathogen has entered the host, a different set of virulence factors is activated at the higher temperature to facilitate access to the host enterocytes (Maurelli, 1989). Virulence of Y. enterocolitica is still poorly understood and there is a need for further work to clarify this immensely complex area. Listeria monocytogenes L. monocytogenes produces a number of virulence factors, which are manifested as proteins on the surface of the cells (Doyle, 2001). Surface protein p104 is involved in adhesion to the intestinal cells (having been previously ingested in food) while other surface proteins, internalin A and internalin B, are required for entry of the organism into the intestinal epithelium cells and hepatocytes in the liver, respectively. Other virulence factors include listeriolysin O (a haemolysin), ActA protein, phospholipases, metalloprotease, Clp proteases, ATPases and protein P60. There has been considerable discussion about whether virulence can be enhanced by growth of L. monocytogenes at low temperature. Gray and Killinger (1966) originally suggested that this might be the case and Czuprinski et al. (1989) reported that virulence of some strains of L. monocytogenes grown at 4 ºC increased when injected intravenously into mice. Conte et al. (1994) suggested that L. monocytogenes grown at low temperatures could synthesise the virulence factors necessary for invasion of host enterocytes. However, Czuprinski et al. (1989) observed that L. monocytogenes strain EGD released less haemolysin into the medium when grown at 4 ºC, compared with growth at 22 and 37 ºC. Furthermore, Brackett and Beuchat (1990) and Myers and Martin (1994) did not observe any changes in pathogenicity of L. monocytogenes grown at low temperature compared with growth at 37 ºC. It would be reasonable to conclude that L. monocytogenes can maintain its virulence characteristics when cultivated at low temperatures, but virulence is unlikely to be enhanced when the organism is subsequently ingested via the oral route. Aeromonas spp. Aeromonas is a food- and waterborne pathogen that can grow at refrigeration


519 temperatures and can produce several types of virulence-asociated factors (Beuchat, 1991). As in the case of Y. enterocolitica, the temperature of cultivation can affect a number of these virulence factors (Gonzalez-Serrano et al., 2002). Majeed et al. (1990) found that A. hydrophila and A. sobria produced both enterotoxin and haemolysin at 5 ºC, but A . caviae produced neither. Virulence factors are found more often, or are better expressed, at 37 ºC in clinical isolates, while isolates from food and the environment express them more efficiently at low temperatures. Flexible pili, which promote intestinal colonisation, were encountered in greater numbers when the culture was grown at 7 and 22 ºC, than when growth was at 37 ºC, and adhesion to cell culture lines was best at 22 ºC and 7 ºC for some environmental strains. Haemolysin, cytotoxin, proteases and lipopolysaccharide levels increased and virulence (tested in mice) was greater in strains of Aeromonas grown at 20 ºC compared with those grown at 37 ºC (Kirov et al., 1993; Kirov, 1997). Bacillus cereus Although most strains of B. cereus are mesophilic, this organism will be considered with the psychrotrophic foodborne pathogens, since the psychrotrophic strains are of particular interest. The presence of psychrotrophic strains of B. cereus is of particular concern in complete pre-prepared foods (‘ready-meals’), since when the organisms achieve the stationary phase of growth (106 CFU/g), an emetic exotoxin is produced. The diarrhoeal toxin (enterotoxin) is produced in the small intestine during sporulation and can also be synthesised in microbiological media during the mid-to late exponential phase of growth (Fermanian et al., 1996). Rowan and Anderson (1998) reported enterotoxin formation by psychrotrophic B. cereus in milk-based infant foods at 4 ºC after 23 days, at 6 ºC after 15 days and at 8 ºC after 10 days. Further evidence that the diarrhoeal toxin is produced in foods was provided by Mahakarnchanakul and Beuchat (1999), who observed that B. cereus produced enterotoxin in chicken gravy stored at 10 ºC after storage for two days (psychrotrophic strain) and five days (mesophilic strain), but no enterotoxin production was recorded in mashed potato at 10 ºC. They concluded that temperatures of less than 2 ºC should be used for storage of pasteurised ready-to-eat foods.

20.4.2 Effect of low temperature on virulence in mesophilic foodborne pathogens Campylobacter Campylobacter is responsible for more cases of foodborne illness in the UK and USA than any other foodborne pathogen, yet it fails to grow in microbiological media or foods at temperatures below 30 ºC. It survives at low temperatures, although not as well as other Gram-negative pathogens such as verocytotoxigenic E. coli and salmonellae. However, during survival at temperatures below 30 ºC, Campylobacter continues to respire and respiration has been recorded at 4 ºC, suggesting that the substrate transport and electron transfer mechanisms are still


520 active at this temperature (Hazeleger et al., 1998). Constitutive protein synthesis also continues to some extent at 4 ºC, although Csps are not produced (Hazeleger et al., 1998). These authors theorised that the absence of Csps in C. jejuni may be a reason why it fails to grow below 30 ºC. Reduced temperature does not appear to affect the fatty acid composition of the cell membrane of Campylobacter (Hazeleger et al., 1995). Chan et al. (2001) reported that clinical isolates generally showed better survival than poultry isolates of C. jejuni at 4 ºC and postulated that such strains may have a lower infective dose and may be of enhanced virulence to humans. Despite these studies, there is a paucity of information about the effect of low temperature on the virulence characteristics of Campylobacter and mechanisms that cause observed differences in tolerance to low temperatures. Salmonella Bolton et al. (1999) tested recent isolates of salmonellae for invasiveness, compared to a well-characterised invasive strain of Salmonella Typhimurium (strain TML). Nine of the ten strains showed invasiveness characteristics similar to strain TML, but the less invasive one (strain GM3) was histotoxic, probably the reason for the limited invasiveness. Salmonella serotypes Choleraesius (strain A50) and Dublin (strain 3246) from systemic salmonellosis in pigs and calves, respectively, were also tested. The Dublin strain, when grown at 37 ºC and tested immediately for invasiveness, damaged the mucosal cells in a similar (limited) way to the GM3 strain. However, if the Dublin strain was grown at 37 ºC and stored overnight at 4 ºC before testing, it did not damage the mucosa in the same way. This suggests a reduced invasiveness of freshly grown organisms compared to those stored at 4 ºC. However, the strain GM3 was unaffected by storage at 4 ºC. Bolton et al. (1999) concluded that the invasiveness was strain dependent. Vibrio There are two main Vibrio species of pathogenic importance: V. parahaemolyticus and V. cholerae. These have different pathogenic mechanisms. Strains of V. cholerae (and other Vibrio species) have been reported to exist at low temperatures in a viable but non-culturable state (VBNC; Colwell and Huq, 1994), in which the cells remain alive and can metabolise, but cannot be recovered on laboratory media. V. cholerae produces cholera toxin, the serological and genetic aspects of which have been extensively studied (Spangler, 1992; Kaper et al., 1995). Carroll et al. (2001) and Datta and Bhadra (2003) reported that a temperature drop from 35 ºC to 5 ºC resulted in loss of culturability and assumption of a coccoid form. If the temperature was reduced to 15 ºC (the minimum for growth) for 2 h before a further decrease to 5 ºC, there was no adaptation to the lower temperature. There was reduced expression of the cholera toxin at low temperature, probably a consequence of decreased metabolic activity (Carroll et al., 2001). Vibrio parahaemolyticus produces haemolysin (a positive Kanagawa response) which lyses human erythrocytes. This is temperature dependent and does not occur


521 at all at 0–4 ºC, although the haemolysin was absorbed onto the erythrocytes at low temperature (Sakurai et al., 1975). Bates and Oliver (2004) hypothesised that Kanagawa-positive and -negative strains may respond differently to the lower temperature and salinity of sea water, but entry into the VBNC condition occurred whether or not the organisms produced haemolysin. Shigella Virulence in Shigella is associated with a plasmid as well as the chromosome. This organism produces Shiga toxin, the genetic aspects of which are discussed by Maurelli and Lampel (1997). Virulence is regulated by temperature, so that when Shigella is grown at 37 ºC it is virulent, but if cultivated at 30 ºC, the characteristic is lost and the organisms are not able to invade host cells. Virulence can be restored by reincubation at 37 ºC. It appears that by sensing the ambient temperature (i.e. of the host) gene expression is triggered, while during existence outside the host, the organism is not expressing the virulence factor, thereby saving energy (Maurelli and Lampel, 1997). Enteropathogenic E. coli There are numerous reports on survival of enteropathogenic E. coli, in particular verocytotoxigenic E. coli, in various environments at low temperatures. However, there is a surprising dearth of published information on the effect of low temperatures on subsequent expression of virulence characteristics, including production of Shiga toxin, by verocytotoxigenic E. coli. Shiga toxin, produced by certain strains of enteropathogenic E. coli, is immunologically and genetically related to verocytotoxins and the terms are often used interchangeably (Doyle et al., 1997). Ebel et al. (1996) reported on the temperature dependence of production of virulence proteins associated with Shiga toxin by verocytotoxigenic E. coli and other enteropathogenic E. coli, but the lowest temperature investigated was 20 ºC. Elhanafi et al. (2004) reported that storage of three strains of E. coli O157:H7 for four weeks at 4 ºC in tryptone soya broth did not affect subsequent expression of the virulence factors Shiga toxin, intimin and haemolysin. However, the cold stress increased freeze–thaw resistance for all three strains. This is clearly an area of work that would benefit from further research. Clostridium spp. Some non-proteolytic Cl. botulinum strains have a minimum growth temperature of 3.3 ºC (Table 20.1). Production of the Cl. botulinum neurotoxin, however, takes ‘weeks’ to be formed at this temperature in otherwise optimal conditions (Dodds and Austin, 1997). Clostridium perfringens fails to grow at temperatures below 12–15 ºC, so relatively little work has been carried out with this organism at conventional ‘low’ temperatures. The organism is common in the environment, but only a few strains possess the plasmid that codes for Cl. perfringens enterotoxin (CPE). The CPE is produced during sporulation in the gut of ingested organisms and causes illness by binding to the epithelial cells of the small intestine, then becomes sequestered into


522 a large complex followed by an increase in permeability of the epithelium, resulting in loss of fluid and salts (McClane, 1997). At 4 ºC, although CPE binding to mammalian epithelial cells was less than at higher temperatures, cytotoxicity to the cells was completely blocked. Blocking of cytotoxicity is likely to be attributable to the failure of the CPE to form a complex at 4 ºC in the host cell (McClane and Wnek, 1990). Staphylococcus aureus The lowest temperature for growth of Staph. aureus is 7 ºC (Table 20.1). It would be prudent to assume that any condition supporting growth of this organism would also allow production of enterotoxin. At 7 ºC, a conservative estimate suggests that sufficient enterotoxin to cause illness would be produced after two to three weeks. However, at 10 ºC Staph. aureus would grow sufficiently to produce toxin in about one week. This demonstrates the importance of good control of refrigeration for perishable foods.

20.5

Conclusions

Psychrotolerant foodborne pathogens are capable of growth at low temperatures and mesophilic ones can survive under these conditions. With the exception of Campylobacter, pathogens are able to modify their cell membrane structure and produce cold shock proteins, which allow the organisms to continue to grow, or at least to survive, in chill conditions. Pathogens that can grow at refrigeration temperatures may show enhanced expression of virulence factors compared with those that do not grow at such temperatures. This could have significant public health implications for chilled foods. Furthermore, even mesophilic organisms such as salmonellae, Shigella and pathogenic E. coli, which do not grow at chill temperatures, show temperature-related modulation of virulence factors.

20.6 Future trends Further research on the effects of low temperature on pathogenicity and virulence of foodborne pathogens is needed, particularly for Y. enterocolitica, A. hydrophila and other pathogenic Aeromonas spp. and Bacillus cereus. Another area where clarification is required is the production of cold-related stress proteins. Weber and Marahiel (2003) comment that ‘there are no systematic analyses available that compare the possible differences between exponentially and stationary growing cells, even though in the natural environment, bacteria most likely exist in the stationary phase’. Furthermore, there is considerable scope for further research on cross-protection by stress proteins, in particular where protection is conferred by certain proteins against multiple stresses, while others fail to offer comprehensive protection. Research on Campylobacter is needed to try to establish the ‘missing link’


523 associated with this organism, including physiological investigation of its survival capabilities at low temperature. This should help to elucidate reasons why it is so important as a pathogen in the developed world, despite being apparently unable to multiply at refrigeration temperatures. Further research into the nature of the lag phase is needed, since prolongation of lag is fundamental to food preservation. In association with this, continuing development of predictive microbiology, based on an understanding of microbial physiology, will allow more accurate predictions of growth and survival of foodborne pathogens at low temperatures.

20.7


For a discussion of the molecular basis for bacterial cold shock responses and adaptation to low temperatures, the reader should refer to the papers by D’Amico et al. (2002) and Weber and Marahiel (2003). Temperature regulation of virulence genes in pathogenic bacteria is discussed by Maurelli (1989), and recent developments in bacterial cold shock responses are reviewed by Phadtare (2004).

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525 GOVERDE, R.L., HUIS IN’T VELD, J.H., KUSTERS, J.G. AND MOOI, F.R (1998) ‘The psychrotrophic bacterium Yersinia enterocolitica requires expression of pnp, the gene for polynucleotide phophorylase for growth at low temperature (5 degrees C)’, Molecular Microbiology 28, 555–69. GRAY, M.L. AND KILLINGER, A.H. (1966) ‘Listeria monocytogenes and listeric infections’, Bacteriological Reviews 30, 309–82. HAZELEGER, W.C., JANSE, J.D., KOENRAAD, P.M.F.J., BEUMER, R.R., ROMBOUTS, F.M. AND ABEE, T. (1995) ‘Temperature-dependent membrane fatty acid and cell physiology changes in coccoid forms of Campylobacter jejuni’, Applied and Environmental Microbiology 61, 2713–9. HAZELEGER, W.C., WOUTERS, J.A., ROMBOUTS, F.M. AND ABEE, T. (1998) ‘Physiological activity of Campylobacter jejuni far below the minimal growth temperature’, Applied and Environmental Microbiology 64, 3917–3922. HEBRAUD, M. AND POTIER, P. (1999) ‘Cold shock response and low temperature adapation in psychrotrophic bacteria’, Journal of Molecular Microbiology and Biotechnology 1, 211–9. HERBERT, R.A. AND SUTHERLAND, J.P. (2000) Chill storage. In: Eds Lund B.M., BairdParker A.C. and Gould G.W., The Microbiological Safety and Quality of Foods, Gaithersburg, Aspen Publishers, 101–21. ICMSF (1996) Microbiological Specifications of Food Pathogens. International Commission for Microbiological Specifications in Foods. London, Blackie Academic and Professional. Volume 5. JONES, P.G. AND INOUYE, M. (1994) ‘The cold shock response – a hot topic’, Molecular Microbiology 11, 811–8. JONES, P.G., VANBOGELEN, R.A. AND NEIDTHARDT, F.C. (1987) ‘Induction of proteins in response to low temperature’, Journal of Bacteriology 169, 2092–5. KAPER, J.B., MORRIS, B.G. AND LEVINE, M.M. (1995) ‘Cholera’, Clinical Microbiology Reviews 8, 48–86. KIROV, S. M. (1997) ‘Aeromonas and Plesiomonas species’. In: Eds Doyle M.P., Beuchat, L.R. and Montville, T.J., Food Microbiology: Fundamentals and Frontiers, Washington, ASM Press, 265–87. KIROV, S.M., ARDESTANI, E.K. AND HAYWARD, L.J. (1993) ‘The growth and expression of virulence factors at refrigeration temperature by Aeromonas strains isolated from foods’, International Journal of Food Microbiology 26, 159–68. KOUTSOUMANIS, K.P., KENDALL, P.A. AND SOFOS, J.N. (2003) ‘Effect of food processingrelated stresses on acid tolerance of Listeria monocytogenes’, Applied and Environmental Microbiology 69, 7514–16. LEISTNER, L. (2000) ‘Basic aspects of food preservation by hurdle technology’, International Journal of Food Microbiology 55, 181–6. LEYER, G. J. AND JOHNSON, E.A. (1993) ‘Acid adaptation induces cross-protection against environment stresses in Salmonella typhimurium’, Applied and Environmental Microbiology 59, 1842–7. LI, H., BHADURI, S. AND MAGEE, W. (1998) ‘Maximizing plasmid stability and production of released proteins in Yersinia enterocolitica’, Applied and Environmental Microbiology 64, 1812–5. MAHAKARNCHARNAKUL, W. AND BEUCHAT, L.R. (1999) ‘Influence of temperature shifts on survival growth and toxin prodution by psychrotrophic and mesophilic strains of Bacillus cereus in potatoes and chicken gravy’, International Journal of Food Microbiology 47, 179–87. MAJEED, K.N., EGAN, A.F. AND MAC RAE, I.C. (1990) ‘Production of enterotoxins by Aeromonas spp. at 5 ºC’, Journal of Applied Microbiology 69, 332–337. MAURELLI, A.T. (1989) ‘Temperature regulation of virulence genes in pathogenic bacteria: a general strategy for human pathogens’, Microbial Pathogenicity 7, 1–10. MAURELLI, A.T. AND LAMPEL, K.A. (1997) ‘Shigella species’. In: Eds Doyle M.P., Beuchat,


526 L.R. and Montville, T.J., Food Microbiology: Fundamentals and Frontiers, Washington, ASM Press, 171–91. MAYR, B., KAPLAN, T., LECHNER, S. AND SCHERER, S. (1996) ‘Identification and purification of a family of dimeric major cold shock protein homologues from the psychrotrophic Bacillus cereus WSBC 10201’, Journal of Bacteriology 178, 2916–25. MCCLANE, B.M. (1997) ‘Clostridium perfringens’. In: Eds Doyle M.P., Beuchat, L.R. and Montville, T.J., Food Microbiology: Fundamentals and Frontiers, Washington, ASM Press, 305–26. MCCLANE, B.M. AND WNEK, A.P. (1990) ‘Studies of Clostridium perfringens enterotoxin action at different temperatures demonstrate a correlation between complex formation and cytotoxicity’, Infection and Immunity 58, 3109–15. MORITA, R.Y. (1975) ‘Psychrophilic bacteria’, Journal of Bacteriology 39, 146–67. MYERS, E.R. AND. MARTIN, S.C. (1994) ‘Virulence of Listeria monocytogenes propagated in NaCl containing media at 4, 25 and 37 ºC’, Journal of Food Protection 57, 475–8. PERALES, I. AND GARCIA, M.I. (1990) ‘The influence of pH and temperature on the behaviour of Salmonella enteriditis phage type 4 in home-made mayonnaise’, Letters in Applied Microbiology 10, 19–22. PHADTARE, S. (2004) ‘Recent developments in bacterial cold shock response’, Current Issues in Molecular Microbiology 6, 125–36. PIERSON, M.D. AND REDDY, N.R. (1988) ‘Clostridium botulinum – status summary’, Food Technology 42, 196–8. RATKOWSKY, D.A., OLLEY, J. MCMEEKIN, T.A. AND BALL, A. (1982) ‘Relationship between temperature and growth rate of bacterial cultures’, Journal of Bacteriology 149, 1–5. RATKOWSKY. D.A., LOWRY, R.K., MCMEEKIN, T.A., STOKES, A.N. AND CHANDLER, R.E. (1983) ‘Model for bacterial culture growth rate throughout the entire biokinetic temperature range’, Journal of Bacteriology 154, 1222–6. ROBINSON, T.P., OCIO, M.J., KALOTI, A. AND MACKEY, B.M. (1998) ‘The effect of the growth environment on the lag phase of Listeria monocytogenes’, International Journal of Food Microbiology 44, 83–92. ROBINSON, T.P., ABOABA, O.O., KALOTI, A., OCIO, M.J., BARANYI, J. AND MACKEY, B.M. (2001) ‘The effect of inoculum size on the lag phase of Listeria monocytogenes’, International Journal of Food Microbiology 70, 163–73. ROSS, T. AND NICHOLS, D.S. (1999) ‘Microbial ecology of bacteria and fungi in foods: Influence of temperature’. In: Eds Robinson, R., Batt, C.A., Patel, P, Encyclopaedia of Food Microbiology, London, Academic Press, 547–56. ROSSO, L., LOBRY, J.R. AND FLANDROIS, J.P. (1993) ‘An unexpected correlation between cardinal temperatures of microbial growth highlighted by a new model’, Journal of Theoretical Biology 162, 447–63. ROSSO, L., LOBRY, J.R., BAJARD, S. AND FLANDROIS, J.P. (1995) ‘Convenient model to describe the combined effects of temperature and pH on microbial growth’, Applied and Environmental Microbiology 61, 610–6. ROWAN, N.J. AND ANDERSON, J.G. (1998) ‘Diarrhoeal enterotoxin production by psychrotrophic Bacillus cereus present in reconstituted milk-based infant formulae (MIF)’, Letters in Applied Microbiology 26, 161–5. SAKURAI, J., BAHAVAR, M.A., JINGUJI, Y. AND MIWATANI, T. (1975) ‘Interaction of thermostable direct haemolysin of Vibrio parahaemolyticus with human erythrocytes’, Biken Journal 18, 187–92. SIMONSEN, B., BRYAN, F.L. AND CHRISTIAN, J.H.B. (1987) ‘Prevention and control of foodborne salmonellosis through application of hazard analysis and critical control point (HACCP)’, International Journal of Food Microbiology 4, 227–47. SORHAUG, T. (1992) ‘Temperature control’. In: Encyclopaedia of Microbiology, Vol. 4, London, Academic Press, 201–11.


527 SPANGLER, B.D. (1992) ‘Structure and function of cholera toxin and the related Escherichia coli heat-labile enterotoxin’, Microbiological Reviews 56, 622–47. TATINI, R. (1973) ‘Influence of food environments on growth of Staphylococcus aureus and production of various toxins’, Journal of Milk and Food Technology 36, 559–63. UYTTENDAELE, M., TAVERNIERS, I. AND DEBEVERE, J. (2001) ‘Effect of stress by suboptimal growth factors on survival of Escherichia coli O157:H7’, International Journal of Food Microbiology 66, 31–7. VARNAM, A.H. AND EVANS, M.G. (1991) Foodborne Pathogens: An Illustrated text. London, Wolfe Publishing Ltd. WALKER, S.J., ARCHER, P. AND BANKS, J.G. (1990a) ‘Growth of Listeria monocytogenes at refrigeration temperatures’, Journal of Applied Bacteriology 68, 157–62. WALKER, S.J., ARCHER, P. AND BANKS, J.G. (1990b) ‘Growth of Yersinia enterocolitica at chill temperatures in milk and other media’, Milchwissenschaft 45, 503–6. WEBER, M.H. AND MARAHIEL, M.A. (2003) ‘Bacterial cold shock responses’, Science Progress 86, 9–75. WEMEKAMP-KAMPHUIS, H.H., KARATZAZ, A.K., WOUTERS, J.A. AND ABEE, T. (2002) ‘Enhanced levels of cold shock proteins in Listeria monocytogenes upon exposure to low temperature and high hydrostatic pressure’, Applied and Environmental Microbiology 68, 456–63.


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21 Clostridium botulinum M. W. Peck, Institute of Food Research, UK

21.1

Introduction

The botulinum neurotoxins are the most potent substances known, with as little as 30 ng of neurotoxin sufficient to cause illness or even death. Botulinum neurotoxins are produced by six clostridia (Clostridium botulinum Groups I, II, III and IV, and some strains of C. baratii and C. butyricum). The clostridia are sufficiently different to merit six distinct species. There are four types of botulism. Foodborne botulism is a severe, but rare, intoxication caused by consumption of pre-formed botulinum neurotoxin. Infant and wound botulism are both infections, in which neurotoxins are formed in the body. A fourth type, inadvertent botulism, is uncommon. Clostridium botulinum Group I (proteolytic C. botulinum) and C. botulinum Group II (non-proteolytic C. botulinum) are responsible for most cases of foodborne botulism. Proteolytic C. botulinum forms very heat-resistant spores, and is a concern in the safe production of canned foods. Non-proteolytic C. botulinum is able to grow and form neurotoxin at 3.0 °C, and is a concern for the safe production of chilled foods. Since spores of proteolytic C. botulinum and non-proteolytic C. botulinum are ubiquitous in the environment, albeit generally at low numbers, food safety is dependent on the application of treatments that destroy spores, or the use of controlling factors that prevent growth and toxin formation. Today, commercial foods have an excellent safety record with respect to foodborne botulism, and most outbreaks are associated with home-prepared foods. It is important that, as new approaches to food processing and new technologies are introduced, the foodborne botulism hazard is appropriately controlled, and that neurotoxigenic clostridia do not become emerging pathogens.


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21.2 Taxonomy and properties of the Clostridium botulinum group The seven botulinum neurotoxins (types A to G) are produced by six Grampositive spore-forming obligately anaerobic clostridia. The six clostridia are physiologically and phylogenetically distinct, and there is not a simple relationship between neurotoxin formed and organism (Table 21.1). The species Clostridium botulinum is a group of four bacteria, and the distinction between these groups is sufficient to justify the creation of four distinct species; however, the name of C. botulinum is retained to emphasize the importance of neurotoxin production (Lund and Peck, 2000). Some strains of C. baratii and C. butyricum are also neurotoxigenic. For each neurotoxigenic organism, there is a non-neurotoxigenic phylogenetically equivalent organism (Hatheway, 1992). Proteolytic C. botulinum (C. botulinum Group I) and non-proteolytic C. botulinum (C. botulinum Group II) are responsible for most cases of foodborne botulism (Lund and Peck, 2000). Occasionally, neurotoxigenic strains of C. baratii and C. butyricum have also been associated with foodborne botulism (Lund and Peck, 2000). Proteolytic C. botulinum is a mesophile that produces heatTable 21.1

Characteristics of the six clostridia that produce the botulinum neurotoxin

Neurotoxigenic organism C. botulinum Group I (proteolytic) C. botulinum Group II (non-proteolytic) C. botulinum Group III C. botulinum Group IV (C. argentinense) C. baratii C. butyricum

Neurotoxins formed A, B, F B, E, F C, D G F E

Non-neurotoxigenic equivalent organism C. sporogenes No name given C. novyi C. subterminale All typical strains All typical strains

Table 21.2 Effect of environmental factors on the growth and survival of the two clostridia most commonly responsible for foodborne botulism

Neurotoxins formed Minimum temperature for growth Minimum pH for growth Minimum water activity for growth NaCl as humectant Glycerol as humectant NaCl concentration preventing growth Spore heat resistance a

Proteolytic C. botulinum

Non-proteolytic C. botulinum

A, B, F 10–12 °C 4.6

B, E, F 2.5–3.0 °C 5.0

0.96 0.93 10 % D121 °C = 0.21 min

0.97 0.94 5% D82.2° C = 2.4/231 mina

Heat-resistance data in buffer without/with lysozyme during recovery. From data in Lund and Peck (2000).


533 resistant spores. Strains of proteolytic C. botulinum form toxins of type A, B or F (or sometimes more than one toxin type). This organism derives energy by degradation of proteins. Non-proteolytic C. botulinum is a psychrotroph and is saccharolytic. Strains of non-proteolytic C. botulinum form a single toxin of either type B, E or F. Spores of non-proteolytic C. botulinum are of moderate heat resistance (Table 21.2). C. baratii type F and C. butyricum type E are both mesophiles.

21.3 Characterisation and types of botulism 21.3.1 Foodborne botulism Foodborne botulism is a severe, but rare, neuroparalytic disease resulting from consumption of pre-formed botulinum neurotoxin. The consumption of as little as 0.1 g of food in which C. botulinum or other neurotoxin-producing clostridia have grown can result in botulism. The botulinum neurotoxin is the most potent substance known; as little as 30 ng of neurotoxin is sufficient to cause illness and even death (Lund and Peck, 2000). Approximately 10 % of cases of foodborne botulism are fatal; this is very high for a foodborne illness. Additionally, full recovery may take several weeks, several months or even longer. Strains of proteolytic C. botulinum and non-proteolytic C. botulinum are responsible for most cases of foodborne botulism, with toxins of types A, B, E or more rarely F involved.

21.3.2 Infant and infectious botulism Infant and infectious botulism are caused by infection and then colonisation of the gastrointestinal tracts of susceptible infants and adults by proteolytic C. botulinum (or, more rarely, neurotoxigenic strains of C. baratii or C. butyricum). The first clinical cases of infant botulism were described in the USA in 1976, although subsequent investigations revealed earlier cases (Arnon, 1992). Infant botulism has now been reported in many countries. In infants, an immature intestinal flora fails to prevent colonisation. Spore germination is then followed by cell multiplication and neurotoxin formation. Infants aged between two weeks and six months are most susceptible (Arnon, 1992). Typical symptoms include extended constipation and flaccid paralysis, and the disease is rarely fatal. Honey and general environmental contamination (e.g. soil, dust) have been identified as sources of spores (Arnon, 1992; Lund and Peck, 2000; Aureli et al., 2002). The association with honey has led to recommendations in several countries that honey jars should carry a warning label indicating that the product is not suitable for infants of less than 12 months of age. It is estimated that between 10 and 100 spores are sufficient to bring about infection (Arnon, 1992). This is based on reports that honey samples that have been associated with infant botulism contain 5–25 spores per g (Midura et al., 1979), and 5–70 spores per g (Arnon et al., 1979). Infectious botulism is a very rare disease that affects adults. It is a similar disease to infant botulism, and


534 occurs when competing bacteria in the normal intestinal flora of adults have been suppressed (e.g. by antibiotic treatment).

21.3.3 Wound and inadvertent botulism Wound botulism was first described in 1943 in the USA (Lund and Peck, 2000). Since the early 1990s, there has been a significant increase in reports of wound botulism in many countries, and this has been primarily associated with intravenous drug users (Passaro et al., 1998; Werner et al., 2000; Sandrock and Murin, 2001; Brett et al., 2004; Hope et al., 2004). Proteolytic C. botulinum types A and B are most frequently implicated in cases of wound botulism. Examples of inadvertent botulism include; three cases of inhalation botulism in Germany in 1962 when veterinary personnel were exposed to an unknown but probably small amount of re-aerosolized botulinum toxin while disposing of animals whose fur was coated with aerosolized type A toxin (Arnon et al., 2001); and four cases of botulism in Florida in 2004 associated with the inadvertent injection of excessive doses of type A toxin given for cosmetic purposes (www.promed.isis.harvard.edu (14 December 2004)).

21.3.4 Botulinum neurotoxins There are seven botulinum neurotoxins (A to G), with the type of neurotoxin formed dependent on the producing organism (Table 21.1). The neurotoxins occur in complexes with other proteins (e.g. haemagglutinin, non-toxin non-haemagglutinin), and were originally distinguished on the basis of antigenic response. Recently, the amino acid sequence and mode of action of all the neurotoxins have also been established (Dodds and Austin, 1997; Lund and Peck, 2000). All seven botulinum neurotoxins comprise a heavy and light chain. The role of the heavy chains is to convey the light chains to the cytosol of the motor neuron. The light chains possess zinc endopeptidase activity. Within the cytosol of the motor neuron, the light chains cleave proteins of the acetylcholine-containing synaptic vesicle docking/fusion complex. Each light chain cleaves a specific protein in this complex at a specific site. The consequence of this cleavage is that acetylcholinecontaining synaptic vesicles are unable to dock, which prevents neurotransmitter release and leads to flaccid paralysis of the muscle. Typically, symptoms of botulism are neurological and initially involve the eyes (blurred and double vision, dilated pupils and drooping eyelids). The effects on the eyes are followed by a descending and progressive paralysis, characterised by dysphagia (difficulty swallowing), dysphonia (difficulty speaking), generalised weakness, nausea/vomiting, dizziness/vertigo and muscle weakness. Flaccid paralysis of the respiratory muscles can result in death if not treated. Rapid treatment with equine antitoxin and supportive therapy has led to a reduction in the fatality rate to approximately 10 % of cases, although full recovery may take many months or even longer. Owing to the need for prolonged supportive care and the long recovery period in an intensive care unit, a large outbreak of botulism could compromise hospital capacity.


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21.4 Epidemiology of foodborne botulism 21.4.1 Historical perspective There is anecdotal evidence that botulism occurred in ancient cultures, and that some dietary laws and taboos may have evolved as a result of the disease (Smith, 1977). The name ‘botulism’ was given to a disease characterised by muscle paralysis, breathing difficulties and a high fatality rate, reported in central Europe in the 18th and 19th centuries. The disease was frequently associated with consumption of blood sausage, and the word ‘botulism’ is derived from the Latin word botulus meaning sausage. Justinus Kerner conducted systematic research on the ‘sausage poison’ in the early part of the 19th century (Erbguth, 2004), and concluded that the poison (toxin):

• • • •

develops in the sausage under anaerobic conditions; is a biological substance; is lethal even in small doses; acts on the motor and autonomic nervous system.

At the end of the 19th century, Emile van Ermengem first isolated a causative organism from home-made raw salted ham and the spleen of a man who later died of botulism (Lund and Peck, 2000). In the early part of the 20th century, a great number of botulism outbreaks occurred across the world, and were often associated with the wider use (commercially and especially at home) of canning processes to extend shelf-life. In the early part of the 21st century, the incidence of foodborne botulism is much lower than a century ago. This is a result of understanding and implementing appropriate effective control measures. Today, most outbreaks of botulism are associated with home-prepared foods, where known control measures have not been implemented. Foodborne botulism involving commercial processing is uncommon, but on the rare occasions when commercial foods are involved, the medical and economic consequences can be significant. The cost per case of foodborne botulism is estimated to be approximately $30 million, compared with $10 000–12 000 for each case of illness associated with Listeria monocytogenes or Salmonella (Setlow and Johnson, 1997).

21.4.2 Recent outbreaks of foodborne botulism Most outbreaks of foodborne botulism over the past few decades have been associated with home-prepared foods, where known control measures have failed to be implemented. In countries with a particularly high incidence, this has often been associated with an increased reliance on the home preservation/bottling/ canning of foods, reflecting difficult economic conditions at the time. For example, in Poland, 1301 outbreaks were reported between 1984 and 1987, in Russia, 542 outbreaks were described between January 1998 and September 1999 (Peck, 2004), and in Georgia 879 cases (706 of which were hospitalised) were reported from 1980 to 2002 (Varma et al., 2004). An outbreak associated with fish


536 consumed at a wedding in 1994 in Georgia involved 173 cases (Varma et al., 2004). Other countries have lower, but significant, rates of foodborne botulism. For example, over the past few decades, approximately 20–40 cases have been reported annually in Italy, Germany, France and the USA. Again, home-prepared foods were primarily responsible (Peck, 2004). Proteolytic C. botulinum and non-proteolytic C. botulinum are responsible for most cases of foodborne botulism and, because they differ physiologically, they present a hazard in different types of foods (Table 21.3). Occasionally, neurotoxigenic strains of C. baratii and C. butyricum have also been associated with foodborne botulism. A suspected outbreak involving C. baratii type F was reported in the USA in 2001 (Harvey et al., 2002), and outbreaks involving C. butyricum type E have been reported in China, India and Italy (Anniballi et al., 2002). Outbreaks of botulism involving proteolytic C. botulinum have generally involved strains forming type A or type B toxin (Table 21.3). Outbreaks of botulism have occurred when the full heating process for low-acid canned foods has not been appropriately delivered to foods. An example is the outbreak in the United Kingdom in 1989. This was due to a failure to adequately heat the hazelnut conserve added to the yoghurt. A strain of proteolytic C. botulinum type B then grew and formed neurotoxin in the hazelnut conserve (O’Mahony et al., 1990). The restaurant-associated outbreak in Texas in 1994 was also attributed to an inadequate heat treatment. This outbreak involved baked potatoes that were subsequently added to potato dip (skordalia) and aubergine dip (meligianoslata). A strain of proteolytic C. botulinum type A formed toxin in the baked potato during cooling and subsequent storage at room temperature (Angulo et al., 1998). An outbreak in 2001 in Texas involved consumption of a temperature-abused commercially produced chilli sauce purchased at a salvage store. The temperature abuse permitted growth and toxin formation by proteolytic C. botulinum type A (Kalluri et al., 2003). Details of other recent outbreaks of botulism involving proteolytic C. botulinum are given in Table 21.3. Outbreaks of botulism involving non-proteolytic C. botulinum are generally associated with strains forming type E toxin (Table 21.3). However, strains forming type B toxin are also important, since it appears that many outbreaks in Europe associated with type B toxin are due to strains of non-proteolytic C. botulinum (Hauschild, 1992; Lucke, 1984). Botulism outbreaks associated with non-proteolytic C. botulinum have most frequently been associated with processed fish and fermented marine products (Table 21.3). Several outbreaks have been associated with salted or smoked fish. One of the largest outbreaks, in Egypt in 1991, involved consumption of commercially produced uneviscerated salted fish (faseikh). Many outbreaks in Alaska and northern Canada have been associated with consumption of home-prepared fermented marine products such as seal, whale or fish eggs. Further details of recent outbreaks of botulism involving nonproteolytic C. botulinum are shown in Table 21.3.


537 Table 21.3 Year

Examples of recent outbreaks of foodborne botulism involving proteolytic C. botulinum and non-proteolytic C. botulinum

Location

Product

Toxin type

Cases (deaths) Factors

Outbreaks involving proteolytic C. botulinum 1985 Canada Commercial garlic-in-oil

B

36

1986

Taiwan

A

9(2)

1987

Canada

Commercial jars of heat-processed unsalted peanuts in water Bottled mushrooms

A

11

1989

UK

Commercial hazelnut yoghurt

B

27(1)

1993

USA

A

8(1)

1993

Italy

B

7

1994

USA

A

30

1994

USA

Restaurant commercial process cheese sauce Commercial canned roasted eggplant in oil Restaurant; potato dip (skordalia) and aubergine dip (meligianoslata) Commercial clam chowder

A

2

1994

USA

Commercial black bean dip

A

1

1996

Italy

Commercial mascarpone cheese

A

8(1)

Homemade pesto/oil Traditionally made cheese preserved in oil Meat roll (matambre)

B A

3 27(1)

A

9

B

2(1)

1997 Italy 1997 Iran 1998

Argentina

1998

UK

Home-prepared bottled mushrooms in oil (imported from Italy)


Bottled; no preservatives; temperature abuse Inadequate heat treatment Underprocessing and/or inadequate acidification Hazelnut conserve underprocessed Recontamination; temperature abuse Insufficient heat treatment; improper acidification Baked potatoes held at room temperature No secondary barrier; temperature No secondary barrier; temperature abuse No competitive microflora; pH >6, temperature abuse pH 5.8, aw 0.97 Unsafe process Cooked and heat-shrink plastic wrap; temperature abuse Unsafe process

Reference St Louis et al. (1988) Chou et al. (1988) CDC (1987), McLean et al. (1987) O’Mahoney et al. (1990) Townes et al. (1996) CDC (1995) Angulo et al. (1998) California Morbidity (1995) California Morbidity (1995) Franciosa et al. (1999) Chiorboli et al. (1997) Pourshafie et al. (1998) Villar et al. (1999) CDSC (1998), Roberts et al. (1998)

538 Table 21.3 cont’d Year

Location

Product

2001

USA

Commercially produced chilli sauce

A

16

2002

South Africa

Commercially produced tinned pilchards

A

2(2)

E

8(1)

E

>91(18)

E

Outbreaks involving non-proteolytic C. botulinum 1987 USA and Israel Commercially produced, uneviscerated salted, air-dried fish (kapchunka) 1991 Egypt Commercially produced uneviscerated salted fish (faseikh) 1992 USA Commercially produced uneviscerated salted fish (moloha) 1995 Canada ‘Fermented’ seal or walrus (4 outbreaks) 1997 Germany Commercial hot-smoked vacuumpacked fish (Raucherfisch) 1997 Argentina Home prepared cured ham 1997 Germany Home smoked vacuum-packed fish (Lachsforellen) 1998 France Frozen vacuum packed scallops 1999 Finland Whitefish eggs 1999 France Grey mullet 2001 Australia Reheated chicken 2001 USA Home prepared fermented beaver tail and paw 2001 Canada Home prepared fermented salmon roe (2 outbreaks) 2002 USA Home prepared muktuk (from Beluga whale) 2003 Germany Home prepared salted air-dried fish


Toxin type

Cases (deaths) Factors Temperature abuse at salvage store Corrosion of tin permitted secondary contamination

Reference Kalluri et al. (2003) Frean et al. (2004)

Lack of refrigeration

Slater et al. (1989) Weber et al. (1993)

8

Putrefaction of fish before salting Insufficient salt

E

9

?

Proulx et al. (1997)

E

2

Suspected temperature abuse

E E

6 4

? Temperature abuse

Jahkola and Korkeala (1997), Korkeala et al. (1998) Rosetti et al. (1999) Anon (1998)

E E E E E

1 1 1 1 3

Temperature abuse (?) Temperature abuse Temperature abuse (?) Poor temperature control Temperature abuse

Boyer et al. (2001) Lindström et al. (2004) Boyer et al. (2001) Mackle et al. (2001) CDC (2001)

E

4

Unsafe process

Anon (2002)

E

12

Unsafe process

McLaughlin et al. (2004)

E

3

Temperature abuse (?)

Eriksen et al. (2004)

CDC (1992)

539

21.5 Incidence of Clostridium botulinum in the environment and in foods Spores of proteolytic C. botulinum and of non-proteolytic C. botulinum are present in soils, sediments and the gastrointestinal tract of animals, and their distribution has been summarised (Dodds, 1992a,b; Dodds and Austin, 1997; Lund and Peck, 2000). Sixteen surveys of the contamination of fruits and vegetables found a consistent, but low level of contamination (Lund and Peck, 2000). The largest most probable number (MPN) estimate was 410 spores/kg. Thirteen surveys of the contamination of meat also found a consistent, but low level of contamination, and the highest MPN estimate was 7 spores/kg. Fish and seafood appear to be more heavily contaminated. While many studies have reported a low level of contamination, several studies reported >100 spores/kg, and two reported >1000 spores/kg, the highest being 5300 spores/kg (Lund and Peck, 2000). It is apparent that in view of the ubiquitous nature of spores of C. botulinum, albeit often at low numbers, raw products cannot be guaranteed free of spores. Foods which are, or can become, anaerobic may allow growth of C. botulinum and must therefore be subjected to treatments that destroy spores, or stored under conditions that prevent growth and toxin formation. It should be noted, however, that some studies on the incidence of spores of C. botulinum in the environment and foods may be subject to certain limitations (Lund and Peck, 2000). For example:

• Many studies have only ascertained the toxin type formed; consequently it can be difficult to establish whether proteolytic C. botulinum or non-proteolytic C. botulinum is present (e.g. if type B or type F toxin is formed). • An underestimate of the level of contamination may arise because (i) the detection limit is not as low as assumed (i.e. detection of one spore per bottle is not achieved), and/or (ii) the use of heat treatments may inactivate spores of non-proteolytic C. botulinum.

21.6 Factors influencing growth, survival and neurotoxin formation 21.6.1 Thermal death of spores of proteolytic C. botulinum Spores of proteolytic C. botulinum are of high heat resistance, and their inactivation in low acid canned foods is of considerable importance. Studies on the thermal death of spores of proteolytic C. botulinum began in the early part of the twentieth century, in response to botulism outbreaks associated with inadequately heated foods. It is generally accepted that the heat resistance of spores of proteolytic C. botulinum is D121 °C = 0.21 min and z = 10 °C (Stumbo et al., 1975). Low acid canned foods receive a ‘botulinum cook’ of 121.1 °C for 3 min, or heat treatments of equivalent lethality at another temperature (Stumbo et al., 1975). This is considered to reduce the number of spores of proteolytic C. botulinum by a factor of 1012 (a 12D process).


540 21.6.2 Factors influencing growth and toxin formation by proteolytic C. botulinum The minimum temperature at which growth and toxin formation occurs is 10– 12 °C. Attempts to establish growth at 10 °C or below have been unsuccessful (e.g. Tanner and Oglesby, 1936; Ohye and Scott, 1953; Smelt and Hass, 1978; Peck, 1999). In an early study, although growth from vegetative inocula was recorded at 10° C, this observation was marred by a temporary rise in the incubator temperature to 20 °C, and growth from a spore inoculum was not observed at 10° C in ten weeks (Tanner and Oglesby, 1936). Growth and toxin formation have been described at 12 °C in 3–4 weeks (Smelt and Haas, 1978; Peck, 1999), and Ohye and Scott (1953) determined growth rates at 12.5 °C. Growth is prevented at pH 4.6 or below, and by 10 % NaCl (Table 21.2). The minimum water activity (aw) permitting growth is 0.93 and 0.96 with glycerol and NaCl, respectively, as humectants (Hauschild, 1989; Lund and Peck, 2000). Other single factors have also been used to control/prevent growth of proteolytic C. botulinum (Hauschild, 1989; Lund and Peck, 2000). For some foods, safety relies on a combination of factors rather than one single factor. The effect of combinations of pH and water activity/NaCl concentration on time to growth has been determined in laboratory medium (Baird-Parker and Freame, 1967), vacuum-packed potatoes (Dodds, 1989) and in lumpfish caviar (Hauschild and Hilsheimer, 1979). Predictive models have been developed that quantify the effect of combinations of environmental conditions on the growth response of proteolytic C. botulinum (Baker and Genigeorgis, 1992; Lund, 1993; Dodds, 1993; Lund and Peck, 2000). These models provide information on interactions between two or more factors, allow predictions to be made for sets of conditions not tested, and are of use in the targeting of challenge testing. Recent advances in computing power and accessibility to powerful computers and the internet have enabled the effect of combinations of factors to be considered in a more effective manner. Two types of tools have been developed: easily browsable databases and predictive microbiology applications. One such database is ComBase (www.combase.cc), which is freely available on the internet. ComBase contains some 30 000 original growth/survival curves and parameters of foodborne pathogens and spoilage microorganisms, including both proteolytic C. botulinum and non-proteolytic C. botulinum. Predictive microbiology packages are also freely available on the internet (e.g. Growth Predictor (www.ifr.ac.uk/Safety/ GrowthPredictor/default.html) and the Pathogen Modeling Program (www.arserrc. gov/mfs/PMP7_Start.htm)). These packages contain predictive models for proteolytic C. botulinum and for non-proteolytic C. botulinum, and enable prediction of the likely response to combinations of controlling factors.

21.6.3 Thermal death of spores of non-proteolytic C. botulinum Spores of non-proteolytic C. botulinum are of moderate heat resistance. Following heating in buffer, the highest heat resistance was reported as D82.2 °C = 2.4 min (Lund and Peck, 2000). Measured spore heat resistance is frequently higher in foods than


541 in buffer. For example, using a thermal death time method, D82.2 °C = 23 min, when spores were heated in meat slurry (Fernandez and Peck, 1997). These heat treatments damaged the spore germination system, and resulted in sublethal injury (Peck et al., 1992b). A proportion of these heat-damaged spores (typically 0.1– 1 %) are permeable to lysozyme, and are able to germinate and give growth in/on media containing lysozyme, giving biphasic survival curves (Sebald and Ionesco, 1972; Alderton et al., 1974; Smelt, 1980; Peck et al., 1992a, 1993; Lund and Peck, 1994; Peck, 1997). Following heat treatment in buffer, D82.2 °C = 231 min was obtained for lysozyme-permeable spores. Lysozymes from many sources increase the measured heat resistance of spores of non-proteolytic C. botulinum, including; hen egg-white lysozyme, other type c lysozymes (e.g. from other bird’s eggs), other enzymes (chitinase, papain), fruit and vegetable extracts, raw foods, egg-yolk emulsion and horse blood (Lund and Peck, 1994; Peck and Stringer, 1996; Stringer and Peck, 1996; Stringer et al., 1999). In studies with meat slurry and no added lysozyme, heat treatments of 85 °C for 36 min, 90 °C for 10 min and 95 °C for 15 min each prevented an inoculum of 106 spores of non-proteolytic C. botulinum leading to growth and toxin formation at 25 °C in 60 days (Peck et al., 1995; Peck and Fernandez, 1995; Graham et al., 1996a; Fernandez and Peck, 1997). When lysozyme was added prior to heating (at 625–2400 units/ml), and heat treatments of 85 °C for 84 min, 90 °C for 34 min or 95 °C for 15 min were applied, growth was observed at 25 °C after 13, 14 and 32 days, respectively (Peck et al., 1995; Fernandez and Peck, 1999). Also in tests with crabmeat, heat treatments at 88.9 °C for 65 min, 90.6 °C for 65 min, 92.2 °C for 35 min, or 94.4 °C for 15 min were required to prevent growth and toxin formation from 106 spores of non-proteolytic C. botulinum at 27 °C for 150 days (Peterson et al., 1997). It has been estimated that the concentration of endogenous lysozyme in crabmeat is approximately 200 µg/g prior to heating (Lund and Peck, 1994). The effect of intrinsic properties of foods and other environmental factors on the thermal inactivation of spores of nonproteolytic C. botulinum has been studied, and predictive models of thermal inactivation have been produced (Juneja and Eblen, 1995; Juneja et al., 1995a,b; Peck and Stringer, 1996; Lindström et al., 2003).

21.6.4 Factors influencing growth and toxin formation by non-proteolytic C. botulinum The minimum temperature at which growth and toxin formation occurs is within the range of 2.5–3.0 °C. Growth and toxin formation have not been detected during incubation at 2.1–2.5 °C for 90 days (Ohye and Scott, 1957; Schmidt et al., 1961; Eklund et al., 1967a,b; Graham et al., 1997). Growth was observed at 3.0 °C after seven weeks, 3.1 °C after six weeks, 3.2 °C after five weeks (Graham et al., 1997), and at 3.3 °C after five weeks (Schmidt et al., 1961; Eklund et al., 1967a,b). It is generally recognised that growth and toxin production do not occur below pH 5.0, or above 5 % NaCl. The minimum aw permitting growth is 0.94 and 0.97 with glycerol and NaCl, respectively, as the humectants (Hauschild, 1989; Graham et al., 1996b; Lund and Peck, 2000). The effect of other single preservative factors on


542 growth of non-proteolytic C. botulinum has been reviewed (e.g. Hauschild, 1989; Lund and Peck, 2000). The effect of redox potential and oxygen concentration on growth from unheated spores has been determined (Lund, 1993). Also, in tests with meat slurry, an initial aerobic atmosphere (20 % O2) did not restrict growth compared with an anaerobic atmosphere (100 % N2) (Peck, 1999). In these tests, the meat was sufficiently reduced so as to support growth and toxin formation by non-proteolytic C. botulinum, although the atmosphere was aerobic. The use of oxygen as a controlling factor is therefore cautioned since, despite the presence of oxygen, the food itself may be sufficiently reduced and support growth and toxin formation (Snyder, 1996). Safety with respect to non-proteolytic C. botulinum may be dependent on more than one preservative factor. Studies on the combined effect of heat treatment and conditions during subsequent chilled storage (e.g. temperature, pH, NaCl, shelflife) have been summarised (Peck and Stringer, 2005). Predictive models for non-proteolytic C. botulinum have been developed that deliver growth curves and that describe the effect of single and multiple factors on the probability of growth, or on time to toxin production at a single inoculum level (Baker and Genigeorgis, 1992; Dodds, 1993; Meng and Genigeorgis, 1993; Lund, 1993; McClure et al., 1994; Graham et al., 1996b; Fernandez and Peck, 1997; Whiting and Oriente, 1997; Fernandez and Peck, 1999; Lund and Peck, 2000; Fernandez et al., 2001). Some of these models are available through predictive modelling packages (e.g. Growth Predictor, Pathogen Modeling Program). Published (and in some cases also unpublished) original growth and death curves are available in ComBase. Process risk models have also been developed for minimally heat-processed refrigerated foods (Barker et al., 1999, 2002, 2005; Carlin et al., 2000).

21.7 Conclusion and future trends Although reducing existing causes of foodborne botulism is an important aim, it is also vital that C. botulinum and other neurotoxin-producing clostridia do not become emerging pathogens. It is therefore essential that new approaches to food processing and new technologies are introduced in a safe manner, and that the foodborne botulism hazard is appropriately controlled. One group of foods recently introduced are minimally heat-processed refrigerated foods (also known as refrigerated processed foods of extended durability (REPFEDs), sous-vide foods, chilled ready-meals and cook–chill foods). These foods address consumer demand in being of high quality, containing few preservatives and requiring minimal preparation time. Microbiological safety relies on a minimal heat treatment and appropriate refrigerated storage, and foodborne botulism has been identified as the principal microbiological safety hazard in these foods (Rhodehamel, 1992; Peck, 1997; Gould, 1999; Carlin et al., 2000; Lindström et al., 2003).

21.8 Acknowledgement The author is grateful to the competitive strategic grant of the BBSRC for funding.


543

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22 Quorum-sensing and virulence in foodborne pathogens M. Griffiths, University of Guelph, Canada

22.1

Introduction

The term ‘quorum-sensing’ was first coined by Fuqua et al., in 1994 to describe the phenomenon whereby bacteria can communicate with each other through the use of signaling molecules that are released into the environment during growth. Bacterial cells are able to ‘sense’ the concentration of these molecules in the environment and when they reach a threshold level several cellular responses are triggered. It is now recognized that there are many ‘languages’ used by bacteria depending on whether they are communicating among their own family, with cells of a different genus or even with their host. The discovery of quorum-sensing was provoked by the finding that luminescent bacteria, such as Vibrio fischeri, emitted light only when large numbers of bacterial cells were present. It was originally thought that this was due to the presence in the growth medium of an inhibitor of luciferase (the enzyme that catalyzes the light reaction) and that this inhibitor was degraded by bacteria when they reached high cell densities (Kempner and Hanson, 1968). This theory was later disproved by Woody Hastings and his colleagues at Woods Hole, Massachusetts, who were able to show that luminescence was triggered by accumulation of an activator which was termed an ‘autoinducer’ (Nealson et al., 1970; Eberhard, 1972). The molecule that was synthesized by V. fischeri activated luminescence when it had accumulated to high enough concentrations and this enabled the bacterium to sense cell density by monitoring the autoinducer levels. The identity of the autoinducer molecule was recognized by Eberhard et al. in 1981 to be


550

Fig. 22.1 The basic structure of acyl homoserine lactone. R1 can be –OH, =O or –H. R2 is an acyl chain of varying carbon number and the chain can be saturated or unsaturated.

N-(3-oxohexanoyl)-homoserine lactone. The genetic basis for V. fischeri bioluminescence was elucidated by Engebrecht and colleagues in the early 1980s (Engebrecht et al., 1983; Engebrecht and Silverman, 1984). This is illustrated in Fig. 22.1. In general, Gram-negative bacteria use acylated homoserine lactones as autoinducers, and Gram-positive bacteria use post-transcriptionally modified peptides to communicate (Bassler, 1999, 2002; Miller and Bassler, 2001). The advantage of quorum-sensing is that it allows bacteria to coordinate their activities to respond to rapid changes in their environment (Bassler, 2002). Such changes may include fluctuations in the availability of nutrients, the increase in populations of other microorganisms that may compete for nutrients, and a rise in the concentration of toxicants. Under nutrient-deprived conditions bacteria may, in fact, search each other out in a collective manner and confine themselves to enclosed spaces (Park et al., 2003). Gram-positive and Gram-negative bacteria both use quorum-sensing to regulate many diverse physiological activities. These processes include symbiosis, competence, conjugation, antibiotic production, motility, sporulation and biofilm formation (Miller and Bassler, 2001). It is also a great advantage for pathogenic bacteria to be able to coordinate production of virulence factors during infection of their host. If the production of compounds that may trigger an immune response in the host can be delayed until there are sufficient cells to overcome the host’s defences then it will increase the chances of the pathogen becoming established. There have been several reviews of quorum-sensing in recent years (Bassler, 1999, 2002; Miller and Bassler, 2001, Greenberg, 2003a,b; Smith et al., 2004); however, this review will concentrate on the role of quorum-sensing in the control of virulence in pathogens of importance in foods.

22.2 Gram-negative bacteria Quorum-sensing in Gram-negative bacteria has been reviewed by Lazdunski et al., 2004. Two quorum-sensing systems have been described in the bioluminescent bacteria Vibrio fisheri and Vibrio harveyi (Fuqua and Greenberg, 2003).

22.2.1 The Vibrio fisheri quorum-sensing paradigm As mentioned in the introduction, quorum-sensing in Gram-negative bacteria is


551 luxR

lux box

luxI

luxC

luxD

luxA

luxB

luxE

luxD

luxA

luxB

luxE

luxI luxR

AHL

luxR

AHL

(a)

luxR

lux box

luxI

luxC

luxR

AHL

AHL

luxI

luxR

AHL

AHL

AHL

AHL

AHL

(b)

AHL

AHL

AHL

Light AHL

AHL

Fig. 22.2 The Vibrio fischeri LuxI/LuxR quorum-sensing circuit. There are five luciferase structural genes (luxCDABE) and two regulatory genes (luxR and luxI) required for quorum-sensing controlled light emission in V. fischeri. The genes are arranged in two adjacent but divergently transcribed units. luxR is transcribed to the left, and the luxICDABE operon is transcribed to the right. At low cell densities (a), the luxICDABE genes are transcribed at a low level, and the small amounts of 3-oxo-C6-homoserine lactone (AHL) produced diffuse out of the cell. At high cell densities (b), AHL accumulates in the local environment and inside the cell. Transcription of luxICDABE is increased by a complex of the LuxR protein and AHL binding to a region of DNA called the lux box. In this way, the AHL autoinduces its own synthesis and, hence, amplifies the quorum-sensing signal.


552 mainly due to the production of acyl homoserine lactones (AHLs) with varying acyl chain lengths. AHL has also been termed autoinducer 1 or AI-1. The basic structure of AHL is shown in Fig. 22.1. The quorum-sensing paradigm is best illustrated by Vibrio fisheri (Fig. 22.2). In V. fischeri there are five structural genes (luxCDABE) and two regulatory genes (luxR and luxI) required for quorumsensing controlled luminescence. The genes are arranged in two adjacent but divergently transcribed units. luxR is transcribed to the left, and the luxICDABE operon is transcribed to the right. At low cell densities, the luxICDABE genes are transcribed at a low level and the small amounts of 3-oxo-C6-homoserine lactone produced by the luxI gene product (AHL synthase) diffuse out of the cell. At high cell densities, AHL accumulates in the local environment and inside the cell. Transcription of luxICDABE is increased by a complex of the LuxR protein (a transcriptional regulator) and AHL binding to a region of DNA called the lux box. In this way the AHL autoinduces its own synthesis and, hence, amplifies the quorum-sensing signal. Binding of the AHL and the target DNA by LuxR allows for either the transcription or suppression of the gene(s) (Swift et al., 2001; Whitehead et al., 2001). It has been proposed that 41 amino acids at the C-terminal of LuxR are important for DNA recognition and binding of the lux box (Trott and Stevens, 2001). Fidopiastis et al., (2002) identified a 606 bp open reading frame (ORF) from V. fischeri that encoded a protein, named LitR, that had about 60 % identity to four related regulator proteins: Vibrio cholerae HapR, Vibrio harveyi LuxR, Vibrio parahaemolyticus OpaR and Vibrio vulnificus SmcR. In cells of V. fischeri in which litR was inactivated, luminescence induction was delayed and the mutants emitted only about 20 % as much light per cell as its parent. Protein-binding studies suggested that LitR enhances quorum-sensing by regulating the transcription of the luxR gene (Fidopiastis et al., 2002). The AHL synthases are members of the LuxI family of proteins. At least 20 luxI homologs have been cloned and sequenced, and the amino acid sequences of the protein products have been deduced. From the deduced amino acid sequences it has been shown that there is a high degree of diversity among the homolog proteins, with homology in fewer than 5 % of the residues (Swift et al., 2001). Several bacteria of importance to the food microbiologist possess LuxR/I analogs (Table 22.1). It has subsequently been shown that V. fischeri possesses two distinct acyl-HSL (homoserine lactone) synthase proteins, the LuxI system described above and AinS (Lupp et al., 2003). Through phenotypic studies of V. fischeri mutants it was established that the AinS signal is the predominant inducer of luminescence expression in culture, whereas the impact of the LuxI signal is apparent only at the high cell densities occurring in symbiosis. AinS apparently regulates activities essential for successful colonization of the symbiotic partner of V. fischeri, the Hawaiian bobtail squid Euprymna scolopes. A V. fischeri ainS mutant failed to persist in the squid light organ. It was suggested that the two quorum-sensing systems in V. fischeri, ain and lux, sequentially induce the expression of luminescence genes and possibly other colonization factors (Lupp et al., 2003).


553

Table 22.1 Foodborne bacteria possessing homologues of the luxI and luxR genes from Vibrio fischeri Bacterium

Lux R/I homolog

Major AHL

Phenotype

Reference

Aeromonas hydrophila

AhyR, AhyI

C4-HSL

Swift et al. (1997, 1999), Lynch et al. (2002)

Enterobacter agglomerans Escherichia coli

EagR, EagI SdiA

3-oxo-C6-HSL None?

Pseudomonas aeruginosa

LasR, Las I

3-oxo-C12-HSL

RhlR, RhlI

C4-HSL

PhzR, PhzI MupR, MupI PpuR, PpuI SwrR, SwrI

Unknown Unknown Unknown C4-HSL

Extracellular protease; biofilm formation Unknown Cell division; regulator of EspD and intimin; motility Exoenzymes; Xcp (secretory pathway); biofilm formation; RhlR (transcriptional regulator); cell–cell spacing Exoenzymes; cyanide; lectins; pyocyanin; rhamnolipid; pili Phenazine antibiotic Polyketide antibiotic Unknown Swarming; protease; exoenzymes

SpnR

Ps. fluorescens Ps. putida Serratia liquefaciens

Salmonella enterica serovar Typhimurium Vibrio cholerae

SdiA

3-oxo-C6-HSL; C6-HSL; C7-HSL; C8-HSL Unknown 3-oxo-C6-HSL; C6-HSL; C7-HSL; C8-HSL None?

HapR

Unknown

Resistance to complement killing; virulence Biofilm formation

Yersinia enterocolitica

Yen R, YenI

C6-HSL

Virulence?

S. marcescens

BsmA, BsmB SpnR, SpnI


Negative regulator of quorumsensing

Swift et al. (1993) Sitnikov et al. (1996), Kanamaru et al. (2000) Chapon-Herve et al. (1997), Gambello et al. (1993), Passador et al. (1993), Gambello and Iglewski (1991), Glessner et al. (1999) Latifi et al. (1995, 1996), Winson et al. (1995), Pearson et al. (1997), Glessner et al. (1999) Shaw et al. (1997) El Sayed et al. (2001) Steidle et al. (2002) Eberl et al. (1996), Lindum et al. (1998), Givskov et al. (1997), Riedel et al. (2001), Labbate et al. (2004) Horng et al. (2002)

Biofilm formation Prodigiosin; NucA (nuclease); surfactant

Labbate et al. (2004) Horng et al. (2002) Ahmer et al. (1998) Hammer and Bassler (2003), Zhu and Mekalanos (2003) Throup et al. (1995), Jacobi et al. (2003)

554 There are also non-LuxI types of AHL synthases, such as LuxM and AinS in Vibrio harveyi and HdtS, which synthesizes three AHLs in Pseudomonas fluorescens (Swift et al., 2001; Whitehead et al., 2001). It was originally believed that the AHLs were freely diffusible through the bacterial membrane, but now it is thought that only the short-chain AHLs are capable of diffusing across the bacterial membrane, whereas long-chain AHLs are actively transported in and out of the cells (Whitehead et al., 2001). In P. aeruginosa, for example, C4-HSL is freely diffusible; however, 3-oxo-C12-HSL is actively transported out of the cell via a proton electromotive force efflux pump but diffuses back in (Pearson et al., 1999). The proteins that act as receptors or regulators for AHLs are known as the LuxR family of transcriptional regulators, and more than 40 LuxR homologs have been identified (Gray and Garey, 2001). As with LuxI, amino acid sequence analysis of the LuxR homologs indicates that they are diverse (Whitehead et al., 2001). Escherichia coli and Salmonella enterica serovar Typhimurium possess a LuxR homolog known as SdiA (Michael et al., 2001; Rahmati et al., 2002) but do not possess a corresponding LuxI homolog. Thus, these organisms do not produce AHLs (Whitehead et al., 2001). It is possible that E. coli and Salmonella Typhimurium can detect the AHLs produced by other intestinal bacteria and, therefore, have no need to synthesize their own (Ahmer et al., 1998; Ahmer, 2004). Other food-related Gram-negative organisms that do not produce AHLs include Klebsiella pneumoniae, V. cholerae, Campylobacter jejuni, and Helicobacter pylori (Williams et al., 2000). Phylogenetic analyses have shown that the luxI/R genes comprise two families with virtually no homology between them and with one family restricted to the γProteobacteria, which includes enteric bacteria, and the other more widely distributed (Lerat and Moran, 2004). Within bacterial phyla, the two LuxI/R families show broad agreement with the ribosomal RNA tree, suggesting that these systems have been continually present during the evolution of groups such as the Proteobacteria and the Firmicutes, which includes such genera as Lactobacillus, Bacillus, Clostridium and Streptomyces. A phylogenetic analysis of 76 individual LuxI and LuxR homologs present in diverse members of the Gram-negative Proteobacteria suggests an early origin for these regulators during the evolution of these organisms (Gray and Garey, 2001). Functional pairs of luxI and luxR genes have possibly co-evolved as regulatory cassettes and may have been acquired by horizontal gene transfer. The authors conclude that signaling pathways can potentially evolve by the sequential integration of pre-existing regulatory circuits acquired from diverse sources (Gray and Garey, 2001). Collins et al. (2005) constructed libraries of luxR mutants and screened them for variants showing increased gene activation in response to octanoyl-homoserine lactone (C8HSL). The wild-type LuxR interacts only weakly with C8HSL. LuxR variants that showed a 100-fold increase in sensitivity to C8HSL also displayed increased sensitivities to pentanoyl-HSL and tetradecanoyl-HSL, and maintained a wildtype or greater response to n-3-oxohexanoyl-L-homoserine lactone (3OC6HSL). These results indicate that AHL-dependent quorum-sensing systems can evolve


555 rapidly to respond to new AHLs, suggesting that there may be an evolutionary advantage to maintaining such plasticity (Collins et al., 2005). It has also been postulated that, as well as the luxR-based system, a luxO-based phospho relay regulatory system exists in V. fischeri. This suggests that the luxO system, originally discovered in V. harveyi, may be a general regulatory mechanism in luminescent bacteria (Miyamoto et al., 2000).

22.2.2 The Vibrio harveyi quorum-sensing paradigm As with Vibrio fischeri, Vibrio harveyi is a bioluminescent marine bacterium in which the luminescence genes are regulated by quorum-sensing; however, unlike V. fisheri, V. harveyi has two quorum-sensing systems. Although one employs an AHL (3-hydroxy-C4-HSL) as the signal molecule (Cao and Meighen, 1989), the system is distinct from that of V. fischeri as the genes involved are not homologous to luxR and luxI. The second system in V. harveyi relies on the accumulation of a molecule, designated AI-2. Both the AHL (or AI-1) and AI-2 systems regulate the luminescence genes via two-component regulatory systems (Bassler et al., 1993) and act synergistically (Mok et al., 2003). The AHL signal is produced by the LuxM protein (Milton et al., 2001) and binds to the LuxN protein. The AI-2 signal molecule is generated by LuxS, S-ribosylhomocysteinase which cleaves the thioether bond in S-ribosylhomocysteine to produce homocysteine and 4,5dihydroxy-2,3-pentanedione (Pei and Zhu, 2004), and is received by the LuxP and LuxQ proteins. LuxN and LuxQ contain both sensor kinase and response regulator domains of two component systems (Freeman et al., 2000). The two receptor systems converge on a protein, LuxO, which is homologous to the response regulator domains of two-component signal transduction systems (Bassler et al., 1994). At low cell densities, LuxO is phosphorylated by LuxQ and LuxN (via a phosphorelay protein, LuxU). In its phosphorylated state, LuxO activates the transcription of a repressor protein which blocks transcription of the luxCDABE genes. At high cell densities when the two signal molecules are present, LuxN and LuxQ dephosphorylate LuxO, preventing it from actvating transcription of the repressor. This allows a transcriptional activator, LuxRvh (Swartzman and Meighen, 1993) (which is not homologous to LuxR from V. fischeri) to activate lux gene expression (Freeman and Bassler, 1999). The quorum-sensing system in V. harveyi is illustrated in Fig. 22.3. It has been suggested by Bassler and her colleagues that V. harveyi uses AI-1 for intraspecies communication and AI-2 for interspecies communication (Miller and Bassler, 2001). To support this idea it has been shown that many species of Gramnegative and Gram-positive bacteria produce AI-2 and, in every case, production of AI-2 is dependent on the function encoded by the luxS gene (Bassler et al., 1997; Bassler, 1999; Surette et al., 1999; Federle and Bassler, 2003). The LuxS protein is the AI-2 synthase, and AI-2 is produced from S-adenosylmethionine in three enzymatic steps. The substrate for LuxS is S-ribosylhomocysteine, which is cleaved to form two products, one of which is homocysteine, and the other is 4,5dihydroxy-2,3-pentanedione (DPD) (Pei and Zhu, 2004) (Fig. 22.4). The crystal


556 AI-2 LuxP

3-OH-C4-HSL

LuxP

P

LuxS LuxN

LuxQ

LuxLM

LuxQ

LuxN

LuxU

P

LuxU

P

P Active form

Inactive LuxO form

LuxO +

P

Repressor of bioluminescence –

LuxR +

luxCDABE

luxCDABE

(a)

(b)

Fig. 22.3 Quorum-sensing systems in Vibrio harveyi. At low cell densities (a), the sensor kinases LuxQ and LuxN autophosphorylate, and subsequently phosphorylate LuxO via the LuxU. In its phosphorylated state, LuxO activates the transcription of a repressor of the luxCDABE genes. At high cell densities (b), the two autoinducer molecules (3-OH-C4-HSL and AI-2) accumulate and interact with the sensors LuxP and LuxN. This causes them to dephosphorylate LuxO (via LuxU) preventing it from activating the expression of the repressor protein. This allows the LuxR protein to activate transcription of the luxCDABE genes.

structure of an AI-2 sensor protein, LuxP, in a complex with the autoinducer has identified the bound ligand as a furanosyl borate diester that bears no resemblance to previously characterized autoinducers. The active AI-2 is generated by the addition of naturally occurring borate to an AI-2 precursor (Chen et al., 2002). The biosynthetic pathway and biochemical intermediates in AI-2 biosynthesis are identical in Escherichia coli, Salmonella Typhimurium, V. harveyi, Vibrio cholerae and Enterococcus faecalis, which suggests that, unlike quorum-sensing via AHLs, AI-2 is a unique, ‘universal’ signal that could be used by a variety of bacteria for communication among and between species (Schauder et al., 2001). Evidence has been presented to show that there is a third V. harveyi quorumsensing system that acts in parallel to Systems 1 and 2. Together, these communication systems act as a three-way coincidence detector in the regulation of a variety of genes, including those responsible for bioluminescence, type III secretion and metalloprotease production (Henke and Bassler, 2004b). Another novel V. harveyi autoinducer has been shown to negatively regulate lead precipitation (Mire et al., 2004). Many different species of bacteria are capable of regulating the V. harveyi lead precipitation phenotype. Moreover, a


557

Fig. 22.4

Pathways for synthesis of signaling molecules. (SAM, S-adenosylmethionine; SAH, S-adenosylhomocysteine; SRH, S-ribosylhomocysteine; MTA, 5'-methylthioadenosine; MTR, 5'-methylthioribose.)


558 quorum-sensing-deficient V. harveyi mutant exhibited little or no response to intercellular signals from other V. harveyi but did respond to some of the heterologous bacteria. Based on these observations, it was proposed that V. harveyi carries at least one quorum sensor that is specifically dedicated to receiving cross-species communication. It also illustrates that there may be many quorum-sensing pathways yet to be identified. Is it possible that Gram-negative bacteria are able to sense peptide signals used for quorum-sensing in Gram-positive bacteria? Two-component signal transduction systems, the main component of the quorum-sensing system in Gram-positive bacteria, are conserved in many Gram-negative bacteria. The PhoP–PhoQ twocomponent system of Salmonella Typhimurium can sense the presence of cationic antimicrobial peptides (Bader et al., 2003). Peptides as signaling molecules have been described in animals, plants and microorganisms and could be an ancient form of intercellular communication (Dirix et al., 2004).

22.3

The definition of a signal molecule

Williams and his colleaques at the University of Nottingham have limited the term ‘cell-to-cell signal molecule’ (CCSM) to describe small, diffusible molecules that have a function in cell-to-cell communication (Winzer et al., 2002). Several different bacterial products are present in spent culture supernatants (conditioned media) but what distinguishes a quorum-sensing or, more generally, cell-to-cell communication molecule from other bacterial metabolites? Winzer et al. (2002) defined the criteria necessary for a molecule to be considered a CCSM as:

• The CCSM is produced during specific stages of growth, under certain physiological conditions, or in response to changes in the environment.

• The CCSM accumulates extracellularly and is recognized by a specific receptor. • Once a critical threshold concentration has been reached, the CCSM generates a concerted response.

• The cellular response involves more than the physiological changes required to metabolize or detoxify the CCSM. This definition has led Williams and his co-workers to suggest that AI-2 may not be a global regulatory system, as LuxS has an additional metabolic function in bacterial cells that they feel has not been taken into account by most of the studies published to date (Winzer et al., 2002).

22.4 Quorum-sensing in Gram-negative foodborne pathogens As stated previously, this review will concentrate on foodborne enteric pathogens. 22.4.1 Vibrio cholerae Vibrio cholerae has several virulence factors including the enterotoxin, which is a


559 product of ctxA and ctxB genes encoding the two subunits of the toxin. Transcription of the ctxAB operon is regulated by several environmental signals, including temperature, pH, osmolarity and certain amino acids (Reidl and Klose, 2002). Several other V. cholerae genes are coregulated in the same manner, including the tcp operon which is concerned with fimbrial synthesis and assembly. The proteins involved in control of this regulon expression have been identified as ToxR, ToxS and ToxT. ToxR and ToxS form a two-component regulatory system with ToxS functioning as a sensor protein that phosphorylates and thus converts ToxR to its active DNA-binding form. ToxT is thought to be a cytoplasmic protein that is a transcriptional activator of the tcp operon. Expression of ToxT is activated by ToxR, while ToxT, in turn, activates transcription of tcp genes for synthesis of tcp pili. Vibrio cholerae has a complex V. harveyi-like quorum-sensing network, consisting of two parallel systems, System 1 and System 2, together with a third uncharacterized signaling pathway that links with the two V. harveyi-like systems to form a single regulatory cascade that regulates virulence gene expression (Camara et al., 2002; Miller et al., 2002). However, in V. harveyi, System 1 consists of the LuxM-dependent autoinducer HAI-1 and the HAI-1 sensor, LuxN; and System 2 consists of the LuxS-dependent autoinducer AI-2 and the AI-2 detector, LuxPQ. V. cholerae possesses System 2 (LuxS, AI-2, and LuxPQ) but does not have obvious homologs of the V. harveyi System 1. Instead, the System 1 of V. cholerae consists of the CqsA-dependent autoinducer CAI-1 and a sensor called CqsS. Using a V. cholerae CAI-1 reporter strain, it has been shown that many other marine bacteria, including V. harveyi, produce CAI-1 activity. These signaling systems act to regulate a variety of genes, including those responsible for bioluminescence, type III secretion and metalloprotease production (Henke and Bassler, 2004b). Quorum-sensing in V. cholerae represses, rather than activates, virulence gene expression at high cell densities. It has been proposed that on initial colonization (low cell density), LuxO represses HapR, itself a repressor of virulence gene expression, thus allowing colonization of the gastrointestinal tract and cholera toxin production (Miller et al., 2002; Zhu et al., 2002). As the bacterium multiplies, the quorum-sensing signal molecules accumulate and activate the two quorumsensing systems, which, in turn, inactivate LuxO. This leads to the production of the protein HapR which represses virulence gene expression while activating the production of a Zn-dependent hemagglutinin metalloprotease, HapA. This protease is thought to promote the detachment of the bacteria from intestinal tissue, thus facilitating the spread of the pathogen. Analysis of strains containing hapR– lacZ and hapA–lacZ fusions confirmed that hapA is transcribed in response to quorum-sensing and nutrient limitation (Silva and Benitez, 2004). There is a third signaling system that influences gene regulation through the LuxO regulatory cascade, as well as other regulators of virulence gene expression to be considered (Reidl and Klose, 2002), which makes the role of signaling systems AI-1 and AI2 in the virulence of V. cholerae in vivo unclear (Camara et al., 2002). A screen for additional components of the V. harveyi and V. cholerae quorum-


560 sensing circuits revealed the protein Hfq (Lenz et al., 2004). Hfq was shown to mediate interactions between small, regulatory RNAs (sRNAs) and specific mRNA targets. Hfq destabilized the mRNA encoding the quorum-sensing regulators LuxR (V. harveyi) and HapR (V. cholerae), implicating sRNAs in the circuit. Lenz et al. proposed that Hfq, together with these sRNAs, creates an ultrasensitive regulatory switch that controls the transition into the high cell density, quorumsensing mode (Lenz et al., 2004). As mentioned above, the production of virulence factors including cholera toxin and the toxin-coregulated pilus is strongly influenced by environmental conditions. The ToxR signal transduction cascade is responsible for sensing and integrating the environmental information and controlling the virulence regulon. In addition to the known components of the ToxR signaling circuit, quorumsensing regulators are involved in regulation of V. cholerae virulence. Using an infant mouse model, it was shown that a luxO mutant was severely defective in its ability to colonize the small intestine (Zhu et al., 2002). Microarray studies revealed that the ToxR regulon was repressed in the luxO mutant, and that this effect was mediated by another negative regulator, HapR. LuxO represses hapR expression early in exponential phase of growth, and constitutive expression of hapR blocks ToxR-regulon expression. Additionally, LuxO and HapR regulate a variety of other cellular processes including motility, protease production and biofilm formation. This suggests a role for quorum-sensing in modulating expression of blocks of virulence genes in a reciprocal fashion in vivo (Zhu et al., 2002). A HapA-deficient mutant was found to contain a point mutation in the luxO quorum-sensing regulator and exhibits a defect in quorum-sensing (Vance et al., 2003). When the mutant luxO allele was transferred to the wild-type, HA protease expression was prevented. The transcription of HapR, an essential positive regulator of HA protease, was constitutively repressed in the LuxO mutant, confirming the results previously published by Zhu et al. (2002). The mutant strains formed enhanced biofilms, which did not appear to be dependent on reduced HA protease expression. It was postulated that LuxO serves as a central ‘switch’ that coordinately regulates virulence-related phenotypes such as protease production and biofilm formation (Vance et al., 2003). Microarray analysis of biofilm-associated V. cholerae and work with mutants has confirmed that expression of the Vibrio polysaccharide synthesis (vps) operons is enhanced in hapR mutants (Hammer and Bassler, 2003; Zhu and Mekalanos, 2003). CqsA, one of two known autoinducer synthases in V. cholerae, acts through HapR to repress vps gene expression. Biofilms deficient in quorum-sensing have lower colonization capacities than those of wild-type biofilms, suggesting that quorum-sensing may promote cellular exit from the biofilm once the organisms have traversed the gastric acid barrier of the stomach (Zhu and Mekalanos, 2003). Several toxigenic strains of V. cholerae possess a naturally occurring frameshift mutation in hapR. Hammer and Bassler suggest that there are distinct environments occupied by this aquatic pathogen where cell–cell communication is crucial, as well as ones where loss of quorum-sensing via hapR mutation confers a selective advantage and bacterial biofilms could represent a complex habitat where such differentiation occurs (Hammer and Bassler, 2003).


561 22.4.2 Vibrio parahaemolyticus Illness caused by Vibrio parahaemolyticus is most commonly associated with consumption of contaminated raw or undercooked shellfish, particularly oysters. Symptoms of illness include watery diarrhea often with abdominal cramping, nausea, vomiting, fever and chills. These symptoms usually occur within 24 hours of ingestion; illness is usually self-limiting and lasts about three days. Severe disease is rare; occurring more commonly in immunocompromised individuals. Henke and Bassler (2004a) used the transposon mini-MulacZ (Cmr) to create chloramphenicol-resistant mutants of V. harveyi. Colonies were plated on heart infusion plates containing chloramphenicol and subsequently transferred to autoinducer bioassay (AB) medium containing 10 % (vol/vol) cell-free culture fluids from the V. harveyi luxS::Tn5 strain MM30 (i.e. HAI-1+ AI-2–) (AB HAI-1+) or 10 % V. harveyi luxM::Tn5 luxS::Cmr strain MM77 (i.e. HAI-1–, AI-2–) (AB HAI-1–). Both AB HAI-1+ and AB HAI-1– plates contained the chromogen X-Gal to visualize β-galactosidase (β-Gal) activity. The β-Gal activity of strains grown to early stationary phase (OD600~1) in AB HAI-1+ liquid medium and AB HAI-1– liquid medium was compared. Of 10 000 fusions assayed, 36 exhibited differential lacZ expression in the presence and absence of HAI-1. Genomic DNA adjacent to the transposon insertions in these mutants was amplified by a two-round polymerase chain reaction(PCR) procedure or by direct cloning of the transposon fusion junctions. The amplified or cloned DNA fragments were sequenced to determine their identities. Using this genetic screen to discover autoinducer-regulated targets in V. harveyi, Henke and Bassler (2004a) showed that genes encoding components of a putative type III secretion system exist in the bacterium. Type III secretion (TTS) systems are specialized secretion apparatuses used by many Gram-negative bacterial pathogens (Cornelis and van Gijsegem, 2000). These pathogens use TTS systems to inject effector virulence factors directly into the cytoplasm of their eukaryotic host cells. TTS was first discovered and studied in Yersinia species (Michiels et al., 1991) and has subsequently been identified in numerous Gram-negative bacterial pathogens, including Vibrio parahaemolyticus (Makino et al., 2003). Over 20 proteins are generally required to form TTS channels (Cornelis and van Gijsegem, 2000). These proteins are highly conserved between pathogens that use TTS systems for virulence and the genes encoding TTS system components are generally clustered in pathogenicity islands Henke and Bassler (2004a) showed that the TTS system is functional in V. harveyi and that expression of the genes encoding the secretion machinery requires an intact quorum-sensing signal transduction cascade. Vibrio parahaemolyticus possesses the genes encoding both of the V. harveyi-like quorum-sensing systems and it also has a TTS system similar to that of V. harveyi. In V. parahaemolyticus, quorum-sensing regulates TTS and, contrary to regulation in E. coli, quorumsensing represses TTS in V. harveyi and V. parahaemolyticus at high cell density (Henke and Bassler, 2004a; Winans, 2004). Vibrio parahaemolyticus strains display reversible phase variation that is manifested by variable colony morphology and is regulated by the transcriptional regulator OpaR (Enos-Berlage et al., 2005). OpaR is a member of the V. harveyi


562 LuxR family of quorum-sensing output regulators (Miyamoto et al., 2003) that also controls expression of multiple genes encoding capsular polysaccharide, lateral flagella, outer-membrane proteins and chitinase (Guvener and McCarter, 2003; Enos-Berlage et al., 2005). V. parahaemolyticus colonies can be opaque, sticky and convex (the OP phenotype), or translucent, non-sticky and flat (the TR phenotype). Both V. parahaemolyticus OP and TR strains form biofilms (EnosBerlage et al., 2005) but the biofilms formed by the respective phenotypes show important differences with respect to kinetics of development and final biofilm architecture. TR submerged biofilms most resemble the archetypal biofilms in which pillars of bacteria are interspersed with open channels that are thought to provide conduits for nutrient and waste exchange. In contrast, OP biofilms are more uniformly populated and lack such channels. The acquisition of nutrients and disposal of waste in these biofilms may be through control of biofilm dispersion by the quorum-sensing signaling pathway (Enos-Berlage et al., 2005). This idea was supported by the observation that OP biofilms show sharp kinetics of detachment; the OP pellicle disintegrates with time; the structural integrity of the submerged OP biofilm loosens with time; and cells in the mature OP biofilm are less closely packed than cells in early OP or mature TR biofilms. Thus, quorum-sensing may play a central role in biofilm development in V. parahaemolyticus.

22.4.3 Vibrio vulnificus Vibrio vulnificus is a halophilic estuarine bacterium and a causative agent of serious foodborne diseases in humans related to consumption of raw seafoods. In healthy individuals, gastroenteritis usually occurs within 16 hours of ingesting the organism but when individuals have chronic underlying disease it may give rise to ‘primary septicemia’. In these individuals, the microorganism enters the bloodstream, resulting in septic shock, rapidly followed by death in many cases (about 50 %). V. vulnificus can also cause necrotizing wound infections. Vibrio vulnificus produced a signalling activity in the culture supernatant that induced luminescence expression in V. harveyi through signaling system 2, but there was no evidence for the presence of the V. harveyi signaling system 1 (Kim et al., 2003; McDougald et al., 2003). From a cosmid library of V. vulnificus type strain ATCC 29307, the AI-2 synthase gene (luxSVv) was identified and showed 80 % identity at the amino acid level with that of V. harveyi (luxSVh). A deletion mutant of the clinical isolate V. vulnificus MO6-24/O exhibited a significant delay in protease production and an increase in hemolysin production. The mutation resulted in an attenuation of lethality to mice by 10- and 750-fold in mice not overloaded with ferric ammonium citrate and mice overloaded with ferric ammonium citrate, respectively. The time required for the death of mice was also significantly delayed in the mutant and cytotoxicity against HeLa cells was also decreased significantly by the mutation. This suggests an important role for the V. vulnificus LuxS quorum-sensing system in the virulence of the pathogen (Kim et al., 2003). The fatal septicemia in human hosts caused by V. vulnificus is dependent on the


563 in vivo production of inflammatory mediators, including tumor necrosis factoralpha (TNF-α). Using the murine macrophage cell-line RAW264.7, it was shown that extracellular products from luxS- and smcR-deficient mutants exhibited weak cytotoxic effects on the macrophage cells (Shin et al., 2004). Mutation of both luxS and smcR delayed the transcription of genes encoding the proinflammatory cytokines TNF-α, interleukin 1beta (IL-1β) and interleukin 6 (IL-6). Also, levels of both TNF-α and nitric oxide induced by luxS- and smcR-deficient mutants were significantly lower than those induced by parent strains, again implicating quorum-sensing as a key regulator of virulence in V. vulnificus. Further evidence to support this has been provided by Shao and Hor (2001). They showed that the expression of the V. vulnificus gene, vvp, encoding a metalloprotease that enhances vascular permeability and disruption of the capillaries, was up-regulated when bacterial growth reached the late log phase. This was due to the presence of a V. harveyi LuxR homolog encoded by the smcR gene of V. vulnificus. An extremely low level of vvp transcription compared with that of the parent strain was found in an isogenic SmcR-deficient (RD) mutant. However, the cytolysin gene, vvhA, was expressed at a higher level in the RD mutant than in the parent strain during the log phase of growth, suggesting that SmcR might not only be a positive regulator of the protease gene but might also be involved in negative regulation of the cytolysin gene (Shao and Hor, 2001). Similar findings were obtained by Kawase et al. (2004).

22.4.4 Escherichia coli As E. coli shifts from exponential growth to stationary growth, many changes occur, including cell division leading to formation of short cells and expression of numerous genes not expressed in exponential phase. Promoters P1 and P2 regulate the ftsQA genes, which encode functions required for cell division in E. coli (Wang et al., 1991; Garcia-Lara et al., 1996). The P1 promoter is RpoS-stimulated and P2 is regulated by SdiA, a member of the LuxR family of transcriptional activators (Sitnikov et al., 1996). However, an AHL synthase gene has not been identified (Michael et al., 2001). SdiA activity is increased in the presence of AHLs. It has also been shown that quorum-sensing regulates DNA replication during the initiation process in E. coli (Withers and Nordstrom, 1998). They suggest that it acts directly on the replication machinery rather than via a transcription/translation mechanism. Withers and Nordstrom (1998) suggest that a quorum-sensing mechanism to regulate initiation of DNA replication might be useful to slow down replication well in advance of entering stationary phase. During rapid growth, the decision of whether to initiate a round of replication may have to be made in a previous generation; therefore, it is important to be able to sense early how many bacteria are present in a specific niche and are potentially capable of utilizing nutrients. Failure to initiate a round of replication may result in the organism either entering stationary phase prematurely, or, if it initiates too late, it may enter stationary phase with incomplete replication; in either case competitiveness will be decreased. The importance of quorum-sensing to increase competitiveness is also


564 suggested by the significant regulatory overlap among several stress and starvation genes and known quorum-sensing genes (DeLisa et al., 2001a). Among other activities controlled by SdiA are the multidrug resistance pump AcrAB (Rahmati et al., 2002). Escherichia coli also contains the AI-2 signaling pheromone (Surette and Bassler, 1998; Surette et al., 1999). Using a DNA microarray and a luxS mutant, DeLisa et al. (2001b) showed that 242 genes, comprising about 5.6 % of the E. coli genome, exhibited significant transcriptional changes (either induction or repression) in response to a 300-fold change in AI-2 levels. There was significant induction of ygeV, a putative σ54-dependent transcriptional activator, and yhbH, a σ54-modulating protein, suggesting that σ54 may be involved in E. coli quorumsensing (DeLisa et al., 2001b). DNA microarrays were also used by Ren et al. (2004b) to study the effect of stationary-phase signals on the gene expression of early exponential-phase cells of an AI-2-deficient strain of E. coli DH5α. They found that 14 genes were induced by supernatants from a stationary culture and 6 genes were repressed, suggesting the involvement of indole (induction of tnaA and tnaL) and phosphate (repression of phoA, phoB and phoU). Three genes were induced by autoclaved stationary-phase supernatant, and 34 genes were repressed. In total, three genes (ompC, ptsA and btuB) were induced and five genes (nupC, phoB, phoU, argT and ompF) were repressed by both fresh and autoclaved stationary-phase supernatants. Supernatant from E. coli DH5α stationary culture was found to decrease AI-2 concentrations in E. coli K-12 by about five-fold. It was suggested that an additional quorum-sensing system in E. coli exists and that gene expression is controlled as a network with different signals working at different growth stages (Ren et al., 2004b). Nataro and Kaper (1998) have described six different classes of pathogenic E. coli: enterotoxigenic E. coli (ETEC), enteroaggregative E. coli (EAEC), enteroinvasive E. coli (EIEC), diffuse adhering E. coli (DAEC), enterohemorrhagic E. coli (EHEC), and enteropathogenic E. coli (EPEC). Of these, quorum-sensing has been implicated in the pathogenesis of both EHEC and EPEC (Sperandio et al., 1999). Enterohemorrhagic E. coli (EHEC) The detection of AI-2 activity in E. coli culture supernatants led Surette and Bassler (1998) to propose that quorum-sensing controls the virulence of pathogenic strains by regulating changes in gene expression during the transition from a non-pathogenic existence outside the host to pathogenesis within the host. The hypothesis that quorum-sensing is involved in detecting the environment within the host is supported by the observation that LEE gene induction occurs only when the medium is conditioned by bacterial growth at 37 °C, but not at 30 °C. In 1999, Sperandio and colleagues (Sperandio et al., 1999) showed that quorum-sensing via AI-2 regulated the expression of the LEE-encoded TTS system in EPEC and EHEC (Fig. 22.5). There are five polycistronic LEE operons in both EHEC and EPEC. LEE 1 to 4 encode secreted proteins and a TTS apparatus, and the LEE 5 operon contains the eae (enterocyte attaching and effacing) and tir (translocated


565 LEE2

LEE1

▲ ▲

(ler)

tir ILEE5

LEE3

(Tir and intimin) ▲

LEE4

(EspADB)

Type III secretion system Esc, Sep

Bacterial membrane Intimin

EspA filament Tir Epithelial cell membrane EspBd ▼

Bacterial membrane

▼

▼

▼ Intimin Tir

Epithelial cell membrane

Fig. 22.5 The type III secretion (TTS) system of enterohemorrhagic E. coli. TTS system is encoded by LEE1, LEE2 and LEE3 operons. LEE4 encodes EscF, which forms the needle complex of the TTSS; EspA, which forms a sheath that involves EscF forming a pilus-like structure; EspB and EspD, which form a pore in the epithelial cell membrane. LEE5, or tir, encodes Tir, which is translocated through the TTSS and inserts itself in the epithelial cell membrane where it serves as the receptor for the bacterial adhesin intimin (also encoded within LEE5). The first gene in the LEE1 operon encodes Ler, which is the transcriptional activator of all other genes within the LEE region. (From Falcao et al., 2004)

intimin receptor) genes encoding the intimin adhesin and its receptor, respectively (Wales et al., 2005). An ORF within LEE 1, termed the LEE-encoded regulator (Ler), up-regulates expression of LEE 2, 3 and 4 (Mellies et al., 1999). The LEE operons, including Ler, are under the influence of global regulators, including the integration host factor (IHF) (Friedberg et al., 1999), and a quorum-sensing mechanism mediated by the AI-2 autoinducer, the production of which is dependent upon the luxS gene (Sperandio et al., 1999, 2003). LEE1 and LEE2 expression was induced three- to six-fold in EHEC and EPEC by culture supernatants from a variety of pathogenic and non-pathogenic E. coli


566 Epinephrine A1-3

A1-2 OM IM

P ??

ATP

ACP

P

OseBC

A1-2? NhDC OseA

Other transcriptional factors

Lor LEE1

LEE2

NWGH/JR NA

LEE3

LEE5

LEE4

LMNOPQR

NC

NE

moaABcheWA

NgMA

mtopEchoRBYZ

Type III secretion (AE lesion)

anst

Flagella and motility

Fig. 22.6 Quorum sensing in enterohemorrhagic E. coli. Both AI-3 and epinephrine are recognized by the same outer-membrane receptor. These signals are imported to the periplasmic space where they interact with sensor kinases. QseC might be the sensor kinase transducing for activation of the flagella regulon. Another sensor kinase transduces these signals to activate transcription of the LEE genes. QseA is one of the transcriptional factors involved in the regulation of ler (LEE1) transcription. Ler activates transcription of the other LEE genes. An lsr operon is involved in recognition and uptake of AI-2. (From Falcao et al., 2004)

strains. This effect was not seen using E. coli DH5α culture supernatant, which is deficient in AI-2 production owing to a frameshift mutation in the luxS gene (Surette and Bassler, 1999; Surette et al., 1999). Restoration of luxS activity in DH5α resulted in these supernatants to induce LEE gene expression. EHEC colonization of the large intestine is facilitated by LEE gene expression induced by AI-2 synthesized by the resident normal flora, so it is possible that mutating luxS in EHEC might have no effect (Sperandio et al., 1999). This may help explain the unusually low infectious dose of EHEC. Thus, AI-2 produced by the normal intestinal flora would serve to alert EHEC that it was in the correct place to initiate colonization. Evidence indicating a role for the quorum-sensing system involving the SdiA protein in the control of colonization by EHEC has also been presented (Kanamaru et al., 2000). The role of AI-2 in control of virulence in EHEC has been put into question by recent research that has shown that EHEC responds to both a novel bacterial quorum-sensing signaling system (AI-3) and a mammalian signaling system to ‘fine tune’ transcription of virulence genes (Sperandio et al., 2003) (Fig. 22.6). The bacterial autoinducer AI-3, and not AI-2, is produced by the resident intestinal flora and the human hormones epinephrine and norepinephrine enable it to signal


567 that it is in the large intestine (Sperandio et al., 2003). It was suggested that LuxS is not devoted to AI-2 production but is involved in the metabolism of Sadenosylmethionine (SAM). This means that expression of genes affected by quorum-sensing, as well as genes differentially expressed because of the interruption of this metabolic pathway, will be altered in a luxS mutation. A luxS knockout affects the synthesis of both autoinducers, AI-2 and AI-3 (Sperandio et al., 2003). Both AI-3 and the hormone epinephrine activate the LEE-encoded TTS system and the flagella regulon in EHEC, and are probably recognized through the same signaling cascade (Falcao et al., 2004). Owing to the non-polar nature of both signals, it is likely that AI-3 and epinephrine are recognized by the same receptor located in the outer membrane of the bacterium. The interaction of these signals with more than one sensor kinase would be advantageous as it would be inefficient for EHEC to produce both the LEE TTS system and flagella at the same time (Sperandio et al., 2003). Using gene arrays, it was observed that about 10 % of the common genome between EHEC and E. coli K-12 is differentially regulated between wild-type EHEC and an isogenic luxS mutant (Sperandio et al., 2001). Among the genes differentially regulated between the wild-type EHEC and the luxS mutant were the genes encoding Stx2, flagella and motility (which may also play a role in pathogenesis) (Sperandio et al., 2001). To date, two novel regulatory systems involved in the quorum-sensing cascade have been described and were named quorum-sensing E. coli regulator A (QseA) and quorum-sensing E. coli regulators B and C (QseBC) (Sperandio et al., 2002a,b). QseA belongs to the LysR family and shares homology with AphB of V. cholerae and PtxR of Pseudomonas aeruginosa. Transcription of the LEE genes is activated by QseA and a sustantial reduction in TTS activity was observed in a qseA mutant. Secretory activity was restored when qseA was complemented (Sperandio et al., 2002a). The TTS activity was absent in a luxS mutant (Sperandio et al., 2003). Apart from QseA, two other novel transcriptional factors were found to be involved in regulation of the TTS system (Falcao et al., 2004). QseBC is a two-component system homologous with Salmonella Typhimurium PmrAB. QseBC is involved in activation of flagella and motility genes (Sperandio et al., 2002b). QseC appears to be the AI-3 and epinephrine sensor for the flagella regulon, but the sensor for the LEE genes remains unknown. Both QseA and QseBC are present in: EHEC, EPEC, uropathogenic E. coli (UPEC), E. coli K-12, Shigella flexneri, Salmonella Typhimurium, Salmonella Typhi, and Yersinia enterocolitica. Whereas the EHEC signaling cascade described above is responsive to AI-3 and epinephrine, the role of AI-2 in EHEC has yet to be established (Sperandio et al., 2003); although EHEC possesses homologs of the lsr genes involved in the uptake of AI-2 in Salmonella Typhimurium (Taga et al., 2001). Quorum-sensing in EHEC has been reviewed by Anand and Griffiths (2003) and Falcao and colleagues (Falcao et al., 2004). Enteropathogenic E. coli (EPEC) The regulation of quorum-sensing is different in EPEC and EHEC. The site of


568 EPEC colonization is the proximal small intestine which has few or no resident microflora. This suggests that quorum-sensing is primarily an inter-species signaling system during EHEC infection, but its main role is for intra-species signaling during EPEC infection (Falcao et al., 2004). Unlike EHEC, TTS in EPEC is diminished but not absent in a luxS mutant (Sperandio et al., 1999, 2003). In EPEC, LEE genes are under additional control by Per, which is absent in EHEC (GomezDuarte and Kaper, 1995; Mellies et al., 1999). EPEC contains a large plasmid, the EPEC adherence factor EAF plasmid which encodes Per (plasmid-encoded regulator). Per consists of three ORFs: perA, perB and perC. PerA activates the expression of the bfp operon encoding the bundle-forming-pilus (BFP) (Tobe et al., 1996) which is required for microcolony formation during colonization. The per loci also activate the expression of ler, which activates expression of the LEE2, LEE3, LEE5 and LEE4 operons in EPEC in a regulatory cascade (Mellies et al., 1999). Thus, for successful colonization and infection, EPEC has to coordinate transcription of the LEE genes with microcolony formation. This is when quorum-sensing regulation may play an active role. Disruption of quorum-sensing signaling in EPEC affects expression of the LEE genes, BFP and the flagella regulon. This results in the interference of both microcolony formation and adherence to epithelial cells (Sircili et al., 2004). Giron et al. (2002) have shown that the flagella of EPEC are directly involved in the adherence of these bacteria and suggest that there is a molecular relationship between the two existing TTS pathways of EPEC, the EAF plasmid-encoded regulator, quorum-sensing and epithelial cells (Giron et al., 2002) and that quorum-sensing in EPEC is necessary for successful colonization of host by coordinating spatial and temporal regulation of virulence genes.

22.4.5 Salmonella It has been proposed that quorum-sensing by pathogens is useful for communicating that bacteria are resident in the host and are not free-living in the environment. For foodborne pathogens such as E. coli and Salmonella Typhimurium, which use quorum-sensing to process both cell density information and metabolic cues, the situation may be more complex than that existing in luminescent bacteria such as V. fischeri, in which cell density may be the sole input for quorum-sensing. For example, signals relaying information regarding the abundance of nutrients could communicate to the bacteria that they should undergo the transition from a freeliving mode to the host-associated mode of existence (Surette and Bassler, 1998). Surette, Bassler and co-workers first demonstrated that Salmonella Typhimurium secreted an organic signaling molecule that can be assayed by its ability to activate one of two specific quorum-sensing systems in V. harveyi (System 2) (Surette and Bassler, 1998, 1999; Surette et al., 1999). They showed that maximal signaling activity was observed during mid- to late exponential phase when Salmonella Typhimurium was grown in the presence of glucose or other preferred carbohydrates. The signal was degraded by the onset of stationary phase or when the carbohydrate source was depleted. Following growth in the presence of glucose,


569 the transfer of Salmonella Typhimurium to a high-osmolarity (0.4 M NaCl) or to a low-pH (pH 5.0) environment resulted in increased signaling activity (Surette and Bassler, 1999). As high osmolarity and low pH are two conditions encountered by Salmonella Typhimurium cells in their passage through the gastrointestinal tract, the authors suggested that quorum-sensing may have a role in the regulation of virulence in Salmonella (Surette and Bassler, 1999). In subsequent work, Surette et al. (1999) identified the genes responsible for AI-2 production in V. harveyi, Salmonella Typhimurium and E. coli, which they named luxSV.h, luxSS.t and luxSE.c, as being highly homologous to one another but not to any other identified gene. These luxS genes defined a new family of autoinducer-production genes (Surette et al., 1999), which have subsequently been found in several other bacteria. The LuxS-dependent autoinducer AI-2 is proposed to function in interspecies cell–cell communication in bacteria. Salmonella Typhimurium produces and releases AI-2 during exponential growth and the molecule is subsequently imported into the bacterial cell via the Lsr ATP binding cassette (ABC) transporter which is regulated by LuxS. AI-2 induces transcription of the lsrACDBFGE operon, the first four genes of which encode the Lsr transport apparatus (Taga et al., 2001). LsrK, a protein that is required for the regulation of the lsr operon and the AI-2 uptake process, is a kinase that phosphorylates AI-2 upon entry into the cell. The phosphorylation of AI-2 results in its sequestration in the cytoplasm where the phospho-AI-2 inactivates LsrR, the repressor of the lsr operon (Taga et al., 2003). Two proteins encoded by the lsr operon, LsrF and LsrG, are necessary for the further processing of phospho-AI-2. It was postulated that transport and processing of AI-2 are required to remove the quorum-sensing signal, to carry the signal to an internal detector and/or to scavenge boron (Taga et al., 2003). Another protein encoded by the Salmonella Typhimurium lsrACDBFGE operon, LsrB, binds a chemically distinct form of the AI-2 signal, (2R,4S)2-methyl2,3,3,4-tetrahydroxytetrahydrofuran (R-THMF), which lacks boron (Miller et al., 2004). Miller et al. (2004) also demonstrated that at least two derivatives of DPD are biologically active, each in the context of a different bacterial species. It remains to be determined whether these or additional DPD derivatives activate gene expression in other AI-2-responsive bacteria. They speculated that all AI-2responsive bacteria have one or more receptors capable of binding a molecule derived from DPD. This diversity of signal molecules may allow species to differentially control behavior by recognizing and responding to different subsets of DPD derivatives that are present under particular environmental conditions. As stated above, components of the Vibrio quorum-sensing systems are present in Salmonella enterica. Salmonellae encode a signal receptor of the LuxR family, SdiA, but not a corresponding signal-generating enzyme (Ahmer et al., 1998). Instead, SdiA of Salmonella detects and responds to signals generated only by other microbial species (Michael et al., 2001). Conversely, E. coli and Salmonella produce the signal-generating component of a second system (a LuxS homolog that generates AI-2), but the mechanism for AI-2 recognition differs substantially from the Vibrio system. The only genes currently known to be regulated by AI-2


570 in Salmonella encode an active uptake and modification system for AI-2. Therefore, it is not yet clear whether Salmonella uses AI-2 as a signal molecule or whether AI-2 has some other function. Owing to pleiotropy, the functions of both SdiA and AI-2 in E. coli are unclear (Ahmer, 2004). The recently identified signaling system, AI-3, is also present in Salmonella (Falcao et al., 2004).

22.4.6 Shigella Shigella spp. are the primary agents of bacillary dysentery, an acute inflammatory disease of the human colonic epithelia (Parsot and Sansonetti, 1996). Factors essential for invasion of host cells include products of three closely linked operons, ipa, mxi and spa, present on the 230 kb Shigella virulence plasmid, and involve factors that mediate host cell cytoskeletal rearrangements, invasion and a TTS system. Increases in temperature are key environmental triggers exploited by Shigella to sense passage into the human gut (Hromockyj and Maurelli, 1989). This thermal regulation is governed by several mechanisms including the chromosomally encoded H-NS, which represses virulence gene expression at 30 °C, and the virulence plasmid-encoded transcription factors VirF and VirB, which are required for the expression of the invasive phenotype at 37 °C (Dorman and Porter, 1998). Additional environmental cues, such as cell density, may also govern virulence gene expression in Shigella (Bahrani et al., 1997) including genes encoding the TTS system and its substrates. The expression of ipa, mxi and spa invasion operons is maximal in stationary-phase bacteria, and conditioned media derived from stationary-phase cultures enhance the expression of these loci (Day and Maurelli, 2001). In contrast, expression of virB peaks in late log phase and is enhanced by a signal(s) present in conditioned media derived from late logphase cultures. Autoinducer 2 (AI-2) is synthesized by Shigella species and was responsible for the observed peak of virB expression. However, the autoinducer had no effect on expression of the invasion operon and, therefore, does not appear to play a role in the virulence of Shigella. This is borne out by the fact that mutants deficient in AI-2 synthesis are fully virulent. AI-2-mediated induction of virB may reflect activity of an AI-2-responsive element epistatic to virB, with H-NS being a possible target. Since a conditioned medium containing AI-2 inhibits DNA replication and cell division, Withers and Nordstrom (1998) suggested that AI-2 signaling may target conserved cellular processes that influence DNA replication. In contrast to other enteric pathogens such as EHEC, EPEC and V. cholerae, which persist in the gut lumen of the host and are exposed to high levels of AI-2 derived from normal flora, Shigella invades host cells and, thus, is only exposed to high AI-2 levels for a short time. Instead of responding to a signal that is present only in environments shared with intestinal flora, Shigella exploits an environmental cue, temperature, which is present regardless of location and of host. Therefore, AI-2 signaling may not be used to control virulence gene expression in bacterial pathogens that colonize host tissues not occupied by normal flora (Day and Maurelli, 2001).


571 22.4.7 Yersinia Bacteria of the genus Yersinia cause an infectious disease known as yersiniosis. In developed countries, most human illness is caused by one species, Y. enterocolitica. Only a few serotypes of Y. enterocolitica cause illness in humans, with the major animal reservoirs for these serotypes being pigs where it is most likely to be found on the tonsils. Infection with Y. enterocolitica can cause a variety of symptoms depending on the age of the person infected. Infection most often occurs in young children, resulting in fever, abdominal pain and diarrhea, which is often bloody. Symptoms typically develop four to seven days after exposure and may last one to three weeks or longer. In older children and adults, right-sided abdominal pain and fever may be the predominant symptoms, which may be confused with appendicitis. In a small proportion of cases, complications such as skin rash, joint pains or septicemia can occur. Infection is most often acquired by consuming contaminated food, especially raw or undercooked pork products, unpasteurized milk, or water. Occasionally Y. enterocolitica infection occurs after contact with infected animals. Other species of bacteria in this family that are human pathogens include Y. pseudotuberculosis, which causes an illness similar to Y. enterocolitica, and Y. pestis, which causes plague. Quorum-sensing was first shown in Y. enterocolitica (Throup et al., 1995) which was shown to produce compounds capable of transcriptionally activating the V. fischeri lux operon. The compounds were shown to be acyl homoserine lactones: N-hexanoyl-L-homoserine lactone (HHL) and N-(3-oxohexanoyl)-Lhomoserine lactone (OHHL). Both HHL and OHHL were synthesized by the product of the yenI gene, which is homologous to the luxI of V. fischeri (Throup et al., 1995). A second ORF, yenR, located downstream of yenI, encoded a luxR homolog. An insertion mutation of yenI was shown to abolish acyl-HSL production (Throup et al., 1995). Transcript analysis of the yenI-mutated gene product coupled with the absence of a characteristic lux box has led to the conclusion that yenI expression is not autoinducible. A functional yenI is required for the expression of a number of unidentified proteins (Throup et al., 1995), but no other changes in phenotype were observed. Two strains of Y. enterocolitica serotype O8, which were virulent using a mouse model, were analyzed in vitro and in vivo using thin-layer chromatography in combination with an E. coli AHL biosensor to identify the AHL species produced (Jacobi et al., 2003). Only OHHL, not HHL, was shown to be produced by Y. enterocolitica O8 in culture supernatant or infected mouse tissue. Since the YenI proteins in the two strains were highly homologous, it is unlikely that structural differences in the proteins account for the differences in AHL production. This is the first report demonstrating AHL production by yersiniae during infection and it raises the question as to the role of quorum-sensing in regulation of virulence genes in Y. enterocolitica or whether OHHL contributes directly to pathogenicity by acting as an immunomodulator, as has been reported for Ps. aeruginosa (Telford et al., 1998). Yersinia pestis and Y. pseudotuberculosis also produce LuxR/I homologs, named ypeR and ypeI in Y. pestis, and ypsR and ypsI in Y. pseudotuberculosis


572 (Atkinson et al., 1999; Yates et al., 2002). Although ypeI and ypeR genes were identified in Y. pestis and it produces signaling molecules (OHHL and HHL), the role of quorum-sensing in this bacterium has not been shown. In addition to the synthesis of OHHL and HHL, Y. pseudotuberculosis also synthesizes a third signaling molecule classified as OHL (N-octanoyl-L-homoserine lactone) (Atkinson et al., 1999; Yates et al., 2002). The regulation of virulence genes in Y. pseudotuberculosis by quorum-sensing is subject to the action of at least two luxR/I homologs described as ypsR/I and ytbR/I (Atkinson et al., 1999). A ypsI mutant did not produce OHHL at 28 °C, but AHL production at both 37 and 22 °C was the same as the wild-type. Changes in phenotype were observed in the mutants; with clumping occurring in the ypsR mutant, over-expression of a major flagellin subunit was observed in the ypsR mutant and both ypsR and ypsI mutants exhibited increased motility. As well as affecting quorum-sensing, temperature also influences the expression of some Yersinia virulence genes (Falcao et al., 2004).

22.4.8 Campylobacter jejuni The genome sequence of C. jejuni NCTC 11168 contains a gene encoding an ortholog of LuxS, which is required for autoinducer-2 (AI-2) production in other bacterial species, but the genome does not contain genes predicted to encode any known AHL synthetase (Elvers and Park, 2002). C. jejuni does produce functional AI-2 activity as demonstrated by the ability of cell-free extracts to specifically induce bioluminescence in V. harveyi BB170, a reporter strain for the quorumsensing system 2. Production of this signaling compound was shown to be dependent upon the product of the C. jejuni luxS gene (Cj1198) (Elvers and Park, 2002). AI-2-mediated quorum-sensing influences the transcription of flaA, the major flagellin gene in C. jejuni (Jeon et al., 2003; Winzer et al., 2003). A null mutation of luxS in C. jejuni strain 81116 reduced flaA transcription to approximately 43 % of that observed in the wild-type and consequently reduced motility (Jeon et al., 2003). However, the luxS mutant had the same level of flagellin protein as the wildtype and transmission electron microscopy showed that the flagellar structure was preserved in the mutant. Elvers and Park (2002) also demonstrated that luxS mutants showed decreased motility. It is generally accepted that flagella are an important virulence factor for C. jejuni (Grant et al., 1993). Agglutination capability was reduced in luxS mutant strains, implying that quorum-sensing might be involved in the formation of surface structures of C. jejuni. These observations suggest that AI-2-mediated quorum-sensing may play a role in regulation of motility and surface properties in C. jejuni (Jeon et al., 2003; Winzer et al., 2003)

22.4.9 Helicobacter pylori Helicobacter pylori is believed to be transmitted orally through the ingestion of


573 contaminated food or water. The organism has been shown to be the causative agent for stomach ulcers and possibly duodenal ulcers. Stomach cancers (gastric adenocarcinomas) are often associated with H. pylori and the presence of H. pylori confers an approximately six-fold increase in the risk of gastric cancer. Helicobacter pylori possesses a homolog of the luxS gene. H. pylori has been shown to synthesize a functional autoinducer (AI-2) that can specifically activate signaling system 2 in V. harveyi (Joyce et al., 2000). However, AI-2 does not appear to be involved in modulating any of the known or putative virulence factors in H. pylori, including growth kinetics in BBH medium, motility, urease activity, Cag-mediated induction of IL-8 production in HEp-2 cells, and H. pylori-induced vacuolization of HEp-2 cells. A luxS null mutant had a two-dimensional protein profile identical to that of its isogenic parent strain. Since H. pylori is unique in its ability to colonize the gastric mucosa, it likely has developed strategies to flourish in the restrictive stomach environment. As there appears to be no other characterized reservoir, H. pylori spends most of its life cycle in this unique ecological niche, that is the gastric lumen of its host. Thus, it may have little need to regulate gene expression to survive the low pH of the stomach. Because survival of H. pylori is host dependent, it must not only be able to grow in the stomach in order to survive, but also must be able to avoid the immune response of the host. Joyce et al. (2000) suggest that density-dependent cell signaling could provide a mechanism for H. pylori to regulate its population size in response to changes in the environment in order to avoid the host immune system. Helicobacter pylori is able to form biofilms. To understand the importance of biofilms to the H. pylori life cycle, Cole et al. (2004) studied the effect of mucin on biofilm formation. They showed that 10 % mucin greatly increased the number of planktonic H. pylori while not affecting biofilm bacteria, resulting in a decline in adherence to a glass surface. This suggests that in the mucus-rich stomach, planktonic growth of H. pylori is favored over biofilm formation. A luxS mutant was able to form biofilms approximately two-fold more efficiently than the parent strain. The authors postulated that H. pylori exists primarily as a biofilm in the environment and, upon encountering gastric mucus, rapidly proliferates as planktonic bacteria in the stomach (Cole et al., 2004). Quorum-sensing may play a role in the switch between these two forms of growth.

22.4.10 Aeromonas hydrophila There is some controversy as to whether A. hydrophila causes human gastroenteritis. Volunteer human feeding studies, even when enormous numbers of cells were ingested, failed to lead to human illness. However, it has been isolated from the stools of individuals with diarrhea that did not contain other known enteric pathogens, which suggests that it may play a role in disease. Putative virulence factors associated with the organism include the production of exotoxins and αand β-hemolysins and the ability to bind to and invade epithelial cells. Spent culture supernatants from both A. hydrophila and A. salmonicida activate a range of biosensors reporting on AHL activity (Swift et al., 1997). The genes


574 encoding a quorum-sensing signal generator and a response regulator were cloned from A. hydrophila and termed ahyRI. Protein sequence homology analysis showed that the gene products were LuxRI homologs. ahyR is transcribed from ahyI, and downstream of ahyI is a gene with close homology to iciA, an inhibitor of chromosome replication in E. coli. This suggests that cell division may be linked to quorum-sensing in Aeromonas (Swift et al., 1997). The major signal molecule synthesized by AhyI was identified as N-(butanoyl)-L-homoserine lactone (BHL). A minor AHL, N-hexanoyl-L-homoserine lactone, was also present. Both AhyR and BHL are required for ahyI transcription (Swift et al., 1997). BHL production was abolished when the ahyI gene was inactivated (Swift et al., 1999). A. hydrophila produces both a serine protease and metalloprotease. Both enzyme activities are significantly reduced in ahyI-negative mutants but the activity can be restored by the addition of exogenous BHL (Swift et al., 1999). Similarly, both exoprotease activities are absent in ahyR-negative mutants, but in these mutants enzyme activity cannot be restored by exogenous BHL (Swift et al., 1999). Thus, exoprotease activity in A. hydrophila is controlled by quorum-sensing. Proteases are major bacterial antigens and quorum-sensing may play a role in evasion of host defences by A. hydrophila. Aeromonas hydrophila readily attaches to stainless steel to produce a thin biofilm with a complex 3D structure. As A. hydrophila possesses an AHLdependent quorum-sensing system based on the ahyRI locus, the presence of the AhyI protein and BHL within the biofilm phase was established by Western blot and AHL biosensor analysis (Lynch et al., 2002). The ability of isogenic ahyI and ahyR mutants of A. hydrophila to form biofilms was assessed in a continuous-flow chamber. The ahyI mutant, which was unable to produce BHL, failed to form a mature biofilm. The ability of the ahyI mutant to establish a biofilm could be partially restored by the addition of exogenous BHL. The ahyR mutant exhibited increased coverage of the available surface without a concomitant effect on biofilm architecture. These data support a role for AHL-dependent quorumsensing in A. hydrophila biofilm development (Lynch et al., 2002).

22.4.11 Pseudomonas aeruginosa Pseudomonas aeruginosa is an opportunistic pathogen, causing urinary tract infections, respiratory system infections, dermatitis, soft tissue infections, bacteremia, bone and joint infections, gastrointestinal infections and a variety of systemic infections. It is primarily a nosocomial pathogen and is a significant problem in patients hospitalized with cancer, cystic fibrosis and burns and in other immunocompromised individuals. The case fatality rate in these patients is 50 %. However, no major outbreaks of foodborne illness due to Ps. aeruginosa have been documented. The ability of Ps. aeruginosa to invade tissues depends upon production of extracellular enzymes and toxins. Two extracellular proteases have been associated with virulence: elastase and alkaline protease. Elastase has several activities that relate to virulence. The enzyme cleaves collagen, IgG, IgA and complement.


575 Alkaline protease interferes with fibrin formation and will hydrolyze fibrin. In addition, Ps. aeruginosa produces three other soluble proteins involved in invasion: a cytotoxin and two hemolysins. The cytotoxin is a pore-forming protein and the two hemolysins are a phospholipase and a lecithinase. The Pseudomonas pigment, pyocyanin, may also be a virulence factor as it impairs the normal function of human nasal cilia, disrupts the respiratory epithelium, and exerts a proinflammatory effect on phagocytes. Pseudomonas aeruginosa possesses two pairs of LuxI/LuxR homologs: LasI/ LasR (Passador et al., 1993) and RhlI/RhlR (Brint and Ohman, 1995). Both LasI and RhlI are autoinducer synthases. LasI catalyzes the formation of the HSL autoinducer N-(3-oxododecanoyl)-homoserine lactone (Pearson et al., 1994) and RhlI produces N-(butyrl)-homoserine lactone (Pearson et al., 1995). The two regulatory circuits act in tandem to control the expression of a number of P. aeruginosa virulence factors. At high cell density, LasR binds its cognate HSL autoinducer, and together they bind at promoter elements immediately preceding the genes encoding a number of secreted virulence factors including elastase, encoded by lasB; a protease encoded by lasA; exotoxinA, encoded by toxA; and alkaline phosphatase, which is encoded by the aprA gene (Miller and Bassler, 2001). The LasR-autoinducer complex also activates the RhlI/RhlR quorum-sensing system of Ps. aeruginosa (Ochsner and Reiser, 1995); whereby rhlR expression is induced. The RhlR/RhlI complex induces the expression of two genes, lasB and aprA, that are also under the control of the LasI/LasR system. The RhlR-autoinducer complex also activates another group of genes that include rpoS, encoding the stationary phase sigma factor σs; rhlAB, encoding rhamnosyltransferase (an enzyme that is involved in the synthesis of the biosurfactant/hemolysin rhamnolipid); genes involved in pyocyanin synthesis; the lecA gene, encoding a cytotoxic lectin; and the rhlI gene (Miller and Bassler, 2001). Two studies have reported a major link between quorum-sensing and σs at the transcriptional level in Ps. aeruginosa, which probably results in coordinated regulation of virulence determinants and survival in stationary phase (Venturi, 2003). A further autoinducer that is not a homoserine lactone has been identified in Ps. aeruginosa and it has been identified as 2-heptyl-3-hydroxy-4-quinolone (denoted PQS for Pseudomonas quinolone signal) (Pesci et al., 1999). PQS partially controls the expression of the elastase gene lasB in conjunction with the Las and Rhl quorum-sensing systems. LasR is required for the expression of PQS, which, in turn, induces transcription of rhlI. In cell-free Ps. aeruginosa culture supernatants, two compounds capable of activating an AHL biosensor were identified as diketopiperazines (DKPs), and not AHLs (Holden et al., 1999). These compounds were also found in cell-free supernatants from Proteus mirabilis, Citrobacter freundii and Enterobacter agglomerans. Although both DKPs were absent from Ps. fluorescens and Ps. alcaligenes, a third DKP was isolated from both pseudomonads. These DKPs may compete for the same LuxR-binding site as AHLs and were found to be capable of activating or antagonizing other LuxR-based quorum-sensing systems. Although


576 the physiological role of these DKPs has not been established, their activity suggests the existence of cross-talk among bacterial signaling systems (Holden et al., 1999).

22.5

Other Gram-negative bacteria of significance in food

Among other organisms that have relevance to food, Serratia marcescens has been found to possess a luxS quorum-sensing system which controls virulence and antibiotic production in the bacterium (Coulthurst et al., 2004). A quorum-sensing system has also been identified in Brucella melitensis, which is responsible for abortion in goats and for Malta fever in humans. The major signaling molecule is N-dodecanoylhomoserine lactone (C12-HSL) (Taminiau et al., 2002). The addition of synthetic C12-HSL to an early log phase culture of either B. melitensis or Brucella suis reduces the transcription of the virB operon, which contains virulence genes required for intracellular survival. This suggests that quorum-sensing may play a role in the control of virulence in Brucella (Taminiau et al., 2002).

22.6

Gram-positive bacteria

While intercellular communication systems in Gram-negative bacteria are often based on homoserine lactones as signaling molecules, it has been shown that autoinducing peptides are involved in intercellular communication in Grampositive bacteria (Sturme et al., 2002). These peptides are exported by dedicated systems, post-translationally modified, and finally sensed by other cells via membrane-located receptors that are part of a two-component regulatory system (Fig. 22.7). The autoinducer is continuously produced at low level and secreted by an ABC transporter protein assisted by an accessory protein. During secretion, the autoinducing peptides become activated by cleavage of their leader sequence. When a threshold level of autoinducer concentration is reached, the histidine kinase, a membrane-bound sensor, is autophosphorylated. The phosphate is then transferred to a cytoplasmic DNA-binding protein, the response regulator, which then activates transcription of the autoinducer structural gene, the regulatory genes and the transporter genes. Depending on the quorum-sensing system, additional genes are co-transcribed, resulting in the development of competence, the production of antimicrobial compounds or the modification of the autoinducing peptide (Kleerebezem et al., 1997). These two-component systems regulate a variety of functions including virulence, genetic competence and the production of antimicrobial compounds in a coordinated manner, taking into account cell density and growth phase. Occasionally the autoinducing peptide has a dual function, such as in the case of nisin that is both a signaling pheromone involved in quorum-sensing and an antimicrobial peptide. It has also been shown that bacteria may contain multiple quorum-sensing systems, underlining the importance of intercellular communication. Finally, in some cases different peptides may be recognized by the same receptor.


577 Post-translationally modified peptide

Membrane transport system

Receptor

P

Response regulator

P kinase

Peptide precursor

Fig. 22.7

Schematic representation of quorum-sensing in Gram-positive bacteria via a two-component regulatory system.

In Gram-positive bacteria, processes controlled by quorum-sensing include virulence in Staphylococcus aureus (Novick and Muir, 1999) and Enterococcus faecalis (Qin et al., 2001), genetic competence in Streptococcus pneumoniae (Cheng et al., 1997) and Bacillus subtilis (Tortosa and Dubnau, 1999), and the production of antimicrobial peptides (AMPs) in many lactic acid bacteria (LAB) (Kleerebezem and Quadri, 2001). As described above, quorum-sensing in Gram-positive bacteria generally consists of the autoinducer, an ABC transporter for secretion of the peptide and a two-component system for sensing autoinducer concentration. However, the Staph. aureus agr- and the E. faecalis fsr-virulence systems use a non-ABC transporter to secrete the autoinducer, and the B. subtilis phr-system is involved in genetic competence and sporulation, in which the signaling peptide is imported into the cytoplasm by an oligopeptide transport system, where it interacts with intracellular receptors (Sturme et al., 2002; Dirix et al., 2004). A luxS system, in conjunction with the AI-2 molecule, is also present in several Gram-positive species, including the probiotic strain Lactobacillus rhamnosus GG (DeKeersmaecker and Vanderleyden., 2003).

22.6.1 Staphylococcus aureus Staphylococcus aureus is a common foodborne pathogen with meat and meat products, poultry and egg products, bakery products and milk and dairy products being common vehicles for infection. However, food handlers are usually the main source of food contamination and illness is usually associated with improper storage of prepared foods. Staph. aureus expresses many potential virulence factors including: (1) surface proteins that promote colonization of host tissues; (2)


578 invasins that promote bacterial spread in tissues (leukocidin, kinases, hyaluronidase); (3) surface factors that inhibit phagocytic engulfment (capsule, Protein A); (4) compounds that enhance survival in phagocytes (carotenoids, catalase production); (5) immunological disguises (Protein A, coagulase, clotting factor); (6) eukaryotic membrane-damaging toxins (hemolysins, leukotoxin, leukocidin); (7) exotoxins that damage host tissues or otherwise provoke symptoms of disease (staphylococcal enterotoxins A to G, toxic-shock syndrome toxin, exfoliatin toxins); and (8) inherent and acquired resistance to antimicrobial agents. Analysis of the Staph. aureus genome has revealed 17 putative two-component systems, all of which are thought to play some role in cell–cell or cell–environment communication (Lyon and Novick, 2004). Four of these two-component systems, agr, sae, arl and srr (srh), are involved in virulence (Novick, 2003), but only the accessory gene regulator, agr, is associated with a peptide signaling system. The agr system (Fig. 22.8) contains a signaling module comprising a secreted autoinducing ligand, AIP; a receptor-histidine kinase, AgrC; and the response regulator, AgrA (Ji et al., 1995; Novick et al., 1995). The signaling module activates the two agr promoters, P1 and P2, but it is the P3 transcript, RNAIII, and not AgrA, that is the intracellular effector of target gene regulation (Novick, 2003). Agr activation results in increased production and secretion of exoproteins involved in virulence (Novick, 2003). Thus, quorum-sensing in Staph. aureus is regulated through the histidine-phosphorylation of the target of RNAIII-activating protein (TRAP) which is highly conserved in staphylococci and inhibition of this protein leads to leads to a decrease in virulence (Vieira-da-Motta et al., 2001; Processed peptide

AgrC

AgrB

Cell membrane

P AgrA

agrA

AgrA

agrC

agrD

SarA

agrB

hid P2 P3

P2 transcript

RNAIII

Positive and negative regulation of target genes

Fig. 22.8 The accessory gene regulatory (agr) system of Staphylococcus aureus.


579 Korem et al., 2003; Ribeiro et al., 2003; Yang et al., 2003). The phosphorylation pattern, structure and gene organization of TRAP deviate from signaling molecules known to date, suggesting that TRAP belongs to a novel class of signal transducers (Gov et al., 2004). AIP is derived from a propeptide, AgrD, by the processing enzyme, AgrB, which removes segments from both the N- and C-terminal ends of the propeptide (Zhang et al., 2002; Novick, 2003; Zhang and Ji, 2004). The length of the AIPs varies from seven to nine amino acids depending on the staphylococcal strains and species. The sequence of the AIPs is highly variable and can be divided into four groups, agr-I–IV, in Staph. aureus, but agrB, D, and C regions vary in concert to maintain the function of AIP. Most AIPs inhibit the activation of heterologous receptors causing repression of accessory gene functions. It has been suggested that this leads to an advantage for the strain producing the greatest AIP concentration or activity (Novick, 2003). Wright et al. (2005) have suggested that, initially, the infecting bacteria grow rapidly, achieving sufficient population density within the first three hours to activate agr. The staphylococcal cells then enter a neutrophil-induced metabolic eclipse lasting for two to three days, followed by agr reactivation concomitantly with abcess development. Only during its short in vivo lifetime does the inhibitory AIP prevent agr expression, and the agr-induced, quorum-dependent synthesis of virulence factors shortly after infection is necessary for the subsequent development of the abscess lesion and bacterial survival. Agr expression also plays a role in biofilm formation which is dependent on environmental conditions (Yarwood and Schlievert, 2003). It has been suggested that detachment of cells expressing agr from biofilms may have important clinical implications (Yarwood et al., 2004). In another paradigm of quorum-sensing, autoinducer concentrations could increase as a result of bacterial cells existing in a confined space, such as a phagocyte or vacuole; rather than as an increase in cell density (Wesson et al., 1998). Evidence for this has been produced in Staph. aureus. Fibrinogen, a component of the inflammatory response, creates spatially constrained microenvironments around bacteria that increase density independently of bacterial numbers and it has been shown to potentiate quorum-sensing-dependent virulence gene expression in Staph. aureus (Rothfork et al., 2003). Quorumsensing has also been linked to infection in Staphylococcus epidermidis (Carmody and Otto, 2004).

22.6.2 Streptococcus Streptococci are responsible for causing a wide variety of human infections and many of these bacteria use quorum-sensing systems to regulate physiological properties, including competence, acid tolerance, biofilm formation and virulence. These quorum-sensing systems are primarily made of small soluble signal peptides that are detected by neighboring cells via a histidine kinase/response regulator pair (Cvitkovitch et al., 2003; Winzer et al., 2003). One such two-component signal


580 transduction system, rgfBDAC, was identified in Strep. agalactiae (Spellerberg et al., 2002). It comprises genes encoding a putative response regulator of 218 amino acids and a putative histidine kinase of 426 amino acids. Comparison of the deduced proteins with the GenBank database revealed a significant similarity to quorum-sensing systems of Gram-positive pathogens and it was thought that rgf regulates the expression of bacterial cell surface components.

22.6.3 Enterococcus faecalis Enterococci are constituents of the normal human microflora, typically colonizing the intestinal tract and skin (Hancock and Perego, 2002). However, they are opportunistic pathogens and can cause disease, mainly in immunocompromised patients. The problem of enterococcal infection has been aggravated by the emergence in the past decade of multiple antibiotic resistance. Despite their pathogenicity, enterococci constitute a major component of the microflora of artisanal cheeses produced in Europe and are considered to play an important role in ripening and aroma development (Hancock and Perego, 2002) and are used as probiotics in humans and animals. These bacteria probably now represent the greatest risk to human health of any bacterial species used for food manufacture. Some strains of E. faecalis express enterococcal cytolysin distantly related to the class of bacteriocins known as lantibiotics. The cytolysin can be encoded by large pheromone-responsive plasmids, or on the chromosome within a pathogenicity island. Expression involves eight genes on the cytolysin operon. This operon is repressed by the activities of two proteins, CylR1 and CylR2, and de-repressed by a quorum-sensing process involving secreted autoinducer CylL(S)' (Nakayama et al., 2001; Haas et al., 2002). The cytolysin operon within the E. faecalis pathogenicity island is associated with other virulence determinants, including aggregation substance and enterococcal surface protein, Esp (Shankar et al., 2004). Another virulence factor of E. faecalis, gelatinase, is regulated in a cell density-dependent fashion by a gelatinase biosynthesis-activating pheromone (GBAP) which has been isolated from culture supernatant of E. faecalis (Nakayama et al., 2001). GBAP is an 11-residue cyclic peptide containing a lactone structure that induces the transcription of two operons, fsrB–fsrC encoding FsrB and a putative histidine kinase FsrC, components of a two component regulatory system, and gelE–sprE encoding gelatinase GelE and serine protease SprE (Nakayama et al., 2001, 2002).

22.6.4 Bacillus spp. Bacillus subtilis Bacillus licheniformis, Bacillus subtilis and Bacillus pumilus constitute the subtilis group, which has been associated with a range of clinical conditions, food spoilage such as ropy bread and incidents of foodborne gastroenteritis (Turnbull, 1997). In B. subtilis, several two-component regulatory systems have been identified. The


581 ComQXPA quorum-sensing system controls cell density-dependent phenotypes such as the production of degradative enzymes and antibiotics and the development of genetic competence (Tran et al., 2000) and biofilm formation (Ren et al., 2004a). The pheromone ComX is a modified ten-amino-acid peptide produced by comQ and comX; with ComQ being involved in a modification of ComX by addition of an isoprenoid (Ansaldi et al., 2002; Bacon et al., 2002). A second quorum-sensing system, the spaRK two-component regulatory system, is controlled by autoinduction by the lantibiotic, subtilin, and transcriptional regulation via sigma factor H (Stein et al., 2002). Extracellular signaling peptides belonging to the Phr family are secreted by B. subtilis, and then, at high cell densities, are actively transported into the cell where they interact with intracellular receptors belonging to a family of aspartyl-phosphate phosphatases, the Rap phosphatases, to regulate gene expression. Immediately downstream of the genes for the Rap phosphatases are the genes for the Phr peptides, forming rap phr signaling cassettes. There are at least seven rap phr signaling cassettes in B. subtilis, and the genome sequence of other Gram-positive, endospore-forming bacteria suggests that similar cassettes may also function in these bacteria. In B. subtilis, the rap phr cassettes regulate sporulation, competence and genes comprising the quorum response (i.e. the response to high cell density) (Carbonell et al., 2002; Pottathil and Lazazzera, 2003). The organism also contains a LuxS system (Hilgers and Ludwig, 2001; Ruzheinikov et al., 2001). To date, no role has been shown for quorum-sensing in regulation of virulence factors in B. subtilis. Bacillus cereus group Members of the B. cereus group include cereus, anthracis and thuringiensis. All three species cause gastroenteritis in humans and B. anthracis can cause more severe infections. The partially annotated genome sequence of B. anthracis contains an ORF (BA5047) encoding an ortholog of luxS, required for synthesis of AI-2 (Jones and Blaser, 2003). A pleiotropic regulator of virulence factors, termed PlcR, is present in B. thuringiensis and B. cereus where it activates the transcription of at least 15 genes encoding extracellular proteins, including phospholipase C, proteases and enterotoxins. Expression of the plcR gene is autoregulated and activated at the onset of stationary phase (Gominet et al., 2001). Using transposon mutagenesis, a fivegene operon encoding polypeptides homologous to the components of the oligopeptide permease (Opp) system of B. subtilis, and with a similar structural organization, was identified. The five B. thuringiensis genes were designated oppA, B, C, D and F. Disruption of the oppB gene resulted in the loss of hemolytic activity and reduced the virulence of the strain against insects. It was also shown that Opp, an oligopeptide permease, was required for the import of small peptides into the cell, and, therefore, plcR expression might be activated by a signaling peptide acting as a quorum-sensing effector (Gominet et al., 2001). Activation of PlcR is under the control of a small peptide: PapR. The papR gene, which encodes a 48-amino-acid peptide, is located 70 bp downstream from plcR on the PlcR regulon. Disruption of the papR gene abolished expression of the PlcR regulon, resulting in a large decrease


582 in hemolysis and virulence in insect larvae. The PapR polypeptide is secreted and reimported via Opp. When inside the cell, a processed form of PapR, presumably a pentapeptide, activates the PlcR regulon by allowing PlcR to bind to its DNA target. This activating mechanism was found to be strain specific, with specificity determined by the first residue of the penta peptide (Slamti and Lereclus, 2002). The fifth amino acid of the pentapeptide is also involved in the specificity of activation with four classes of PlcR–PapR pairs, defining four distinct phenotypes in the B. cereus group (Slamti and Lereclus, 2005). Quorum-sensing between spores may also explain why higher concentrations of B. megaterium spores encourage more rapid germination and more spores to germinate than low spore populations (Caipo et al., 2002).

22.6.5 Clostridium spp. Clostridium perfringens The Gram-positive anaerobic pathogen Clostridium perfringens, causes gastroenteritis and clostridial myonecrosis (or gas gangrene) in humans by producing numerous extracellular toxins and enzymes that act in concert to degrade host tissues. C. perfringens possesses a homolog of the luxS gene (Ohtani et al., 2002). A luxS mutant showed a reduced level of production of α-, κ- and θ-toxins. The transcription of the θ-toxin gene (pfoA) was lower at mid-exponential growth phase, whereas α- and κ-toxin gene transcription was not significantly affected in the luxS mutant. The production of toxins in the luxS mutant was stimulated by the addition of the culture supernatant from the wild-type cells, possibly because of the presence of AI-2. A deletion analysis of the luxS operon showed that only the luxS gene is responsible for cell–cell signaling, and that the metB or cysK genes located upstream of luxS are not involved in regulating toxin production, indicating that cell–cell signaling by AI-2 plays an important role in the regulation of toxin production in C. perfringens (Ohtani et al., 2002). The addition of ascorbic acid, an AI-2 analogue, on AI-2 production, as well as growth, sporulation and enterotoxin production in C. perfringens were examined. The addition of ascorbic acid to supernatants from ground beef resulted in a 100fold decrease in AI-2 activity. The addition of sodium ascorbate, a non-acidic salt of ascorbic acid, also resulted in AI-2 assay inhibition. Spore production decreased in the presence of ascorbic acid. Western immunoblot analyses showed that C. perfringens enterotoxin (CPE) levels were highest after 24 h without ascorbic acid (Novak and Fratamico, 2004). Clostridium difficile There is an increasing incidence of disease associated with Clostridium difficile, particularly hospital-acquired infections. Pseudomembranous colitis is caused by C. difficile that usually live harmlessly in the intestines of about 50–70 % of newborns, 20–50 % of infants and 3 % of adults. Normally, C. difficile compete with other intestinal bacteria, but when someone takes antibiotics that kill the


583 competing bacteria, C. difficile can grow uncontrollably and produce two toxins that cause intestinal illness. Clostridium difficile produces at least one potential signaling molecule shown to be AI-2, and a homolog of luxS has been identified in its genome. A potential transcriptional regulator and sensor kinase, rolA and rolB, are located adjacent to luxS(Cd). Reverse transcriptase polymerase chain reaction (RT-PCR) has been used to confirm the genetic organization of the luxS(Cd) locus. RolA acts as a negative regulator of AI-2 production, but the exogenous addition of AI-2 or 4-hydroxy-5-methyl-3(2H) furanone has no effect on the production of toxins by C. difficile (Carter et al., 2005).

22.6.6 Listeria monocytogenes Concerns about L. monocytogenes have resurfaced over the past few years because of outbreaks associated with processed meats and several product recalls. Legislation has also been introduced in the USA centered on environmental monitoring for Listeria in food-processing plants. The L. monocytogenes genome contains a putative luxS homolog, but culture supernatants of the organism fail to stimulate expression of the virulence genes encoding phospholipase C and listeriolysin (Favrin, 2004). Virulence genes from this organism are controlled by the transcriptional regulator PrfA. Although PrfA synthesis is activated at 37 °C, PrfA-dependent expression remains low in rich medium, but is strongly induced when L. monocytogenes is grown in the presence of activated charcoal (Ermolaeva et al., 1999). This ‘charcoal effect’ is due to the adsorption of a diffusible autorepressor substance released by the organism during exponential growth (Ermolaeva et al., 2004). Studies using an L. monocytogenes strain in which the prfA gene is expressed constitutively at 37 °C indicate that the autoregulatory substance represses PrfA-dependent expression by inhibiting PrfA activity. PrfA presumably functions via an allosteric activation mechanism. The inhibitory effect was overcome by a PrfA* mutation that locks PrfA in the fully active conformation, suggesting that the autorepressor interferes with the allosteric shift of PrfA. The listerial autorepressor appears to be a low molecular weight hydrophobic substance. It is postulated that this diffusible substance mediates a quorum-sensing mechanism by which L. monocytogenes restricts the expression of its PrfA virulence regulon. This autoregulatory pathway could ensure the silencing of virulence genes during extracellular growth at 37 °C. It may also limit damage to the host cell caused by an excess production of cytotoxic PrfA-dependent virulence factors in the PrfA-activating cytosolic compartment during intracellular infection (Ermolaeva et al., 2004).

22.7 Alternative quorum-sensing systems A diffusible signal factor (DSF), which regulates virulence in Xanthomonas


584 campestris pv. campestris, has been identified as cis-11-methyl-2-dodecanoic acid, an α,β unsaturated fatty acid. A range of bacterial pathogens, including several Mycobacterium species, also displayed DSF-like activity. Furthermore, DSF is structurally and functionally related to farnesoic acid (FA), which regulates morphological transition and virulence by Candida albicans, a fungal pathogen. Wang et al. (2004) propose that α,β unsaturated fatty acids represent a new class of extracellular signals for bacterial and fungal cell–cell communications.

22.8 Quorum-sensing and host cells Several recent reports indicate that the signaling molecules (autoinducers) that mediate quorum-sensing in Ps. aeruginosa may also modulate gene expression in host cells, but how this is brought about is unknown. Williams et al. (2004) have shown that N-3-oxododecanoyl-homoserine lactone and N-butyrl-homoserine lactone can both enter eukaryotic cells and activate transcription factors based on their cognate transcriptional activators, LasR and RhlR, respectively. In vitro studies have also indicated that AHLs may function as virulence determinants by modifying cytokine production by eukaryotic cells, and by stimulating the relaxation of blood vessels (Gardiner et al., 2001). In some instances, signaling molecules are specifically targeted by the host as part of an innate defence mechanism against infection. This suggests that humans have evolved mechanisms to interfere with bacterial cell–cell communication (Chun et al., 2004; Hastings, 2004; Sperandio, 2004). For example, in a skininfection model phagocyte-derived oxidants produced by expression of NADPH oxidase, myelo-peroxidase or nitric oxide synthase genes inactivated peptide autoinducers of Staph. aureus, but this mechanism was not observed during infection induced by a quorum-sensing-deficient mutant (Rothfork et al., 2004). Quorum-sensing molecules have also been shown to modulate cell-mediated immunity, both in vitro and in vivo (Boontham et al., 2004).

22.9 Strategies to interfere with quorum-sensing Many methods to interefere with quorum-sensing have been studied and these have been reviewed by Zhang and Dong (2004). A brief description of some of these systems is included below.

22.9.1 Acyl homoserine lactonases Several bacteria, including members of the genus Bacillus, produce enzymes, termed lactonases, which degrade AHL quorum-sensing signals. The enzymes are encoded by the aiiA gene in B. thuringiensis. These signal interference mechanisms are being explored for prevention of bacterial infections (Dong et al., 2002, 2004; Lee et al., 2002) and even food spoilage (Liu and Griffiths, 2003).


585 Porcine kidney acylase I is also able to deacylate N-acylhomoserine lactones (Xu et al., 2003).

22.9.2 Halogenated furanones Halogenated furanones produced by the macroalga Delisea pulchra inhibit AHLdependent gene expression by destabilization of LuxR activity (Manefield et al., 2002). There is evidence that several polyphenolic compounds present in plants can also interfere with quorum-sensing (Huber et al., 2003).

22.10 Quorum-sensing and food microbiology The importance of quorum-sensing to the science of food microbiology has been reviewed by Smith et al. (2004). N-acyl homoserine lactones have been detected in a variety of foods including vacuum-packed meat samples (Bruhn et al., 2004), fish, milk and raw meats (Liu and Griffiths, 2003). Co-inoculation of H. alvei wild-type with an AHL-deficient Serratia proteamaculans B5a, in which protease secretion is regulated by quorum-sensing, caused spoilage of liquid milk, but co-inoculation of AHL-negative strains of H. alvei and S. proteamaculans B5a did not cause spoilage. However, AHL and AHL-producing bacteria are present in vacuum-packed meat during storage and spoilage, but AHL does not appear to influence the spoilage of this particular type of conserved meat, possibly because spoilage in these products is due to growth of lactic acid bacteria and not the aerobic Gram-negative psychrotrophic bacteria that possess AHL-based quorum-sensing circuits (Bruhn et al., 2004). Production of AHL and other quorum signals was not widespread in Gram-negative isolates from a raw vegetable processing line, and did not appear to be related to biofilm formation in vitro (Van Houdt et al., 2004). However, other work has shown that quorum-sensing controls the formation of channels and pillar-like structures to ensure efficient nutrient delivery to cells in a biofilm (Stanley and Lazazzera, 2004). The luminescence-based response of the reporter strain Vibrio harveyi BB170 has been used to screen fresh produce and processed foods for AI-2-like activity and to determine whether specific food additives can act as AI-2 mimics and result in AI-2-like activity (Lu et al., 2004). Maximum AI-2 activity was seen on frozen fish followed by tomato, cantaloupe, carrots, tofu and milk. Some raw meats and cheeses were capable of inhibiting AI-2 activity, and AI-2 activity was almost totally inhibited by sodium propionate (Lu et al., 2004). It is worth cautioning about the use of quorum-sensing reporters in natural systems which may lead to unreliable results. The study of quorum-sensing is in its infancy and there is much to learn about the way that bacteria communicate with their environment and each other. Shedding light on this will open up several avenues for controlling foodborne pathogens and spoilage organisms.


586

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598

Index

accurate mass and time tags (AMTs) 13 acid adaptation systems 101, 136-8, 154— 5, 186-7, 394-5 Campylobacter jejuni 292 Escherichia coli 137-8, 260-2 Listeria monocytogenes 68, 138, 154—5 186, 187-8 ' Salmonella 137, 138, 219, 395 Staphylococcus aureus 345-6 Vibrio spp. 371-2, 378 acidification of food 400 acyl homoserine lactonases 584-5 adherence mechanisms 103^-, 394 Bacillus cereus 316 Escherichia coli 242-6 Salmonella 219-20 Vibrio spp. 369 Aeromonas spp. quorum-sensing 573-4 sublethal injury 183-4 and temperature 518-19 aerotaxis mechanisms 284 affinity-based separations 11 AHL systems 362-5, 552, 554 AI systems 362-5 alcohol disinfectants 489-90 aldehydes 490 allicin 462-3 allylisothiocyanate 402-3 AMTs (accurate mass and time tags) 13 animal antimicrobials 460-2, 467-9 antibiotic programs 110-11 antigen mimicry 395-6

antigenic determinants 38-9 antimicrobials 109, 402-3, 460-74 allicin 462-3 allylisothiocyanate 402-3 animal sources 460-2, 467-9 bacteriocins 464-6 chitosan 460-1 garlic 462 herbs 463-4, 469 hydroxycinnamic acids 463 isothiocyanates 463 lactoferrin 461 lactoperoxidase 461-2, 467 lysozyme 462, 467-9 microbial sources 464-6, 470-2 natamycin 464, 470 nisin 402-3, 465, 470-1 onion 462 overcoming resistance 473-4 plant sources 462^-, 469 resistance and adaptation 460-74 resistance development 467-72 resistance predictions 472-3 resistance responses 466 spices 463-4, 469 AphA regulation 366 array-based genomic sequencing 5-6 Arrhenius equation 511-13 artificial neural networks 63 Bacillus cereus 310-24 adhesion of spores 316 diseases caused by 311-16

600

Index

enterotoxins 313-14, 322 foodborne poisoning 317-23 diarrhoeal 311, 313-14 dissemination along the food chain 319-20 emetic 311-13 foods affected 317-19 germination of spores 316-17 growth requirements 322-3 inactivation 144 quorum-sensing 581-2 reservoirs 319-20 risk assessment 323-4 stress responses heat stress 71, 135, 322-3 osmotic stress 139 sublethal injury 179-81 taxonomy 310 virulence 311-16 diversity of virulence 320-2 and temperature 519 Bacillus subtilis heat stress 7 1 , 135 quorum-sensing 580-1 bacteremia 218 bacteriocins 464-6 bacteriophages 87-90 Baranyi, J. 57-9 bias factors 67 Bifidobacterium 190 biguanides 491-2 bile toleration Campylobacter jejuni 285 Vibrio spp. 372 biofilms 91-2, 103-4, 129, 500 Staphylococcus aureus 342 Vibrio spp. 369 bioinformatics 4, 29-31, 31-2 biosensors 141-2 bipolar pulses 451 bisphenols 490-1 BLAST 29 blocking infections 107-9 botulinum neurotoxins 531, 534, 540, 541-2 botulism 533-8 Brucella melitensis 576 Brucella suis 576 buffered peptone water (BPW) 200 CAMP (cationic antimicrobial peptides) 37 Campylobacter coli 285 Campylobacter jejuni 279-99, 393^4

aerotaxis mechanisms 284 in animals 283-5 antigen mimicry 395-6 chemotactic mechanisms 284 diseases caused by 280-2 in the food chain 282-5, 393 red meats 157 transmission methods 283 genomic sequencing 4, 396 growth requirements 282, 284, 292-3 temperature range 509 host-pathogen interactions 36 motility mechanisms 284 mutant strains 289 pathogenicity islands 86 quorum-sensing 572 stationary phase 287-9 strain-dependent differences 293-4, 296, 298 stress responses 285-5 acid/alkali stress 292 bile toleration 285 cold stress 285-6 and genetic evolution 294-5 heat stress 286-7 iron limitation 284-5 nitrosative stress 297 osmotic stress 290-2 oxidative stress 289-90, 292-3, 297 quorum-sensing 293 starvation 287-9 strain-dependent variations 293^1 sublethal injury 171-4 taxonomy 279 virulence 87, 295-7 eukaryotic-epithelial cell interaction 297 strain-dependent variation 296 and temperature 519-20 toxin production 296 Candida albicans 584 capillary electrophoresis 10, 24-5 capsules of Escherichia coli 246-7 cardinal parameter models (CPM) 62 cardinal temperatures 508-9, 510 CDT (cytolethal distending toxin) 296 cell adhesion see adherence mechanisms cell membrane damage 432, 513-15 cellular basis of pathogenesis 217-22 cereulide 311-13 cheese 187 chemotactic mechanisms 284 chitosan 460-1 chlorine based disinfectants 486

Index cholera 359-60, 373-5 vaccines 377 classification of metabolites 21 cleaning 484, 500 Clostridium botulinum 409-10, 531^-2 botulinum neurotoxins 531, 534, 540, 541-2 foodborne botulisum 533, 535-8 growth requirements 540, 541-2 heat stress 539, 540-1 inadvertent botulisum 534 infant botulisum 533-4 infectious botulisum 533-4 radiation-tolerance 444 sources of 539 sublethal injury 181-2 taxonomy 532-2 toxin formation 540, 541-2 virulence 521 wound botulisum 534 Clostridium difficile 181-2, 582-3 Clostridium perfringens and food poisoning 410 heat stress 135 population kinetic behaviour 69-70 quorum-sensing 582 sublethal injury 181-2 virulence 521-2 "work to be done" 69 CNF (cytotoxic necrotising factors) 250 cold pasteurization 452 cold stress 507-23 Campylobacter jejuni 285-6 and cell membrane integrity 513-15 and cross-protection 135-6, 153 Escherichia coli 136 and homeoviscocity 513-15 and metabolic changes 515-17 and metabolic exhaustion 509-11 resistance and adaptation 507-22 Staphylococcus aureus 344—5 see also temperature comparative proteomics 32-3 concentrations of disinfectants 493, 500 contact times and disinfectants 493-4 controlling pathogenicity 92 cross-protection 128-^15, 428-9, 471 anticipating 142-3 and cold stress 135-6, 153 detecting and quantifying 139^42 Escherichia coli 265 and food preservation technologies 129, 132, 143-4 and heat stress 134-5

601

hurdle concept 143 limited adaptive responses 132 multiple adaptive responses 132 osmotic stress 138-9 preventing 143-4stress hardening 143 see also acid adaptation systems; survival mechanisms CsrA gene 288 curli 244 cytolethal distending toxin (CDT) 296 cytolycins 247 DAEC Escherichia coli strains 255-6 databases 30, 31, 32-3 defense mechanisms 100-3, 393-8 detection technologies 411-12 and cross-protection 139^-2 genomics-based detection 411 and Staphylococcus aureus enterotoxins 339^12 and stress response genes 140 and sublethal injury 199-208 diarrhoeal poisoning 311, 313-14 differential equation models 57-9 diffusible signal factors (DSF) 583^1 diseases and Bacillus cereus 311-16 botulism 533-8 and Campylobacter jejuni 280-2 cholera 359-60, 373-5, 377 and Clostridium perfringens 410 enteric fever 218 and Escherichia coli 241-2, 251-7, 269-70 gastrointestinal infections 218, 241-2 and Salmonella 218 septicemia 218 and Staphylococcus aureus 331-3 and Vibrio spp. 359-61 means of infection 373-4 prevention strategies 375-6 see also foodborne poisoning disinfectants and sanitizers 484-502 acquired resistance 498-9 alcohols 489-90 aldehydes 490 biguanides 491-2 and biofilms 500 bisphenols 490-1 chlorine based compounds 486 and cleaning 484, 500 concentrations 493, 500 contact times 4 9 3 ^ -

602

Index

halogen releasing agents 486-7 hypochlorous acid 486 intrinsic resistance 498 iodine 487 and mechanical energy 494 and metabolic state of microorganisms 499 monitoring devices 494-7 neutralising components 494 peroxygens 488-9 and predictive modelling 496-7 product choice 493 quaternary ammonium compounds 487-8 resistance and adaptation 484-502 resistance prediction 499-501 revival of injured cells 500 stress responses 497-9 target areas 497 and temperature 493, 500 DNA alterations and pathogenicity 78-91 acquisition of new DNA 78-9 mutator strains 78, 80-1 pathogenicity islands 82-6 recombination of genes 82 DNA damage 429-30 dormancy of Vibrio spp. 372-3 drug development 38 drug resistance 38 drying foods 399 EaggEC Escherichia coli strains 255 EGF (epidermal growth factor) receptor 103 EHEC Escherichia coli strains 248, 2 5 6 7, 258, 259, 269, 564-7 EIEC Escherichia coli strains 254-5 emetic poisoning 311-13 enteric fever 218 enteroaggregative feat-stable enterotoxins 250 Enterococcus faecalis 580 enterotoxins inB. cereus 313-14, 322 in 5. aureus 333-42 environmental conditions 53, 59-66, 78 see also host-pathogen interactions; stress responses; temperature environmental cues 366-8 environmental interactions 257-60 environmental stress 153 enzyme damage 431-2 EPEC Escherichia coli strains 245-6, 251^1,567-8

epidermal growth factor (EGF) receptor 103 epithelial cells 297, 394 Ermengem, Emile van 535 Escherichia coli 240-70 animal reservoirs 268-9 attaching and effacing mechanisms 244-5 bacteriophages 89 characterisation/classification 241 CsrA gene 288 DAEC strains 255-6 diseases caused by 241-2, 251-7, 2 6 9 70 EaggEC strains 255 EHEC strains 248, 256-7, 258, 259, 269 EIEC strains 254-5 EPEC strains 245-6, 251-4 ETEC strains 84, 86-7, 243-4, 254 fimbriae 243^1 in food processing environments 258-9, 270 gastrointestinal infections 241-2 genome 116-17,242 genomics-based detection 411 growth predictions 66 growth requirements 257 horizontal DNA transfer 79-80 host-pathogen interactions 36, 251-7 inactivation 144 metabolome composition 22 mutator strains 81 normal physiological range 71 0157:H7 strain 393 pathogenicity islands 242, 245 pathotypes 251-7 quorum-sensing 563-8 resuscitation of injured cells 201, 202 risk assessment and control 265-8 in soil environments 257-8 stress responses 260-5 acid adaptation systems 137-8, 260-2 cold stress 136 cross-protection 265 heat stress 115, 122-3, 134, 135, 263-4, 425-7, 432 metal ion stress 265 osmotic stress 139, 262-3 oxidative stress 264 regulatory genes 116-22, 132-3, 260 sublethal injury 174-7 and turgor pressure 262-3 vesicle-mediated gene transfer 90-1

Index virulence 86-7, 2 4 0 - 1 , 242-60 adherence and colonisation 242-6 capsules 246-7 environmental interactions 257-60 exotoxins 247-50 gene expression 259-60 lipopolysaccharides 246 pathotypes 251-7 and risk assessment 266-7 and temperature 521 in waste environments 258 and water activity 292-3 in water environments 258 in yogurt 187 ESI-MS 26-7 ETEC Escherichia coli strains 84, 86-7, 243-4, 254 eukaryotic-epithelial cell interaction 297 evolution of bacteria 78 exotoxins in Escherichia coli 247-50 CNF (cytotoxic necrotising factors) 250 cytolycins 247 enteroaggregative feat-stable enterotoxins 250 haemolysins 247-8 heat-labile enterotoxins 248 heat-stable enterotoxins 248-9, 250 lymphostatin 250 plasmid-encoded toxin (Pet) 250 verocytotoxins 249-50 see also toxins expression proteomics 7-15 fermentation 400 fimbriae 243-4 fingerprinting 26-9 fluorescence staining 145 food microbiology 585 food preservation and processing 129, 132, 399^103 acidification 400 cold pasteurization 452 and cross-protection 129, 132, 143^4 drying 399 and Escherichia coli 258-9, 270 fermentation 400 freezing 156-7, 400 see also cold stress HACCP (Hazard Analysis and Critical Control Points) 391, 392 heating 399-400, 401, 422-3 see also heat stress high-intensity pulsed electric fields 401, 450-3

603

hydrostatic pressure 157, 185, 401, 446-50 inactivation techniques 442 inhibitory procedures 442 ionizing irradiation 434-5, 443-6 natural antimicrobials see antimicrobials and pathogenicity 91-2 preservatives 402 process criteria 391-2 product structuring 403 resistance and adaptation 399^411 and Staphylococcus aureus 343, 347-8 and sublethal injury 156-8, 190 ultra violet light 401 ultrasound energy 401 see also detection technologies; risk assessment food structuring 403 foodborne botulisum 533, 535-8 foodborne poisoning 317-23 and Clostridium perfringens 410 diarrhoeal poisoning 311, 313-14 dissemination along the food chain 319-20 emetic poisoning 311-13 foods affected 317-19 preventing 347-8 see also diseases footprinting 26-9 free radicals 122-3 freezing 156-7, 400 see also cold stress fruit and vegetables 215 functional proteomics 8, 15-20 Fur regulator 397-8 GAD proteins 155-6 Gamma model 6 1 - 2 garlic 462 gas chromatography separation 23^4 gastrointestinal infections 218, 241-2 gastrointestinal mucosa 102-3, 107 genetic evolution 294-5 genetic exchange 78-9, 80-91 genomic sequencing 3, 4 - 5 , 502 Campylobacter jejuni 4, 396 Escherichia coli 116-17, 242, 259-60, 411 Listeria monocytogenes 154 Salmonella 216-17 see also regulatory genes genomics-based detection 411-12 germination of 6. cereus spores 316-17

604

Index

glycosylation 16-17 Gompertz model 56-7 Gram-negative bacteria, quorum-sensing 550-76 Gram-positive bacteria, quorum sensing 576-83 growth kinetics 511-13 growth rate models 54-63 and lag times 64 simulation of growth 6 7 - 8 validation and prediction 66-8 growth requirements Bacillus cereus 322-3 Campylobacter jejuni 282, 284, 292-3, 509 Clostridium botulinum 540, 541-2 Escherichia coli 257 optimum growth temperatures 6 0 - 1 , 508-9,510 Salmonella 222-7 Staphylococcus aureus 342-3 see also stationary phase growth-no-growth interface 142-3 Guillain-Barre Syndrome (GBS) 282 HACCP (Hazard Analysis and Critical Control Points) 391, 392 haemolysins 247-8 halogen releasing agents 486-7 halogenated furanones 585 heat stress Bacillus cereus 71, 135, 322-3 Bacillus subtilis 71, 135 Campylobacter jejuni 286-7 cell membrane damage 432 Clostridium botulinum 539, 540-1 Clostridium perfringens 135 countering resistance to 432-5 and cross-protection 134-5 and DNA damage 429-30 Escherichia coli 115, 122-3, 134, 135, 263^1, 425-7, 432 Lactobacillus spp. 134 Listeria monocytogenes 7 1 , 135, 156, 188-9,425-8,433-4 modelling 71 protein and enzyme damage 431-2 resistance and adaptation 422-36 resistance development 424-9 resistance predictions 423-4 and ribosome damage 430 Salmonella 425 Staphylococcus aureus 344-5, 430-1 sublethal injury 115, 134-5

targets of heat damage 429-32 thermal death kinetics 423-4 time-temperature combinations 425-6 heat-labile enterotoxins 248 heat-stable enterotoxins 248-9, 250 heating and food preservation 399-400, 401,422-3 Helicobacter pylori host-pathogen interactions 36 quorum-sensing 572-3 and starvation 288 herbs 463-4, 469 Hfq protein 120 high-intensity pulsed electric fields 207, 401,450-3 high-pressure processing, resistance and adaptation 446-50 homeoviscocity 513-15 horizontal DNA transfer 79-80 host cell quorum-sensing 584 host defense mechanisms 100-3, 393-8 host-pathogen interactions 33-8, 78, 9 9 111 antigen mimicry 395-6 blocking infections 107-9 Campylobacter jejuni 36 cell adhesion 103-4 defense mechanisms 100-3, 393-8 Escherichia coli 36, 251-7 Helicobacter pylori 36 immunological defence mechanisms 100, 101-3, 395-6 invasion into host tissue 104-5 Listeria monocytogenes 37 microbial antagonism 396-7 Salmonella 36, 37 survival mechanisms 33-8, 100-2, 222-7, 395-6 Vibrio spp. 36 virulence expression 105-6 see also cross-protection; stress responses hurdle concept 143 hydrogen peroxide 402, 488-9 hydrostatic pressure 157, 185, 347, 401, 446-50 hydroxycinnamic acids 463 hyperosmotic stress 291-2 hypochlorous acid 486 hyposmotic stress 290-1 ICAT (isotopically coded affinity tags) 14 IgA antibodies 395 immunological defence mechanisms 100,

Index 101-3, 395-6 in silico analyses 29-32 inactivation techniques 144, 442 inadvertent botulisum 534 inductive heating 401 infant botulisum 533-4 infectious botulisum 533-4 inhibitory procedures 442 innate defence mechanisms 394-5 interpolation region 66-7 intestinal immunity 100, 101-3, 395-6 intrinsic resistance 498 iodine 487 ionizing irradiation 434-5, 443-6 iron stress 284-5, 397-8 isoelectric focusing 8 isolation protocols 163 isothiocyanates 463 isotope labelling 14, 26 isotope tracer techniques 25-6 isotopically coded affinity tags (ICAT) 14 Kerner, Justinus 535 kinetics of growth 511-13 Lactobacillus johnsonii 134 Lactobacillus plantarum 190 Lactococcus lactis 134 lactoferrin 461 lactoperoxidase 461-2, 467 lag times 63-6, 7 2 - 3 , 203-5, 511 intermediate lag time 71-2 minimum lag time 65 relative lag times (RLT) 64 stress and single cell lag times 72-3 LC/CE separations 10-11 LEE pathogenicity island 83-4 limited adaptive responses 132 lipopolysaccharides 246 liquid chromatography separation 23^4 liquid media repair 200-2 liquid-based separations 10-11 Listeria monocytogenes acid tolerant response 68, 138, 154-5, 186, 187-8 genome sequence 154 heat stress 71, 135, 156, 188-9, 425-8, 433-4 host-pathogen interactions 37 isolation protocols 163 osmotic stress 139, 154-5 pathogenicity islands 85 quorum-sensing 583 and ready-to-eat (RTE) foods 110

605

reference map 33 resuscitation of injured cells 201 sublethal injury 158-69, 187-8 virulence 518 low temperatures see cold stress; temperature lymphostatin 250 lysozyme 462, 467-9 M cells 102,221 macrorestriction analysis 411-12 MALDI-MS 26-7 mass spectrometry 11-13, 26-7 mayonnaise 509, 511 measurement of metabolites 22-3 mesophilic organisms 508 metabolic changes and low temperatures 515-17 metabolic exhaustion 509-11 metabolic labelling 14 metabolic profiling 21-2 metabolic state of microorganisms 499 metabolite target analysis 22 metabolome analyses 3-6, 2 1 - 6 2D-TLC (two-dimensional thin-layer chromatography) 23 arguments for 6 bioinformatics 31-2 capillary electrophoresis separation 2 4 5 classification of metabolites 21 databases 30 future trends 39-40 gas chromatography separation 23^4 in silico approaches 29-32 isotope tracer techniques 2 5 - 6 liquid chromatography separation 23-4 measurement of metabolites 22-3 network organizations 25-6 separation strategies 23-5 metabolomic fingerprinting and footprinting 22, 26-9 metabonomics 21-2 metal ion stress 265 microarray-based analyses 13, 37, 141 microbial antagonism 396-7 microbial antimicrobials 464-6, 470-2 microflora in the colon 102, 107 microwave heating 401 minimum lag time 65 modelling stress responses 53-73 accuracy factors 67 artificial neural networks 63 bias factors 67

606

Index

cardinal parameter models (CPM) 62 differential equation models 57-9 Gamma model 61-2 Gompertz model 56-7 growth rate models 54-63 and lag times 64 simulation of growth 67-8 validation and prediction 66-8 heat treatment effects 71 interpolation region 66-7 lag time models 63-6 intermediate lag time 71-2 stress and single cell lag times 72-3 NTPR (normal physiological temperature range) 71-2 polynomial models 60 primary models 54-9 secondary models 59-66 square-root type models 60-1 stress effect models 68-73 and temperature ranges 71-2 molecular basis of pathogenesis 217-22 molecular biological proteome analyses 19-20 monitoring devices for disinfectants 494-7 monopolar pulses 451 motility mechanisms 284 mRNA 119-20, 133, 140-1, 153-4 MS-based identifications 11-13 MudPIT technology 11 multiparametric flow cytometry 208 multiple adaptive responses 132 multiple quorum-sensing systems 368 mutator strains 78, 80-1 Campylobacter jejuni 289 Escherichia coli 81 and ionising radiation 446 Salmonella 81 Staphylococcus aureus 342-3 Mycobacterium spp. 584 natamycin 464, 470 natural antimicrobials see. antimicrobials network organizations 25-6 neural networks 63 neutralising components in disinfectants 494 nisin 402-3, 465, 470-1 nitrosative stress 297 NMR techniques 26, 27-8 NTPR (normal physiological temperature range) 71-2 0157:H7 Escherichia coli strain 393

ohmic heating 401 onions 462 optimum growth temperatures 508-9, 510 osmotic stress Bacillus cereus 139 Campylobacter jejuni 290-2 and cross-protection 138-9 Escherichia coli 139, 262-3 hyperosmotic stress 291-2 hyposmotic stress 290-1 Listeria monocytogenes 139, 154-5 Staphylococcus aureus 346-7 oxidative stress Campylobacter jejuni 289-90, 292-3, 297 Escherichia coli 264 Vibrio spp. 372 Oxyrase 201 pathogenicity 78-93 acquisition of new DNA 78-9 bacteriophages 87-90 controlling pathogenicity 92 evolution of bacteria 78 and food processing environment 91-2 genetic exchange 78-9, 80-91 mutator strains 78, 80-1 pathogenicity islands 82-6 predicting pathogenicity 92 recombination of genes 82 vesicle-mediated gene transfer 90-1 virulence 38-9, 79-80 phage-encoded 88 and plasmids 86-7 pathogenicity islands 82-6 Campylobacter jejuni 86 Escherichia coli 242, 245 Listeria monocytogenes 85 Salmonella 84-5, 216 Staphylococcus aureus 85 Vibrio spp. 85 Yersinia 84 peptide chemistry 12, 19 CAMP (cationic antimicrobial peptides) 37 peptide sequencing 12 peracetic acid 488-9 peroxygens 488-9 phage-encoded virulence 88 phosphorylation 16 plant antimicrobials 462^1, 469 plasmid-encoded toxin (Pet) 250 plasmids 86-7 polymerase chain reaction (PCR) 207

Index polynomial models 60 population kinetics 54, 69-70 post-translational modifications (PTMs) 15-17 pre-enrichment 200-1 predictive microbiology 142 and antimicrobials 472-3 and disinfectants 496-7, 499-501 and heat stress 423-4 and pathogenicity 92 prediction algorithms 29 preservatives 402 see also food preservation and processing preventing cross-protection 143-4 primary models 54-9 probiotics 108-9 process criteria 391-2 processing see food preservation and processing product structuring 403 protective agents 202-3 Protein Quaternary Structure (PQS) database 20 ProteinChip technology 13 proteins 17-18 biochemical activities 18-19 damage to 431-2 protein-carbohydrate interactions 18 protein-protein interactions 17-18 quantification of protein expression 1 3 15 subcellular localizations 19 proteolytic labelling 15 proteome analyses 3-21 2D-SDS PAGE separations 8-10, 12, 32,33 affinity-based separations 11 arguments for 6 bioinformatics 29-31 comparative proteomics 32-3 databases 30, 31, 32-3 expression proteomics 7-15 functional proteomics 8, 15-20 future trends 39-40 in silica approaches 29-32 information content 7 LC/CE separations 10-11 liquid-based separations 10-11 microarray-based approaches 13, 37, 141 molecular biological approaches 19-20 MS-based identifications 11-13 peptide sequencing 12

607

post-translational modifications (PTMs) 15-17 protein biochemical activities 18-19 protein complexes 17-18 protein subcellular localizations 19 protein-protein interactions 17-18 quantification of protein expression 1 3 15 strategies 6-21 stress protein detection 141 structural proteomics 8, 20-1 subcellular proteomics 19 proteomic fingerprinting and footprinting 26-9 Pseudamanas aeruginosa heat stress 135 host-pathogen interactions 36-7 quorum-sensing 574-6, 584 psychrophile organisms 508 psychrotroph organisms 508 pulsed electric fields 207, 401, 450-3 quaternary ammonium compounds 487-8 quorum-sensing 36-7, 549-85 acyl homoserine lactonases 584-5 Aerotnonas hydrophila 573-4 AHL systems 362-5, 552, 554 AI systems 362-5 AphA regulation 366 Bacillus cereus 581-2 Bacillus subtilis 580-1 Brucella melitensis 576 Brucella suis 576 Campylobacter jejuni 293, 572 Candida albicans 584 Clostridium difficile 582-3 Clostridium perfringens 582 diffusible signal factors (DSF) 583^1 Enterococcus faecalis 580 and environmental cues 366-8 Escherichia coli 563-8 EHEC strains 564-7 EPEC strains 567-8 and food microbiology 585 in Gram-negative bacteria 550-76 in Gram-positive bacteria 576-83 halogenated furanones 585 Helicobacter pylori 572-3 and host cells 584 Listeria monocytogenes 583 multiple quorum-sensing systems 368 Mycobacterium spp. 584 Pseudomonas aeruginosa 574-6, 584 Salmonella 568-70

608

Index

Serratia marcescens 576 Shigella 570 signal interference methods 584-5 signal molecules 558 signal-mediated cross-talk 368 Staphylococcus aureus 577-9 Streptococcus 579-80 Vibrio spp. 361-8 V. choleras 558-60 V. fischeri 549-55 V. harveyi 555-8 V. parahaemolyticus 561-2 V. vulnificus 562-3 Xanthomonas campestris 583-4 Yersinia 571-2 radiation-tolerance 444 radio frequency heating 401 ready-to-eat (RTE) foods 110 recombination of genes 82 reference maps 33 regulatory genes 116-22, 132-3, 222-7, 260, 336-9, 367, 404-6 see also genomic sequencing relative lag times (RLT) 64 reservoirs of Bacillus cereus 319-20 of Escherichia coli 268-9 resistance and adaptation 38, 404 acquired resistance 498-9 to cold stress 507-22 to disinfectants and sanitizers 484-502 to food preservation 399-411 to heat stress 422-36 to high-pressure processing 446-50 to high-voltage pulsed electric fields 450-3 to host defense mechanisms 100, 101— 3, 393-8 to ionising irradiation 443-6 to natural antimicrobials 460-74 resuscitation of injured cells 200-3, 500 liquid media repair 200-2 protective agents 202-3 solid media repair 202 ribosome damage 430 risk assessment 391-2 Bacillus cereus 323-4 Escherichia coli 265-8 Vibrio spp. 373-7 RNA regulators 119-20, 133, 153 Roberts, T.A. 57-9 RpoS genes 116-22, 121, 132-3, 369-71, 405

SAGE (sequential analysis of gene expression) 6 Salmonella 215-28 acid adaptation systems 137, 138, 219, 395 adhesins 219-20 bacteriophage-encoded virulence 89-90 biofilm formation 104 cellular basis of pathogenesis 217-22 in cheese 187 CsrA gene 288 diseases caused by 218 EGF (epidermal growth factor) receptor 103 in fruit and vegetables 215 genome 216-17 growth 222-7 host-pathogen interactions 36, 37 inactivation 144 in mayonnaise 509, 511 molecular basis of pathogenesis 217-22 mutator strains 81 pathogenicity islands 84-5, 216 pre-enrichment 200-1 quorum-sensing 568-70 regulatory genes 222-7 RpoS activity 121 stress resistance 156, 227-8 acid adaptation systems 137, 138, 219, 395 heat stress 425 sublethal injury 169-71, 206 survival and genetic regulation 222-7 vesicle-mediated gene transfer 90-1 virulence genetic regulation 222-7 molecular basis 216-22 and temperature 520 secondary models 59-66 selective enrichment procedures 159-62 separation strategies 23-5 septicemia 218 Serratia marcescens 576 Shigella heat stress 135 quorum-sensing 570 reference map 33 virulence 521 siderophores 398 SIgA antibodies 395 SigB regulon 344 sigma factors 369-71, 404-5 see also regulatory genes signal interference quorum-sensing 584-5

Index signal molecules 558 signal-mediated cross-talk 368 soil environments 257-8 solid media repair 202 spices 463-4, 469 spores 409-11, 429-30, 432 square-root type models 60-1 Staphylococcus aureus 331^49 biofilm development 342 diseases caused by 331-3 enterotoxins 333-42 detection of 339-42 extrinsic production factors 337 genetics and regulation 336-9 and food preservation and processing 343 preventing food poisoning 347-8 growth conditions 342-3 mutant strains 342-3 pathogenicity islands 85 quorum-sensing 577-9 stress responses 343-7 acid stress 345-6 cold stress 344-5 heat stress 344-5, 430-1 hydrostatic pressure 347 osmotic stress 346-7 SigB regulon 344 sublethal injury 177-9 taxonomy 331-2 as a terrorist weapon 333 virulence 87, 88-9, 522 starvation Campylobacter jejuni 287-9 Helicobacter pylori 288 stationary phase 287-9 sterilization 129 STI peptides 249 stomach acidity 100-1 Streptococcus 579-80 Streptomyces coelicolor 287 stress effect models 68-73 physiological state estimates 69 and single cell lag times 7 2 - 3 stress hardening 143 stress protein detection 141 stress responses 115-24, 129, 393-8, 4 0 4 11 bile toleration 285, 372 definition of stress 128 detecting stress response genes 140 and disinfectants 497-9 dormancy 372-3 and fluorescence staining 145

609

and free radicals 122-3 and genetic evolution 294-5 heterogeneity of responses 407-9 hydrostatic pressure 347 iron stress 284-5, 397-8 metal ion stress 265 nitrosative stress 297 osmotic stress Bacillus cereus 139 Campylobacter jejuni 290-2 Escherichia coli 139, 262-3 Staphylococcus aureus 346-7 oxidative stress Campylobacter jejuni 289—90, 2 9 2 3,297 Escherichia coli 264 Vibrio spp. 372 and predictive microbiology 142 regulatory genes 116-22, 132-3, 2 2 2 7, 260, 336-9, 367, 404-6 starvation 287-9 see also acid adaptation systems; cold stress; heat stress structural proteomics 8, 20-1 subcellular proteomics 19 sublethal injury 122-3, 128, 190 Aeromonas hydrophila 183-4 Bacillus cereus 179-81 Campylobacter spp. 171-4 characterising injuries 205-6 Clostridium spp. 181-2 consequences for food safety 184-5 detecting 199-208 by environmental stress 153 Escherichia, coli 174-7 by heat stress 115, 134—5 injury detection 203-8 lag times 203-5 Listeria monocytogenes 158-69, 187-8 processing conditions producing 156-8 resuscitation of injured cells 200-3 Salmonella 169-71, 206 Staphylococcus aureus 177'-9 Vibrio spp. 182-3 and virulence 185-9 suicide response hypothesis 123 survival mechanisms 33-8, 100-2, 222-7, 395-6 see also cross-protection; host-pathogen interaction; stress responses TAP (tandem affinity purification) 18 taxonomy Bacillus cereus 310

610

Index

Campylobacter jejuni 279 Clostridium botulinum 532-2 Escherichia coli 251-7 Staphylococcus aureus 331-2 Vibrio spp. 358-9 temperature and disinfectants 493, 500 heat stress and time-temperature combinations 425-6 and kinetics of growth 511-13 and modelling stress responses 71-2 NTPR (normal physiological temperature range) 71-2 optimum growth temperatures 6 0 - 1 , 508-9,510 thermal pasteurization 129 and virulence 106, 517-22 see also cold stress; heat stress terrorist weapons 333 thermal death kinetics 423-4 thermal pasteurization 129 thermophile organisms 509 toxins botulinum neurotoxins 531, 534, 540, 541-2 from Campylobacter jejuni 296 CDT (cytolethal distending toxin) 296 cholera toxin 359-60, 373-5 from Clostridium botulinum 540, 541-2 enterotoxins inB. cereus 313-14, 322 in S. aureus 333-42 pyrogenic toxin superantigens (PTSAgs) 333 radiation resistance 445-6 verotoxin (VT) 88 see also exotoxins in Escherichia coli transcriptomics 5-6, 412 triclosan 490-1 turgor pressure 262-3 2D-SDS PAGE separations 8-10, 12, 32, 33 2D-TLC (two-dimensional thin-layer chromatography) 23 Type-1 fimbriae in Escherichia coli 243 ultra violet light 401 ultrasound energy 401 vaccines 107-8, 110-11, 377-8 verocytotoxins 249-50 verotoxin (VT) 88 vesicle-mediated gene transfer 90-1

viable but non-culturable (VBNC) state 372-3 vibrational spectroscopy 28-9 Vibrio spp. 358-79, 394 adhesion mechanisms 369 biofilm formation 369 contamination sources 373-4 diseases caused by 359-61 means of infection 373-4 prevention strategies 375-6 genetic regulators 367 host-pathogen interactions 36 pathogenicity islands 85 quorum-sensing 361-8 reference map 33 risk assessment 373-7 stress responses 369-73 acid stress 371-2, 378 bile tolerance 372 dormancy 372-3 oxidative stress 372 sigma factors 369-71 sublethal injury 182-3 taxonomy 358-9 V. alginolyticus 361 V. angustum 368 V cholerae 359-60, 366, 375-6, 5 5 8 60 V.fischeri 361, 549-55 V. fluvialis 361 V. harveyi 555-8 V. hollisae 361 V. mimicus 361 V. parahaemolyticus 361, 561-2 V. vulnificus 360-1, 562-3 viable but non-culturable (VBNC) state 372-3 virulence 366, 520-1 and water contamination 374 viral pathogens 99 virulence 38-9, 79-80, 87, 105-6, 295-7, 311-16 Aeromonas spp. 518-19 and antimicrobial therapy 109 Bacillus cereus 320-2, 519 bacteriophage-encoded 89-90 Campylobacter jejuni eukaryotic-epithelial cell interaction 297 strain-dependent variation 296 and temperature 519-20 toxin production 296 Clostridium botulinum 521 Clostridium perfringens 521-2

Index determinants of 38-9 Escherichia coli 86-7, 240-1, 242-60, 521 gene expression 259-60 lipopolysaccharides 246 Listeria monocytogenes 518 phage-encoded 88 and plasmids 86-7 Salmonella 89-90, 216-27, 520, 521 Staphylococcus aureus 87, 88-9, 522 and sublethal injury 185-9 and temperature 106, 517-22, 519 Vibrio spp. 366, 520-1 Yersinia 87, 517-18 see also adherence mechanisms; toxins viruses 444-5 waste environments 258 water activity 292-3

water contamination 374 water environments 258 water stress see osmotic stress 'work to be done' 69 wound botulism 534 Xanthomonas campestris 583-4 xenobiotics 21 yeast two-hybrid (Y2H) system 17-18 Yersinia DNA transfer 80 pathogenicity islands 84 quorum-sensing 571-2 virulence 87, 517-18 yogurt 187 zoonotic pathogens 393-4 Zwietering, M.H. 61-2

611

Understanding Pathogen Behaviour Virulence, Stress Response and Resistance

Understanding Pathogen Behaviour Virulence, Stress Response and Resistance

Stress Response: Methods and Protocols

Stress Response. Methods and Protocols

Understanding Drugs and Behaviour

Understanding drugs and behaviour

Understanding Drugs and Behaviour

Stress Response - Methods, Protocols

Escherichia coli: Virulence Mechanisms of a Versatile Pathogen

Treatment of Stress Response Syndromes

Thermal Stress Resistance of Materials

Environmental Stress and Cellular Response in Arthropods

Exercise and stress response: the role of stress proteins

Pathogen

Exercise and stress response: the role of stress proteins

Thermal stress resistance of materials

Thermal Stress Resistance of Materials

Crowding and Disease Virulence

Crowding and Disease Virulence

Understanding Business: Environments (Understanding Business Behaviour)

Understanding Business: Organisations (Understanding Business Behaviour)

Item Response Theory (Understanding Statistics)

Understanding Post Traumatic Stress Syndrome

(NAS Colloquium) Virulence and Defense in Host--Pathogen Interactions (National Academy of Sciences Colloquium Series)

The Unfolded Protein Response and Cellular Stress, Part A

The Unfolded Protein Response and Cellular Stress, Part B

Understanding Global Environmental Politics: Domination, Accumulation, Resistance

How Horses Feel and Think: Understanding Behaviour, Emotions and Intelligence

Pharmacology in Drug Discovery: Understanding Drug Response

Breaktime and the School: Understanding and Changing Playground Behaviour

Understanding Pathogen Behaviour Virulence, Stress Response and Resistance