Molecular Approaches to Soil, Rhizosphere and Plant Microorganism Analysis (Cabi Publishing)

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Molecular Approaches to Soil, Rhizosphere and Plant Microorganism Analysis

1 Z:\CABI\A5224 - Cooper and Rao\Make-up\A5291 - Cooper - Voucher Proofs.vp 12 September 2006 12:33:49




Molecular Approaches to Soil, Rhizosphere and Plant Microorganism Analysis

Edited by

J.E. Cooper and J.R. Rao Department of Applied Plant Science, Queen's University Belfast, Belfast, UK



CABI is a trading name of CAB International CABI Head Office Nosworthy Way Wallingford Oxfordshire OX10 8DE UK Tel: +44 (0)1491 832111 Fax: +44 (0)1491 833508 E-mail: [email protected] Website: www.cabi.org

CABI North American Office 875 Massachusetts Avenue 7th Floor Cambridge, MA 02139 USA Tel: +1 617 395 4056 Fax: +1 617 354 6875 E-mail: [email protected]

© CAB International 2006. All rights reserved. No part of this publication may be reproduced in any form or by any means, electronically, mechanically, by photocopying, recording or otherwise, without the prior permission of the copyright owners. A catalogue record for this book is available from the British Library, London, UK. A catalogue record for this book is available from the Library of Congress, Washington, DC. ISBN-10: 1 84593 062 2 ISBN-13: 978 1 84593 0622

Typeset by AMA DataSet Ltd, UK. Printed and bound in the UK by Biddles Ltd, King’s Lynn.



Contents

Contributors Preface

vii ix

1

Genomic Analyses of Microbial Processes in Biogeochemical Cycles Shilpi Sharma, Heidrun Karl and Michael Schloter

2

Applications of Nucleic Acid Microarrays in Soil Microbial Ecology Alexander Loy, Michael W. Taylor, Levente Bodrossy and Michael Wagner

18

3

Metagenomics for the Study of Soil Microbial Communities Helen L. Steele and Wolfgang R. Streit

42

4

In Vivo Expression Technology (IVET) for Studying Niche-specific Gene Expression by Plant- and Soil-colonizing Bacteria Hans Rediers and René De Mot

55

Analysing Microbial Community Structure by Means of Terminal Restriction Fragment Length Polymorphism (T-RFLP) Christopher B. Blackwood

84

Characterization of Phylloplane and Rhizosphere Microbial Populations Using PCR and Denaturing Gradient Gel Electrophoresis (DGGE) Maureen O’Callaghan, Nicola Lorenz and Emily L. Gerard

99

5

6

7

Molecular Tools for Studying Plant Growth-promoting Rhizobacteria (PGPR) Ashley Franks, Robert P. Ryan, Abdelhamid Abbas, G. Louise Mark and Fergal O’Gara

8

Detection of Autotrophic Sulphur- and Iron-oxidizing Bacteria Using Labelled Fatty Acid Methyl Esters (FAMEs) André Lipski

1

116

132

v



vi

Contents

9

Molecular Analyses of Soil Denitrifying Bacteria Laurent Philippot and Sara Hallin

146

10

Ecology of Streptomyces in Soil and Rhizosphere Janice L. Strap and Don L. Crawford

166

11

Molecular Ecology of Ectomycorrhizal Fungal Communities: New Frontiers Ian C. Anderson

183

12

Molecular Ecology of Arbuscular Mycorrhizal Fungi: a Review of PCR-based Techniques Dirk Redecker

198

Transcriptomics for Determining Gene Expression in Symbiotic Root–Fungus Interactions Philipp Franken and Franziska Krajinski

213

13

14

Differentiation of Nitrogen-fixing Legume Root Nodule Bacteria (Rhizobia) Kristina Lindström, Paula Kokko-Gonzales, Zewdu Terefework and Leena A. Räsänen

236

15

Molecular Markers for Studying the Ecology of Rhizobia Angela Sessitsch

259

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Molecular Characterization of Bacterial Plant Pathogens Scott A. Godfrey and Robert W. Jackson

272

Index


293


Contributors

Abdelhamid Abbas, The Biomerit Research Centre, Department of Microbiology, National University of Ireland (UCC), Cork, Ireland. Ian C. Anderson, The Macaulay Institute, Craigiebuckler, Aberdeen AB15 8QH, Scotland, UK. Christopher, B. Blackwood, Department of Biological Sciences, Kent State University, Kent OH 44242, USA Levente Bodrossy, Department of Bioresources/Microbiology, ARC Seibersdorf research GmbH, A-2444 Seibersdorf, Austria. J.E. Cooper, Department of Applied Plant Science, Queen’s University Belfast, Newforge Lane, Belfast BT9 5PX, Northern Ireland, UK Don L. Crawford, Director, Environmental Science Program, 216 Morrill Hall, University of Idaho, Moscow, Idaho 83844 3006, USA. René De Mot, Centre of Microbial and Plant Genetics, Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, B-3001 Heverlee, Belgium. Philipp Franken, Institute for Vegetables and Ornamental Crops, Theodor-EchtermeyerWeg, D-14979 Grossbeeren, Germany. Ashley Franks, The Biomerit Research Centre, Department of Microbiology, National University of Ireland (UCC), Cork, Ireland. Emily M. Gerard, AgResearch, Biocontrol and Biosecurity Group, PO Box 60, Lincoln, Canterbury, New Zealand. Scott A. Godfrey, School of Biological and Molecular Sciences, Oxford Brookes University, Oxford OX3 0BP, UK. Sara Hallin, Department of Microbiology, Swedish University of Agricultural Sciences, Box 7025, SE 750 07 Uppsala, Sweden. Robert W. Jackson, Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, UK. Heidrun Karl, Institute of Soil Ecology, GSF-National Research Center for Environment and Health, PO Box 1129, D-85764 Neuherberg, Germany. Paula Kokko-Gonzales, Department of Applied Chemistry and Microbiology, Biocenter 1, PO Box 56, FIN-00014 University of Helsinki, Finland. Franziska Krajinski, Department of Molecular Genetics, University of Hannover, Herrenhäuser Strasse 2, D-30419 Hannover, Germany. vii



viii

Contributors

Kristina Lindström, Department of Applied Chemistry and Microbiology, Biocenter 1, PO Box 56, FIN-00014 University of Helsinki, Finland. André Lipski, Universität Osnabrück, Abteilung Mikrobiologie, Fachbereich Biologie/ Chemie, D-49069 Osnabrück, Germany. Nicola Lorenz, The Ohio State University, School of Environment and Natural Resources, 2021 Coffey Road, Columbus, OH 43210, USA. Alexander Loy, Department of Microbial Ecology, University of Vienna, A-1090 Vienna, Austria. G. Louise Mark, The Biomerit Research Centre, Department of Microbiology, National University of Ireland (UCC), Cork, Ireland. Maureen O’Callaghan, AgResearch, Biocontrol and Biosecurity Group, PO Box 60, Lincoln, Canterbury, New Zealand. Fergal O’Gara, The Biomerit Research Centre, Department of Microbiology, National University of Ireland (UCC), Cork, Ireland. Laurent Philippot, UMR Microbiologie et Géochimie des Sols, INRA-Université de Bourgogne, CMSE, 17, rue Sully, B.V. 86510, 21065 Dijon Cedex, France. J.R. Rao, Department of Applied Plant Science, Queen’s University Belfast, Newforge Lane, Belfast BT9 5PX, Northern Ireland, UK Leena A. Räsänen, Department of Applied Chemistry and Microbiology, Biocenter 1, PO Box 56, FIN-00014 University of Helsinki, Finland. Dirk Redecker, Institute of Botany, University of Basel, Hebelstrasse 11, 4056 Basel, Switzerland. Hans Rediers, Hogeschool voor Wetenschap & Kunst – De Nayer Instituut, Jan De Nayerlaan 5, B-2860 Sint-Katelijne-Waver, Belgium. Robert P. Ryan, The Biomerit Research Centre, Department of Microbiology, National University of Ireland (UCC), Cork, Ireland. Michael Schloter, Institute of Soil Ecology, GSF-National Research Center for Environment and Health, PO Box 1129, D-85764 Neuherberg, Germany. Angela Sessitsch, ARC Seibersdorf research GmbH, Department of Bioresources, A-2444 Seibersdorf, Austria. Shilpi Sharma, Institute of Soil Ecology, GSF-National Research Center for Environment and Health, PO Box 1129, D-85764 Neuherberg, Germany. Helen L. Steele, Molecular Enzyme Technology, Biofilm Centre, Universität DuisburgEssen, Lotharstrasse 1, D-47057 Duisburg, Germany. Janice L. Strap, Environmental Biotechnology Institute, Food Research Center 103, University of Idaho, Moscow, Idaho 83844 1052, USA. Wolfgang R. Streit, Molecular Enzyme Technology, Biofilm Centre, Universität Duisburg-Essen, Lotharstrasse 1, D-47057 Duisburg, Germany. Michael W. Taylor, Department of Microbial Ecology, University of Vienna, A-1090 Vienna, Austria. Zewdu Terefework, Department of Applied Chemistry and Microbiology, Biocenter 1, PO Box 56, FIN-00014 University of Helsinki, Finland. Michael Wagner, Department of Microbial Ecology, University of Vienna, A-1090 Vienna, Austria.



Preface

The profusion of molecular techniques now available to microbial ecologists allows previously unobtainable information to be gathered on the microflora of soils, plants and their rhizospheres. Most of these methods target nucleic acids and a high proportion are in some way dependent on the polymerase chain reaction (PCR). A researcher embarking on a new project is faced with the problem of choosing the most appropriate techniques from what appears to be a bewildering range of options. It is the purpose of this book to provide critiques, written by internationally recognized experts, of many of the molecular techniques that are currently employed for studying soil and plant microorganisms at the community, population, taxonomic and functional group levels. In so doing, it supplies the critical and comparative bases upon which an informed choice of technique can be made. The book is not intended as a bench manual but technical information on, for example, PCR primer sequences and experimental protocols is to be found in a number of chapters and the extensive reference lists will lead the reader to relevant articles for each topic. J.E. Cooper J.R. Rao Belfast

ix





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Genomic Analyses of Microbial Processes in Biogeochemical Cycles Shilpi Sharma,* Heidrun Karl and Michael Schloter

Institute of Soil Ecology, GSF-National Research Center for Environment and Health, PO Box 1129, D-85764 Neuherberg, Germany

Introduction Without the cycling of elements, continuation of life on earth would be impossible, since essential nutrients would rapidly be taken up by organisms and locked in a form that cannot be used by others. The reactions involved in elemental cycling are often chemical in nature, but biochemical reactions also play an important role. Microbes are of prime significance in this process. Microbial and biochemical characteristics are used as potential indicators of soil quality because of their central role in cycling of carbon and nitrogen and their sensitivity to change (Nannipieri et al., 2003). In the cycling of carbon, photosynthetic plants and microbes are the primary producers of organic carbon compounds and these provide nutrients for other organisms, which act as consumers of organic carbon and break down organic material in the processes of respiration and fermentation. Certain bacteria are also capable of anaerobic carbon cycling. Methane can itself act as a carbon and energy source for methaneoxidizing bacteria, which can generate sugars and amino acids from methane found in their environments, again contributing to the cycling of carbon compounds.

Even though the amount of nitrogen in the atmosphere is > 70%, it cannot be used directly by the majority of life forms. Bacteria are the only organisms capable of the biological fixation of nitrogen. Fixed nitrogen may be obtained through the death and lysis of free-living nitrogen-fixing bacteria. Nitrogen-fixing bacteria, however, frequently form close associations with plants and, in the case of rhizobia, can supply the plant with all of its fixed nitrogen demands. In return, they receive a supply of organic carbon compounds. Fixation aside, cycling of nitrogen involves interconversions among inorganic nitrogen compounds as well as conversions from inorganic to organic nitrogen, and vice versa. Many bacteria reduce nitrates to nitrites and some further reduce nitrites to ammonia. Ammonium salts may then be incorporated into organic polymers by the process of assimilatory nitrate reduction. Nitrates may be used by some bacteria instead of oxygen for a type of respiration referred to as dissimilatory nitrate reduction. Bacteria of the genus Pseudomonas, micrococci and Thiobacillus species can reduce nitrates to liberate nitrogen gas into the environment. Bacteria that can generate nitrogen gas from the reduction of nitrates are commonly found in organically rich

*Corresponding author; Phone: +49 89 3187 3054, Fax: +49 89 3187 3376, E-mail: [email protected] ©CAB International 2006. Molecular Approaches to Soil, Rhizosphere and Plant Microorganism Analysis (Cooper and Rao)


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soils, compost heaps and in sewage treatment plants. For the continued cycling of nitrogen, organic nitrogen compounds must be broken down to release ammonia. Putrefactive metabolism yields considerable quantities of ammonia from biopolymers that contain nitrogen. Bacteria may also produce urease, an enzyme that breaks down urea to liberate carbon dioxide, water and ammonia. Nitrifying bacteria are responsible for the biological oxidation of ammonia to nitrate. The carbon and nitrogen cycles are two of the most important cycles responsible for nutrient processing in soil. In addition, soil is the site for numerous other activities which also finally contribute to nutrient turnover. Biodegradation of xenobiotics within the complex soil ecosystem is mostly the result of proto-cooperation, i.e. the combined, mutually beneficial activities of various types of organisms having different abilities at the same time and site. Additionally, microorganisms have a role in the biochemical transformation of metal ions. Bacteria such as Thiobacillus ferrooxidans and iron bacteria of the genus Gallionella are capable of oxidizing ferrous (Fe2+) iron into ferric (Fe3+) iron. Many bacteria can reduce ferric iron to its ferrous state by using it as an electron acceptor instead of O2. Bacteria are also important in the transformation of manganese ions, where reactions similar to those seen with iron are observed (Heritage et al., 1999). Microbial cycling of heavy metals is especially important in contaminated sites. One of the resistance or detoxification mechanisms of mercury is enzymatic reduction of Hg2+ to Hg0. This occurs in both Gram-negative and Gram-positive aerobic bacteria from a variety of natural and clinical environments across the globe, and as such has become the best studied mechanisms of mercury resistance. These are a few examples from the known wide array of processes contributing to biogeochemical cycles. However, it needs to be mentioned that there might be various cycle components of which we are still unaware. The discovery of anaerobic ammonia oxidation (ANAMMOX) further

strengthens this view. ANAMMOX is a relatively newly revealed process (Jetten et al., 1997; Strous et al., 1997) in which nitrite and ammonia combine to produce dinitrogen gas.

Approaches to the Study of Microbial Processes in Biogeochemical Cycles Non-genomic methodologies Microbial processes contributing to biogeochemical cycles are regulated at various steps as outlined in Fig. 1.1. Studies analysing the role of microbes in biogeochemical cycles can be performed at various levels. Some parameters that have been used to determine microbiological activity have been heat output, nitrogen mineralization, thymidine incorporation, nitrification rate, leucine incorporation and potential denitrification activity (Alef and Nannipieri, 1995). The determination of microbial carbon, nitrogen, phosphorus and sulphur contents by fumigation techniques has allowed a better quantification of nutrient dynamics in soil. Conventional methods, such as the most probable number (MPN) and platecounting techniques (using specially formulated media), were designed to determine the total numbers and/or species of microorganisms in a particular soil. However, they provide only limited information regarding the functional diversity of the microbial community. Apart from the disadvantage that such methods are laborious, we are now aware that only 1–10% of microorganisms are culturable. Other approaches include generation of patterns of organic substrate utilization by the soil microbial communities, the two most commonly used methods being the BIOLOG® plate method (Garland and Mills, 1991; Zak et al., 1994) and the in situ substrateinduced respiration (SIR) technique (Degens and Harris, 1997). The latter has the advantage of assessing catabolic diversity without extracting and cultivating organisms from the soil. However, these methods too suffer from the following limitations: (i) the



Genomic Analyses of Microbial Processes in Biogeochemical Cycles

3

Microbial structure/ gene pool Regulation by environmental factors, repression, co-repression and induction Transcript pool Biogeochemical cycles

Regulation by environmental factors, feedback inhibition, RNA interference and riboswitches Enzyme pool Regulation by feedback inhibition, modification and effector molecules Microbial function/ turnover processes

Regulation by environmental factors

Fig. 1.1. Relationship between microbial community structure and function contributing to biogeochemical cycles.

inoculum density and incubation time are arbitrary; and (ii) the substrate utilization profiles obtained do not reflect the nature or structure of the original microbial communities from which the samples were taken due to the regulation at every step as outlined in Fig. 1.1. Soil enzyme activities have been suggested as suitable indicators of soil quality as they are a measure of microbial activity and they are therefore strictly related to nutrient cycles and transformations. Moreover, as claimed by several authors (van Beelen and Doelman, 1997; Trasar-Cepeda et al., 2000), changes in soil enzyme activities may be regarded as early and sensitive indicators of the impact of natural and anthropogenic factors on ecosystems. Enzyme activities related to the cycling of the biologically important nutrients carbon, nitrogen, phosphorus and sulphur have been investigated in soils. Soil dehydrogenase reflects the soil oxidative power. Being present in all microorganisms, it may give a measure of the total viable microbial cells. Invertase and β-glucosidase catalyse hydrolytic processes

in the breakdown of organic matter. Urease, phosphatase and arylsulphatase play important roles in the mineralization of nitrogen, phosphorus and sulphur compounds. They may give indications of the soil’s potential to perform specific biochemical reactions, and are important contributors to soil fertility. Enzyme assays, however, do not allow any identification of the microbial species directly involved in the measured processes. Moreover, stable extracellular enzyme activities are associated with soil colloids and persist even in harsh environments that would limit intracellular activity. Thus, only strictly intracellular enzyme activities can truly reflect microbial activity (Fig. 1.1). Unfortunately, the presently available assays do not distinguish the contribution of intracellular from extracellular and stabilized enzyme activities, and thus they do not give valid information on the distribution and comparative importance of reactions mediated by microbes (Nannipieri et al., 2002). Active autotrophic bacteria, such as those involved in sulphur and iron cycling, can be directly characterized in microbial



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communities by labelling their fatty acid methyl esters with [13C]bicarbonate (FAME analysis; see Lipski, Chapter 8 this volume).

Genomic technologies The latest and most powerful technologies revolutionizing microbiology are based on genomics – the mapping and sequencing of genomes and analysis of gene and genome function. These provide a connecting link between biochemical measurements and enzyme analysis. Moreover, the entire functional potential of a system can be overviewed by analysing its genomics. In general, genomic techniques have some significant advantages over traditional methods: (i) only very small samples (in the range of millilitres up to a litre) are required for most analyses; (ii) the sensitivity of many methods is very high, thus enabling the researcher to detect even single specific cells among thousands of others; (iii) dead or non-culturable cells can be analysed; and (iv) species-specific data (such as sequences) can be obtained without the need to culture or even isolate a species. However, we emphasize that all these advantages are relative and depend on the researcher’s requirements, e.g. free DNA from dead cells could overinterpret the community structure. Genomic techniques based on the polymerase chain reaction (PCR) have several drawbacks. Amplification by PCR can be inhibited if contaminants are not removed by the purification process, and preferential or selective amplification in the presence of DNA from mixed communities can occur. Another bias of these techniques is the production of chimeric or heteroduplex DNA molecules. Despite these biases, genomic analyses still prove to be the fastest and most reliable in extracting information from complex environments. More than 100 microbial genome sequences have been completed, providing information on > 300,000 predicted genes, with approximately half of these being of unknown function and potentially novel. These novel genes represent exciting new opportunities for future research and

potential sources of biological resources for exploration and application. However, the determination of the composition of microbial communities in soil is not in itself sufficient for understanding nutrient transformations. A holistic approach, based on the division of the systems into pools and the measurement of fluxes linking these pools, is comparatively more efficient. Some of the genes most commonly targeted to study processes in biogeochemical cycles have been listed in Table 1.1.

Methods for Detection and Quantification of Gene Expression in Biogeochemical Cycles Nucleic acids can be used as markers in a number of different approaches. Selection of the most appropriate one depends on the desired level of resolution of results. So a researcher needs to evaluate their benefits and limitations critically before deciding on any one method. In many cases, a combination of several techniques can be well suited to answer the desired questions. Various genomic approaches employed in studying microbial processes in biogeochemical cycles have been summarized in Table 1.2. In the following sections, specific examples are cited in some detail to inform the selection of appropriate techniques and to evaluate the results generated by them.

PCR and reverse transcription– PCR (RT–PCR) Early molecular studies used gene probe technology to screen for the presence or absence of structural genes in a soil population (Felske et al., 1996; Griffiths et al., 2000). The detection of DNA sequences directly from the soil community has proven to be a useful tool to improve our understanding of soil processes. Another approach for studying specific microbial activities is to investigate the expression of genes by analysing mRNA transcripts via their reverse




Table 1.1.

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Functional targets of microbial processes in nutrient cycling.

Target genes

Process/nutrient cycle

Reference

alkB amoA

Alkane hydroxylation Ammonification

cbbL cbh1 chi dsrAB lipA, lipC mcrA mer mmoX mnp mxaF

Autotrophic CO2 fixation Cellobiohydrolase Chitinase gene Dissimilatory (bi) sulfite reduction Lignin peroxidation Methyl coenzyme M reduction Mercury resistance Methane monooxygenase Manganese peroxidase Methanol dehydrogenation

narG nifH nirK

Nitrate reduction Nitrogen fixation Nitrite reduction

nirS

Nitrite reduction

nosZ pmoA

Nitrous oxide reduction Methane monooxygenase

Tesar et al., 2002; Whyte et al., 2002 Wu et al., 2001; Avrahami et al., 2002; Radajewski et al., 2002 Selesi et al., 2005 Lamar et al., 1995; Vallim et al., 1998 Williamson et al., 2000; Metcalfe et al., 2002 Loy et al., 2002 Lamar et al., 1995; Stuardo et al., 2004 Ritchie et al., 1997; Castro et al., 2004 Bruce, 1997 Ritchie et al., 1997; Morris et al., 2002 Bogan et al., 1996 McDonald and Murrell, 1997; Morris et al., 2002; Radajewski et al., 2002 Philippot et al., 2002; Deiglmayr et al., 2004 Widmer et al., 1999; Poly et al., 2001 Avrahami et al., 2002; Prieme et al., 2002; Throbäck et al., 2004; Sharma et al., 2005 Wu et al., 2001; Prieme et al., 2002; Rösch et al., 2002; Throbäck et al., 2004 Rösch et al., 2002; Throbäck et al., 2004 Kolb et al., 2003; Horz et al., 2005

transcription to complementary DNA (cDNA) and its amplification by PCR (RT–PCR). The usefulness of transcription analysis is based on the short half-life of mRNA, which is sometimes in the order of minutes (Alifano et al., 1994). Regulation at the transcription level almost immediately affects the rate of protein synthesis, and detection of gene expression can be used for investigation of real, defined microbial processes (i.e. phenomena occurring under in situ conditions). Thus, detection of mRNA provides a strong indication of gene expression at the time of sampling, which can be correlated with the prevailing physicochemical conditions (Nogales et al., 2002; Alfreider et al., 2003). Primers for genes involved in nutrient cycling can be used to detect and quantify the gene/transcript copies in the environment. The technique relies on the availability of large numbers of sequences in databases for the design of efficient primers. The amplicon achieved can be further analysed for diversity

studies and identification and characterization of the microbial members. Fingerprinting techniques Fingerprinting techniques allow a researcher to screen rapidly for similarities and differences between samples. They are useful when performing multiple sample analyses of complex communities and are based on differences in the gene sequence from different organisms. One such method is terminal restriction fragment length polymorphism (T-RFLP; see also Blackwood, Chapter 5 this volume). The technique itself depends on the amplification of DNA with a primer set, one of which is fluorescently end labelled, and restriction of the resulting product with frequently cutting enzymes. Due to sequence variations, the terminal restriction site for each species in the community should be different. The output is digital and provides



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Table 1.2.

S. Sharma et al.

Various genomic approaches employed to study biogeochemical cycles in soil.

Molecular approach

Target

Reference

PCR/RT–PCR-cloning

mnp nifH, nirS, nirK, nosZ mcrA cbbL amoA, nirK nirK, nirS, nosZ Sulphate reducers narG nirK, nirS amoA, nifH pmoA nirK narG amoA narG, nifH, nirK, nirS, nosZ sub, npr cbh1, lipA, lipC mnp amoA Ammonia oxidizers Nitrifiers, ammonia-oxidizers & nitrite-oxidizers amoA, nirK, nirS, pmoA dsrAB amoA, nifH, nirK, nirS amoA, dsrAB, nifH, nirK, nirS, pmoA mxaF, mmoX, pmoA amoA, methylotrophs PAH degraders

Bogan et al., 1996 Rösch et al., 2002 Castro et al., 2004 Selesi et al., 2005 Avrahami et al., 2002 Throbäck et al., 2004 Dar et al., 2005 Philippot et al., 2002 Wolsing & Prieme, 2004 Yeager et al., 2005 Kolb et al., 2003 Henry et al., 2004 López-Gutiérrez et al., 2004 Bruns et al., 1999 Mergel et al., 2001 Sharma et al., 2004 Lamar et al., 1995 Bogan et al., 1996 Bjerrum et al., 2002 Briones et al., 2002 Meyer et al., 2005

PCR/RT–PCR-DGGE-cloning

PCR-RFLP/T-RFLP-cloning

Real-time PCR

PCR-probe hybridization

cPCR

FISH

Macroarray/Microarray

SIP

information on the size of the product in base pairs (i.e. species) and the intensity of fluorescence or relative abundance of the various community members. T-RFLP analysis of nifH (Widmer et al., 1999), nirK (Avrahami et al., 2002) and narG (Philippot et al., 2002) has been used to compare denitrifying community patterns in different environments (see also Philippot and Hallin, Chapter 9 this volume). Wolsing and Prieme (2004) explored the temporal and spatial variation of communities of soil denitrifying bacteria at sites receiving mineral fertilizer and cattle manure using T-RFLP analyses of PCR-amplified

Wu et al., 2001 Loy et al., 2002 Taroncher-Oldenburg et al., 2003 Tiquia et al., 2004 Morris et al., 2002 Radajewski et al., 2002 Singleton et al., 2005

nitrite reductase (nirK and nirS) gene fragments. The analyses were performed three times during the year: in March, July and October. Fragments of the nirK and nirS genes were amplified using primer sets nirK1F and nirK5R for nirK, which give a 512–515 bp product, and nirS1F and nirS6R for nirS, which give an approximately 890 bp product (Braker et al., 1998). nirK gene fragments could be amplified in all three months, whereas nirS gene fragments could be amplified only in March. For T-RFLP, the forward primers were fluorescently labelled with FAM (nirS1F) or TET (nirK1F). Each PCR product was divided in three equal




portions and digested for 4 h with 10 U of the three endonucleases: MspI, TaqI and HhaI (New England BioLabs, Ipswich, Massachusetts, USA). After DNA digestion, size standards labelled with Texas red (MegaBACE ET900-R, Amersham Biosciences, Uppsala, Sweden) were added to the samples, which were desalinated using Sephadex G-50 (Amersham Biosciences), denatured at 94°C for 1 min, and kept on ice before injection in the MegaBACE 1000 DNA Sequencing System sequencer (Amersham Biosciences), where the T-RFLP run was done. Samples were injected at 3 kV for 3 min, and separation was performed at 7 kV for 180 min in MegaBACE Long Read Matrix (Amersham Biosciences). After the T-RFLP run, the raw data were analysed by the software program MegaBACE Genetic Profiler Version 1.5 (Amersham Biosciences). Sites treated with mineral fertilizer or cattle manure showed different communities of nirK-containing denitrifying bacteria, since the T-RFLP patterns of soils treated with these fertilizers were significantly different. These sites differed significantly from the control plot (no fertilizer treatment). The developing view that diversity is likely to be functionally significant (e.g. Conrad, 1996; Cavigelli and Robertson, 2000) may imply that the observed differences in population structure result in differences in the nitrogen dynamics in the soils and that, for instance, the denitrification rate and N2O emissions may be altered. A substantial difference in the ratio between nitrous oxide reductase activity and denitrification activity among sites, which may influence N2O emission rates from the soils, was observed. The change in the ratio may be linked to the observed changes in community structure or directly to changes in abiotic parameters known to influence the ratio of nitrous oxide : nitrogen emission, such as concentrations of soil nitrate and easily degradable organic carbon (Tiedje, 1988). However, neither pH, concentrations of inorganic nitrogen nor dissolved organic carbon differed between control soil and other soils. Another commonly used method for fingerprinting genes and transcripts amplified

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from soil is denaturing gradient gel electrophoresis (DGGE; see O’Callaghan et al., Chapter 6 this volume), which allows for separation of amplicons of equal length but different sequence on a denaturing gradient of urea and formamide. The technique has the advantage that it is theoretically possible to separate very similar sequences as well. In contrast to T-RFLP, DGGE can detect not only the major differences but also the sequences of the predominant populations without the need for large clone libraries. With the possibility to sequence differences or shifts in the denitrifying communities directly, redundant sequencing or screening of hundreds of clones can be avoided. Sharma et al. (2005) used DGGE with amplicons derived from RT–PCR of nirK (nitrite reductase) genes from rhizosphere samples of Vicia, Lupinus and Pisum plants. They used primer set nirK1F and nirK5R (Braker et al., 1998) to amplify specifically nirK genes from the soil samples and then resolved the products on a denaturing gradient of 50–60%. Denaturing gels were visualized by silver staining using standard protocols. Differences in the denitrifying communities between the three rhizospheres were evident, with the profiles of Vicia and Lupinus being more similar to each other than to the profile derived from Pisum rhizosphere. This could be correlated to a study by Mayer et al. (2003) wherein similar rhizodeposition values were obtained in Vicia and Lupinus rhizospheres compared with Pisum rhizosphere. Cloning The amplicon obtained by PCR/RT–PCR from soil samples is a mixture of sequences derived from a complex community. For resolution of this mixed product and to identify particular sequences specifically, cloning is employed wherein each copy of the vector receives only one fragment (from one organism). Thus, the technique results in the highest level of resolution. A PCR– cloning-based approach was used by Selesi et al. (2005) to detect ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO) form I large-subunit genes (cbbL) as a functional



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marker of autotrophic bacteria that fix carbon dioxide via the Calvin–Benson–Bassham cycle in agricultural soil. Two different primer sets were constructed, targeting the green-like and red-like phylogenetic groups of cbbL genes. PCR products of the expected sizes (1100 bp for green-like and 800 bp for red-like genes) from soil samples as well as from bacterial cultures were eluted from agarose gels with a NucleoSpin extraction kit (Macherey & Nagel, Düren, Germany). Eluted PCR products were ligated into the vector pCR2.1-TOPO (Invitrogen, Carlsbad, California, USA) and transformed into competent Escherichia coli cells provided with a TA cloning kit (Invitrogen). Plasmids from the cbbL libraries were isolated by use of a NucleoSpin plasmid kit (Macherey & Nagel). Clones containing putative cbbL genes were screened by EcoRI restriction endonuclease digestion. Clones which harboured a correctly sized red-like cbbL insert were screened by RFLP. Restriction fragments were analysed in 3.5% (w/v) agarose gels (PeqLab, Erlangen, Germany). The green-like cbbL sequences revealed a very low level of diversity, being closely related to the cbbL genes of Nitrobacter winogradskyi and Nitrobacter vulgaris. In contrast, the redlike cbbL gene libraries revealed a high level of diversity in two fertilized soils and less diversity in unfertilized soil. The majority of environmental red-like cbbL genes were only distantly related to already known cbbL sequences and even formed separate clusters. This approach was thus capable of detecting an as yet unravelled diversity of CO2 fixation in soil bacteria. Hybridization Probes specific for a particular group of organisms can be used for hybridization of DGGE gels transferred to nylon hybridization membranes (Stephen et al., 1998) or even sequences generated by cloning (Bruns et al., 1999). This is a rapid method to detect the presence and activity of specific organisms even if the initial primers used for amplification have been generic, such as universal bacterial primers targeting the 16S region.

A rather different approach was applied by Sharma et al. (2004). In a study analysing functional diversity in the rhizospheres of three grain legumes, Sharma et al. (2004) combined the technique of RNA arbitrarily primed (RAP) PCR with hybridization. RAP-PCR (Welsh et al., 1992) is analogous to differential display RT–PCR (DDRT–PCR) for eukaryotic mRNA (Liang and Pardee, 1992) but it also samples non-polyadenylated RNAs. RAP-PCR was performed using two different primers: M13 reverse and a 10-mer primer. Dot-blot hybridization for bacterial serine peptidases (sub) and neutral metallopeptidases (npr) was performed on RAP-PCR products transferred onto positively charged nylon membranes according to the protocol described by Bach et al. (2001) with digoxigenin (DIG)-labelled probes. It was observed that while serine peptidase was expressed in all the three rhizosphere soils, neutral metallopeptidase could be detected in Vicia and Pisum rhizospheres only. This was further confirmed by RT–PCR. Lack of neutral metallopeptidase expression could be one of the factors for the observed reduced mineralization of lupin rhizodeposits in a related study (Mayer et al., 2003). Quantitative PCR To quantify the number of gene copies/ transcript level in environmental samples, quantitative PCR methods such as real-time PCR, TaqMan PCR and competitive PCR (cPCR) have been employed. The advantage of real-time PCR over other PCR-based quantification methods is that it focuses on the logarithmic phase of product accumulation rather than on end-product abundance. This technique is therefore more accurate, since it is less affected by amplification efficiency or depletion of a reagent. In addition, real-time PCR measures template abundance over a large dynamic range of around six orders of magnitude (Heid et al., 1996). Finally, this method reduces the risk of contamination, as no post-PCR processing is required. The main disadvantage of real-time PCR is the need for a special thermocycler and reagents that are expensive




compared with the equipment utilized by other PCR-based quantification methods. The real-time PCR technique is based on the use of the 5′ nuclease assay, first described by Holland et al. (1991) and further improved by the use of fluorescent TaqMan methodology and the ABI Prism 7700 sequence detection system (PE Applied Biosystems, Foster City, California, USA). The system requires the design of a forward and a reverse primer, in addition to a probe that hybridizes between them. The probe is fluorescently labelled at both ends (Lee et al., 1993). The fluorescent dye at the 5′ end serves as a reporter, and its emission spectrum is quenched by the dye at the 3′ end of the probe. During the elongation step of each PCR cycle, the DNA polymerase cleaves the annealed probe with its 5′ nuclease activity. Once separated from the quencher, the reporter fluorescence is detected, resulting in an increase in fluorescence emission. The fluorescence increases logarithmically as the PCR proceeds, until a reagent becomes limiting. A threshold fluorescence intensity is defined within the logarithmic phase. The higher the amount of initial template DNA, the earlier the fluorescence will cross the defined threshold. The copy number of the initial target DNA is thereby determined by comparison with a standard curve (Grüntzig et al., 2001). Henry et al. (2004) used real-time PCR to quantify the nirK gene (see also Philippot and Hallin, Chapter 9 this volume). To design the nirK primers, nirK sequences from cultivated strains, from complete and unfinished bacterial genomes and from environmental nirK libraries, were aligned using the ClustalX software V.101 (Thompson et al., 1997) and compared with select conserved regions. Two degenerate primers, (5′ → 3′) nirK876 (ATYGGCGGVAYGGCGA) and nirK1040 (GCCTCGATCAGRTTRTGGTT), were designed to amplify a 165 bp fragment (nirK from Sinorhizobium meliloti 1021 was used as reference sequence for numbering). The standard curve was created using tenfold dilution series of three linearized plasmids containing the different nirK genes from environmental samples. The real-time PCR assay was linear over seven orders of

9

magnitude and sensitive enough to detect 102 copies. Real-time PCR analysis of different soil samples showed nirK densities of 9.7 × 104–3.9 × 106 copies/g of soil. Soil real-time PCR products were cloned and sequenced. Analysis of 56 clone sequences revealed that all cloned real-time PCR products exhibited high similarities to previously described nirK. However, phylogenetic analysis showed that most sequences of environmental origin were not related to nirK from cultivated denitrifiers.

Fluorescence in situ hybridization (FISH) FISH is a relatively new technology utilizing fluorescently labelled DNA probes to detect genes or groups of organisms in environmental samples. The sample DNA is first denatured, then a fluorescently labelled probe is added to the denatured sample mixture where it hybridizes with DNA at the target site as it re-anneals back into a double helix. The probe signal can then be seen through a fluorescence microscope and the sample DNA scored for the presence or absence of the signal. Apart from allowing direct visualization of bacteria in the environment, FISH also has the added advantage of being able to detect active cells by targeting rRNA. Most studies have focused mainly on nutrient-rich environments where the abundance and ribosome content of cells are relatively high. The application of FISH in nutrient-poor environments such as soil and roots is more problematic due to lower cell numbers and the presence of interfering autofluorescence from roots and soil particles. Nevertheless, the method has been reliably used for identification and quantification of ammonia-oxidizing bacteria (AOB) (Mobarry et al., 1996; Briones et al., 2002; Meyer et al., 2005) as described below. For quantification of AOB in the root environment of different rice cultivars, Briones et al. (2002) employed FISH using rRNA-targeted oligonucleotide probes. In this study, they found that the biofilm coating the root surface can be detached by moderate sonication, concentrated, and then



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probed using FISH. All hybridization procedures were performed as described by Amann (1995) using previously published hybridization and washing stringencies (Amann et al., 1990; Mobarry et al., 1996). The following oligonucleotide probes were used: Eub338, specific for the domain Bacteria (Amann et al., 1990), Nso190, specific for AOB of the Betaproteobacteria (Mobarry et al., 1996), and Nsm156, specific for the Nitrosomonas lineage (Mobarry et al., 1996). Probe Eub338 was labelled with 5-carboxyfluorescein (FAM; Sigma-Genosys), while probes Nso190 and Nsm156 were labelled with 6-carboxy-x-rhodamine (6-ROX; Sigma-Genosys). Eub338 was used as a positive control for confirming that cells stained with either Nso190 or Nsm156 were true bacteria. Since this required double hybridizations, the first hybridization was carried out on the probe with higher thermal stability (Nso190 or Nsm156) followed by the second probe (Eub338). A thin (usually < 100 µm thick) biofilm was observed on the surface of all root samples examined. No significant differences were detected in the abundances of AOB among the three rice cultivars. However, significantly higher (P < 0.05) levels of Nitrosomonas cells were detected in the root biofilms of rice cultivar IR63087-1-17, which corresponded to the highest gross nitrification rates measured among the three varieties and unplanted soil. Since the abundances were within the detection limits of FISH, these authors were able to present, for the first time, population estimates of AOB on the rice root surface. The results also showed an enrichment of AOBs on root surfaces. Several studies have used FISH together with microautoradiography (FISH–MAR). This combined approach allows in situ identification and provides information on substrate utilization in complex microbial communities (Lee et al., 1999; Ouverney and Fuhrman, 1999). Ouverney and Fuhrman (1999) used these combined techniques to determine in situ nutrient uptake by members of specific picoplankton groups on a single slide. The cells were triple-labelled with a general stain, a fluorescent oligonucleotide

probe and a tritiated substrate. Within the same microscopic field, it was possible to determine not only the percentage of a specific prokaryotic phylogenetic group in a mixed sample but also the distribution of nutrient uptake within each subgroup.

Macroarray/microarray Microarray analysis represents the most recent advance in quantitative gene expression measurement, but as yet it has been used sparingly for environmental analysis. The principle underlying a microarray experiment is that DNA/mRNA from a given source is used to generate a labelled sample, sometimes referred to as a target, which is hybridized in parallel to a large number of DNA sequences that are immobilized onto a solid surface in an ordered array. Tiquia et al. (2004) constructed a 50-mer oligonucleotide microarray using 763 genes involved in nitrogen cycling: nitrite reductase (nirS and nirK), ammonia monooxygenase (amoA), nitrogenase (nifH), methane monooxygenase (pmoA) and sulphite reductase (dsrAB) to examine the specificity, sensitivity and quantitative aspects of using arraybased hybridization to analyse environmental samples. The 50-mer probes were designed using a modified version of the software, PRIMEGENS (http://compbio.ornl.gov/ structure/primegens/) (Xu et al., 2001). Each individual gene sequence was compared against the entire sequence database using the Basic Local Alignment Search Tool (BLAST®; NCBI) and aligned with other sequences showing > 85% similarity using dynamic programming. Based on these global optimal alignments, segments of 50-mer with < 85% nucleotide identity to the corresponding aligned regions of any BLAST hit sequences were selected as potential probes. From those, probes were further selected by considering GC content, melting temperature and self-complementarity. Two methods were used to label the DNA fluorescently. For genomic DNA labelling, from 1 ng to 1 µg of DNA was mixed with 1 µg of random octamers, denatured by boiling for 5 min and immediately chilled




on ice. DNA targets from plasmid clones were also indirectly labelled by PCR amplification using gene-specific primers. The hybridization solution contained 3× standard saline citrate (SSC) (1× SSC contains 150 mM NaCl and 15 mM trisodium citrate), 1 µg of unlabelled herring sperm DNA (Promega, Madison, Wisconsin, USA), 0.30% sodium dodecylsulphate (SDS) and 50% formamide. To determine the effect of temperature on signal intensity, hybridization was carried out at 45, 50, 55, 60, 65, 70 and 75°C. The results indicated that the saturation point of probe concentration was 100 pmol/µl after printing. Oligonucleotide probes hybridized strongly to their complementary sequences at 50°C, and the hybridization signal intensity was much stronger than that at 55, 60 or 65°C. The detection limit of the functional gene array-based hybridization in the presence of non-target DNAs under the standard hybridization condition was therefore approximately 60 ng. This technique has great potential especially because of the following advantages: rapidity; multiple gene analysis; high throughput; quantitation; and no amplification biases (see Loy et al., Chapter 2 this volume). However, it is still in its infancy with respect to its application to environmental samples, and further research is needed to employ the method optimally in complex environments.

Stable isotope probing (SIP) SIP is a culture-independent technique that enables the isolation of nucleic acids from microorganisms actively involved in a specific metabolic process. It attempts to link an organism’s identity with its biological function under conditions approaching those encountered in situ (Radajewski et al., 2000, 2003). Information on transformation rates of nutrients in soil has been obtained with 14C- or 13C-labelled, or 15N-enriched compounds. In the holistic approach, the system is partitioned into pools with a functional meaning, and fluxes between these pools represent abiotic (such as leaching and

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volatilization) or biotic transformations. Then, the distribution of the isotope (reflecting the behaviour of the added compound) between the various pools can be followed, and the behaviour of the added compound can be discriminated with respect to that of the native carbon or nitrogen. The principle of SIP is based on the natural abundance of 13C being approximately 1%. Consequently, addition of 13C-labelled substrate to an environmental sample will result in 13C labelling of actively dividing microorganisms when it is used as a carbon source and incorporated into DNA during its replication. The heavy DNA can be separated by CsCl density gradient centrifugation from [12C]DNA of microorganisms that do not assimilate the labelled substrate (Radajewski and Murrell, 2001). In a study by Radajewski et al. (2002), SIP was used to characterize the active methylotroph populations in forest soil (pH 3.5) microcosms that were exposed to 13CH OH (99 ± 4% pure, 99 ± 2% 13C; Isotec3 Sigma Aldrich) or 13CH4 (99% pure, 99% 13C; Linde Gas, Höllriegelskreuth, Germany). Distinct 13C-labelled DNA fractions were separated from total community DNA by CsCl density gradient centrifugation. Analysis of 16S rDNA sequences amplified from the [13C]DNA revealed that bacteria related to the genera Methylocella, Methylocapsa, Methylocystis and Rhodoblastus had assimilated the 13C-labelled substrates, which suggested that moderately acidophilic methylotroph populations were active in the microcosms. Analysis of AOB-selective 16S rDNA amplification products identified Nitrosomonas and Nitrosospira sequences in the [13C]DNA fractions, suggesting certain AOB assimilated a significant proportion of 13CO2, possibly through a close physical and/or nutritional association with the active methylotrophs. Other sequences retrieved from the [13C]DNA were related to the 16S rDNA sequences of members of the Acidobacterium division, the Betaproteobacteria and the order Cytophagales, which implicated these bacteria in the assimilation of reduced one-carbon compounds or in the assimilation of the by-products of methylotrophic carbon metabolism.



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Despite the remarkable simplicity of the SIP technique, its suitability for resolving certain structure/function relationships may be limited (Radajewski et al., 2000). In these experiments, it was critical that sufficient 13C was incorporated into the DNA of the functionally active methylotrophs to permit collection of a [13C]DNA fraction. Factors including 13C turnover due to predation or substrate transformation, 13C dilution due to the assimilation of other carbon substrates or 13C assimilation without DNA replication might have influenced the community composition within the [13C]DNA fractions. Nonetheless, this technique is very promising because we might be able to calculate the active microbial carbon pool by multiplying the ratio between labelled and total DNA fractions by the content of microbial carbon in the soil. In addition, the taxonomic and functional characterization allows us to understand more precisely the changes in the composition of microbial communities affected by the added carbon substrate. Instead of measuring the composition of soil microflora, it might be less laborious and time-consuming to monitor the behaviour of key species which can function as indicators of the status of that microflora. Further applications of stable isotopes in soil microbial ecology are considered by Lipski (Chapter 8 this volume).

Metagenomics A relatively new approach to addressing the genomics of uncultured microorganisms is metagenomics. This involves extraction of DNA from an environmental sample, cloning the DNA into a suitable vector, transforming the clones into a host bacterium, preferably E. coli, and screening the resulting transformants. An exciting potential of metagenomics is the retrieval of a community-wide assessment of metabolic and biogeochemical functions because, theoretically, a metagenomics database will contain DNA sequences for all the genes in the microbial community (Handelsman, 2004; Steele and Streit, Chapter 3 this volume).

Tyson et al. (2004) nearly completely sequenced the metagenome of a community in acid mine drainage. In one of the most extreme environments on earth, the microbial community forms a pink biofilm, which floats on the surface of the mine water. The only sources of carbon and nitrogen are the gaseous forms in the air. The community structure is simple and the GC contents of the genomes belonging to the dominant taxa differ considerably. Genomic DNA was extracted from 1 g of biofilm (containing 109–1010 cells), homogenized and embedded in a 0.8% agarose plug. Plugs were treated with lysozyme (5 mg/ml) at 37°C for 3 h and afterwards with proteinase K (0.2 mg/ml) at 50°C for 24 h. High molecular weight DNA (> 20 kb) was excised and precipitated after running a pulsed-field gel electrophoresis. Approximately 3–5 µg of isolated DNA was randomly sheared to 3–4 kb fragments and blunt end repaired using DNA polymerase I Klenow fragment. The 3–4 kb fragments were again extracted from an agarose gel and purified prior to blunt end ligation into pUC18 (Roche). The ligation product was electroporated into E. coli DH10B. Random shotgun sequencing yielded 103,462 high quality reads covering a total of 76.2 Mbp. The authors were able to reconstruct almost complete genome sequences of the two dominating species designated as Leptospirillum group II and Ferroplasma group I. Interestingly, the ability to fix nitrogen appeared to be limited to a non-dominant species of Leptospirillum group III. In this group, a complete nitrogen fixation operon with homology to that of Leptospirillum ferrooxidans was detected (e.g. nifH is 84% identical). These findings led to the suggestion that the survival of the biofilm is dependent on a numerically poorly represented key species. The transferability of this approach to other environments with higher species richness is limited. Nevertheless, despite its time-consuming nature, high cost- and labour-intensiveness, metagenomics harbours great potential for the discovery of novel genes (see Steele and Streit, Chapter 3 this volume).




Conclusions and Future Directions From recent molecular ecological studies, it is apparent that most carbon- and nitrogencycling gene sequences are divergent from those of the model organisms on which most of our existing appreciation of biogeochemical cycles is based. Hence our understanding of ecologically relevant bacteria involved in these cycles is limited. Genomic tools would continue to expand our knowledge of participating microorganisms and the processes that are driven by them. Although genomic analyses of microbial processes allow investigation of a community without culture biases, the methods employed are not free from limitations. As mentioned earlier, the fundamental Table 1.3.

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genomic tool, PCR, itself suffers in this regard by introducing biases at the amplification stage. Biases can also be introduced at an even earlier stage, e.g. during DNA extraction. Some of the limitations of various molecular techniques have been pointed out in Table 1.3. The model systems used to prepare mRNA for an expression study are in all aspects equivalent to the models used in proteomics. Both approaches have their advantages and limitations. One important difference between the two is molecular stability: while mRNAs are relatively shortlived molecules, proteins, in general, tend to be more stable. Consequently, short-term changes in expression/synthesis may perhaps be most conveniently studied at the mRNA level. On the other hand, since regulation often also occurs at post-transcriptional

Comparison of various genomic techniques for analysis of microbial processes in soil.

Technique

Advantages

Disadvantages

PCR & RT–PCR

Rapid, sensitive, easy

Fingerprinting

Rapid means to screen large number of samples for comparative microbial community analysis Rapid, sensitive, easy, quantitative

Semiquantitative because of the kinetics of PCR product accumulation, dynamic range only 1000 fold Major contribution by dominant microorganisms

cPCR Real-time PCR

TaqMan-PCR

Microarray

FISH–MAR

SIP Metagenomics

Wide dynamic range (up to 107 fold), high sensitivity (∼5 copies), high precision, no post-PCR steps, high throughput, multiplexing possible, no radioactivity Extremely sensitive, specific, multiplex detection, quantitative, probes can be applied in real-time PCR

Rapid, multiple gene analysis, high throughput, sensitive, quantitative, no amplification biases Rapid, high efficiency of hybridization and detection, insight into spatial niches High sensitivity, enables coupling of structure and function Increased chances of identification of new genes/operons


Post-PCR processing of PCR products needed PCR product increases exponentially, variation increases with cycle number, overlap of emission spectra, expensive instrument and reagents Additional expensive fluorophor probe needed, PCR product increases exponentially, variation increases with cycle number, expensive instrument and reagents Expensive equipment, diverse target sequences in environmental samples, humic acids inhibit hybridization Prior information needed for design of special fluorescent probes, radioactivity involved, dependent on the permeability of cells and accessibility of the target Discrimination by microbes against heavy isotope Labour-intensive, costly and time-consuming


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stages, mRNA levels may be misleading in some instances, and a determination of the protein may be more indicative of general metabolic potential. Hence it is important to evaluate each method critically before

applying it to any study of nutrient cycling in soil. A combination of approaches (e.g. RT–PCR and proteomics) could better serve to improve our understanding of gene expression and post-translational modifications.

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Degens, B.P. and Harris, J.A. (1997) Development of a physiological approach to measuring the catabolic diversity of soil microbial communities. Soil Biology and Biochemistry 29, 1309–1320. Deiglmayr, K., Philippot, L., Hartwig, U.A. and Kandeler, E. (2004) Structure and activity of the nitrate-reducing community in the rhizosphere of Lolium perenne and Trifolium repens under long-term elevated atmospheric pCO2. FEMS Microbiology Ecology 49, 445–454. Felske, A., Engelen, B., Nubel, U. and Backhaus, H. (1996) Direct ribosome isolation from soil to extract bacterial rRNA for community analysis. Applied and Environmental Microbiology 62, 4162–4167. Garland, J.L. and Mills, A.L. (1991) Classification and characterization of heterotrophic microbial communities on the basis of patterns on community-level, sole-carbon-source utilization. Applied and Environmental Microbiology 57, 2351–2359. Griffiths, R.I., Whiteley, A.S., O’Donnel, A.G. and Bailey, M.J. (2000) Rapid method for the coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition. Applied and Environmental Microbiology 66, 5488–5491. Grüntzig, V., Nold, S.C., Zhou, J. and Tiedje, J.M. (2001) Pseudomonas stutzeri nitrate reductase gene abundance in environmental samples measured by real-time PCR. Applied and Environmental Microbiology 67, 760–768. Handelsman, J. (2004) Metagenomics: application of genomics to uncultured microorganisms. Microbiology and Molecular Biology Reviews 68, 669–685. Heid, C.A., Stevens, J., Livak, K.J. and Williams, P.M. (1996) Real time quantitative PCR. Genome Research 6, 986–994. Henry, S., Baudoin, E., López-Gutiérrez, J.C., Martin-Laurent, F., Brauman, A. and Philippot, L. (2004) Quantification of denitrifying bacteria in soils by nirK gene targeted real-time PCR. Journal of Microbiological Methods 59, 327–335. Heritage, J., Evans, E.G.V. and Killington, R.A. (1999) Microbiology in Action. Cambridge University Press, Cambridge, UK. Holland, P.M., Abramson, R.D., Watson, R. and Gelfand, D.H. (1991) Detection of specific polymerase chain reaction product by utilizing the 5′–3′ exonuclease activity of Thermus aquaticus DNA polymerase. Proceedings of the National Academy of Sciences of the USA 88, 7276–7280. Horz, H.P., Rich, V., Avrahami, S. and Bohannan, B.J.M. (2005) Methane-oxidizing bacteria in a California upland grassland soil: diversity and response to simulated global change. Applied and Environmental Microbiology 71, 2642–2652. Jetten, M.S.M., Horn, S.J. and van Loosdrecht, M.C.M. (1997) Towards a more sustainable wastewater treatment system. Water Science and Technology 35, 171–180. Kolb, S., Knief, C., Stubner, S. and Conrad, R. (2003) Quantitative detection of methanotrophs in soil by novel pmoA-targeted real-time PCR assays. Applied and Environmental Microbiology 69, 2423–2429. Lamar, R.T., Schoenike, B., Vanden Wymelenberg, A., Stewart, P., Dietrich, D.M. and Cullen, D. (1995) Quantitation of fungal mRNAs in complex substrates by reverse transcription PCR and its application to Phanerochaete chrysosporium-colonized soil. Applied and Environmental Microbiology 61, 2122–2126. Lee, L.G., Connell, C.R. and Bloch, W. (1993) Allelic discrimination by nick-translation PCR with fluorogenic probes. Nucleic Acids Research 21, 3761–3766. Lee, N., Nielsen, P., Andreasen, K., Juretschko, S., Nielsen, J., Schleifer, K. and Wagner, M. (1999) Combination of fluorescent in situ hybridization and microautoradiography – a new tool for structure–function analyses in microbial ecology. Applied and Environmental Microbiology 65, 1289–1297. Liang, P. and Pardee, A.B. (1992) Differential display of eukaryotic messenger-RNA by means of the polymerase chain reaction. Science 257, 967–971. López-Gutiérrez, J.C., Henry, S., Hallet, S., Martin-Laurent, F., Catroux, G. and Philippot, L. (2004) Quantification of a novel group of nitrate-reducing bacteria in the environment by real-time PCR. Journal of Microbiological Methods 57, 399–407. Loy, A., Lehner, A., Lee, N., Adamczyk, J., Meier, H., Ernst, J., Schleifer, K.H. and Wagner, M. (2002) Oligonucleotide microarray for 16S rRNA gene-based detection of all recognized lineages of sulfatereducing prokaryotes in the environment. Applied and Environmental Microbiology 68, 5064–5081. Mayer, J., Buegger, F., Jensen, E.S., Schloter, M. and Hess, J. (2003) Turnover of grain legume N rhizodeposits and effect of the rhizodeposition on the turnover of crop residues. Biology and Fertility of Soils 39, 153–164. McDonald, I.R. and Murrell, J.C. (1997) The methanol dehydrogenase structural gene mxaF and its use as a functional gene probe for methanotrophs and methylotrophs. Applied and Environmental Microbiology 63, 3218–3224.



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Applications of Nucleic Acid Microarrays in Soil Microbial Ecology Alexander Loy,1,* Michael W. Taylor,1 Levente Bodrossy2 and Michael Wagner1

1Department

of Microbial Ecology, University of Vienna, A-1090 Vienna, Austria; of Bioresources/Microbiology, ARC Seibersdorf Research GmbH, A-2444 Seibersdorf, Austria

2Department

Introduction Sediments and soils harbour the highest abundance and diversity of microorganisms found on earth (Whitman et al., 1998; Curtis et al., 2002; Torsvik and Øvreås, 2002; Torsvik et al., 2002; Gans et al., 2005). One cubic centimetre of soil may contain billions of prokaryotes belonging to thousands of different species. This enormous biocomplexity poses considerable challenges to microbial ecologists who struggle to unravel the diversity and dynamics of prokaryotes in soils (Tringe et al., 2005). Traditional, cultivation-based approaches are able to retrieve only a minute fraction of total soil biodiversity, and those microbial strains which are successfully isolated are often not abundant and thus presumably of minor ecological importance in these systems. The realization of such a profound ‘cultivation bias’ was brought about by the introduction of an array of molecular, mainly nucleic acid-based techniques for microbial community analyses at the end of the last millennium. One of the key cultivationindependent techniques in the molecular tool box employs the amplification of

phylogenetic marker genes, such as the 16S rRNA gene, from environmental samples by using conserved primers for polymerase chain reaction (PCR), followed by the establishment of clone libraries and comparative sequence analyses of the obtained clones. Although also not entirely free of stochastic and systematic errors (von Wintzingerode et al., 1997; Polz and Cavanaugh, 1998), this PCR-based approach is, in principle, capable of unmasking the microbial richness of an environmental sample. A major drawback to the use of this technique in soil environments is that as many as a few thousand clones must be analysed in order to cover the phylogenetic richness hidden in a prokaryotic gene library. This fact has hampered the wide application of gene library surveys in soil microbial ecology in the past (Borneman and Triplett, 1997; Dunbar et al., 1999; McCaig et al., 1999), although continuous advances in high throughput sequencing now render this approach easier and much more cost-effective. Nevertheless, studying spatial and temporal changes in the microbial community will remain tedious and time-consuming due to the large number of samples that have to be analysed.

*Corresponding author; Phone: +43 1 4277 54207, Fax: +43 1 4277 54389, E-mail: [email protected] 18

©CAB International 2006. Molecular Approaches to Soil, Rhizosphere and Plant Microorganism Analysis (Cooper and Rao)



Applications of Nucleic Acid Microarrays in Soil Microbial Ecology

The invention of DNA microarrays has supplied microbial ecologists with an elegant, potential solution to this problem. The essence of DNA microarray technology is that, in a single hybridization experiment, dozens to tens of thousands of diagnostic DNA probes can be simultaneously exploited to detect and differentiate among target genes (or gene products) in complex nucleic acid mixtures. The high parallelism of microarrays thus opens the door for largescale comparisons among different samples. While DNA microarrays were introduced, and are still most widely applied, for comparative measurements of gene expression on a genomic scale (Schena et al., 1995; Brown and Botstein, 1999; Lander, 1999), it was as early as 1997 that the use of this technology was first proposed for microbial community composition analyses (Guschin et al., 1997). Since that time, microbial ecologists have seen a considerable increase in the number of publications describing technical improvements of DNA microarrays for environmental studies (Liu et al., 2001; Wu et al., 2001; Urakawa et al., 2002, 2003), yet successful application of this technology in the environmental field became feasible only recently (Loy et al., 2002, 2004, 2005; Wilson et al., 2002; Bodrossy et al., 2003; Rhee et al., 2004; Stralis-Pavese et al., 2004). In this chapter, we introduce the general methodological principles of DNA microarrays and highlight crucial steps in developing and applying this hybridization format for the analysis of complex microbial communities in the environment. Special emphasis will be given to soil habitats. Microarrays for microbial community analysis have been classified into three main categories (Zhou and Thompson, 2002; Zhou, 2003) depending on the combination of probe types and target molecules exploited: ● ●

●

Community genome arrays (CGAs); rRNA-based oligonucleotide microarrays (PhyloChips, phylogenetic oligonucleotide arrays); and Functional gene arrays (FGAs).

Benefits and caveats of these three microarray types will be illustrated and discussed.

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We further present established microarray protocols for PhyloChips and FGAs, together with hints for possible improvements of their use. Although DNA microarray technology is constantly being improved, examples from our own research and from the literature demonstrate that DNA microarrays can already be routinely applied in a massively parallel fashion to reveal microbial community structure. Finally, we emphasize that application of microarray technology is not restricted to identification of genes/ microorganisms in the environment, but additionally has great potential for ecophysiological characterization of microbial populations.

General Methodology The basic principle of DNA microarray technology is simple, i.e. identification of an unknown nucleic acid mixture (targets) by hybridization to numerous known diagnostic nucleic acids (probes), which are immobilized in an arrayed order on a miniaturized solid surface (Fig. 2.1). However, this conceptual simplicity belies the technological complexities associated with the technique. Numerous options exist on the methodological path from target preparation and microarray fabrication to a successful microarray experiment. Although this methodological complexity offers many options for ameliorating problems commonly associated with microarray technology (such as low sensitivity), it is also the source of many possible systematic and random errors that collectively hamper standardization. Careful and thorough development and subsequent performance evaluation with targets of known identity and quantity are therefore required before newly developed microarrays can reliably be applied for the characterization of unknown microbial communities in the environment. Here, we only briefly describe some technical aspects that must be considered before beginning work with microarrays, since these have already recently been reviewed elsewhere (Bodrossy and Sessitsch, 2004; Loy and Bodrossy, 2006; Taylor et al., 2006).



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Fluorescent dye

Free unknown nucleic acid: target

Immobilized nucleic acid: probe

Spacer Probe spot

Solid miniaturized support, e.g. glass slide

Fig. 2.1.

Schematic illustration of the DNA microarray principle.

Microarray manufacturing Depending on the surface structure, solid supports used for microarray analyses can be divided into two- (2D) and three-dimensional (3D) formats. Typical 2D microarrays are the standard 1 × 3 inch (25 × 75 mm) planar glass slides that are widely used for the production of ‘home-made’ microarrays with appropriate robotics. Probes, often synthesized with suitable linker molecules to reduce steric hindrance during hybridization (Shchepinov et al., 1997), are tethered (‘spotted’) to a chemically activated slide surface. The great advantages of the 1 × 3 inch glass slide format are the generally low costs and the flexibility of the probe layout on the microarray, i.e. the user can easily change the spotting pattern for each spotting run. Another 2D format that has already been applied for microbial community analyses is the Affymetrix GeneChiparray® (Affymetrix, Santa Clara, California, USA) (Wilson et al., 2002; Desantis et al., 2005). Here, thousands of different oligonucleotide probes are synthesized at a very high

density directly on the chip surface by employing the photolithographic masking technique (Barone et al., 2001). In contrast to the 2D arrays, a 3D structure considerably increases the available surface area on the array and allows the deposition of higher probe loads, thus promoting a higher sensitivity of the microarray analysis. Examples of this format type include the polyacrylamide gel-pad slides (Yershov et al., 1996; Fotin et al., 1998) or the porous microarrays from PamGene (PamGene, Hertogenbosch, The Netherlands) (Anthony et al., 2003; Wu et al., 2004b) and Infineon Technologies’ Array Tip Platform (Infineon Technologies, Munich, Germany).

Target sequence databases and probe design One of the most important rules for PhyloChips and FGAs is that the quality of a newly developed microarray probe set largely depends on the quality and size of




the underlying sequence database. Furthermore, the marker gene also influences the choice of probe types (single-stranded oligonucleotides or double-stranded PCR products) and thus of the physical characteristics (length, free binding energies, position of mismatches to non-target sequences, etc.) and the (partially predictable) hybridization behaviour of the probes (reviewed in Loy and Bodrossy, 2006). Probe types which are typically used for CGAs, PhyloChips and FGAs are given below in the respective sections.

Target preparation and labelling The initial stages of microarray sample preparation mirror those associated with many microbial community analysis techniques. DNA or RNA is extracted from the sample of interest and, if desired, PCR amplification is performed to enrich sequences from target organisms (note that in the case of soil samples, PCR inhibitors may necessitate the use of extra cleaning steps during extraction). Purified nucleic acid or PCR amplificate is then quantified and ready for labelling. Here, numerous options exist. Fluorescence-based detection of target sequences is most commonly used, with the cyanine dyes Cy3 and Cy5 proving popular due to their non-overlapping spectral characteristics, high molecular extinction coefficients and high quantum yields. Two detailed procedures for Cy-based labelling of nucleic acids (labelling via random priming and in vitro transcription) are provided in the Methods sections, but other strategies (e.g. direct incorporation of labelled deoxynucleotides during PCR or post-labelling strategies with aminoallylated or biotinylated nucleotides) offer feasible alternatives (Small et al., 2001; Wilson et al, 2002; Vora et al., 2004). Direct detection of environmentally retrieved target nucleic acids (DNA, rRNA and mRNA) is favourable compared with PCR-mediated target amplification, known to introduce biases in nucleic acid composition. However, direct detection of low abundance organisms may be problematic in complex environments such as soils.

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Various options are available to enhance target detection and they are described elsewhere in this chapter. A further consideration is the effect of target fragment size. Probes are attached to the microarray surface, so any complementary target sequence must first negotiate its way toward the slide surface before hybridization is possible (Southern et al., 1999). Long fragments are therefore disadvantageous, since physical (steric) effects due to complex secondary structures of the target nucleic acids may reduce the potential for binding. Shorter fragments of nucleic acids should therefore be created either during or after labelling (see Methods sections below).

Sensitivity and specificity In environmental microarray analyses, two key considerations are detection sensitivity (i.e. how well you can detect a target gene/microorganism in a given sample) and specificity (i.e. the extent to which you can differentiate among various targets and non-targets). The methodological complexities of microarray analyses are well illustrated by the interplay between sensitivity and specificity. Both differ depending on the experimental parameters used, and must be thoroughly evaluated for each newly developed microarray since many methods which increase one result in a concomitant decrease in the other. Sensitivity in particular is likely to be a major factor in soils, if one is trying to detect certain – possibly low abundance – groups against a background of enormous microbial diversity and abundance. Recent estimates indicate that organisms comprising 1–5% of the total community are detectable using current technologies (Bodrossy et al., 2003; Peplies et al., 2004; Tiquia et al., 2004; Wu et al., 2004a; Loy et al., 2005). This detection limit translates into a maximum of 20–100 different populations that can be detected in a single microarray experiment at the moment. It is assumed that these detectable populations represent the numerically important fraction of the microbial community in a sample. Potential means for



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increasing sensitivity and thus the number of populations that could be distinguished include, for example, post-hybridization procedures such as tyramide signal amplification (Denef et al., 2003) and the use of longer and/or high quality probes (Relogio et al., 2002).

each probe on a given array because target binding capacity can differ greatly among probes. Two-colour experiments, in which environmental samples and reference DNAs are labelled with different fluorophores (such as the aforementioned Cy dyes), offer further potential for semi-quantitative analyses of community structure (Cho and Tiedje, 2002; Bodrossy et al., 2003).

Data analysis and quantification Modern equipment such as laser-based, fluorescence-detecting scanners and their associated software greatly facilitate the acquisition and analysis of microarray images. Upon completion of a microarray hybridization, the pertinent data can be extracted in a matter of minutes. Positive probes can be readily identified from scanned slides by digital image analysis if the signal-to-noise ratio (SNR) of a probe spot exceeds an arbitrary threshold. Normalization of signal intensities is a common approach to account for variations among slides and the often significant inherent differences in probe–target affinity. The microarray format also offers the opportunity for replicate spots to be printed, thus allowing for differences in spot characteristics due to the manufacturing process. With appropriate equipment, such as precise heating devices linked to a fluorescence detector, a series of images can be taken at various slide washing temperatures to yield non-equilibrium dissociation (melting) curves (Liu et al., 2001; Li et al., 2004). The advantage of such curves is that they enable all probes on an array to be analysed at their optimal stringencies. Quantification of microorganisms using diagnostic microarrays is highly desirable, yet plagued with difficulties due to the many potential biases introduced at different stages (from array manufacture and sample processing, through to hybridization and washing of slides). Evidence of a linear relationship between signal intensity and target nucleic acid concentration (obtained via spiking experiments, e.g. Tiquia et al., 2004; Desantis et al., 2005) is encouraging, but it must be emphasized that such validation should in principle be performed for

Community Genome Arrays The principle of CGAs (Zhou, 2003) is comparable with the membrane-based technique known as reverse sample genome probing (RSGP) (Voordouw et al., 1991, 1992, 1993; Shen et al., 1998; Voordouw, 1998). Both methods employ whole genomic DNA from isolated strains as probes for hybridization, but differ in the type of labelling (fluorescence and radioactivity for CGAs and RSGP, respectively) and the array format (microarray on a solid support and macroarray on a membrane for CGAs and RSGP, respectively). RSGP further enables quantitative analysis of microbial communities by using known concentrations of bacteriophage λDNA as reference spots on the array (Voordouw et al., 1993). Due to the nature of the probes, cross-hybridization is a potential problem and largely depends on: (i) the experimental conditions; (ii) the choice of reference genomes on the array; and (iii) the genomic complexity in the environmental sample (Voordouw, 1998; Wu et al., 2004a). Current data hint that species level resolution can be achieved (Wu et al., 2004a). One point of criticism is that an unknown environmental community can only be described in terms of its cultivable members represented on the array, even though the actual hybridization is independent of cultivation. Considering this drawback, the increased use of cultivation-independent metagenomic approaches employing large-insert cloning (DeLong, 2002; Handelsman, 2004; Riesenfeld et al., 2004) could lead to an important extension of the CGA approach by providing a hitherto unavailable source for large genomic fragments from uncultivated microorganisms.




However, identification of the microorganisms representing these environmental genomic DNA fragments is a demanding challenge unless diagnostic genes (e.g. those encoding rRNAs) are present on the fragments. Application examples for CGAs are scarce (Krause et al., 2004). Evaluation of a prototype CGA (based on 67 strains) showed signal intensities above background if the number of cells that were spiked in autoclaved soil exceeded 2.5 × 105 (Wu et al., 2004a). In the same study, principal component analyses distinguished samples obtained from soils, river sediments and marine sediments on the basis of CGA hybridization intensity data and physico-chemical parameters.

rRNA-based Oligonucleotide Microarrays The latest releases of the benchmark microbial taxonomy textbooks The Prokaryotes and Bergey’s Manual of Systematic Bacteriology are structured according to small subunit (SSU) rRNA sequence-based phylogeny, which is regarded as the taxonomic backbone for the classification of novel microbial species. Increasing efforts to describe the large fraction of as yet uncultivated microorganisms on our planet are reflected in the publicly available RDP II (Cole et al., 2005) and ARB (Ludwig et al., 2004) SSU rRNA sequence databases that represent the largest and most rapidly growing data sets for an individual gene. These encompassing data sets, the ubiquitous distribution of this gene in the domains of life and the low frequency of lateral gene transfer events render SSU rRNA an ideal target molecule for the design of phylogenetic probes for DNA microarray-based microbial identification (Fox et al., 1980; Woese, 1987; Ludwig and Schleifer, 1999). Due to high overall conservation of rRNA gene sequences (for some genera, even resolution of different species is not possible) (Ludwig et al., 1998b), short oligonucleotide probes ranging from approximately 15 to 25 nucleotides provide higher

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discrimination among perfectly matching and mismatching targets (i.e. single-mismatch discrimination under optimal conditions) and are thus the preferred choice. The great advantage of using rRNAs or their genes (in comparison with protein-coding genes) as target molecules is that hierarchically nested probes can be designed for the microorganisms of interest according to the multiple nested probe concept (Ludwig et al., 1998a; Liu et al., 2001; Loy et al., 2002). Consequently, a target organism/group is only unambiguously identified if all probes in a redundant and nested probe set for this organism/group are positive. This probe design strategy enhances the reliability of the microarray and additionally allows the detection of hitherto unrecognized microorganisms if they are related to those directly targeted by the PhyloChip (Taylor et al., 2006). Because traditional end-point PCR changes the composition and the level of targets originally present in the sample, the final aim of using PhyloChips for microbial community analyses is directly to detect and quantify rRNA isolated from an environmental sample. While proof-of-concept studies have shown that direct rRNA detection is possible (Small et al., 2001; Adamczyk et al., 2003; El Fantroussi et al., 2003; Peplies et al., 2004), the currently assumed relative detection limit of about 5% of the total rRNA pool in a sample would presumably hamper the wide application of this approach in highly diverse environments such as soils. However, advances in microarray technology by, for example, adapting enzymatic signal amplification approaches for microarray analysis (Small et al., 2001; Denef et al., 2003), the use of high quality probes (Relogio et al., 2002) or the development of high sensitivity microarray hybridization detection devices (Blom et al., 2002; McKendry et al., 2002; Epstein et al., 2003; Francois et al., 2003; Thompson et al., 2005) hold much promise for enhancing the sensitivity of direct detection of extracted rRNA in the near future. In the meantime, physical or enzymatic (e.g. PCR) pre-enrichment of target genes/ organisms will remain an important initial



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step to increase the specificity and sensitivity of follow-up microarray analyses (Hashsham et al., 2004). The use of PCR primer pairs which selectively enrich the target genes compared with the non-targeted DNA background significantly improves the detection limit and can allow the identification of low abundance microorganisms representing < 1% of the total bacterial community (Loy et al., 2005).

Methods In the following, a protocol for PhyloChip hybridization using PCR for initial target amplification and 18-mer oligonucleotide probes is presented (de novo development and evaluation of PhyloChips is not covered). Although this approach has already been successfully applied for PhyloChip analyses of sulphate-reducing prokaryotes (Loy et al., 2002, 2004), nitrifying bacteria (M.W. Taylor, A. Loy, J. Adamczyk and M. Wagner, 2005, unpublished data), members of the betaproteobacterial order Rhodocyclales (Loy et al., 2005) and Enterococcus species (Lehner et al., 2005) in different clinical, food and environmental samples, it should be noted that other microarray protocols (such as the one exemplarily presented below for FGAs) might be equally well suited, and vice versa. Microarray manufacturing and processing Oligonucleotides for in-house microarray printing can be obtained from various commercial suppliers. We recommend the use of high quality (purified by high performance liquid chromatography or polyacrylamide gel electrophoresis) oligonucleotides due to their better performance in microarray experiments compared with their desalted counterparts (Relogio et al., 2002). Sequences, specificities and other characteristics of probes from some published PhyloChips can be viewed via the probeBase webpage (http:// www.microbial-ecology.net/probebase/) (Loy et al., 2003). The 5′ end of each oligonucleotide probe should be tailed with a spacer element (e.g. a simple T-spacer

consisting of several dTTPs) to reduce steric hindrance and thus increase on-chip accessibility of spotted probes to target DNA (Shchepinov et al., 1997; Southern et al., 1999). In addition, the 5′-terminal nucleotide of each oligonucleotide is aminated to allow covalent coupling of the oligonucleotides to aldehyde group-coated glass slides (e.g. CSS-100, CEL Associates, Pearland, Texas, USA). Prior to printing of the final microarray, the optimal spotting concentration of the probes and the spotting buffer should be empirically tested (Peterson et al., 2001). We generally use 50 or 100 pmol/µl in 50% (v/v) dimethylsulphoxide to reduce evaporation during the printing procedure. Subsequently, PhyloChips are printed under controlled environmental conditions (temperature ∼20–22°C, humidity ∼50–55%) using a suitable spotting device. Individual probes should be spotted at least in triplicate on a microarray to enable a statistical correction for errors. After spotting, DNA microarrays are dried overnight at room temperature in the dark to allow efficient cross-linking. Thereafter, slides are washed twice at room temperature in 0.2% sodium dodecylsulphate (SDS) and subsequently twice with double-distilled water with vigorous agitation to remove unbound oligonucleotides and the SDS. After careful air drying, slides are incubated for 5 min in fresh sodium borohydride solution (1.0 g of NaBH4 in 300 ml of phosphate-buffered saline and 100 ml of ethanolabsolute) to reduce all remaining reactive aldehyde groups on the glass slides. The reaction is stopped by adding ice-cold ethanolabsolute. Reduced slides are washed four times (twice with 0.2% SDS and then twice with double-distilled water), air-dried and can be stored in the dark at room temperature for up to about 3 months (note that the microarray quality decreases with storage time). PCR amplification of 16S rRNA genes Almost complete 16S rRNA gene fragments are amplified from environmental DNA using universal, general bacterial and/or archaeal primers (Table 2.1). As mentioned above, the use of target group-selective



S-D-Bact-0008-a-S-18 S-D-Bact-1529-a-A-17 S-D-Arch-0008-a-S-18

S-D-Arch-0109-a-S-17

S-D-Arch-1196-a-A-18 S-*-Proka-1492-a-A-19 S-D-Univ-1389-a-A-18

S-D-Univ-1389-b-A-18 S-D-Univ-1389-c-A-18

BACT8F (616V) BACT1529R (630R) ARCH8F (A3Fb)

ARCH109F (A109F)

ARCH1196R (UA1204R) PROKA1492R (1492R) UNIV1389aR

UNIV1389bR UNIV1389cR

ACG GGC GGT GTG TAC AAA ACG GGC GGT GTG TGC AAG

TTM GGG GCA TRC IKA CCT GGY TAC CTT GTT ACG ACT T ACG GGC GGT GTG TAC AAG

ACK GCT CAG TAA CAC GT

AGA GTT TGA TYM TGG CTC CAK AAA GGA GGT GAT CC TCY GKT TGA TCC YGS CRG

Sequence (5′-3′)c

Juretschko et al., 1998 Juretschko et al., 1998 Lopez-Garcia et al., 2001

Most Bacteria Most Bacteria Most Archaea, not Korarchaeota and Nanoarchaeota Most Archaea, not Korarchaeota and Nanoarchaeota Most Archaea Most Bacteria and Archaea Most Bacteria, not Epsilonproteobacteria Most Eucarya Most Archaea

Loy et al., 2002 Loy et al., 2002

Baker et al., 2003 Loy et al., 2002 Loy et al., 2002

Whitehead and Cotta, 1999

Selected referencesc

Specificity

bName

short names used in the literature are indicated in parentheses. of SSU rRNA gene-targeted oligonucleotide primer based on the nomenclature of Alm et al. (1996). cPlease note that for some primers variants (differing in length and/or type and number of degenerate nucleotides) exist in the literature e.g. see Baker et al. (2003).

aSynonymous

Full nameb

Selected universal, bacterial, and archaeal PCR primers for amplification of near complete small-subunit rRNA genes.

Short namea

Table 2.1.


Applications of Nucleic Acid Microarrays in Soil Microbial Ecology 25


26

A. Loy et al.

primers is recommended for improved detection and differentiation of target organisms (Loy et al., 2005). It must be kept in mind that the binding position of the PhyloChip probes should be located on the amplified 16S rRNA gene fragments. Positive controls containing purified DNA from suitable reference organisms are included in all of the PCR amplification experiments along with negative controls (no DNA added). Reaction mixtures containing 25 pmol of each primer are prepared in a total volume of 50 µl by using an appropriate PCR system. Additionally, additives such as 20 mM tetramethylammonium chloride (TMAC) can be added to each amplification mixture to enhance the specificity of the PCR (Kovárová and Dráber, 2000). Thermal cycling is carried out by an initial denaturation step at 94°C for 1 min, followed by 30 cycles of denaturation at 94°C for 40s, annealing (the temperature depends on the primer pair) for 40 s, and elongation at 72°C for 1 min 30 s. Cycling is completed by a final elongation step at 72°C for 10 min. It is suggested to run several PCRs in parallel to account for tube-to-tube variation (stochastic PCR biases) and to pool the reaction products prior to further analysis. Random prime fluorescence labelling of PCR products Prior to labelling, PCR amplificates are purified using the QIAquick PCR purification kit (Qiagen, Hilden, Germany). Subsequently, the amount of DNA is determined spectrophotometrically by measuring the optical density at 260 nm. Purified PCR products are labelled with Cy3 or Cy5 using the DecaLabel™ DNA labelling kit (Fermentas GmbH, St Leon-Rot, Germany). The random prime labelling approach creates random, relatively short, fluorescently labelled DNA fragments, which display lower steric hindrance during hybridization and thus promote enhanced duplex yield. Some fluorescent dyes are prone to degradation (Fare et al., 2003), and thus exposure to light and ozone should be minimized during all subsequent steps. Reaction mixtures

typically containing 200–300 ng of purified PCR product and 10 µl of decanucleotides in reaction buffer in a total volume of 45 µl are denatured at 95°C for 10 min and immediately placed on ice. After addition of 3 µl of the deoxynucleotide Mix C (containing no dCTP), 1 µl of Cy3- or Cy5-dCTP (Amersham Biosciences, Freiburg, Germany) and 1 µl of Klenow fragment (exo–, 5 U/µl), labelling reactions are incubated at 37°C for 45 min. For more efficient labelling, it is recommended to repeat the addition of Mix C, Cy3- or Cy5-dCTP, and Klenow fragment and incubation at 37°C for 45 min. Labelling is completed by a final addition of 4 µl of dNTP-Mix and incubation at 37°C for 60 min. To remove unincorporated deoxynucleotides and decanucleotides, the labelling mixture is purified with the QIAquick nucleotide removal kit (Qiagen) using double-distilled water for DNA elution. The labelling efficiency of the target nucleic acids can be determined by measuring the optical density at 260 nm (nucleic acids) and 550 nm (Cy3, extinction coefficient: 150,000 l/mol/cm) or 650 nm (Cy5, 250,000 l/mol/cm) in a spectrophotometer and applying the Beer–Lambert law. To assess the purity of the nucleic acid after labelling, an absorbance spectrum from 200 to 300 nm should be recorded. Incorporation of Cy3 or Cy5 in target nucleic acid is calculated by dividing the amount of nucleic acid (measured in µg) by the amount of Cy3 or Cy5 (measured in pmol) in the labelled sample. It is also possible to add 0.5 pmol of a Cy3- or Cy5-labelled oligonucleotide that is fully complementary to a nonsense probe included on the microarray and serves as a positive control for hybridization (Loy et al., 2002). Finally, labelled DNA is vacuumdried and stored in the dark at –20°C to avoid dye degradation. Reverse hybridization on PhyloChips Vacuum-dried Cy-labelled PCR products (typically 400–600 ng) are resuspended in 20 µl of hybridization buffer containing 5× standard saline citrate (SSC), 1% blocking reagent, 0.1% n-lauryl sarcosine, 0.02%




SDS and 5% formamide, denatured for 10 min at 95°C and immediately placed on ice. In the next step, the solution is pipetted onto the spotted area of the microarray, covered with a coverslip and inserted into a tight hybridization chamber (for example, see http://cmgm.stanford.edu/pbrown/mguide/ HybChamber.pdf) containing 50–100 µl of hybridization buffer for subsequent equilibration. Hybridization is performed overnight at 42°C in a water bath or hybridization oven. For comparison, hybridization times should be kept constant for different experiments. It is worth noting that dynamic hybridization systems with active mixing (e.g. see Methods for FGAs below) can considerably decrease the hybridization time and improve sensitivity and specificity of the microarray analysis (Adey et al., 2002; McQuain et al., 2004; Schaupp et al., 2005). After hybridization, slides are immediately washed under stringent conditions for 5 min at 55°C in 50 ml of washing buffer containing 3 M TMAC, 50 mM Tris–HCl, 2 mM EDTA and 0.1% SDS. TMAC is added to the buffer to alleviate differences in the GC content of the 18-mer probes and hence to fine-tune them for uniform thermodynamic behaviour (Jacobs et al., 1988; Maskos and Southern, 1992). After the stringent wash, slides are rinsed twice with ice-cold double-distilled water, air-dried (air stream must not contain oil or any autofluorescent particles), and stored in the dark at room temperature until scanning.

Scanning of PhyloChips and image analysis Fluorescence images of the PhyloChips are recorded by scanning the slides with an appropriate microarray scanner, and the fluorescence signals are quantified by using the respective software tools. The resolution of the scan depends on the spot size because approximately 100 image pixels per spot should be analysed for statistical reasons, i.e. if spots are 100–150 µm in diameter, a scanning resolution of 10 µm is sufficient. A grid of individual circles defining the location of each spot on the

27

array is usually superimposed on the image to designate each fluorescent spot to be quantified. Mean and/or median signal intensity is determined for each spot and for the local background area surrounding the respective spot. The mean signal intensity of each spot and the local background area surrounding each spot is determined. Subsequently, for each spot, the SNR is calculated and the mean and standard deviation/error for replicate probe spots is determined. Various formulae for SNR calculation on microarrays have been applied in the past (Loy et al., 2002; Bodrossy et al., 2003; Schadt et al., 2005). Finally, a probe is interpreted as positive if its SNR exceeds a previously determined or arbitrarily set threshold value. It should be noted that because of the inherent variation among replicate microarray analyses (Chandler et al., 2003; Stralis-Pavese et al., 2004), signals near (either slightly above or below) the threshold value for a positive probe must be interpreted with caution. It is thus generally recommended to perform a minimum of three replicate hybridizations per microarray experiment and/or to confirm the results subsequently by microarrayindependent methods.

PhyloChips of importance for soil ecology and selected applications A 16S rRNA-based oligonucleotide microarray (SRP-PhyloChip) currently carrying approximately 200 probes, which target all recognized lineages of sulphate-reducing prokaryotes (SRPs) at multiple hierarchical and identical phylogenetic levels, was recently introduced in microbial ecology research (Loy et al., 2002). While SRPs contribute largely to mineralization of organic compounds in ocean sediments, their ecological function in oxygen-limited terrestrial ecosystems might be more specialized due to the generally lower availability of sulphate (Wind and Conrad, 1995; Wind et al., 1999; Scheid and Stubner, 2001). Nevertheless, SRPs have also attracted the attention of soil ecologists because of their capability for alleviating anthropogenic



28

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damage (through oil spills or acidic rain) to earth environments (Alewell and Giesemann, 1996; Caldwell et al., 1998; Alewell and Novak, 2001; Chang et al., 2002; Noh et al., 2003). A study of SRP diversity in two fens (Schlöppnerbrunnen I and II, Germany), which receive periodically changing loads of sulphate due to a profound acidic rain history, used the aforementioned microarray method and the SRP-PhyloChip to

A

A

B

B C

D

E

A

F 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

A

screen for the vertical distribution of SRPs in acidic fen soil samples differing in their geochemistry (Loy et al., 2004). Although for each soil type microarray hybridization fingerprints did not change considerably over soil depth, suggesting a stable vertical distribution of SRP richness, clear differences between the two soil types were observed (Fig. 2.2). Furthermore, the presence of Syntrophobacter-related SRPs in both fens

B C

D

E

F

Bacteria EUB338D25, F2 UNIV1390aD26, F5

Deltaproteobacteria DELTA495aC2, E2

Subgroups of the Deltaproteobacteria SRB385C5, E5 SRB385DbC6, E6

Desulfobacterales, Syntrophobacterales DSBAC355C7

Desulfonema spp. other species of the Desulfobacterales DSN658B2 DSS658C11 Syntrophobacter spp. Desulfovirga spp. Desulforhabdus spp. SYBAC986C17

Desulfomonile spp. DSMON95C18 DSMON1421C19

B C

D

E

F 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

A

B C

D

E

F

Bacteria EUB338D25, F2 UNIV1390aD26, F5

Deltaproteobacteria DELTA495aC2, E2

Subgroups of the Deltaproteobacteria SRB385C5, E5 SRB385DbC6, E6

Desulfobacterales, Syntrophobacterales DSBAC355C7

Syntrophobacter spp. Desulfovirga spp. Desulforhabdus spp. SYBAC986C17

Fig. 2.2. Application of a PhyloChip for diversity surveys of sulphate-reducing prokaryotes in 22.5–30 cm depth of two acidic fen soils SbI (A) and SbII (B) (Loy et al., 2004). Duplicate probe spots having a signal-to-noise ratio ≥ 2.0 are indicated by bold boxes and were considered to be positive. The dotted bold boxes indicate that only one of the duplicate spots had a positive signal-to-noise ratio. The flow charts illustrate the presence of distinct groups of sulphate-reducing prokaryotes in the analysed samples as inferred from positive signals of sets of probes with nested and/or parallel specificity (multiple nested probe concept).




and the additional presence of SRPs of the genus Desulfomonile in Schlöppnerbrunnen I, as indicated by the SRP-PhyloChip analyses, could be confirmed by selective 16S rRNA gene sequence retrieval and phylogenetic treeing. Most strikingly, parallel comparative sequence analyses of genes encoding the α and β subunits (dsrAB) of the dissimilatory (bi)sulphite reductase, a key enzyme in the energy metabolism of SRPs (Wagner et al., 2005; Zverlov et al., 2005), further detected numerous previously unrecognized SRP lineages in the fens. This pinpointed a general but often overlooked feature of microarrays, namely that the technique can only detect target genes/microorganisms which are covered by the probes. A functional group of microorganisms with considerable ecological and economic importance in terrestrial ecosystems is the nitrifying bacteria, which are further subclassified into ammonia- (AOB) and nitriteoxidizing bacteria (NOB). While soil-dwelling nitrifiers have a generally significant impact on the biogeochemical cycling of nitrogen, their activity is detrimental and thus undesired in agricultural soils. In essence, nitrifiers convert positively charged ammonium ions (e.g. from fertilizers) that are less mobile in the negatively charged clay-containing soil matrix, to negatively charged nitrate ions that are quickly leached out of the soil by rainwater. A common agricultural practice is thus the addition of nitrification inhibitors to reduce oxidation of ammonium to nitrate and hence nitrogen loss from soils. To improve our understanding of this important functional group, an encompassing microarray for nitrifying and anaerobic ammonium-oxidizing (ANAMMOX) bacteria (Nitrifier-PhyloChip) is currently being developed (M.W. Taylor, A. Loy, J. Adamczyk and M. Wagner, 2005, unpublished data) and should enable monitoring of these microorganisms in soils. This array contains about 200 probes in the style of previous PhyloChips, i.e. 18-mer oligonucleotide probes designed according to the multiple nested probe concept. Preliminary data demonstrating the detection of ammonia oxidizers of the Nitrosospira lineage in a soil lysimeter are included

29

in Fig. 2.3. All probes which perfectly match 16S rRNA gene sequences retrieved from the sample were positive in the assay. Members of the Nitrosospira lineage are phylogenetically highly related (Koops et al., 2003), yet specific probes on the Nitrifier-PhyloChip enabled identification of the organisms as ‘cluster 3’ nitrosospiras. The SRP- and Nitrifier-PhyloChips both target prokaryotes that share an important ecophysiological function (i.e. dissimilatory sulphate reduction and nitrification, respectively), and hence identification of these organisms in the environment is indirectly linked with a specific biogeochemical process. A slightly different rationale was the basis for the design of the RHC-PhyloChip that targets members of the order Rhodocyclales, a taxonomically defined group within the class Betaproteobacteria (Loy et al., 2005). Although this order comprises a physiologically heterogeneous group of bacteria, some of them carry out important tasks in soil environments, e.g. some Azoarcus species fix nitrogen in association with plant roots (Engelhard et al., 2000), and Dechloromonas species attenuate the soil contaminant perchlorate (Coates et al., 1999). The RHC-PhyloChip could therefore prove useful for deciphering the diversity and ecology of Rhodocyclales in soils. Other rRNA gene-targeted oligonucleotide microarrays of potential interest for soil ecologists include a recently developed microarray for selected microorganisms associated with the composting process (FrankeWhittle et al., 2005) and a universal high density Affymetrix microarray targeting numerous operational taxonomic units of pro- and eukaryotes (Wilson et al., 2002; Desantis et al., 2005).

PhyloChips for functional community analyses – the isotope array approach As mentioned above, detection of native environmental rRNA using PhyloChips is a primary goal in order to avoid PCR bias and promote quantification of probe-defined populations in terms of ribosomal content. While for some microorganisms the number



30

A. Loy et al.

A

70

Signal-to-noise ratio

60 50 40 30 20 10

EUB338 NIT1 NIT2 NIT3 NIT4 NIT5 NIT6 NIT7 NIT8 NIT9 NIT10 NIT11 NIT12 NIT13 NIT14 NIT15 NIT16 NIT17 NIT18 NIT19 NIT20 NIT21 NIT22 NIT23 NIT24 NIT25 NIT26 NIT27 NIT28 NIT29 NIT30 NIT31 NIT32 NIT33 NIT34 NIT35 NIT36 NIT37 NIT38 NIT39 NIT40 NIT41 NIT42 NIT43

0

Selected nitrifier-PhyloChip probes

B

Nitrosospira sp. A16 Nitrosospira sp. L115 “Cluster 3” Nitrosospira sp. AF Soil lysimeter clone ARH1 Soil lysimeter clone ARH6 Nitrosovibrio (Nitrosospira) tenuis Nv1 Nitrosospira sp. C-141 Soil lysimeter clone ARH11 Soil lysimeter clone ARH114 Nitrosospira sp. Nsp1 Nitrosospira sp. Nsp40 Nitrosospira briensis Nsp10 Nitrosospira sp. Nsp2 Nitrosolobus (Nitrosospira) multiformis NL13 Nitrosospira sp. TCH716 Nitrosospira sp. Nsp57 Nitrosospira sp. lll7ul Nitrosospira sp. lll2 Nitrosospira sp. Ka4 Nitrosospira sp. Nsp65A

NIT8

NIT24 EUB338 NIT28 NIT2 NIT3 NIT4 NIT6

5%

Fig. 2.3. Detection of ammonia-oxidizing bacteria of the Nitrosospira lineage in a soil lysimeter using the Nitrifier-PhyloChip (M.W. Taylor, A. Loy, J. Adamczyk and M. Wagner, 2005, unpublished data). (A) Probe signal-to-noise ratios (SNRs) for general probes targeting ammonia-oxidizing bacteria and specific probes targeting members of the Nitrosospira lineage. The dashed line indicates the adopted SNR threshold of 2.0; shaded bars indicate those probes with an SNR ≥ 2.0. Probe names in bold signify those probes which perfectly match 16S rRNA gene sequences retrieved from the same soil sample (see below). (B) 16S rRNA gene-based phylogenetic tree of the Nitrosospira lineage, calculated using maximum-likelihood analysis. Clone sequences in bold were retrieved from the same soil lysimeter sample on which the microarray data are based (courtesy of P. Salkowitsch, University of Vienna). The scale bar represents 5% estimated sequence divergence. Parentheses indicate the perfectly matched target organisms of the probes. Please note that probes EUB338, NIT2, NIT3, NIT4 and NIT6 differ in their respective specificities. Probe NIT31, which was positive in (A), targets a closely related group (Nitrosospira spp. A16, L115 and AF) but has a single mismatch to the soil-derived sequences.




of ribosomes is representative of the level of cellular activity (Ramos et al., 2000), others, especially slow-growing bacteria, maintain high ribosome levels even when the cells are starved or inactivated (Wagner et al., 1995; Schmid et al., 2001). Hence, cellular ribosomal content is generally an unreliable indicator of microbial activity in an environmental context. Furthermore, the ability to catalyse a biogeochemically relevant process is mostly not confined to a phylogenetically homogenous (monophyletic) group of microorganisms. Consequently, rRNAbased identification alone does not usually allow direct inference of physiological properties and thus the function of a microorganism in its environment. In order to bridge the gulf between microbial community structure and function, the so-called isotope array approach was developed recently (Adamczyk et al., 2003). This technique represents an important extension of PhyloChips and takes advantage of selective isotopic labelling of active microbial populations and the highly parallel microarray format. In brief, after short- to mediumterm incubation of a 14C-labelled substrate with the environmental sample under the desired conditions, total RNA is extracted, fluorescently labelled and subsequently hybridized to a PhyloChip. In a following step, fluorescent and radioactive probe signals on the hybridized PhyloChip are recorded with fluorescence- and β-imagers, respectively. Only those probe-defined populations which have actively metabolized the added substrate will show both fluorescent and radioactive signals on the microarray. As an alternative to using a specific 14C-labelled substrate (some desired radioactive substrates might simply not be available or else are relatively expensive), it is also possible to combine the use of unlabelled substrates and [14C]bicarbonate as a general activity marker (Roslev et al., 2004). For radioactive labelling of their RNA, this approach makes use of the presumably widespread capability of microorganisms to assimilate CO2 heterotrophically for radioactive labelling of their rRNA (Hesselsoe et al., 2005). It should be noted that due to the limited resolution of available β-imagers,

31

the isotope array technique currently requires the use of large probe spots (> 500 µm in diameter), and thus high density microarrays containing small spots such as the Affymetrix GeneChips™ (Wilson et al., 2002; Desantis et al., 2005) are not suited for this purpose at present. We have recently applied the isotope array approach in conjunction with the RHCPhyloChip (Loy et al., 2005) to determine substrate spectra of members of the Rhodocyclales in a full-scale waste water treatment plant in Denmark (M. Hesselsoe, A. Loy and M. Wagner, 2005, unpublished data). In separate experiments, several unlabelled electron donors were added to the activated sludge samples and incubated under toxic or denitrifying conditions in the presence of [14C]bicarbonate and an inhibitor of autotrophic CO2 assimilation. The results demonstrated that diverse members of the Rhodocyclales, including Zoogloea species and Accumulibacter-related bacteria, were actively involved in denitrification in this waste water treatment plant. We expect that similar experiments could prove useful for studying microbial community structure and function in soil systems.

Functional Gene Arrays (FGAs) As the name implies, FGAs contain DNA probes targeting genes (or gene products) that encode key enzymes conferring a specific functional capability on the respective microorganisms. Some examples of functional enzymes catalysing different steps in the global nitrogen, sulphur and carbon cycles are nitrite reductase (nirS) for denitrification, ammonia monooxygenase (amoA) for ammonia oxidation, nitrogenase (nifH) for nitrogen fixation (Wu et al., 2001; Taroncher-Oldenburg et al., 2003), dissimilatory (bi)sulphite reductase (dsrAB) for sulphate reduction (Wagner et al., 2005), methyl-coenzyme M reductase (mcrA/mrtA) for methanogenesis and particulate methane monooxygenase (pmoA) for methane oxidation (Bodrossy et al., 2003; Stralis-Pavese et al., 2004). The great advantage of FGAs is that microorganismal identification is



32

A. Loy et al.

directly linked to a physiological trait. However, it must be considered that some functional genes were affected by lateral gene transfer events (e.g. Klein et al., 2001) which hamper unambiguous phylogenetic positioning of the microorganisms concerned. Additionally, the design of non-degenerate, perfectly matching probes targeting a phylogenetically broader group of microorganisms according to the multiple nested probe concept is complicated due to the highly variable third codon (‘wobble’) position of protein-coding functional genes (Loy and Bodrossy, 2006). On the other hand, this high variability is beneficial for FGAs employing short oligonucleotides as probes, because it potentially allows differentiation among species of the same genus or even strains of the same species. An overview of possible marker genes for different biogeochemical processes and the current extent of available sequence data was compiled recently (Schadt et al., 2005). A technical difference from PhyloChips is the nature and length of possible probes on FGAs. Probes can be purchased as short (typically 15- to 30-mer) (Bodrossy et al., 2003; Stralis-Pavese et al., 2004) or long (typically 50- to 100-mer) (Denef et al., 2003; Taroncher-Oldenburg et al., 2003; Tiquia et al., 2004) single-stranded oligonucleotides as well as being self-made by PCR amplification of the target gene (double-stranded probes) (Wu et al., 2001; Cho and Tiedje, 2002; Dennis et al., 2003). The length of the probe has significant implications for its hybridization characteristics (Wetmur, 1991). For example, target binding capacity correlates with probe length, and thus long oligonucleotide probes and PCR products display improved sensitivity (Kane et al., 2000; Letowski et al., 2004). In contrast, short oligonucleotide probes enable the discrimination of single nucleotide mismatches under ideal circumstances (Urakawa et al., 2002, 2003), whereas longer probes reportedly only discriminate among sequences with less than about 75–87% similarity (Wu et al., 2001; Cho and Tiedje, 2002; Taroncher-Oldenburg et al., 2003; Tiquia et al., 2004). Consequently, the choice of probes determines the actual performance

of a newly developed FGA in terms of sensitivity and specificity, and hence strongly depends on the biological question being asked. Methods In analogy to the PhyloChip protocol described above, an example protocol for FGA hybridization employing short oligonucleotide probes with similar theoretical melting temperatures is presented. Using this protocol, an encompassing oligonucleotide FGA targeting the particulate methane monooxygenase (pmoA) gene was developed and applied for monitoring methanotrophic microorganisms in the environment (Bodrossy et al., 2003; Stralis-Pavese et al., 2004). Manufacturing of slides, scanning of hybridized microarrays and image analysis are essentially performed as outlined for PhyloChips and are thus not explicitly mentioned in this section. PCR amplification of functional genes The functional gene of choice is amplified from an environmental DNA extract by using appropriate primers for PCR. In order to allow for subsequent labelling by in vitro transcription, the primer representing the strand to be labelled (usually the reverse primer) must include the T7 promoter sequence (5′-TAATACGACTCACTATAG-3′) at the 5′ end. For environmental DNA amplification, we suggested performing PCRs at low annealing temperatures to maximize coverage of the naturally occurring diversity of functional genes, which might possess mismatches in the respective primer target sites. Furthermore, a combination of high DNA template concentration and low cycle numbers has been shown to alleviate known PCR biases (Polz and Cavanaugh, 1998). If desired, a novel two-step PCR approach (Rudi et al., 2003), which shows much promise in reducing erroneous target amplification, can also be incorporated in the protocol. As recommended for rRNA gene amplification, several parallel PCRs should be performed and pooled prior to further analysis.




Fluorescence labelling of PCR products by in vitro transcription Before fluorescence labelling, PCR products are purified using the HighPure PCR purification kit (Roche Diagnostics GmbH, Mannheim, Germany) and adjusted with ultrapure water to a DNA concentration of 50 ng/µl. All subsequent steps should be performed under RNase-free conditions and using RNase-inactivated (e.g. diethylpyrocarbonate (DEPC)-treated) reagents. Reaction mixtures for in vitro transcription contain 400 ng of purified PCR product, 4 µl of 5× T7 RNA polymerase buffer (Gibco-BRL, Gaithersburg, Maryland, USA), 2 µl of dithiothreitol (100 mM), 0.5 µl of RNase inhibitor (RNAsin 40 U/µl, Promega), 1 µl of T7 RNA polymerase (40 U/µl) (GibcoBRL), 1 µl of ATP (10 mM), 1 µl of CTP (10 mM), 1 µl of GTP (10 mM), 0.5 µl of UTP (10 mM) and 1 µl of Cy3-UTP or Cy5-UTP (5 mM) in a 1.5 ml microcentrifuge tube and are incubated at 37°C for 4 h. Immediately after the incubation, in vitro-transcribed RNA is purified using the RNeasy kit (Qiagen) according to the manufacturer’s instructions. In the final step of the purification protocol, RNA is eluted from the column with 50 µl of double-distilled water. The RNA yield and dye incorporation rate can be measured spectrophotometrically (see Methods for PhyloChips). Subsequent fragmentation of RNA is initiated by adding 10 mM ZnCl2 and 25 mM Tris–HCl (pH 7.4) at 60°C for 30 min and finally stopped by adding 10 mM EDTA (pH 8.0) and cooling on ice. After addition of 1 µl of RNase inhibitor (RNAsin 40 U/µl), fragmented and labelled RNA targets can be stored at –20°C. For quality control reasons, target RNA can be analysed by polyacrylamide or agarose gel electrophoresis.

Reverse hybridization on oligonucleotide FGAs In order to allow for active mixing of the hybridization solution, hybridization is carried out in a custom-tailored aluminium block used as an insert for a temperature-controlled Belly Dancer (Stovall

33

Life Science, Inc., Greensboro, North Carolina, USA). HybriWell (Grace BioLabs, Inc., Bend, Oregon, USA) stick-on hybridization chambers (200 µl volume) are applied onto the slides and must fully cover the printed microarray area. Thereafter, assembled slides are pre-heated at 55°C on top of the hybridization block. For each hybridization, 124 µl of DEPC-treated water, 2 µl of 10% SDS, 4 µl of 50× Denhardt’s reagent (Sigma), 60 µl of 20× SSC (3 M sodium chloride, 0.3 M sodium citrate, pH 7.0) and 10 µl of target RNA (corresponding to ∼400 ng of RNA) are incubated in a 1.5 ml reaction tube at 65°C for 1–15 min. Pre-heated hybridization mixtures are pipetted onto the microarray surface via one of the ports in the stick-on hybridization chamber (note that air bubbles should be avoided). The ports are subsequently sealed with seal spots (Grace BioLabs) and the microarrays are hybridized overnight at 55°C in the pre-heated Belly Dancer at 30–40 r.p.m. circulation and maximum bending (∼10°). Upon hybridization, HybriWell chambers must be removed and slides are immediately immersed in the first washing buffer (2× SSC and 0.1% (w/v) SDS). Sequential washing is performed at constant temperature (∼22°C) and in buffers with decreasing salt concentrations, i.e. increased stringency. For the initial wash, slides are vigorously agitated in 2× SSC and 0.1% (w/v) SDS for 5 min, followed by two washes in 0.2× SSC for 5 min each, and a final wash in 0.1× SSC for 5 min. After drying of the microarray using an airgun with a cotton wool filter, slides are stored in the dark at room temperature prior to scanning.

FGAs of importance for soil ecology and selected applications Most of the FGAs published to date consisted of oligonucleotide probes and were mainly designed for method development and performance evaluation (Wu et al., 2001; Denef et al., 2003; Taroncher-Oldenburg et al., 2003; Tiquia et al., 2004). While these studies gave important insights into the capacity and current limits of the FGA technique for microbial ecology research, examples of FGA



34

A. Loy et al.

application with environmental samples remain few. Recently, a microarray carrying 1662 oligonucleotide probes (50 nucleotides in length) for genes involved in aerobic and anaerobic degradation processes and resistance to metals was manufactured and applied to profile microbial communities in selected contaminated and pristine soil samples (Rhee et al., 2004). Real-time PCRbased quantification of representative genes was performed in parallel and generally correlated well with the signal intensities of the respective probes on the microarray. However, some of these probes had SNRs below the threshold value for a positive signal (Rhee et al., 2004), confirming that enhanced detection sensitivity would improve the applicability of FGAs for surveying conspicuous genes/microorganisms in a complex environmental DNA background (Denef et al., 2003). An in silico and empirically evaluated FGA based on 68 short oligonucleotide probes (18–28 nucleotides in length) is available for environmental screening of methane-oxidizing bacteria (MOB, methanotrophs), a guild unified by the ability to use methane as a carbon and energy source (Bodrossy et al., 2003; Stralis-Pavese et al., 2004). The probes target pmoA, coding for the 27 kDa subunit of particulate methane monooxygenase (pMMO) which catalyses the first step in the methane oxidation pathway and is present in almost all known methanotrophs (Hanson and Hanson, 1996; Knief et al., 2003; Theisen and Murrell, 2005). Because oxic soil systems such as landfill sites constitute an important sink for the greenhouse gas methane due to the beneficial activity of MOB, the pmoAtargeted FGA was applied to profile MOB diversity at various soil depths in 24 lysimeters simulating different landfill site conditions (e.g. different plant covers, presence and absence of methane and carbon dioxide) (Stralis-Pavese et al., 2004). While type Ib MOB (genus Methylocaldum) appeared to thrive in almost all samples analysed, strong signals of probes targeting some type II MOB indicated high abundance of members of the Methylocystis group only

in methane-consuming lysimeters (Fig. 2.4). This study nicely demonstrated the high throughput capacity of DNA microarrays, rendering large-scale (> 100 samples were analysed in total) comparisons of different samples feasible in terms of time and effort.

Potential of FGAs for microbial population transcriptomics Given the high parallelism achievable with one analysis, DNA microarrays theoretically push the door open for surveying the metatranscriptome, i.e. the transcriptome of the whole microbial community in an ecosystem. The concept seems simple: extraction of mRNA from an environmental sample, conversion of the mRNA into fluorescently labelled cDNA via reverse transcriptase activity, and subsequent detection and differentiation of the labelled transcriptome on a DNA microarray. However, while microarray-based transcriptome analysis on a genomic level is routinely performed for single organisms, the extension to complex microbial communities in the environment is an extraordinarily demanding challenge. Although a first step in the right direction has already been made (e.g. a test FGA enabled the detection of catabolic gene transcripts in a microbial community from a pulp mill effluent (Dennis et al., 2003)), limited knowledge about the enormous metagenomic complexity (Tringe et al., 2005) currently constrains the application and interpretation of the results of such an approach in soils and in other complex ecosystems. Nevertheless, a realizable task in the near future is to focus the expression analysis on a certain habitat and on selected functional genes. To accomplish this, a comprehensive functional gene(s) sequence database for the habitat of interest (established, for example, by clone library surveys) serves as the basis for the development of a ‘habitat-specific’ oligonucleotide FGA, which in principle should allow the identity of the targeted microorganisms and the expression status of their functional genes to be revealed.




35

a

Type la

Type lb

Type ll

M

RA 14 LP 21

ps

a .c

M os tM O B

pmoA-targeted oligonucleotide probes

Soil depth

1.0

10 cm 20 cm 30 cm 50 cm 60 cm 10 cm 20 cm 30 cm 50 cm 60 cm

0.8 0.6 0.4 0.2

GA+

G+

Normalized signal intensity

10 cm 20 cm 30 cm 50 cm

M+

60 cm 10 cm 20 cm 30 cm 50 cm 60 cm 10 cm 20 cm 30 cm 50 cm 60 cm

0.0

P+

BS+

Fig. 2.4. Application of a pmoA-targeted oligonucleotide FGA for diversity surveys of methane-oxidizing bacteria (MOB) in a landfill site simulating lysimeters gassed with methane (Stralis-Pavese et al., 2004). Normalized signal intensities of selected probes (columns) and soil types/depths (rows) of replicate analyses are shown in grey scales. The signal intensity of each probe was first normalized to a positive control probe on the same microarray (to account partly for slide-to-slide variation) and subsequently to an experimentally determined reference value for this probe (to account partly for inherent signal intensity differences among different probes). GA, grass–alfalfa cover; G, grass cover; P, poplar cover; M, Miscanthus cover; BS, bulk soil with no plants; +, lysimeter received methane.

Summary and Outlook Microarrays for microbial community analyses represent powerful screening tools even at their currently incomplete state of

development, and are thus invaluable high capacity instruments in the molecular kitchen of microbial ecologists. Bearing in mind that biological and methodological variability constrains absolute quantitative



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measurements, and detection sensitivity might be low, microarray-based diversity surveys need to be extended by complementary techniques such as molecular and phylogenetic inventorying via clone libraries (Loy et al., 2004), quantitative real-time PCR (Kolb et al., 2003) (note that quantitative fluorescence in situ hybridization in soil samples might be complicated by the high endogenous autofluorescence of soil material) or quantitative membrane hybridization (Hristova et al., 2000). Beyond their application for microbial diversity analysis,

microarrays can additionally be used to measure cellular activity and infer ecophysiological function of microbial populations in the environment by tracking isotopelabelled substrate utilization (Adamczyk et al., 2003) or metatranscriptome analysis (Dennis et al., 2003). Finally, we anticipate that development of habitat-specific microarrays based on encompassing sequence data sets from a habitat of interest will allow reliable monitoring of temporal and spatial changes in microbial community structure and function.

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Voordouw, G., Voordouw, J.K., Jack, T.R., Foght, J., Fedorak, P.M. and Westlake, D.W.S. (1992) Identification of distinct communities of sulfate-reducing bacteria in oil fields by reverse sample genome probing. Applied and Environmental Microbiology 58, 3542–3552. Voordouw, G., Shen, Y., Harrington, C.S., Telang, A.J., Jack, T.R. and Westlake, D.W.S. (1993) Quantitative reverse sample genome probing of microbial communities and its application to oil field production waters. Applied and Environmental Microbiology 59, 4101–4114. Vora, G.J., Meador, C.E., Stenger, D.A. and Andreadis, J.D. (2004) Nucleic acid amplification strategies for DNA microarray-based pathogen detection. Applied and Environmental Microbiology 70, 3047–3054. Wagner, M., Rath, G., Amann, R., Koops, H.-P. and Schleifer, K.-H. (1995) In situ identification of ammonia-oxidizing bacteria. Systematic and Applied Microbiology 17, 251–264. Wagner, M., Loy, A., Klein, M., Lee, N., Ramsing, N.B., Stahl, D.A. and Friedrich, M.W. (2005) Functional marker genes for identification of sulphate-reducing prokaryotes. Methods in Enzymology 397, 469–489. Wetmur, J.G. (1991) DNA probes: applications of the principles of nucleic acid hybridization. Critical Reviews in Biochemistry and Molecular Biology 26, 227–259. Whitehead, T.R. and Cotta, M.A. (1999) Phylogenetic diversity of methanogenic archaea in swine waste storage pits. FEMS Microbiology Letters 179, 223–226. Whitman, W.B., Coleman, D.C. and Wiebe, W.J. (1998) Prokaryotes: the unseen majority. Proceedings of the National Academy of Sciences of the USA 95, 6578–6583. Wilson, K.H., Wilson, W.J., Radosevich, J.L., DeSantis, T.Z., Viswanathan, V.S., Kuczmarski, T.A. and Andersen, G.L. (2002) High-density microarray of small-subunit ribosomal DNA probes. Applied and Environmental Microbiology 68, 2535–2541. Wind, T. and Conrad, R. (1995) Sulfur compounds, potential turnover of sulfate and thiosulfate, and numbers of sulfate-reducing bacteria in planted and unplanted paddy soil. FEMS Microbiology Ecology 18, 257–266. Wind, T., Stubner, S. and Conrad, R. (1999) Sulfate-reducing bacteria in rice field soil and on rice roots. Systematic and Applied Microbiology 22, 269–279. Woese, C.R. (1987) Bacterial evolution. Microbiological Reviews 51, 221–271. Wu, L., Thompson, D.K., Li, G., Hurt, R.A., Tiedje, J.M. and Zhou, J. (2001) Development and evaluation of functional gene arrays for detection of selected genes in the environment. Applied and Environmental Microbiology 67, 5780–5790. Wu, L., Thompson, D.K., Liu, X., Fields, M.W., Bagwell, C.E., Tiedje, J.M. and Zhou, J. (2004a) Development and evaluation of microarray-based whole-genome hybridization for detection of microorganisms within the context of environmental applications. Environmental Science and Technology 38, 6775–6782. Wu, Y., de Kievit, P., Vahlkamp, L., Pijnenburg, D., Smit, M., Dankers, M., Melchers, D., Stax, M., Boender, P.J., Ingham, C., Bastiaensen, N., de Wijn, R., van Alewijk, D., van Damme, H., Raap, A.K., Chan, A.B. and van Beuningen, R. (2004b) Quantitative assessment of a novel flow-through porous microarray for the rapid analysis of gene expression profiles. Nucleic Acids Research 32, e123. Yershov, G., Barsky, V., Belgovskiy, A., Kirillov, E., Kreindlin, E., Ivanov, I., Parinov, S., Guschin, D., Drobishev, A., Dubiley, S. and Mirzabekov, A. (1996) DNA analysis and diagnostics on oligonucleotide microchips. Proceedings of the National Academy of Sciences of the USA 93, 4913–4918. Zhou, J. (2003) Microarrays for bacterial detection and microbial community analysis. Current Opinion in Microbiology 6, 288–294. Zhou, J. and Thompson, D.K. (2002) Challenges in applying microarrays to environmental studies. Current Opinion in Biotechnology 13, 204–207. Zverlov, V., Klein, M., Lucker, S., Friedrich, M.W., Kellermann, J., Stahl, D.A., Loy, A. and Wagner, M. (2005) Lateral gene transfer of dissimilatory (bi)sulfite reductase revisited. Journal of Bacteriology 187, 2203–2208.



3

Metagenomics for the Study of Soil Microbial Communities Helen L. Steele1,* and Wolfgang R. Streit2,*

Molecular Enzyme Technology, Biofilm Centre, University of Duisburg-Essen, Lotharstrasse 1, D-47057 Duisburg, Germany

Introduction Soil is a highly complex and variable matrix comprising a wide range of habitats and supporting some of the most species-rich, biochemically diverse, microbial communities in nature. The activity and diversity of soil microbial communities fluctuate in response to alterations in the environmental conditions. While many of the reactions carried out by soil microbial communities have been identified, it is still not possible to assign specific processes to identified microorganisms within mixed species communities and thereby relate community structure to soil function (Nannipieri et al., 2003). Studies based on 16S rDNA/rRNA have extensively redefined and expanded our knowledge of soil microbial diversity and have started to reveal the extent of the uncultured fraction of the microbial world. Estimates of diversity have traditionally placed it in the range of 3000–11,000 genomes/g of soil, with < 1% being accessible through cultivation techniques (Torsvik and Øvreås, 2002; Curtis and Sloan, 2004). The most recent estimates, based on the reanalysis of community DNA reassociation kinetics

with improved computational methods, place diversity as much as two to three orders of magnitude higher than this (Curtis and Sloan, 2005; Gans et al., 2005). These provide a broad framework for soil microbial ecology studies and emphasize the overwhelming genomic dominance of the uncultured fraction. Pure culture analysis of soil microorganisms has revealed that they are a rich source of novel therapeutic compounds such as antibiotics (Raaijmakers et al., 1997), anticancer agents (Shen et al., 2002) and immunosuppressants (Skoko et al., 2005), as well as a wide range of biotechnologically valuable products (AcostaMartínez and Tabatabai, 2000; Ullrich et al., 2004; Inoue et al., 2005). However, there is a vast amount of information held within the genomes of uncultured microorganisms, and metagenomics is one of the key technologies that can be used to access and investigate this potential (Handelsman, 2004; Pettit, 2004; Streit and Schmitz, 2004; Streit et al., 2004). Metagenomics concerns the extraction, cloning and analysis of the entire genetic complement of a habitat (Handelsman, 1998); it allows the investigation of the wide diversity of individual genes and their products as well as analysis

*Corresponding authors; 1Phone: +49 203 379 4482, Fax: +49 203 379 4489, E-mail: [email protected]; +49 203 379 4382, Fax: +49 203 379 4489, E-mail: [email protected]

2Phone:

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Metagenomics for the Study of Soil Microbial Communities

of entire operons encoding biosynthetic or degradative pathways. Metagenomics also makes it possible to answer key ecological questions by enabling scientists to relate potential functions to specific microorganisms within multispecies soil communities. This chapter describes metagenomic methodologies, their applications in novel drug discovery and their contribution to advances in soil and rhizosphere ecology.

Methodology The heterogeneous nature of soil must be taken into consideration when deciding sampling strategy and size. A metagenomic library potentially contains DNA from all the genomes present in the original sample; many microscale alterations in diversity are combined and averaged, and diversity is presented as if for a homogeneous sample. One of the main factors to be considered when generating a metagenomic library is the extraction of DNA from a habitat. Whether or not a metagenomic library represents all microorganisms in a sample depends on the degree of cell lysis and the quality of DNA for cloning purposes. In practice, libraries contain a representative selection of DNA from most microorganisms present. As well as DNA from the microbial community, metagenomic libraries will also contain clones of free DNA from the soil (Niemeyer and Gessler, 2002). The choice of cloning strategy depends on whether the clones will be subjected to functional screening for a particular trait or analysed using a sequencing-based approach.

DNA extraction techniques When isolating DNA from the environment for construction of large insert metagenomic libraries, there are three main considerations: the first is that DNA should be extracted from as broad a range of microorganisms as possible so that it is representative of the original microbial population; secondly, it must not become too broken up

43

during the extraction procedure as high molecular weight DNA is required; and, thirdly, it must be free from contaminating substances which interfere with downstream processing such as restriction and ligation. Soil microbial communities are composed of a mixture of archaea, bacteria and protists displaying a diversity of cell wall characteristics and varying in their susceptibility to lysis (Kauffmann et al., 2004). Additionally, the microorganisms present in soil are typically in a starved state, which means that they are physically smaller and difficult to lyse; this has an influence on the size of the DNA fragments that can be extracted from them (Bertrand et al., 2005). Even though various kits are commercially available for DNA isolation from environmental samples, many laboratories have developed their own methods with the aim of optimizing extraction and reducing bias caused by unequal lysis of different members of the soil microbial community (Frostegård et al., 1999; Krsek and Wellington, 1999; Miller et al., 1999). There are direct, in situ, extraction techniques where the cells are lysed in the soil sample and then the DNA recovered, and indirect techniques where the cells are removed from the soil and then lysed for DNA recovery. A comparison of the direct and indirect techniques for analysis of diversity of bacterial subgroups present in soil indicated that there was no difference in the diversity spectrum obtained (Courtois et al., 2001). Normally, more DNA is obtained from direct lysis and extraction, but the purity is lower in comparison with indirect extraction. Soil is a particularly complex matrix containing many substances which can be co-extracted during DNA isolation. Humic acids are one example and they must be removed before the DNA can be processed further. To remove such unwanted compounds, a range of purification techniques has been developed. Sephadex G-200 spin columns have proven to be one of the best ways to remove contaminants from soil DNA (Miller et al., 1999). A pulsed-field electrophoresis procedure using a two-phase agarose gel, with one phase containing



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H.L. Steele and W.R. Streit

polyvinylpyrrolidone (PVPP), can also be used for removal of humics (Quaiser et al., 2002): the humic acids bind to the PVPP immobilized in the gel, and clean DNA fragments > 30 kb can be extracted. One way to improve the quality of the extracted DNA and to enhance the likelihood of detecting a clone possessing a specific trait is to use a pre-cultivation step. This alters and reduces the overall diversity of the original microbial community and at the same time enriches for microorganisms carrying the target trait (Entcheva et al., 2001). Pre-cultivation is the laboratory equivalent of taking environmental samples which have been exposed to conditions that naturally enhance for the target trait. Examples can be found in searches for lipase and esterase enzymes in sites contaminated with oils (Elend et al., 1966), and for thermostable enzymes in the sediments of hydrothermal springs (Rhee et al., 2005). The range of products obtained from metagenomic libraries generated with and without a pre-cultivation step was recently reported (Daniel, 2005).

Cloning strategies As noted above, the cloning strategy will depend on whether downstream analysis involves a functional screening or a purely sequencing-based approach (Fig. 3.1). The decision to generate small or large insert libraries depends on whether individual genes or gene products are the target of the study or if the aim is to detect entire operons encoding biosynthetic or degradative pathways. In many cases, the generation of large insert libraries is required in order to analyse the size, complexity and diversity of the soil metagenome. Large insert libraries from the soil metagenome have been generated using cosmids (Entcheva et al., 2001; Courtois et al., 2003), bacterial artificial chromosomes (BACs) (Rondon et al., 2000) or fosmids (Quaiser et al., 2002, 2003; Treusch et al., 2004). Generally, the size of the insert is between 27 and 45 kb, and libraries contain from 5000 to 36,000 clones. The largest amount of metagenomic DNA from a single soil type

(sandy soil) is stored in two libraries which contain, in total, 2.2 Gbp of DNA held within 55,710 clones (Quaiser et al., 2002, 2003; Treusch et al., 2004).

Clone analysis In order to be representative of the genomic diversity of the original sample, soil metagenomic libraries have to contain thousands of clones. Often the traits being searched for in the library are only carried by a small fraction of the original population, or only a subset of the population is of interest. Therefore, thousands of clones must be screened before a small number of positive ones can be detected. For example, in a screening of 1532 clones for agarolytic activity, only four positives were detected (Voget et al., 2003). Screening can involve the search for gene expression, function, or species of interest independent of gene expression. Screening Where only a specific subset of the microbial population is of interest, then it is possible to screen libraries for their specific 16S rDNA or 16S rRNA. The most commonly used screening method, which is independent of gene expression, is polymerase chain reaction (PCR)-based detection of the 16S rRNA-encoding gene. This has been applied successfully for the detection of clones containing archaeal DNA by using primers specific for archaeal 16S rDNA (Stein et al., 1996; Quaiser et al., 2002). A wider range of clones can be detected by developing primers for other genes which are specific to the target organisms or processes, as in the case of a population analysis in hydrothermal vent sites (Nercessian et al., 2005). A variant of the fluorescent in situ hybridization (FISH) method, called large insert library fluorescent in situ hybridization (LIL-FISH), has been developed for detection of 16S rRNA in metagenomic libraries (Leveau et al., 2004). This combines detection of gene expression with identification, using specific FISH probes, of the microorganism from which the cloned




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Environmental sample

DNA isolation from environment

Ligation into vectors

Expression vectors

or

Sequencing vectors

Transformation into E. coli or P. putida or S. lividans

Screening for desired clones or sequencing

Fig. 3.1.

Steps involved in generating a metagenomic library from an environmental sample.

DNA was obtained. Escherichia coli is the most commonly used host in the generation of metagenomic libraries. The presence of host E. coli cell rRNA means that only probes which do not hybridize with it can be used, otherwise all clones would give a positive signal. The potential to combine

this technique with fluorescence-activated cell sorting (FACS) would allow the screening of thousands of cells. One limitation of this method is that the cells have to be treated with paraformaldehyde prior to hybridization with the FISH probes, and this means that they are effectively dead and cannot be



46


cultured for further analysis. Fortunately, it is still possible to access the cloned DNA by PCR amplification after paraformaldehyde treatment (Wallner et al., 1997). As well as the application of a phylogenetic step to identify the spectrum of positive clones, it is also possible to differentiate between clones from previously identified and characterized microorganisms and those containing novel genes and operons from the uncultured portion of the microbial community. A microarray-based method for screening metagenomic libraries can be used for this purpose (Sebat et al., 2003). It is also possible to refine the search for novel clones by partial sequencing of positive ones followed by database searches to identify and exclude genes which have already been analysed and published (Ginolhac et al., 2004). Functional screening This offers a greater chance of finding completely new enzymes than the purely sequencing-based approach because the trait is detected first and the responsible gene is then characterized (Lorenz and Schleper, 2002). Functional screening is, however, limited by the need to express metagenomic genes in a heterologous background: the E. coli host cell. Recent studies, analysing 32 different genomes for the presence of expression signals which could function in an E. coli background, indicated that the level of metagenomic gene expression which could be achieved using this system was only 40% (Gabor et al., 2004a). Many genes have expression requirements which cannot be fulfilled by E. coli. To overcome this, and extend the range of functional screening, Streptomyces lividans and Pseudomonas putida can serve as alternative hosts (Martinez et al., 2004). S. lividans is a particularly useful host for functional screening of soil metagenomic libraries for novel polyketide synthase genes (Courtois et al., 2003) as well as for the detection of a range of other novel metabolites (Wang et al., 2000). Substrate-induced gene expression screening (SIGEX) has been developed for rapid identification of clones displaying

catabolic gene expression (Uchiyama et al., 2004). This method uses an operon trap gfp expression vector for generation of the metagenomic clones. The clones are then incubated in the presence of the target substrate, which acts as inducer, and positive clones are identified by FACS. This makes it possible to screen large metagenomic libraries for a specific phenotype in a very short time (Uchiyama et al., 2005). Sequencing approaches One feature for which metagenomic research is renowned is the scale of the sequencing projects that are required to cover the metagenome, the largest one to date being the sequencing and partial reconstruction of the metagenome of the Sargasso Sea (Venter et al., 2004). In this single project, 1.045 billion base pairs of sequence was analysed in terms of gene content, diversity and relative abundance of the microorganisms present. A total of 1800 genomic species were detected, of which 148 belonged to completely new bacterial phylotypes, and 1.2 million new genes were discovered. One of the main benefits of a large-scale project like this is that it reveals a great deal about the diversity of genes. For example, while the detection of a rhodopsin-like open reading frame in a marine bacterium proved for the first time that this phototrophic process was being carried out by bacteria in marine surface waters (Béjà et al., 2000), the sequencing of the Sargasso Sea metagenome revealed just how widespread this process is in diverse marine bacteria (Venter et al., 2004). Recently, it was shown that proteorhodopsin phototrophy is carried out by 13% of bacteria inhabiting marine surface waters (Sabehi et al., 2005). Considering marine environments have a much lower diversity than soils, the equivalent metagenomic analysis of soil would require an even larger effort. Bioinformatics One important consequence of metagenome sequencing projects has been the development




of new software programs to deal with the large quantities of data which they generate. Some programs, such as GenDB, were intended for annotation of prokaryotic sequences (Meyer et al., 2003). DOTUR software was used to determine whether a library contains sufficient genes for it to be considered representative of the diversity in the original microbial community (Schloss and Handelsman, 2005). It has been calculated that to analyse accurately only the 16S gene diversity of a soil sample, 10,000 sequences would have to be analysed, which is 100 times more than is currently the case (Schloss and Handelsman, 2005). With LIBSHUFF software, it is possible to determine whether differences between libraries of 16S genes arise by chance or if they are ecologically significant (Schloss et al., 2004). Many software programs have been developed in direct response to the data processing needs of metagenomic research. They enable scientists to evaluate fully the range of data and assess complex communities at the system level (Brooksbank et al., 2005).

Soil Metagenomics for Biotechnology Metagenomic analysis of uncultured microbial populations has started to reveal the wealth of products encoded therein which have potential for widespread applications in biotechnology, medicine, bioremediation and microbial ecology (Steele and Streit, 2005).

Biocatalysts for biotechnology One of the central themes of metagenomic research is the detection and characterization of novel biocatalysts with biotechnological applications. Some examples are certain enzymes involved in the breakdown or modification of lipid or polysaccharide compounds, as well as others catalysing steps in the synthesis of important therapeutic products such as vitamins and antibiotics (Table 3.1). Metagenomics is only starting to reveal the range and diversity of enzymes present in soil environments, and

47

at present many more have been detected than characterized (Table 3.1). As more enzymes are characterized, more information is made available about their levels of activity, substrates and potential product ranges. Microorganisms that have adapted to a variety of soil environments carry enzymes which catalyse the same reaction but with different activity optima. This natural variation can be investigated, and enzymes displaying the appropriate characteristics can be used directly for biotechnological applications. Alternatively, a number of enzymes displaying some of the required characteristics can be exploited for creation of the ideal biocatalyst by means of gene reassembly (Richardson et al., 2002).

Therapeutics Uncultured soil microorganisms constitute a rich resource for biosynthesis of medically important products such as antibiotics and anticancer drugs (Gillespie et al., 2002; Pettit, 2004). Correspondingly, they also contain a vast array of genomic information on medically important bacterial mechanisms such as antibiotic resistance (Riesenfeld et al., 2004). The increase in bacteria displaying resistance to antibiotics is starting to limit the range of application of these compounds. As more bacterial resistance mechanisms are discovered through metagenomics and elucidated, the development of compounds which inhibit resistance, effectively extending the functional range of antibiotics again, can be envisaged. While the detection and characterization of novel antimicrobial compounds have obvious therapeutic benefits, there is also a great deal of interest in elucidating the ecological role of antibiotics; for example, phenazines have been shown to function as electron shuttles for microbial mineral reduction (Hernandez et al., 2004). The search for novel antibiotics and antibiotic resistance mechanisms extends beyond soil communities to include metagenomic analysis of oral biofilms and bromeliad tank water (Diaz-Torres et al., 2003; Brady and Clardy, 2004).



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Table 3.1.


Soil metagenome-derived biocatalysts.

Enzyme

Clones* Characterization

Reference

Lipase/esterase 2/2 4/4

1/1 6/8 1/4 1/1

Polysaccharide modifying α-amylases

8/8 3/15

α-1,4 glucanases β-agarases

1/1 1/1 1/1 1/4

Cellulases

4/4

Antibiotic biosynthesis Amidases Polyketide synthases

N-acyltyrosine synthases

DNA restriction analysis to determine clone diversity Substrate specificity, molecular mass of lipase & temperature range of activity DNA sequence level Substrate specificity & molecular mass of lipases/esterases pH range and temperature range of enzyme activity & substrate specificity Molecular mass, pH range, temperature range and substrate specificity DNA restriction analysis to determine clone diversity Performed gene reassembly to create the optimal enzyme DNA sequence level pH range and substrate specificity DNA sequence level Molecular mass and temperature optima Temperature and pH optima

Rondon et al., 2000 Henne et al., 2000

Voget et al., 2003 Lee et al., 2004 Rhee et al., 2005 Elend et al., 2006

Rondon et al., 2000 Richardson et al., 2002 Voget et al., 2003 Yun et al., 2004 Voget et al., 2003 Voget et al., 2003 Healy et al., 1995

Biocatalytic potential and activity Enzyme activity Phylogenetic analysis and substrate prediction Genes were sequenced, N-acyltyrosines detected

Gabor et al., 2004b Courtois et al., 2003 Ginolhac et al., 2004

Terragines Turbomycin A & B Indirubin

Wang et al., 2000 Gillespie et al., 2002 MacNeil et al., 2001

10

Aminoglycoside and tetracycline resistance

Riesenfeld et al., 2004

7/7

Molecular and physiological characterization Substrate and product spectrum, thermostability Enzyme activity

Entcheva et al., 2001

6/6 13 3/40 11

Brady et al., 2004

Antibiotics 5 3 1

Antibiotic resistance genes

Vitamin biosynthesis Biotin Vitamin C: 2,5-diketoD-gluconic acid reductases Nicotinamide: nitrile hydratases

2/2 9/22

Eschenfeldt et al., 2001 Liebeton and Eck, 2004

Continued




Table 3.1.

49

Continued

Enzyme

Clones* Characterization

Oxidoreductases/dehydrogenases 4-hydroxybutyrate 5/36 dehydrogenase Alcohol oxidoreductase 16/24 Other biocatalysts Diol dehydratase Proteases

5/7 1/1

Nitrilases

1/200

Reference

Enzyme activity

Henne et al., 1999

Substrate spectrum

Knietsch et al., 2003b

Catalytic efficiency & stability Enzyme activity, molecular mass and pI Activity and substrate range

Knietsch et al., 2003a Gupta et al., 2002 DeSantis et al., 2002

*Number of characterized clones/the number of positive clones.

Bioremediation Soil microbial communities have a large capacity for bioremediation. This may be attributed to the diversity and genetic adaptability of bacteria in soil, and their range of metabolic strategies for utilization of all naturally occurring, and most anthropogenic, compounds (Díaz, 2004). Often, the most successful bioremediation strategies are based on stimulation of indigenous soil populations. The input of carbon from plant roots is one way to achieve this, although other compounds in rhizodeposits may also be responsible for the increase in microbial activity (Shaw and Burns, 2005). A comparison of the range of enzymes obtained from bacterial isolates with those obtained from the soil metagenome has revealed that most enzymes (and with them the greatest potential for bioremediation) are held within the unculturable fraction of the population (Marchesi and Weightman, 2003). Metagenomics, in combination with functional screening or functional gene PCR screening, is one of the best ways to gain information about the range of catabolic pathways in soil microbial communities and to understand better the natural process of bioremediation (Eyers et al., 2004; Erwin et al., 2005).

Assigning Functions to Soil Microorganisms Bacteria in the soil environment rarely exist as distinct monospecies populations, but

more commonly inhabit multispecies biofilms where complex metabolic cooperation and interdependency develop. One of the most significant contributions of metagenomics to the field of microbial ecology is the attribution of functions to specific microorganisms in natural mixed species biofilms. As mentioned earlier, it is possible to screen metagenomic libraries for the presence of clones containing large inserts of DNA which include the 16S rDNA specific to a target organism. In these cases, the 16S gene is used as a phylogenetic anchor, although other genes can also be used to extend the number of positive clones identified (Nercessian et al., 2005). Sequencing of surrounding genes on the clone can reveal information about the potential role of that microorganism in the environment. While the 16S sequence does not provide a definitive guide to physiological diversity, it is still highly useful as a phylogenetic anchor and it has yielded information about unculturable Archaea from marine environments (Stein et al., 1996; Moreira et al., 2004). Combining analysis of a 16S rRNA gene clone library with shotgun sequencing of metagenomic DNA, it was possible to reconstruct a low diversity mixed species biofilm and gain a wealth of information about the metabolic co-dependency and the potential roles of the individuals in the population (Tyson et al., 2004). Partial reconstruction of the Sargasso Sea metagenome also revealed a vast array of



50


genomic information which is still being processed and evaluated (Venter et al., 2004). This metagenomic approach is a successful strategy to obtain system level information about low diversity habitats. However, soil microbial communities are so highly diverse that in a recent study where 100 Mbp were sequenced, 150,000 reads resulted in < 1% showing sequence overlap (Tringe et al., 2005). Also, events such as horizontal gene transfer may make it practically impossible to reconstruct the complete soil metagenome accurately. While metagenomics can be used to assign potential function to microorganisms, gene expression and function must be confirmed through techniques such as stable isotope probing (Dumont and Murrell, 2005). Soil metagenomics has provided much information about the diversity and function of soil microbial communities, and encouraged the development of new cultivation systems for previously uncultured prokaryotes (Zengler et al., 2002).

Influence of environment on the genome – evolution of species In recent years, it has become clear that the level of microbial diversity extends beyond that defined by the 16S rDNA standard, with some microorganisms carrying identical 16S genes while at the same time displaying different phenotypes

(Rodríguez-Valera, 2002; Jaspers and Overmann, 2004; Hahn and Pöckl, 2005). Genomes are strongly influenced by the bacterial response and adaptation to their habitats (Fadiel et al., 2005). Sequencing of the metagenome of a habitat can provide insights into evolutionary processes occurring in mixed species biofilms (Allen and Banfield, 2005).

The Future of Metagenomics Through metagenomics, it is possible to access and investigate the complexity of all soil microorganisms, from the detection of novel therapeutic compounds to the elucidation of bacterial community genome evolution. Soil contains a mixture of prokaryotes, eukaryotes and viruses, but, to date, there are very few metagenomic studies covering viral and fungal soil communities (Anderson and Cairney, 2004; Edwards and Rohwer, 2005). As the prokaryotic and eukaryotic communities do not exist in isolation, but interact at many levels, metagenomic analyses of soil should be extended to include eukaryotes. Only then will it be possible to understand fully the array of complex interactions taking place in soils and to release the potential therein. Adaptation of functional screening methods to allow expression of eukaryotic genes and processing of their products could open up previously untapped sources for novel compounds and enzymes.

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4

In Vivo Expression Technology (IVET) for Studying Niche-specific Gene Expression by Plant- and Soil-colonizing Bacteria Hans Rediers1 and René De Mot2,* 1Hogeschool

voor Wetenschap & Kunst-De Nayer Instituut, Jan De Nayerlaan 5, B-2860 Sint-Katelijne-Waver, Belgium; 2Centre of Microbial and Plant Genetics, Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, B-3001 Heverlee, Belgium

Introduction The study of microorganisms under welldefined laboratory conditions (‘in vitro’) rendered a wealth of information on physiological and genetic mechanisms that contribute to ecological fitness. The value of such simplified in vitro approaches is evident from several studies that analysed the bacterial response to conditions mimicking, in part, those encountered in complex environments such as the rhizosphere or phyllosphere, or upon infection of plants. For instance, the behaviour of saprophytic, symbiotic and phytopathogenic bacteria was studied in vitro by analysing their response to plant root exudates (Peters et al., 1986; Engstrom et al., 1987; Van Bastelaere et al., 1993; Dunn et al., 2003; Fischer et al., 2003; Miché et al., 2003). Also, the response of bacteria to other inhabitants of the rhizosphere has been studied in vitro (Smith et al., 1999; Whipps, 2001; Dunn et al., 2003). The emergence of genome-scale and high throughput techniques, such as transcriptomics, proteomics and metabolomics, has created a new impulse to study bacterial behaviour

in well controlled laboratory conditions (Schoolnik, 2002; Mostertz et al., 2004; Palma et al., 2004), and some of these are covered in other chapters. In the past 15 years, several techniques that enable the study of bacteria in complex environmental niches were devised (Lee and Camilli, 2000; Rainey and Preston, 2000). Complementary strategies, such as differential fluorescence induction (DFI) (Valdivia and Falkow, 1997), signature-tagged mutagenesis (STM) (Hensel et al., 1995), differential display using arbitrarily primed polymerase chain reaction (AP-PCR) (McClelland et al., 1995; Fislage, 1998), subtractive and differential hybridization (Ito and Sakaki, 1996, 1997) and selective capture of transcribed sequences (SCOTS) (Graham and Clark-Curtiss, 1999), are reviewed elsewhere (Chiang et al., 1999; Handfield and Levesque, 1999; Hautefort and Hinton, 2000; Mahan et al., 2000). In this chapter, in vivo expression technology (IVET) as a molecular tool to study differential gene expression of plant- and soil-colonizing bacteria is discussed. IVET is a high throughput promoter-trapping

*Corresponding author; Phone: +32 16 32 9681; Fax: +32 16 32 1963, E-mail: [email protected] ©CAB International 2006. Molecular Approaches to Soil, Rhizosphere and Plant Microorganism Analysis (Cooper and Rao)


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strategy that enables the identification of bacterial genes showing elevated levels of expression in a particular niche and has been applied to soil, the rhizosphere or during interaction with plant hosts. Genes that show specific upregulation in a particular niche are likely to be involved in adaptation to these living conditions and to contribute to ecological performance in that environment (Preston et al., 2001).

IVET Methodologies Development of IVET More than 15 years ago, Osbourn et al. (1987) were the first to use an in vivo promotertrapping system, consisting of a promoterless chloramphenicol resistance gene, to isolate Xanthomonas campestris genes upregulated during infection of turnip seedlings. Later, this technique was modified and the term in vivo expression technology (acronym IVET) was coined (Mahan et al., 1993). These authors used an auxotrophic Salmonella purA mutant in combination with a promoter trap containing the promoterless purA gene. Promoters specifically induced during mice infection were selected on the basis of their ability to drive expression of purA provided on the promoter trap, thereby complementing the purine auxotrophy. In general, any gene encoding an essential growth factor (egf ) necessary to sustain growth in the studied niche can be used to design an auxotrophy-based IVET selection strategy (Fig. 4.1). DNA fragments are randomly cloned into the promoter trap that contains a promoterless egf gene, transcriptionally linked to a reporter gene (rep). Only when an active promoter is cloned in the promoter trap is the egf gene expressed and the conditionally compromised strain able to grow in the wild. Bacteria harbouring active promoters are subsequently isolated from the environmental niche after a period of selection pressure. Constitutive promoters are eliminated by screening for in vitro reporter gene activity.

Initially, IVET was adapted and used to study various animal pathogens (reviewed in Mahan et al., 2000; Rediers et al., 2005), but in recent years it was also successfully applied to study the soil-dwelling and plantassociated bacteria Pseudomonas fluorescens (Rainey, 1999; Gal et al., 2003; Silby and Levy, 2004), Pseudomonas stutzeri (Rediers et al., 2003) and Pseudomonas putida (Ramos-González et al., 2005), the legume symbiont Sinorhizobium meliloti (Oke and Long, 1999), and the phytopathogens Ralstonia solanacearum (Brown and Allen, 2004), Erwinia chrysanthemi (Yang et al., 2004), Pseudomonas syringae pv. syringae (Marco et al., 2003) and P. syringae pv. tomato (Boch et al., 2002). That IVET has become a very popular molecular tool to study bacterial life in complex environments is reflected in the increasing number of papers that have been published since that of Mahan et al. (1993) (Fig. 4.2). Several IVET studies revealed new insights into the behaviour of bacteria in the wild. In follow-up research, the role of some novel IVET-identified genes was elucidated.

IVET selection strategies The choice of egf gene to be mutated is of primary importance. It has to be ascertained that the mutation is not complemented by niche-derived metabolites, such as those originating from a plant host or associated microorganisms. In addition, the nature of the auxotrophic mutation can influence the strength of the selection in the wild. For instance, when the generated auxotrophy is only lethal for actively growing cells but not impairing cell survival, selection can be adjusted by altering the time lapse between inoculation and re-isolation (Rainey and Preston, 2000). Whether low or transiently expressed genes will be detected depends on the strength of the selection regime. While the choice of egf gene is critical, as discussed above, the only prerequisite of the transcriptionally linked reporter gene is the ability for easy monitoring in vitro.



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G

5 4

Fig. 4.1. Schematic representation of the basic IVET strategy. IVET consists of two components: a conditionally compromised strain that is mutated in a gene encoding an essential growth factor (egf ) and a promoter trap carrying a promoterless egf gene transcriptionally linked to a reporter gene (rep). Bacterial DNA of the target strain is cloned randomly into the promoter trap (pIVET), in front of the promoterless egf gene (step 1). Subsequently, these constructs are integrated into the chromosome of the egf mutant strain (step 2). Complementation of the egf mutation can take place in strains that carry a promoter active in a specific ecological niche (pivi) , for instance in a biofilm (A), during colonization of soil-borne fungi (B), in a polluted environment (C), in bulk soil (D) or during interaction with a plant host, e.g. upon infection (E), symbiosis (F) or root colonization (G) (step 3). These strains are re-isolated from the environment, and promoters that are equally active outside this environment are eliminated based on the in vitro reporter gene activity (step 4). Strains bearing promoters that are upregulated in the wild are subjected to a second IVET screening to eliminate false positives (step 5). The selected gene fusions are recovered and sequenced to identify the trapped promoters.



58


90

Cumulative number

80 70 60 50 40 30 20 10 0 1993–1996

1997–1999

2000–2002

2003–2005

Period

In several IVET studies, lacZ, encoding β-galactosidase (Silhavy and Beckwith, 1985), was used. However, to study bacteria in a close relationship with a plant host, the gusA reporter gene, encoding β-glucuronidase (Jefferson, 1989), is perhaps more suitable, because many plants possess an endogenous β-galactosidase that can interfere with the analysis of bacterial gene expression. Improved reporters that encode luminescent (e.g. lux) or fluorescent proteins (e.g. gfp) enabled the in situ monitoring of gene expression in single cells (Gould and Subramani, 1988; Chalfie et al., 1994). The green fluorescent protein (GFP) was used in IVET studies of E. chrysanthemi, P. syringae and P. stutzeri (Marco et al., 2003; Rediers et al., 2003; Yang et al., 2004). However, the disadvantage of such reporter genes is the need for specialized equipment to monitor their activity. Variations of the auxotrophy-based selection strategy were introduced to study bacterial gene expression in soil and plant environments by using alternative egf genes: panB, involved in pantothenate biosynthesis (Rainey, 1999); dapB or asd, involved in biosynthesis of the lysine and peptidoglycan precursor diaminopimelate (Gal et al., 2003; Rediers et al., 2003; Espinosa-Urgel and Ramos-González, 2004; Silby and Levy, 2004; Zhang et al., 2004a); metXW, necessary for methionine biosynthesis (Marco

Fig. 4.2. Cumulative number of research papers on IVET studies of animal pathogens (white bars), and of other, mainly plantassociated, bacteria (black bars). Also shown are the number of publications reporting the further (in-depth) functional characterization of IVET-identified genes (grey bars).

et al., 2003); and trpEG (Brown and Allen, 2004), involved in tryptophan biosynthesis. To explore genes upregulated during oomycete colonization by P. putida, pyrB, necessary for de novo biosynthesis of pyrimidine nucleotides, was used (Lee and Cooksey, 2000). However, since it is not always possible to generate a suitable conditionally compromised strain, other IVET selection strategies were devised (reviewed in Rediers et al., 2005). The antibiotic resistance-based IVET selection requires a promoter trap containing a promoterless antibiotic resistance gene instead of an egf gene. Target genes are isolated simply by administering the antibiotic to the host. However, drug administration to growth media often affects plant growth and perturbs the ecological niche of the microorganism under study. Especially for bacteria infecting plants, antibiotic resistance-based selection is rarely suitable since antibiotics are seldom translocated throughout all plant tissues. This explains why the antibiotic resistance-based IVET selection strategy was almost exclusively used to study animal pathogens (Rediers et al., 2005), the only exception being the ‘IVET-avant-la lettre’ to isolate X. campestris genes upregulated during turnip infection (Osbourn et al., 1987). An influential IVET modification involves the recombinase-based IVET (RIVET) selection strategy (Camilli and Mekalanos, 1995).




In this case, a promoterless site-specific DNA resolvase is cloned in the promoter trap. Active promoters drive expression of the DNA resolvase, which mediates excision from the genome of an antibiotic resistance cassette that is flanked by the resolvase recognition sites. Promoter activity in the wild can be analysed by screening the bacteria for the loss of antibiotic resistance after isolation from the environmental niche. Until now, RIVET has only been used to analyse differential gene expression in animal pathogens (Rediers et al., 2005). For the plant symbiont S. meliloti and the phytopathogen P. syringae pv. tomato, system-specific IVET selection strategies were designed to identify genes that are upregulated during a particular stage of the bacterium–plant interaction (Oke and Long, 1999; Boch et al., 2002). In these cases, active promoters drive expression of a gene encoding an ‘essential interaction factor’ (eif ) that is required at a specific stage of the interaction. Only fusions containing active promoters sustain a successful interaction and can be isolated by screening for a scorable host phenotype such as symbiosis (nitrogenfixing nodules) or disease. A bacA-based IVET selection strategy was designed to isolate S. meliloti genes that are upregulated after the initiation of nodulation but before nitrogen fixation takes place (Oke and Long, 1999). BacA is necessary for differentiation of S. meliloti into nitrogen-fixing differentiated cells (bacteroids) that are packed within the nodule (Ichige and Walker, 1997). In this system, nodule organogenesis can only be completed when a fused promoter drives bacA expression in an appropriate spatio-temporal pattern. Using this approach, several genes known to be involved in the nodulation process were isolated, thus validating the strategy. Moreover, genes that were previously not known to play a role in nodulation were isolated, thereby expanding the knowledge of the early stages of symbiosis. Likewise, a system-specific IVET selection strategy was devised to explore P. syringae pv. tomato gene expression during the early stages of Arabidopsis infection, using an hrcC knockout strain (Boch et al., 2002).

59

hrcC encodes an essential component of the type III secretion system (TTSS). Since a functional TTSS is required for P. syringae to infect susceptible plant hosts, only promoters that are active during the early stage of plant infection are able to drive expression of the promoterless hrcC gene provided on the promoter trap, thereby resulting in plant pathogenesis. The isolation of numerous known hrp and hrc genes demonstrated that this strategy indeed selects for promoters that are active in the early stages of the infection process. Again, this approach enabled the isolation of novel virulence determinants. The ‘habitat-inducible rescue of survival’ (HIRS) is another system-specific IVET selection strategy used to study the epiphytic colonization and infection of bean leaves by the phytopathogen P. syringae pv. syringae (Marco et al., 2003). This IVET relies on a metXW mutant that displays normal growth in humid conditions but is auxotrophic for methionine when exposed to low humidity. Therefore, the auxotrophy-based selection becomes active when plants are transferred from high to low humidity conditions, and hence the timing and strength of in situ selection pressure can be easily altered. In contrast to the system-specific selection strategies mentioned above where selection targets genes that are upregulated during the early stages of infection or symbiosis, this technique rather traces genes that enable adaptation to and survival in the new environment. By initially growing the plants in moist conditions, the metXW mutant is able to grow and multiply in the plant host without selection pressure, ensuring a sufficient number of bacteria in planta before the selection regime starts. The differential gene expression of the plant pathogen E. chrysanthemi during spinach infection was analysed by means of the so-called ‘GFP-based IVET leaf array’. This approach is based on differential fluorescence resulting from the expression of a promoterless gfp gene present on the promoter trap (Yang et al., 2004). Actually this is a variant of DFI rather than of IVET since no positive selection for promoters in the wild is involved.



60


Benefits and shortcomings of IVET The major advantage of IVET is the powerful positive selection of genes that are specifically induced by environmental parameters, and this in a high throughput and genomewide manner. Since IVET does not imply the disruption of target genes, upregulated genes that are also essential for survival in the wild can be isolated, in contrast to mutagenesis techniques. Moreover, IVET does not require sophisticated and expensive equipment (in contrast to, for example, DFI) and can be very easily developed using standard molecular biology techniques. Application of IVET is not hampered by the lack of genome sequence data. For instance, P. stutzeri A15 is a rice root colonizer with only a few known DNA sequences, but IVET was successfully applied to isolate genes that are upregulated in the rice rhizosphere (Rediers et al., 2003). However, the availability of a (draft) genome sequence of the target microorganisms, or close relatives, certainly facilitates subsequent characterization of trapped promoters and their cognate genes. In this case, partial DNA sequences of both ends of the transcriptional fusion of interest suffice to identify the open reading frame(s) and corresponding promoter(s) that are present. Even so, the re-isolation of the integrated plasmids from IVET-positive clones, using standard molecular cloning procedures, can sometimes be tedious, and therefore alternative methods were developed. Recovery of the desired transcriptional fusion by transduction is a possibility, but is not widely applicable due to the lack of suitable phages. Conjugative cloning, or ‘retro-transfer’, is a technique that can be more widely used to rescue chromosomally integrated plasmids. In this approach, genetic loci necessary for mobilization of the integrated plasmid into a suitable Escherichia coli host are supplied by a helper plasmid (Rainey et al., 1997). There is virtually no limitation to the complexity of the ecological niche that constitutes the natural habitat of the bacterium under study (Rediers et al., 2005). IVET was applied to study elevated gene expression during rhizosphere colonization of sugar beet

(Rainey, 1999; Gal et al., 2003), rice (Rediers et al., 2003) and maize (Ramos-González et al., 2005); during epiphytic survival on bean leaves (Marco et al., 2003); during infection of spinach leaves (Yang et al., 2004), Arabidopsis (Boch et al., 2002), turnip (Osbourn et al., 1987) and tomato plants (Brown and Allen, 2004); during alfalfa symbiosis (Oke and Long, 1999); upon growth and survival in soil (Silby and Levy, 2004); and during bacterial colonization of a phytopathogenic oomycete (Lee and Cooksey, 2000). However, some aspects of IVET prevent it from entering the Valhalla of molecular techniques. First, it is not possible to isolate niche-repressed promoters. Furthermore, the subset of isolated genes depends on the synthetic growth medium used to eliminate constitutive promoter activity and on the degree of the selection pressure that is applied in the wild. A too stringent selection pressure will prevent the isolation of false-positive clones, but will miss weakly or transiently expressed promoters. Also, after isolation of IVET genes, the physiological role has to be elucidated by mutational analysis of each individual gene. On the other hand, expression analysis of the isolated genes is facilitated by the presence of the reporter gene in the promoter trap. By assessing the reporter gene activity, the niche-specific upregulation of gene expression can be quantified. The reporter gene also enables the visualization of gene expression in situ. For instance, the IVET study of rice root colonization by P. stutzeri allowed the identification of a putative antisense transcript (Rediers et al., 2003). Histochemical staining revealed that this transcriptional fusion displays higher expression at the tips of young lateral roots and suggests local upregulation of the transcript (Fig. 4.3).

IVET-identified Genes of Plant- and Soil-colonizing Bacteria IVET-identified genes that emerged from studies of soil and plant environments are compiled in Table 4.1 and assigned to nine




61

Fig. 4.3. Visualization of rice root-colonizing P. stutzeri A15 carrying an IVET-identified fusion with spatially confined expression. Histochemical staining of rice roots colonized by a P. stutzeri A15 IVET clone, containing a transcriptional fusion that encodes a (putative) α-mcp antisense transcript. Expression of the transcriptional fusion, and thus of the β-glucuronidase reporter gene, results in blue staining. The microscopic image of a root with lateral roots shows that this transcriptional fusion displays higher expression at the tips of lateral roots (darker areas, indicated with arrows) and suggests spatial upregulation of the transcript (adapted from Rediers et al., 2003).

Table 4.1.

Overview of IVET-isolated genes of soil- and plant-colonizing bacteria.

Gene or fusion

Protein function or possible role

Organism (host)

CLASS I: GENES INVOLVED IN MOTILITY OR CHEMOTAXIS Flagella/pili biosynthesis fliF Flagellar M-ring protein P. fluorescens (sugar beet) fliO Flagellar assembly protein P. putida (maize) α-fliM

Rsc0726

Antisense transcript – flagellar C-ring protein (switch complex) Type IV fimbrial biogenesis (PilW-related protein)

Chemotaxis Trg Chemotaxis receptor protein (MCP) cheR Chemotaxis protein (methyltransferase) cheY Two-component response regulator Antisense transcript – α-mcp chemotaxis receptor protein (MCP)

P. stutzeri (rice)

Reference

Gal et al., 2003 Ramos-González et al., 2005 H. Rediers, unpublished results

R. solanacearum (tomato)

Brown and Allen, 2004

E. chrysanthemi (spinach)

Yang et al., 2004


Yang et al., 2004



P. stutzeri (rice)

Rediers et al., 2003

Continued



62


Table 4.1.

Continued

Gene or fusion


Organism (host)

CLASS II: GENES INVOLVED IN NUTRIENT SCAVENGING Metal ion acquisition Siderophore-dependent iron uptake pvsA Pyoverdine synthetase P. fluorescens (sugar beet) ufrA Siderophore receptor P. fluorescens (sugar beet) Siderophore-independent iron uptake yfeA Periplasmic (chelated) E. chrysanthemi (spinach) iron-binding protein of ABC transporter hmuT Periplasmic hemin-binding E. chrysanthemi (spinach) protein of ABC transporter hmuU Permease of hemin ABC E. chrysanthemi (spinach) transporter hmuS Hemin-degrading protein E. chrysanthemi (spinach) Sodium/potassium uptake nex10 Putative potassium channel S. meliloti (nodules) Rsc1951 Solute/sodium symporter R. solanacearum (tomato) Phosphate acquisition phoB Response regulator of two-component system phoU Regulator of transport system pstB ATP-binding protein of high-affinity ABC transporter phnC ATP-binding protein of ABC transporter phnD Periplasmic protein of ABC transporter ppk Polyphosphate kinase iiv8 Antisense transcript – polyphosphate kinase Sulfate acquisition rap2-16 Periplasmic binding protein of ABC transporter Amino acid acquisition Rsp1575 Periplasmic binding protein of ABC transporter ipx46 ATP-binding component of ABC transporter livMH Permease for high-affinity transport of branched amino acids hutT Permease for histidine uptake putP Sodium/proline symporter

iiv6

Sodium/serine symporter

Reference

Gal et al., 2003 Gal et al., 2003 Yang et al., 2004

Yang et al., 2004 Yang et al., 2004 Yang et al., 2004 Oke and Long, 1999 Brown and Allen, 2004


Yang et al., 2004

E. chrysanthemi (spinach) E. chrysanthemi (spinach)

Yang et al., 2004 Yang et al., 2004


Yang et al., 2004


Yang et al., 2004

E. chrysanthemi (spinach) P. fluorescens (soil)

Yang et al., 2004 Silby and Levy, 2004

P. putida (maize)

Ramos-González et al., 2005

R. solanacearum (tomato) P. syringae (Arabidopsis)


P. fluorescens (sugar beet)

Rainey, 1999


Rainey, 1999

P. putida (maize)

Ramos-González et al., 2005 Silby and Levy, 2004

P. fluorescens (soil)

Boch et al., 2002

Continued




Table 4.1.

Continued

Gene or fusion


Sugar uptake nagE N-acetylglucosamine phosphotransferase system (IIABC) Rsp0536 Transmembrane sugar-proton symporter rhiT Rhamnogalacturonide transporter yicJ Sodium galactoside symporter Uptake of organic acids dctD Two-component response regulator of C4-dicarboxylate transport dctS Two-component sensor of C4-dicarboxylate transport Rsc1598 Two-component sensor (DctS homologue) lldP L-lactate permease kgtP2 α-ketoglutarate permease iiv20 PcaT-like dicarboxylate transporter (major facilitator superfamily) Uptake of miscellaneous compounds potF2 Putrescine transport protein rap2-21 γ-aminobutyrate permease Transport of unknown substrates Structural components of ABC transporters orf2 Rsc1376 pup-31 rhi-37 Periplasmic-binding proteins pup-59 Rsc0044

63

Organism (host)

Reference


Yang et al., 2004



E. chrysanthemi (spinach) Yang et al., 2004 E. chrysanthemi (spinach)

Yang et al., 2004

P. syringae (bean)

Marco et al., 2003


Rainey, 1999



P. fluorescens (sugar beet) R. solanacearum (tomato) P. fluorescens (soil)

Gal et al., 2003 Brown and Allen, 2004 Silby and Levy, 2004

P. fluorescens (sugar beet) P. putida (maize)

Gal et al., 2003 Ramos-González et al., 2005

P. putida (Phytophthora) R. solanacearum (tomato) E. chrysanthemi (spinach) P. fluorescens (sugar beet)

Lee and Cooksey, 2000 Brown and Allen, 2004 Yang et al., 2004 Gal et al., 2003

E. chrysanthemi (spinach) R. solanacearum (tomato)

Yang et al., 2004 Brown and Allen, 2004

CLASS III: GENES OF CENTRAL INTRACELLULAR METABOLISM Intermediary metabolism aceE Pyruvate dehydrogenase P. putida (maize) subunit aceE Pyruvate dehydrogenase P. syringae (Arabidopsis) subunit aceA Isocitrate lyase (glyoxylate P. putida (maize) pathway) fumC Fumarase (TCA cycle) R. solanacearum (tomato) ppc Phosphoenolpyruvate E. chrysanthemi (spinach) carboxylase (TCA cycle)

Ramos-González et al., 2005 Boch et al., 2002 Ramos-González et al., 2005 Brown and Allen, 2004 Yang et al., 2004

Continued



64


Table 4.1.

Continued

Gene or fusion


pckA

Phosphoenolpyruvate carboxykinase (gluconeogenesis) 2,3-biphosphoglycerateindependent phosphoglycerate mutase (Embden–Meyerhoff pathway) 6-phosphogluconate dehydrogenase (pentosephosphate pathway)

pgm

gnd

Fatty acid metabolism phbA Acetyl-CoA acetyltransferase (synthesis) fadD Long-chain fatty acid CoA ligase (degradation) iiv3 Acyl-CoA synthetase (degradation) fadE Acyl-CoA dehydrogenase (degradation) iiv4 Antisense transcript – 2,4-dienoyl-CoA reductase (degradation) Phospholipid metabolism eutR Ethanolamine operon regulator dgk Diacylglycerol kinase (diacylglycerol recycling) Sugar metabolism xylA Xylose isomerase xylR Xylose operon regulator (AraC family) glgA Glycogen synthase glgX Glycogen operon protein iiv11 Xylanase/chitin deacetylase Amino acid synthesis argG Argininosuccinate synthase (arginine biosynthesis) trpD3 Anthranilate phosphoribosyl transferase (tryptophan biosynthesis) pheA Chorismate mutase-P/ prephenate dehydratase (phenylalanine/tyrosine biosynthesis) ilvI Acetolactate synthase subunit (branched amino acid biosynthesis)

Organism (host)

Reference

S. meliloti (nodules)

Oke and Long, 1999

P. putida (maize)


P. putida (maize)




P. syringae (Arabidopsis)

Boch et al., 2002


Silby and Levy, 2004


Gal et al., 2003



E. chrysanthemi (spinach) Yang et al., 2004 P. putida (Phytophthora)

Lee and Cooksey, 2000

P. fluorescens (sugar beet) E. chrysanthemi (spinach)

Rainey, 1999 Yang et al., 2004

R. solanacearum (tomato) R. solanacearum (tomato) P. fluorescens (soil)

Brown and Allen, 2004 Brown and Allen, 2004 Silby and Levy, 2004






Yang et al., 2004


Gal et al., 2003

Continued




Table 4.1.

Continued

Gene or fusion


ilvl

Acetolactate synthase subunit (branched amino acid biosynthesis) Diaminopimelate decarboxylase (LysA homologue, lysine biosynthesis) N-succinyldiaminopimelate aminotransferase (lysine biosynthesis) Transcription regulator of LysR family (methionine biosynthesis) Glutamate dehydrogenase (glutamate biosynthesis) Cytoplasmic L-asparaginase I (aspartate biosynthesis)

dcdA

dapC

metR gdhA ansA

Amino acid degradation gcvP Glycine cleavage system (P protein) gcvH1 Glycine cleavage system (H protein) Nucleotide synthesis pyrG CTP synthase (de novo synthesis pyrimidine nucleotides) purF Amidophosphoribosyltransferase (de novo synthesis purine nucleotides) upp Uracil phosphoribosyltransferase (salvage pathway) Rsc0204 Thymidine/pyrimidinenucleoside phosphorylase (salvage pathway) xdhA Xanthine dehydrogenase (salvage pathway) Peptide and protein synthesis Non-ribosomal peptide synthesis Rsp1419 Peptide synthetase Ribosomal synthesis α-prfC Antisense transcript – peptide chain release factor 3 Antisense transcript – α-cii61 16S rRNA gene Amino acid-tRNA synthetases alaS Alanyl-tRNA synthetase cysS Cysteinyl-tRNA synthetase glnS Glutaminyl-tRNA synthetase

65

Organism (host)

Reference


Boch et al., 2002


Boch et al., 2002




Yang et al., 2004




Yang et al., 2004




Boch et al., 2002






Yang et al., 2004









P. stutzeri (rice)

Rediers, 2006

R. solanacearum (tomato) E. chrysanthemi (spinach) R. solanacearum (tomato)

Brown and Allen, 2004 Yang et al., 2004 Brown and Allen, 2004

Continued



66


Table 4.1.

Continued

Gene or fusion


Organism (host)

Reference

gltX

Glutamyl-tRNA synthetase

P. putida (maize) R. solanacearum (tomato)

Ramos-González et al., 2005 Brown and Allen, 2004




Oke and Long, 1999

R. solanacearum (tomato) R. solanacearum (tomato) P. syringae (Arabidopsis) R. solanacearum (tomato) R. solanacearum (tomato) R. solanacearum (tomato) R. solanacearum (tomato)

Brown and Allen, 2004 Brown and Allen, 2004 Boch et al., 2002 Brown and Allen, 2004 Brown and Allen, 2004 Brown and Allen, 2004 Brown and Allen, 2004






Oke and Long, 1999




Yang et al., 2004



P. stutzeri (rice)



Boch et al., 2002




Yang et al., 2004



gatA

Glutamyl-tRNA (gln) amidotransferase (subunit A) tyrS Tyrosyl-tRNA synthetase Protein folding groEL5 Chaperonine Protein degradation pepN Aminopeptidase N Rsp0196 Prolyl aminopeptidase ipx41 Carboxypeptidase Rsc1476 Carboxypeptidase Rsp0603 Serine protease Rsc3101 Serine protease α-Rsc2654 Antisense transcript – serine protease lon ATP-dependent protease Cofactor biosynthesis Rsc0082 S-adenosylmethionine8-amino-7-oxononanoate aminotransferase (biotin biosynthesis) nifS Cysteine desulfurase (synthesis nitrogenase metallocluster) Rsc2193 Nicotinate nucleotide adenylyltransferase (nucleotide cofactor biosynthesis) pncA Pyrazinamidase/ nicotinamidase (nucleotide cofactor biosynthesis) hemB δ-aminolevulinic acid dehydratase (tetrapyrrole synthesis) cii-11 Putative tetrapyrrole methylase (PA4422 homologue) (tetrapyrrole synthesis) ipx45 Lipoprotein (thiamine biosynthesis) nadB2 L-aspartate oxidase (quinolinate biosynthesis) folB Dihydroneopterin aldolase (folate biosynthesis) iiv21 Molybdenum cofactor biosynthesis

Continued




Table 4.1.

Continued

Gene or fusion


Organism (host)

Conversion of miscellaneous or unknown compounds mdcA Malonate decarboxylase P. fluorescens (sugar beet) glcF Glycolate oxidase (Fe-S subunit) R. solanacearum (tomato) gph Phosphoglycolate phosphatase R. solanacearum (tomato) rhi-4 MorB-like reductase (complex P. fluorescens (sugar beet) N-compounds) vanR Transcriptional regulator of P. fluorescens (sugar beet) GntR family (aromatic compounds) pcaC Carboxymuconolactone P. stutzeri (rice) decarboxylase (aromatic compounds) gabD1 Succinate semialdehyde R. solanacearum (tomato) dehydrogenase (putrescine degradation) ipx39 Haloacid dehalogenase-like P. syringae (Arabidopsis) hydrolase pup-28 Dioxygenase E. chrysanthemi (spinach) rhi-74 Putative amidohydrolase P. fluorescens (sugar beet) (plant nitrilase-like) Energy metabolism Rsc0087 NADH dehydrogenase nex8 NADH-ubiquinone oxidoreductase subunit Rsc1280 Transmembrane 4Fe-S ferredoxin α-Rsc0329 Antisense transcript – 2Fe-S ferredoxin phaZ Polyhydroxybutyrate depolymerase

67

Reference

Gal et al., 2003 Brown and Allen, 2004 Brown and Allen, 2004 Rainey, 1999 Gal et al., 2003



Boch et al., 2002 Yang et al., 2004 Gal et al., 2003

R. solanacearum (tomato) S. meliloti (nodules)

Brown and Allen, 2004 Oke and Long, 1999







CLASS IV: GENES INVOLVED IN STRESS RESPONSE AND ADAPTATION Oxidative stress Glutathione metabolism Rsc2501 γ-glutamyltranspeptidase R. solanacearum (tomato) Brown and Allen, 2004 lsfA Glutathione peroxidase P. fluorescens (sugar beet) Rainey, 1999 ykmA Glutathione peroxidase P. fluorescens (sugar beet) Rainey, 1999 a-iiv25 Antisense transcript – P. fluorescens (soil) Silby and Levy, 2004 glutathione synthase (glutaminyl transferase) Peroxidases/catalases bcp Peroxiredoxin (bacterioferritin R. solanacearum (tomato) Brown and Allen, 2004 co-migratory protein) bcp Peroxiredoxin (bacterioferritin P. stutzeri (rice) Rediers et al., 2003 co-migratory protein) Rsc2800 Peroxidase R. solanacearum (tomato) Brown and Allen, 2004 nex1 Peroxiredoxin S. meliloti (nodules) Oke and Long, 1999 catF Catalase P. syringae (Arabidopsis) Boch et al., 2002

Continued



68


Table 4.1.

Continued

Gene or fusion


Organism (host)

Reference

R. solanacearum (tomato) E. chrysanthemi (spinach)

Brown and Allen, 2004 Yang et al., 2004

P. fluorescens (sugar beet) E. chrysanthemi (spinach)

Gal et al., 2003 Yang et al., 2004

Osmoregulation mdoG Synthesis of periplasmic β-glucans



Low oxygen Rsc2859 Putative formate dehydrogenase



E. chrysanthemi (spinach) P. fluorescens (sugar beet)

Yang et al., 2004 Rainey, 1999

P. fluorescens (sugar beet) R. solanacearum (tomato) R. solanacearum (tomato)

Rainey, 1999 Brown and Allen, 2004 Brown and Allen, 2004


Boch et al., 2002

R. solanacearum (tomato) R. solanacearum (tomato)

Brown and Allen, 2004 Brown and Allen, 2004


Gal et al., 2003


Rainey, 1999



P. putida (maize)

Ramos-González et al., 2005 Rainey, 1999

Other Rsp1530 msrA pqiB indA

L-ascorbate oxidase Peptide methionine sulfoxide reductase Paraquat-inducible protein Indigoidine biosynthesis protein

Detoxification Metal ion efflux and homeostasis cutC Copper homeostasis protein copRS Two-component response regulator of copper resistance ragC Cation efflux protein Rsp1597 Putative cation efflux pump czcS Two-component sensor kinase for regulation of Zn, Co, Cd resistance ipx33 Metal transporting P-type ATPase Antibiotic resistance Rsc1323 Multidrug resistance protein acrA Periplasmic component of RND-type efflux pump acrF Transmembrane protein of RND-type efflux pump rosA Transmembrane protein (fosmidomycin resistance) α-Rsc3205 Antisense transcript – transmembrane protein Methylglyoxal detoxification gloA Lactoylglutathione lyase (glyoxalase I) ycbL Glyoxylase II enzyme family


Other stress-related proteins ctc General stress protein

P. putida (maize)

hslU


Ramos-González et al., 2005 Brown and Allen, 2004

R. solanacearum (tomato) P. stutzeri (rice)

Brown and Allen, 2004 Rediers et al., 2003


Yang et al., 2004

grpE yhbH ynaF

Stress-inducible protease (ATPase component) Heat shock protein 24 RpoN modulator protein (nutritional adaptation) Universal stress protein

Continued




Table 4.1.

Continued

Gene or fusion


dinF

DNA-damage inducible transmembrane protein (SOS response)

Organism (host)

Reference



CLASS V: GENES INVOLVED IN REGULATION Two-component regulatory systems ipx48 Sensor kinase P. syringae (Arabidopsis) ipx49 Sensor kinase P. syringae (Arabidopsis) colS Sensor kinase P. putida (maize)

iiv7 vsrB pehR Rsc1597 vsrD

Sensor kinase Response regulator Response regulator Response regulator (LuxR-like) Response regulator (LuxR-like)

69

P. fluorescens (soil) R. solanacearum (tomato) R. solanacearum (tomato) R. solanacearum (tomato) R. solanacearum (tomato)

Boch et al., 2002 Boch et al., 2002 Ramos-González et al., 2005 Silby and Levy, 2004 Brown and Allen, 2004 Brown and Allen, 2004 Brown and Allen, 2004 Brown and Allen, 2004

Transcriptional regulators rap2-44 Regulator (AraC family)

P. putida (maize)

rap1-12

Regulator (AsnC family)

P. putida (maize)

Rsc2094 Rsp1574 a-iiv13

R. solanacearum (tomato) R. solanacearum (tomato) P. fluorescens (soil)

Rsp1644 srpS pup-24 rap1-4

Regulator (LysR family) Regulator (LysR family) Antisense transcript – regulator (LysR family) Regulator (MarR family) Regulator (Crp family) Regulator (Cro/CI family) Regulator (ArsR family)

crp relB nex7 rhi-1 Rsc0280 Rsc1997 iiv9

CRP regulator RelB protein Regulator (TspO/MBR family) Putative repressor (DnrO-like) Regulator (MerR family) Regulator (GntR family) Regulator (GntR family)

E. chrysanthemi (spinach) E. chrysanthemi (spinach) S. meliloti (nodules) P. fluorescens (sugar beet) R. solanacearum (tomato) R. solanacearum (tomato) P. fluorescens (soil)

Brown and Allen, 2004 Yang et al., 2004 Yang et al., 2004 Ramos-González et al., 2005 Yang et al., 2004 Yang et al., 2004 Oke and Long, 1999 Rainey, 1999 Brown and Allen, 2004 Brown and Allen, 2004 Silby and Levy, 2004

Transcription factors greA Transcription elongation factor hrpA ATP-dependent RNA helicase rpoS Stationary phase sigma factor Rsp1668 σ54-interacting protein

R. solanacearum (tomato) R. solanacearum (tomato) R. solanacearum (tomato) R. solanacearum (tomato)

Brown and Allen, 2004 Brown and Allen, 2004 Brown and Allen, 2004 Brown and Allen, 2004

Other hfq pta pup-44

P. stutzeri (rice) P. stutzeri (rice) E. chrysanthemi (spinach)

Rediers et al., 2003 Rediers et al., 2003 Yang et al., 2004

Global regulator Phosphotransacetylase Zn-finger-containing protein

R. solanacearum (tomato) E. chrysanthemi (spinach) E. chrysanthemi (spinach) P. putida (maize)

Ramos-González et al., 2005 Ramos-González et al., 2005 Brown and Allen, 2004 Brown and Allen, 2004 Silby and Levy, 2004

Continued



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Table 4.1.

Continued

Gene or fusion


Organism (host)

Reference

CLASS VI: GENES INVOLVED IN CELL ENVELOPE STRUCTURE AND MODIFICATION Peptidoglycan ipx10 Lytic transglycosylase P. syringae (Arabidopsis) Boch et al., 2002 pup-36 Membrane protein with E. chrysanthemi (spinach) Yang et al., 2004 C-terminal transglycosylase domain ampG Muropeptide transporter P. syringae (Arabidopsis) Boch et al., 2002 pup-33 Periplasmic murein E. chrysanthemi (spinach) Yang et al., 2004 peptide-binding protein of ABC transporter Exopolysaccharide biosynthesis algA Alginate biosynthesis algD GDP-mannose-6dehydrogenase (alginate biosynthesis) Outer membrane proteins Rsc1627 Lipoprotein ompW Outer membrane protein pup-38 Outer membrane protein wssE Cellulose synthase subunit

oprD2

Porin

Inner membrane proteins yidC Membrane integration of proteins Adhesion/invasion nex18 Putative fasciclin-like adhesion molecule

P. syringae (Arabidopsis) P. putida (maize)

Boch et al., 2002 Ramos-González et al., 2005

R. solanacearum (tomato) R. solanacearum (tomato) E. chrysanthemi (spinach) P. fluorescens (sugar beet) P. putida (Phytophthora)

Brown and Allen, 2004 Brown and Allen, 2004 Yang et al., 2004 Gal et al., 2003

P. putida (maize)



Oke and Long, 1999

CLASS VII: GENES INVOLVED IN VIRULENCE AND SECRETION Secretion SRP pathway tig Trigger factor P. fluorescens (sugar beet) General secretory pathway secA Translocation ATPase S. meliloti (nodules) secB Protein chaperone P. putida (maize)

outFG Component of Out system gspK Type II secretory protein TTSS structural components hrcC rscC HrcC homologue hrpK hrpJ hrpG

Lee and Cooksey, 2000

Rainey, 1999

E. chrysanthemi (spinach) R. solanacearum (tomato)

Oke and Long, 1999 Ramos-González et al., 2005 Yang et al., 2004 Brown and Allen, 2004

R. solanacearum (tomato) P. fluorescens (sugar beet) P. syringae (Arabidopsis) P. syringae (Arabidopsis) P. syringae (Arabidopsis)

Brown and Allen, 2004 Rainey, 1999 Boch et al., 2002 Boch et al., 2002 Boch et al., 2002

Continued




Table 4.1.

Continued

Gene or fusion


hrcQb hrpA hrpA hrpB TTSS effector proteins avrPphD avrPpiB virPphA dspA dspE Type IV secretion iiv18 VirB4-like ATPase Virulence factors plyD Pectin lyase pme Membrane-bound pectinesterase ogl Oligogalacturonate lyase syrE Syringomycin synthetase (phytotoxin production) cfl-cfa Coronatine biosynthesis (phytotoxin production) pup-29 MsgA-like virulence protein iaaM Tryptophan 2-monooxygenase (auxin biosynthesis)

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Organism (host)

Reference

P. syringae (Arabidopsis) P. syringae (Arabidopsis) E. chrysanthemi (spinach) E. chrysanthemi (spinach)

Boch et al., 2002 Boch et al., 2002 Yang et al., 2004 Yang et al., 2004

P. syringae (Arabidopsis) P. syringae (Arabidopsis) P. syringae (Arabidopsis) E. chrysanthemi (spinach) E. chrysanthemi (spinach)

Boch et al., 2002 Boch et al., 2002 Boch et al., 2002 Yang et al., 2004 Yang et al., 2004



P. syringae (Arabidopsis) R. solanacearum (tomato)

Boch et al., 2002 Brown and Allen, 2004

E. chrysanthemi (spinach) P. syringae (Arabidopsis)

Yang et al., 2004 Boch et al., 2002


Boch et al., 2002

E. chrysanthemi (spinach) E. chrysanthemi (spinach)

Yang et al., 2004 Yang et al., 2004

CLASS VIII: GENES INVOLVED IN NUCLEIC ACID METABOLISM dnaN DNA polymerase III subunit R. solanacearum (tomato) (DNA synthesis) helA Helicase (DNA topology) P. fluorescens (sugar beet) helB Helicase (DNA topology) P. fluorescens (sugar beet) helC Helicase (DNA topology) P. fluorescens (sugar beet) parC DNA topoisomerase IV subunit P. putida (maize) (DNA topology) recJ Single-stranded DNA-specific P. putida (maize) exonuclease (DNA repair) uvrA Exonuclease ABC subunit R. solanacearum (tomato) (DNA repair) orn Oligoribonuclease P. fluorescens (RNA degradation) (sugar beet) crcB Chromosome condensation E. chrysanthemi (spinach) protein (cell division) hsdM Type IA restriction-modification P. putida (maize) system subunit

Brown and Allen, 2004 Zhang et al., 2004a Zhang et al., 2004a Zhang et al., 2004a Ramos-González et al., 2005 Ramos-González et al., 2005 Brown and Allen, 2004 Zhang et al., 2004b Yang et al., 2004 Ramos-González et al., 2005

Continued



72


Table 4.1.

Continued

Gene or fusion


Organism (host)

Reference

CLASS IX: GENES INVOLVED IN TRANSPOSITION AND SITE-SPECIFIC RECOMBINATION Mobile DNA elements tnpA Transposase (family 11; IS4-like) P. syringae (Arabidopsis) Boch et al., 2002 tnpA Transposase (family 17; E. chrysanthemi (spinach) Yang et al., 2004 IS200-like) vgrG Vgr-like protein (located on E. chrysanthemi (spinach) Yang et al., 2004 multi-copy genetic elements) Integrases/recombinases Rsp1303 Integrase xerD Site-specific DNA recombinase

R. solanacearum (tomato) P. syringae (bean)

Brown and Allen, 2004 Marco et al., 2003

The (predicted) function of the genes upregulated in the respective environmental niche is indicated. Most assignments of genes and gene product functions are based on similarity to known genes as described in the original papers. Some of these assignments were updated through homology searches with available gene sequences. Genes for which mutational analysis unequivocally demonstrated their role during bacterial life in the wild are indicated in bold. Abbreviations: ABC = ATP-binding cassette; CRP = cAMP receptor protein; MCP = methyl-accepting chemotaxis protein; RND = Resistance Nodulation Division; SRP = signal recognition particle; TCA = tricarboxylic acid; TTSS = Type III secretion system. Abbreviated genus names: E. = Erwinia; P. = Pseudomonas; R. = Ralstonia; S. = Sinorhizobium.

functional classes. In addition, several genes were isolated without significant similarity to known genes or with ‘function unknown’, collectively referred to as FUN genes (Hinton, 1997).

Having FUN with IVET From Fig. 4.4, which illustrates the relative proportion of different functional classes of IVET-identified genes, it is apparent that approximately one-third of the IVETidentified genes are FUN genes, underscoring the fact that a lot of knowledge is still lacking about the lifestyle of bacteria in plant and soil niches. FUN genes can indeed encode previously unrecognized traits that contribute to ecological fitness or are important for interaction with a eukaryotic host (Rediers et al., 2005). For instance, the S. meliloti nex4 gene, lacking similarity to any known gene, was isolated in an IVET screening for early stage symbiosis genes (Oke and Long, 1999). It was observed that the nex4 transcriptional fusion was expressed in a restricted zone (nodule tip) of the

alfalfa root nodule, suggesting a strictly regulated expression in response to specific plant signals. Subsequent mutational analysis showed that the nex4 mutant was significantly attenuated in nodule formation and showed a decreased nitrogen-fixing capacity (Oke and Long, 1999). Further work demonstrated that this FUN gene constitutes an important trait for establishment of a successful Sinorhizobium–alfalfa symbiosis. Another FUN gene, the iiv2 orphan gene from P. fluorescens Pf0-1, was shown to contribute to bacterial fitness in soil as the corresponding mutant proliferated more slowly in bulk soil than the wild type (Silby and Levy, 2004). In Table 4.1, antisense gene fusions were categorized in the class of the corresponding sense genes, but they might also be considered as FUN genes as their biological significance remains unknown. Since the first IVET study that mentioned the isolation of transcriptional fusions that are apparently orientated in the wrong direction to drive reporter gene expression (Camilli and Mekalanos, 1995), several other IVET studies have isolated antisense transcripts from




I 2% FUN 32%

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II 11% III 22%

IX 2% VIII 3%

VII 7%

VI 4%

V 8%

IV 9%

Fig. 4.4. Distribution of IVET-isolated genes among different functional classes. The percentages of genes involved in chemotaxis and motility (class I), nutrient scavenging (class II), central metabolism (class III), adaptation to environmental stresses (class IV), regulation (class V), cell envelope structure and modification (class VI), virulence and secretion (class VII), nucleic acid metabolism (class VIII), transposition and site-specific recombination (class IX), and FUN genes (genes with unknown function or without significant similarity to known genes) are presented in the diagram.

P. fluorescens SBW25 and Pf0-1 (Rainey, 1999; Silby and Levy, 2004), P. stutzeri (Rediers et al., 2003), P. putida (RamosGonzález et al., 2005) and R. solanacearum (Brown and Allen, 2004). The frequent identification of antisense fusions (∼3% of the total IVET-identified genes) suggests that the associated promoter activity may fulfil a biologically significant role. It was hypothesized that antisense fusions are able to downregulate gene expression by driving the expression of small non-coding RNA molecules that would result in the degradation of the corresponding sense mRNA in processes such as RNA interference or antisense inhibition (Camilli and Mekalanos, 1995; Merrell and Camilli, 2000; Silby et al., 2004). Besides affecting transcription and translation processes, small RNAs can also affect protein activity. As small non-coding RNAs are suitable for subtle gene regulation, it has been postulated that they are important for environmental adaptation (Repoila et al., 2003).

Having more fun: functional classification of IVET genes Of the genes to which a (possible) function could be assigned, the largest proportion (22%) was found in class III, which

contains genes that are involved in central intracellular processes, such as amino acid and protein metabolism, intermediary metabolic pathways or cofactor biosynthesis. Although in the past metabolic genes were considered to be merely involved in housekeeping functions, and therefore of less interest, each IVET study resulted in the isolation of several metabolic genes. Such nichetriggered upregulation of certain metabolic pathways (and probably also downregulation of others) reflects the importance of metabolic versatility for bacterial fitness. The ability to adapt to changing nutritional conditions in the wild is also reflected in the proportion of IVET genes (11%) that play a role in nutrient scavenging. Genes of this class also provide information about the environmental stimuli that are present in a particular ecological niche. For instance, when present in a plant environment, several bacteria show increased expression of genes involved in acquisition of amino acids and organic acids. These compounds are present in plant root exudates (Jaeger et al., 1999; Aulakh et al., 2001) and can be utilized during life in the plant environment (Simons et al., 1997). This is in line with the observation that P. fluorescens shows chemotaxis towards amino acids present in these root exudates (de Weert et al., 2002). In contrast, such genes were



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hardly ever isolated from animal pathogens, suggesting that amino acids and organic acids are scarcely available for uptake and subsequent utilization during animal infection (Rediers et al., 2005). In class V, global regulators, regulators with unknown target genes and regulators of genes with unknown function are listed. The ability of bacteria to modulate gene expression in response to changing environmental cues in a particular ecological niche is likely to contribute to their ecological success by enabling a better adaptation and survival when experiencing, for example, nutrient limitation or stress conditions. It is therefore not surprising that a large proportion of IVET genes encode regulatory proteins. Together with the regulatory genes that are categorized with their known target genes, genes assigned a putative regulatory role account for 12% of the total genes listed in Table 4.1. Genes that are upregulated to deal with environmental stress (class IV) constitute almost 10% of the total IVET genes. Most of these are involved in detoxification or show increased expression in response to oxidative stress. Plants are equipped with a defence mechanism against invading microorganisms, the so-called oxidative burst, that results in the generation of superoxide and hydrogen peroxide (commonly referred to as reactive oxygen species, ROS), which are toxic for potential microbial invaders (Lamb and Dixon, 1997; Imlay, 2003). Therefore, increased expression of enzymes that reduce ROS can add to the bacterial fitness in this environment. In contrast to IVET studies of animal pathogens, increased expression of genes involved in acid stress response was not detected in bacteria that reside in the plant environment (Rediers et al., 2005). These data demonstrate that the lack of upregulation of certain genes can also shed more light on the conditions encountered in a specific ecological niche. In class VII (7% of total), genes involved in secretion, including virulence factors, are combined. It was shown in several IVET studies that different types of secretion mechanisms are specifically induced in the plant environment. Interestingly, the class

VI genes involved in cell wall modification, which constitute 4% of the total IVETisolated genes, were predominantly isolated from bacteria that infect their host. Altering the cell wall surface appears to be important for interaction with host cells as well as for adaptation to environmental stress. Classes VIII and IX contain genes involved in nucleic acid modification (3%), and in transposition and recombination processes (2%), respectively. Probably, the genes of class VIII are primarily involved in DNA repair and in the modulation of gene expression, whereas genes of class IX are thought to be involved in the generation of genetic variability, thereby improving bacterial fitness.

New Insights into Bacterial Life in the Wild from IVET Studies Although no reported IVET screening has covered an entire bacterial genome (yet), several of the isolated genes have already provided new insights into the traits that determine ecological fitness of bacteria or that are important for establishing an intimate interaction with a plant host. Indeed, for a subset of IVET genes, subsequent mutational analysis demonstrated a significant role in the studied niches.

Exploring plant symbiosis Oke and Long (1999) were among the first to introduce IVET in the study of bacterial gene expression in a plant environment. As discussed above, these authors devised a system-specific bacA-based screen that selects for S. meliloti promoters that are upregulated in the early stages of symbiosis. Together with a few genes that were previously known to be involved in the nodulation process, several new genes were isolated. Mutational analysis of nex1, which encodes a peroxiredoxin, demonstrated that this gene plays an important role in symbiosis as the mutant was impaired in symbiotic nitrogen fixation. Expression analysis in the nodule, using the




gusA reporter gene provided on the promoter trap, revealed for the first time that rhizobia display antioxidant activity in nodules. Recently, it was observed that expression of another rhizobial peroxiredoxin, PrxS, protects Rhizobium etli when inhabiting root nodules (Dombrecht et al., 2005). An intriguing gene with increased expression during alfalfa symbiosis, that upon inactivation results in decreased symbiotic nitrogen fixation, is nex10. Nex10 resembles the regulatory β-subunit of eukaryotic voltage-gated potassium channels. Its function in prokaryotes is unknown, but possible roles in osmoregulation or pH adaptation have been postulated (Milkman, 1994; Oke and Long, 1999). nex18 is another IVET-identified gene that was subsequently shown by mutational analysis to play a role in successful symbiotic nitrogen fixation (Oke and Long, 1999). Nex18 shares a resemblance with the major mycobacterial antigen MPB70 that is structurally related to fasciclin I, an adhesion molecule found in some eukaryotes (Carr et al., 2003). In addition, this IVET approach also isolated several FUN genes, of which nex4 was unequivocally shown to play an important role in the establishment of symbiosis, as discussed above.

Scrutinizing plant disease Pectin, as a major plant cell wall polymer, constitutes a primary target for hydrolytic enzymes from phytopathogenic bacteria. IVET studies confirmed upregulation of pectin-degrading enzymes during plant infection by P. syringae (Boch et al., 2002), R. solanacearum (Brown and Allen, 2004) and E. chrysanthemi (Yang et al., 2004). Also, enhanced expression of the cognate type II secretion machineries was evident for E. chrysanthemi (Yang et al., 2004) and R. solanacearum (Kang et al., 1994). Host infection-specific upregulation of several other virulence factors was demonstrated through IVET. Several genes for known and putative TTSS effector molecules were isolated from E. chrysanthemi,

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P. syringae and R. solanacearum, along with genes encoding components of the TTSS machinery (Boch et al., 2002; Brown and Allen, 2004; Yang et al., 2004). IVET also highlighted the importance of phytopathogen genes, playing a role in peptidoglycan recycling, that are thought to be involved in the integration of macromolecular transport systems, such as the TTSS, in the cell wall (Koraimann, 2003). One such gene, P. syringae ipx10, encodes a lytic transglycosylase and displayed increased expression during Arabidopsis infection. Detailed expression analysis revealed a sixfold induction of ipx10 during infection, and subsequent mutational analysis unequivocally demonstrated the importance of this lytic transglycosylase in virulence (Boch et al., 2002). Also, in several animal pathogens, lytic transglycosylases are upregulated during host infection and are required for full virulence (Rediers et al., 2005). IVET also enabled the isolation of different sugar uptake systems that were specifically induced during E. chrysanthemi and R. solanacearum infection of their respective plant host (Brown and Allen, 2004; Yang et al., 2004). It was subsequently established by mutational analysis that the rhamnogalacturonide transporter, encoded by rhiT, contributes to systemic infection of spinach by E. chrysanthemi (Yang et al., 2004). These findings support the notion that plant-infecting bacteria thrive on plantderived nutrients released by the hydrolytic attack of host tissues. Genes involved in the uptake of organic acids, as well as those that regulate C4dicarboxylate metabolism, were frequently isolated with IVET from bacteria that interact with a plant host. This is not surprising as C4-dicarboxylates, such as malate and succinate, are present in plants and root exudates (Aulakh et al., 2001). The P. syringae dctD gene, which displays increased expression during bean infection (Marco et al., 2003), encodes the response regulator of the DctBD two-component regulatory system. In the presence of dicarboxylates, DctBD induces expression of a C4-dicarboxylate : cation (H+ or Na+) symporter, encoded by dctA. Interestingly, in independent IVET



76


studies, it was shown that a dctS homologue, encoding the response regulator of the DctRS two-component regulatory system, was induced in both P. fluorescens (Rainey, 1999) and R. solanacearum (Brown and Allen, 2004) during sugar beet root colonization and tomato infection, respectively.

Soil- and plant-colonizing bacteria: more secrets revealed Several IVET techniques were developed to study different Pseudomonas species in their natural habitat (Espinosa-Urgel and Ramos-González, 2004). These comprise plant pathogens (P. syringae; discussed in the previous section), plant growth-promoting rhizobacteria (PGPR), such as P. fluorescens SBW25 (Rainey, 1999; Gal et al., 2003; Zhang et al., 2004a), P. putida strains 06909 (Lee and Cooksey, 2000) and KT2440 (RamosGonzález et al., 2005), soil-colonizing P. fluorescens Pf0-1 (Silby and Levy, 2004) and the nitrogen-fixing facultative rice endophyte P. stutzeri A15 (Rediers et al., 2003). Genes involved in flagellar assembly were upregulated in both P. fluorescens and P. putida upon rhizosphere colonization, indicating that chemotaxis and motility are important traits for successful root colonization. Conversely, two putative antisense genes (α-mcp and α-fliM) were found to be upregulated in P. stutzeri A15 during rice root colonization (Rediers, 2006). Upregulation of these antisense transcriptional fusions may play a role in the downregulation of chemotactic motility. For some bacteria, it is known that their flagellation pattern is altered in response to changing environmental conditions (reviewed in Fraser and Hughes, 1999; McCarter, 2004). We hypothesize that P. stutzeri A15 may upregulate genes involved in chemotaxis and motility at an early stage of rice root colonization but, once established, it may suppress these genes by upregulation of the corresponding antisense transcripts. In doing so, the infection efficiency of this endophyte may increase as the formation

and maintenance of microcolonies would be encouraged. This is in agreement with the isolation of antisense chemotaxis genes in bacteria infecting mammalian hosts (Merrell and Camilli, 2000; Rediers et al., 2005). Adhesion to the host surface is a prerequisite for efficient colonization. In this regard, IVET identified the P. fluorescens wssE gene as being specifically induced in sugar beet rhizosphere (Gal et al., 2003). Being part of the wss operon for synthesis of acetylated cellulose polymers, wssE encodes a cellulose synthase subunit (Spiers et al., 2003). However, acetylated cellulose polymers are thought to play a role in colony development and bacterial cell–cell contact, rather than mediating adhesion to the plant surface (Gal et al., 2003; Spiers et al., 2003). Nevertheless, mutational analysis demonstrated that the wss operon contributes to ecological fitness on the leaf surface and, to a lesser extent, in the sugar beet rhizosphere (Gal et al., 2003). IVET was used to study the differential gene expression of P. fluorescens strains both in sugar beet rhizosphere (Rainey, 1999; Gal et al., 2003; Zhang et al., 2004a) and in bulk soil (Silby and Levy, 2004). In sugar beet rhizosphere, P. fluorescens showed increased expression of two glutathione peroxidases and a paraquat-inducible protein, all involved in the oxidative stress response (Rainey, 1999; Gal et al., 2003). Interestingly, in bulk soil, upregulation of a P. fluorescens transcriptional fusion (iiv25), antisense to a glutathione synthase gene, was detected (Silby and Levy, 2004). Apparently, P. fluorescens increases expression of genes for protection against oxidative stress encountered in the sugar beet rhizosphere, but downregulates these genes when residing in bulk soil, where less oxidative stress is experienced. This illustrates that IVET is also useful for comparing differential gene expression in different ecological niches, such as root rhizosphere versus bulk soil. The IVET approach would also be suitable to assess rhizosphere-triggered gene expression during colonization of different host plants, revealing genes that are upregulated in response to host-specific signals, and hence rendering more information about the




bacterial mechanisms that determine preferential host colonization. Likewise, by comparing subsets of IVET genes associated with rhizosphere colonization with those linked with endophytic survival, new insights may be generated into the bacterial determinants enabling an endophytic lifestyle. Zhang et al. (2004a) used IVET to identify genes with increased expression in sugar beet rhizosphere that are contained on the indigenous plasmid pQBR103. The pQBR103 plasmid is also known for its transfer proficiency, and is frequently present in pseudomonads isolated from sugar beet fields (Zhang et al., 2004a). Its presence provides a competitive advantage for root colonization. Several genes of this plasmid, including three encoding helicases, show increased expression during sugar beet colonization. It was subsequently demonstrated that a mutation in one such helicase, encoded by helA, results in attenuated sugar beet colonization (Zhang et al., 2004a). Notably, IVET also identified a P. fluorescens gene, iiv18, encoding a VirB4-like ATPase, with increased expression in bulk soil (Silby and Levy, 2004). VirB-like proteins are an important component of the type IV secretion machineries that can also mediate the conjugation of certain plasmids and are involved in DNA uptake and release. Being involved in genetic exchange, such systems may allow better adaptation to changing environments in the wild. In addition, dedicated type IV secretion systems are involved in the translocation of effector molecules into eukaryotic host cells (Cascales and Christie, 2003). The approach used by Zhang et al. (2004a) can be useful for focusing on the significance of plasmid-borne genes for ecological fitness in other plant- or soil-colonizing bacteria that often carry a large proportion of their genes on these extrachromosomal elements. The power of IVET is also clear from the fact that expression of the isolated genes, which display increased expression in the wild, is often difficult to induce in the laboratory. These genes probably respond to complex stimuli that are hard to mimic in vitro. For instance, the expression of the P. stutzeri A15 bcp gene, encoding a peroxiredoxin that is specifically induced

77

during rice root colonization, was assessed in vitro by measuring the activity of βglucuronidase. Imposing oxidative stress by exposure of cells to different peroxides failed to increase bcp expression, except for hydrogen peroxide which induced bcp expression in cells grown in minimal but not in rich growth medium (Rediers, 2006). Typically, the bcp gene is involved in protection against ROS and it is probably induced to cope with the oxidative defence mechanisms of the rice root when challenged with a potential invader (Iwano et al., 2002). IVET also revealed increased expression of the R. solanacearum bcp gene during infection of tomato plants (Brown and Allen, 2004). Using a different approach, an elevated Bcp level was detected in Frankia, in response to root exudates of its symbiotic host, Alnus glutinosa (Hammad et al., 2001). Interestingly, the repeated isolation of IVET genes encoding peroxidases and catalases from phytopathogens, from a plant endophyte and from a plant symbiont highlights the need for bacteria that invade plant tissues to deploy defence mechanisms against oxidative damage. Apparently, rhizospherecolonizing bacteria are confronted to a lesser extent with this type of threat. A second example illustrating the complexity of niche-specific triggers is the iiv7 gene, encoding a sensor kinase, that was isolated from P. fluorescens in bulk soil. Mutational analysis of iiv7 unequivocally demonstrated that this sensor kinase plays a significant role in the survival of P. fluorescens in bulk soil (Silby and Levy, 2004). Despite its predicted role in metal resistance, the authors failed to induce expression in vitro by growing the bacterium in the presence of sublethal concentrations of metal ions (Silby and Levy, 2004). Another IVET-identified regulatory protein is P. stutzeri A15 YhbH (Rediers et al., 2003), which was originally designated as the ‘RpoN modulator protein’. However, increasing evidence suggests that YhbH fulfils a more general regulatory role, beyond the RpoN-dependent regulation of nitrogen metabolism. In E. coli, it was observed that YhbH accumulates in stationary phase and binds to ribosomes, thereby storing them in



78


an inactive form (Maki et al., 2000). We speculate that YhbH may be involved in adaptation to rhizosphere environments where bacteria experience conditions similar to those faced during stationary growth phase in culture media. The physiological status in stationary growth phase might reflect the bacterial fitness in the wild, since it often coincides with an increased resistance to unfavourable conditions, such as nutritional deprivation and oxidative stress. To evaluate the growth phase dependency of P. stutzeri A15 yhbH inducibility, the cell density and the gusA reporter gene activity of the yhbH IVET clone were monitored simultaneously at regular time intervals. In both rich and minimal growth medium, yhbH expression displayed a growth phasedependent pattern (Bonnecarrère et al., 2003). The power of IVET for the discovery of unanticipated traits is illustrated by the isolation of rscC, encoding a component of the TTSS of non-pathogenic P. fluorescens (Rainey, 1999). The expression of the TTSS in P. fluorescens is specifically induced in sugar beet rhizosphere, but, in contrast to the homologous TTSS in P. syringae, not upon leaf colonization. Although TTSS in P. fluorescens was demonstrated to be functional, an rscC mutation did not affect root colonization capacity (Preston et al., 2001). The identification of a functional TTSS in P. fluorescens was rather unexpected because TTSSs were previously only reported in pathogenic bacteria and symbiotic rhizobia. However, recent studies revealed that TTSSs are more widespread than originally thought (Mazurier et al., 2004; Pühler et al., 2004; Rezzonico et al., 2004).

Conclusions and Perspectives Analysis of ecological performance is far from straightforward as it is a complex phenotype, not determined by a single gene, but rather by complex epistatic interactions among many different gene products. In the past, in vitro approaches for studying bacteria under well-defined laboratory conditions have demonstrated their value, but

they are not very suitable to study bacterial behaviour in complex environmental niches, such as soil or a plant environment. Many genes and corresponding traits important for bacterial life in a natural environment are likely to remain unidentified with in vitro techniques because of the poor knowledge about the (combination of) environmental cues a bacterial resident experiences and the researcher’s inability to simulate these triggers appropriately. In order to circumvent at least some of these limitations, various genetic approaches have been devised to study bacterial behaviour in complex environments. The major disadvantage of mutagenesis techniques such as STM is that they are not able to identify genes that are also required for normal growth, whereas this is not an impediment for IVET. Several IVET screenings yielded genes involved in housekeeping functions. Their increased expression in the wild indicates that these genes do contribute to ecological fitness. In addition, a gene for which the corresponding null mutation does not result in a scorable phenotype cannot be identified with STM. Compared with IVET, microarrays have the advantage that gene expression can be quantified in a high throughput mode and on a genome-wide scale. However, microarray analysis only reports for a particular gene its average expression in a population at the time of sampling. Therefore, spatial differences in upregulation of genes due to microheterogeneity of the environment will be masked (Boyce et al., 2002), while with IVET it is possible to isolate an individual clone expressing a gene in such an environment. Also in the case of microarrays, isolation of representative bacterial mRNA from many natural niches is difficult: due to the instability of the molecule, the yield is often too low, and contamination with mRNA originating from other microorganisms in the studied niche may occur (Hinton et al., 2004). In addition, application of microarrays requires expensive equipment and is dependent on the availability of annotated genome sequences. Promoter-trapping techniques such as IVET are based upon the assumption that many genes important for adaptation to the




changing conditions experienced in the natural habitat display increased expression when bacteria reside in these habitats. The power of IVET to study plant- and soilcolonizing bacteria is obvious from the following. It enabled the identification of several genes that are upregulated in the wild, but that were previously not known to be involved in bacterial fitness. On the other hand, the IVET approach was repeatedly validated by the isolation of numerous genes whose contribution to bacterial life in the wild was previously established. Moreover, independent IVET screenings resulted in the isolation of certain genes that are upregulated in different bacteria when occupying the same environmental niche. Further validation came from several follow-up studies that unravelled the functional role of IVETidentified genes, among them a number of FUN genes, by expression and mutational analysis. A major advantage of IVET is the presence of the reporter gene on the promoter trap; this facilitates the quantification of IVET gene expression in vitro in order to identify inducing stimuli, and permits visualization of gene expression in situ. The power of IVET for exploration of differential gene expression in complex environmental niches is also reflected in the detection of genes that are upregulated in the wild, but whose expression is difficult or impossible to induce in vitro. Promoters that drive niche-specific expression are a valuable deliverable from IVET studies that can find application for spatially and/or temporally controlled expression of heterologous genes. For instance, such regulatory elements may find application in the design of improved PGPR strains with tightly controlled expression of their biocontrol genes in order to minimize the inherent metabolic burden associated with their expression.

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Considering the effort involved in assembling the necessary components of an IVET system for a particular strain (construction of a mutant and of a genomic library in a suitable IVET vector) and the need for further functional characterization of IVET-identified genes, careful consideration should be given to the choice of the strain. Genetic tractability is an important consideration when choosing the target strain. Hence, most published studies involve strains for which appropriate genetic tools are available (Rediers et al., 2005). However, once set up, the same system can in principle be used in different ecological niches relevant for the chosen strain (for instance, soil, rhizosphere, biofilm, aquatic environment, bacterial consortium, colonized eukaryotic host, etc.). Comparative analysis of IVET data has expanded our view of the mechanistic differences and commonalities between beneficial and pathogenic bacteria and between bacteria that colonize animal or plant hosts (Rediers et al., 2005). It is expected that application of IVET to previously largely unexplored niches will continue to disclose secrets about bacterial behaviour in natural habitats. An attractive but as yet unexplored application for IVET is in the field of biodegradation and bioremediation of pollutants. Although a substantial amount of knowledge has accumulated on the bacterial metabolic pathways involved in breakdown or detoxification of a large variety of natural compounds and xenobiotics, taking place in either soil, rhizosphere or inside plants (Barac et al., 2004; Kuiper et al., 2004; Paul et al., 2005), little is known about other genetic determinants that contribute to a bacterium’s biodegradative performance in these complex environments.

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McClelland, M., Mathieu-Daude, F. and Welsh, J. (1995) RNA fingerprinting and differential display using arbitrarily primed PCR. Trends in Genetics 11, 242–246. Merrell, D.S. and Camilli, A. (2000) Detection and analysis of gene expression during infection by in vivo expression technology. Philosophical Transactions of the Royal Society B: Biological Sciences 355, 587–599. Miché, L., Belkin, S., Rozen, R. and Balandreau, J. (2003) Rice seedling whole exudates and extracted alkylresorcinols induce stress-response in Escherichia coli biosensors. Environmental Microbiology 5, 403–411. Milkman, R. (1994) An Escherichia coli homologue of eukaryotic potassium channel proteins. Proceedings of the National Academy of Sciences of the USA 91, 3510–3514. Mostertz, J., Scharf, C., Hecker, M. and Homuth, G. (2004) Transcriptome and proteome analysis of Bacillus subtilis gene expression in response to superoxide and peroxide stress. Microbiology 150, 497–512. Oke, V. and Long, S.R. (1999) Bacterial genes induced within the nodule during the Rhizobium–legume symbiosis. Molecular Microbiology 32, 837–849. Osbourn, A.E., Barber, C.E. and Daniels, M.J. (1987) Identification of plant-induced genes of the bacterial pathogen Xanthomonas campestris pathovar campestris using a promoter-probe plasmid. EMBO Journal 6, 23–28. Palma, M., DeLuca, D., Worgall, S. and Quadri, L.E. (2004) Transcriptome analysis of the response of Pseudomonas aeruginosa to hydrogen peroxide. Journal of Bacteriology 186, 248–252. Paul, D., Pandey, G., Pandey, J. and Jain, R.K. (2005) Accessing microbial diversity for bioremediation and environmental restoration. Trends in Biotechnology 23, 135–142. Peters, N.K., Frost, J.W. and Long, S.R. (1986) A plant flavone, luteolin, induces expression of Rhizobium meliloti nodulation genes. Science 233, 977–980. Preston, G.M., Bertrand, N. and Rainey, P.B. (2001) Type III secretion in plant growth-promoting Pseudomonas fluorescens SBW25. Molecular Microbiology 41, 999–1014. Pühler, A., Arlat, M., Becker, A., Göttfert, M., Morrissey, J.P. and O’Gara, F. (2004) What can bacterial genome research teach us about bacteria–plant interactions? Current Opinion in Plant Biology 7, 137–147. Rainey, P.B. (1999) Adaptation of Pseudomonas fluorescens to the plant rhizosphere. Environmental Microbiology 1, 243–257. Rainey, P.B. and Preston, G.M. (2000) In vivo expression technology strategies: valuable tools for biotechnology. Current Opinion in Biotechnology 11, 440–444. Rainey, P.B., Heithoff, D.M. and Mahan, M.J. (1997) Single-step conjugative cloning of bacterial gene fusions involved in microbe–host interactions. Molecular and General Genetics 256, 84–87. Ramos-González, M.I., Campos, M.J. and Ramos, J.L. (2005) Analysis of Pseudomonas putida KT2440 gene expression in the maize rhizosphere: in vivo expression technology capture and identification of root-activated promoters. Journal of Bacteriology 187, 4033–4041. Rediers, H. (2006) Study of the Interaction of the Plant Growth-promoting Bacterium Pseudomonas stutzeri A15 with its Plant Host Using In Vivo Expression Technology (IVET). PhD Thesis, Katholieke Universiteit Leuven, Dissertationes de Agricultura. No. 689. Rediers, H., Bonnecarrère, V., Rainey, P.B., Hamonts, K., Vanderleyden, J. and De Mot, R. (2003) Development and application of a dapB-based in vivo expression technology system to study colonization of rice by the endophytic nitrogen-fixing bacterium Pseudomonas stutzeri A15. Applied and Environmental Microbiology 69, 6864–6874. Rediers, H., Rainey, P.B., Vanderleyden, J. and De Mot, R. (2005) Unraveling the secret lives of bacteria: use of in vivo expression technology and differential fluorescence induction promoter traps as tools for exploring niche-specific gene expression. Microbiology and Molecular Biology Reviews 69, 217–261. Repoila, F., Majdalani, N. and Gottesman, S. (2003) Small non-coding RNAs, co-ordinators of adaptation processes in Escherichia coli: the RpoS paradigm. Molecular Microbiology 48, 855–861. Rezzonico, F., Défago, G. and Moënne-Loccoz, Y. (2004) Comparison of ATPase-encoding type III secretion system hrcN genes in biocontrol fluorescent pseudomonads and in phytopathogenic Proteobacteria. Applied and Environmental Microbiology 70, 5119–5131. Schoolnik, G.K. (2002) Microarray analysis of bacterial pathogenicity. Advances in Microbial Physiology 46, 1–45. Silby, M.W. and Levy, S.B. (2004) Use of in vivo expression technology to identify genes important in growth and survival of Pseudomonas fluorescens Pf0-1 in soil: discovery of expressed sequences with novel genetic organization. Journal of Bacteriology 186, 7411–7419. Silby, M.W., Rainey, P.B. and Levy, S.B. (2004) IVET experiments in Pseudomonas fluorescens reveal cryptic promoters at loci associated with recognizable overlapping genes. Microbiology 150, 518–520.




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Silhavy, T.J. and Beckwith, J.R. (1985) Uses of lac fusions for the study of biological problems. Microbiology Reviews 49, 398–418. Simons, M., Permentier, H.P., de Weger, L.A., Wijffelman, C.A. and Lugtenberg, B.J.J. (1997) Amino acid synthesis is necessary for tomato root colonization by Pseudomonas fluorescens strain WCS365. Molecular Plant-Microbe Interactions 10, 102–106. Smith, L.M., Tola, E., de Boer, P. and O’Gara, F. (1999) Signalling by the fungus Pythium ultimum represses expression of two ribosomal RNA operons with key roles in the rhizosphere ecology of Pseudomonas fluorescens F113. Environmental Microbiology 1, 495–502. Spiers, A.J., Bohannon, J., Gehrig, S.M. and Rainey, P.B. (2003) Biofilm formation at the air–liquid interface by the Pseudomonas fluorescens SBW25 wrinkly spreader requires an acetylated form of cellulose. Molecular Microbiology 50, 15–27. Valdivia, R.H. and Falkow, S. (1997) Fluorescence-based isolation of bacterial genes expressed within host cells. Science 277, 2007–2011. Van Bastelaere, E., De Mot, R., Michiels, K. and Vanderleyden, J. (1993) Differential gene expression in Azospirillum spp. by plant root exudates: analysis of protein profiles by two-dimensional polyacrylamide gel electrophoresis. FEMS Microbiology Letters 112, 335–342. Whipps, J.M. (2001) Microbial interactions and biocontrol in the rhizosphere. Journal of Experimental Botany 52, 487–511. Yang, S., Perna, N.T., Cooksey, D.A., Okinaka, Y., Lindow, S.E., Ibekwe, A.M., Keen, N.T. and Yang, C.H. (2004) Genome-wide identification of plant-upregulated genes of Erwinia chrysanthemi 3937 using a GFP-based IVET leaf array. Molecular Plant-Microbe Interactions 17, 999–1008. Zhang, X.X., Lilley, A.K., Bailey, M. and Rainey, P.B. (2004a) The indigenous Pseudomonas plasmid pQBR103 encodes plant-inducible genes, including three putative helicases. FEMS Microbiology Ecology 51, 9–17. Zhang, X.X., Lilley, A.K., Bailey, M.J. and Rainey, P.B. (2004b) Functional and phylogenetic analysis of a plant-inducible oligoribonuclease (orn) gene from an indigenous Pseudomonas plasmid. Microbiology 150, 2889–2898. Zhang, X.-S. and Cheng, H.-P. (2006) Identification of Sinorhizobium meliloti early symbiotic genes by use of a positive functional screen. Applied and Environmental Microbiology 72, 2738–2748. Zhang, X.-X., George, A., Bailey, M.J. and Rainey, P.B. (2006) The histidine utilization (hut) genes of Pseudomonas fluorescens SBW25 are active on plant surfaces, but are not required for competitive colonization of sugar beet seedlings. Microbiology 152, 1867–1875. Zhao, Y., Blumer, S.E. and Sundin, G.W. (2005) Identificaation of Erwinia amylovora genes induced during infection of immature pear tissue. Journal of Bacteriology 187, 8088–8103.

Please note: Application of the HRS selection strategy (Marco et al., 2003) for the isolation of novel plant-inducible genes from P. syringae pv. syringae was reported by Marco et al. (2005). A second system-specific selection strategy suitable for identification of symbiotic genes of S. Meliloti, using a plasmid-borne promoterless exoY gene, was described by Zhang and Cheng (2006). Antibiotic resistance-based selection was applied by Czelleng et al. (2006) to identify Pseudomonas viridiflava genes expressed during infection of green pepper fruit. An approach similar to the GFP-based IVET leaf array (Yang et al., 2004), but with gusA as a reporter gene, enabled identification of Erwinia amylovora genes induced during infection of pear tissue (Zhao et al., 2005). RIVET was used by Zhang et al. (2006) as a reporter system to demonstrate expression of the histidine utilization operon of P. fluorescens upon phyllosphere and rhizosphere colonization.



5

Analysing Microbial Community Structure by Means of Terminal Restriction Fragment Length Polymorphism (T-RFLP) Christopher B. Blackwood Department of Biological Sciences, Kent State University, Kent, OH 44242, USA

Introduction How will distributions of multiple, interacting populations respond to changes in the environment, and what ecosystem effects will the populations have? These are the general questions in community ecology, and they are just as relevant to ask when studying microorganisms as when studying macroorganisms. However, to begin to answer these questions, we require a method to determine ‘community structure’ or, in other words, which species are present in an environment, and what their abundances are. Often the method of choice for ecologists studying macroorganisms is to enter an environment, look for the organisms of interest and to count them. Obviously, microbial ecologists are forced to take another approach. Culture-independent, molecular methods were established as our best strategy to determine in situ microbial community structure by the development of two complementary molecular approaches. Torsvik et al. (1990) used DNA reassociation kinetics to show that bacterial diversity present in soil was hundreds of times greater than commonly found by cultivation of bacteria (perhaps many thousands of times greater,

see Dykhuizen, 1998; Gans et al., 2005). The data from DNA reassociation kinetics are drawn from the entire community in a sample, but provide only a single statistic with which to describe the community (C0t1/2, which can be used to calculate a theoretical number of species). The method has provided our best estimate of the number of bacterial species present in soil, and perhaps of the abundance distribution of those species (Gans et al., 2005). However, the picture provided by reassociation curves is still only a ‘rough map’ (Curtis and Sloan, 2005) as it lacks any descriptive detail about the phylogenetic and functional make-up of the community. Pace et al. (1986) described sequencing of small subunit (SSU) rRNA or DNA obtained directly from the environment. Cloning and sequencing of the ribosomal gene has since shown that the majority of environments on Earth harbour a multitude of uncultured microbial taxa. Phylogenetic analysis of ribosomal sequences can place detected organisms in a particular genus or species, or indicate the presence of a novel phylogenetic clade. However, Dunbar et al. (2002) calculated that to document 50% of bacterial species in a soil community would require analysis of tens of thousands of clones.

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This has not been accomplished and, unless multiple samples are analysed in this way, no information about the variability of community composition from one sample to the next is obtained. Hence cloning and sequencing studies provide detail but lack breadth. A variety of community profiling (or ‘fingerprinting’) techniques has been developed to fill the gap between the two methods discussed above, finding a compromise between breadth and detail. The method discussed in this chapter is terminal restriction fragment length polymorphism (T-RFLP), or terminal restriction fragment (T-RF) analysis, which was developed independently in several laboratories (Bruce et al., 1997; Liu et al., 1997; Clement et al., 1998). Community profiling methods involve amplification of a gene by the polymerase chain reaction (PCR), followed by differentiation of sequences (corresponding to different taxa) based on some property of the amplicons. Community profiling methods have been

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widely adopted because they provide a more detailed picture of soil communities than methods such as DNA reassociation, while being simple and rapid enough to allow an ecologically and statistically relevant number of samples to be compared. This chapter begins with a review of significant contributions to the field of soil ecology made using T-RFLP, and discussion of an example of T-RFLP data. This is followed by details that need to be considered when performing T-RFLP or interpreting data. The chapter ends with a discussion of the advantages and disadvantages of PCR-based community profiling methods in general, and T-RFLP in particular, and modifications to T-RFLP that can be used to ameliorate some of the disadvantages.

T-RFLP in Action The basic steps of T-RFLP analysis are shown in Fig. 5.1. After extraction of mixed

T-RFLP Generates a community profile using length heterogeneity in the terminal fragment of target gene Soil DNA extraction Mixed community genomic DNA

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Fig. 5.1. Illustration of the steps involved in T-RFLP.



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community DNA, a gene is amplified from all organisms of interest using PCR. One end of each PCR amplicon is tagged by a fluorescent molecule attached to a PCR primer. The amplicons are then cut with a restriction enzyme and electrophoresed to separate DNA fragments by size. Taxa with different sequences are differentiated according to the length of their T-RFs, which are the only restriction fragments with the fluorescent primer. T-RFs are visualized by excitation of the fluor, and are represented by peaks in fluorescence in an electropherogram. The presence, height or area of peaks is used to compare community profiles.

Microbial communities across landscape and plant scales Soil bacterial communities from different geographic locations, and different soil types, have been shown to differ dramatically in both forest (Hackl et al., 2004) and agricultural systems (Girvan et al., 2003). Similar results have also been obtained for fungi (Edel-Hermann et al., 2004). It is unknown whether these large-scale differences between communities are a result of soil characteristics, the plant community or limited dispersal of soil microbes. However, soil chemistry was found by Kennedy et al. (2004) to be more important than identity of plant species in influencing rhizosphere bacteria. At a given location, differing ecosystem management regimes, which affect both soil characteristics and plant communities, can cause bacterial (Blackwood and Paul, 2003) and fungal (Brodie et al., 2003; Edel-Herman et al., 2004) communities to diverge. Kuske et al. (2002) found that soil bacterial communities under arid grass species were different from those in interspaces between plants. The species of plant did not seem to have a large impact, in agreement with the study by Kennedy et al. (2004) mentioned above. Kuske et al. (2002), as well as LaMontagne et al. (2003), also found that bacterial community composition changes with depth into the soil profile.

Microbial communities in soil microsites: a case study From culture-based microbiological inquiries, the rhizosphere has been recognized as a soil microenvironment which harbours a community of microorganisms that is different from non-rhizosphere (bulk) soil. This was confirmed using T-RFLP by Blackwood and Paul (2003). The bacterial community residing in another soil microenvironment, the light fraction, was also examined. The light fraction is composed of soil organic matter particles which are less dense than soil minerals and are isolated by flotation or centrifugation. Blackwood and Paul (2003) showed that the bacterial community within this soil fraction had a unique structure, which was more similar to the community in the rhizosphere than to that in the bulk soil (or heavy fraction). In the agricultural systems studied, the light fraction served as a habitat for a community of active microorganisms, and represented a pool of labile carbon approximately five times as large as the roots and rhizosphere. Figure 5.2 is an example of raw T-RFLP data analysed in Blackwood and Paul (2003). Peaks in the same position (same size in bp) in different samples are assumed to be derived from the same taxa. A change in relative peak height from one sample to another indicates a difference in relative abundance of the taxa. Many of these differences must accumulate for communities to be found to be significantly different overall. Relative peak height is analysed because differences in overall fluorescence (measured as the sum of peak heights) can vary randomly due to pipetting error, amplification efficiency or purification efficiency (see the ‘Community analysis’ section of this chapter). Many of the T-RFs affiliated with particular habitats in Fig. 5.2 seem to be comprised of relatively small peaks, but small peaks do not necessarily imply that the organisms they represent are unimportant. Large peaks could be caused by high biomass of the corresponding organisms, or by differences in ribosomal operon copy number, bias in PCR or bias in DNA extraction. In addition, it may not




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T-RF size (bp) Fig. 5.2. Section of T-RFLP profiles analysed in Blackwood and Paul (2003). Primers used were 8F-Hex and 1392R; the restriction enzyme used was RsaI. Only a portion of the full range analysed (50–500 bp) is shown. T-RFs which are affiliated with one soil fraction or cropping system are outlined and the environment is indicated at the top; T-RFs affiliated with the environment in both years of the analysis are marked with an asterisk. H = heavy fraction, L = light fraction, C = continuous maize, A = alfalfa

make sense to compare the biomasses of different taxa within the same sample when the taxa could have unrelated physiologies and ecological roles. Hence, differences in height of peaks at different positions within one profile are not accorded much significance, while differences in height of peaks

at the same position in different profiles represent important differences between communities. Hellinger distance was used to incorporate this situation into the statistical analysis. This parameter is a distance metric that reduces the influence of large peaks, but still retains information about changes



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in relative peak height (see the ‘Community analysis’ section of this chapter for details). While consistent and significant differences were seen between soil fractions, the process of assigning a phylogenetic affiliation to interesting T-RFs revealed an area of T-RFLP needing improvement (Blackwood and Paul, 2003). Some of the T-RFs in Fig. 5.2 were consistently associated with the same treatment across 2 years, demonstrating a robust relationship between soil habitat and a particular bacterial taxa. In an attempt to assign a phylogenetic affiliation to these T-RFs, in silico digests were performed using the software TAP-TRFLP. In most cases, this resulted in quite a few sequences which could have generated each T-RF. For example, the 55–58 bp category from RsaI digests could correspond to sequences from 13 different genera. Overall, T-RFs analysed could be matched to 0–29 different genera. Digests with two restriction enzymes were performed, but no database sequences were found which could have generated T-RFs from different digests associated with the same treatment. There is a variety of reasons why attempts to match T-RFs to database sequences can have unsatisfactory results (Dunbar et al., 2001; Engebretson and Moyer, 2003; Kaplan and Kitts, 2003). Strategies for more robust phylogenetic inference from T-RFLP profiles are discussed below. T-RFLP analysis has also revealed some surprising similarities between soil microenvironments. For example, different macroaggregate size classes (0–2, 2–4 and 4–6.3 mm diameter aggregates) were found to have statistically indistinguishable bacterial profiles, and only marginal differences were found between communities in external surface layers of aggregates versus internal aggregate cores (Blackwood et al., 2006). We hypothesize that slow growth of bacteria in bulk soil relative to the turnover of soil macroaggregates prevents divergence of communities in these microenvironments. In another example, the microflora of the earthworm gut appears to be quite similar to surrounding soil unless the earthworm has been feeding on plant litter (Egert et al., 2004). This is in contrast to the situation for

soil-feeding termites, where the microbial community of the gut includes dominant species not found in soil (Schmitt-Wagner et al., 2003; Donovan et al., 2004).

T-RFLP analysis of functional groups In the experiments discussed so far, very broad phylogenetic groups have been targeted for analysis (bacteria, archaea or fungi). This ensures that major changes in community structure will not be missed, but it can be difficult to associate changes in general community structure with specific biogeochemical processes. When a specific process is of interest, the ecological guild or functional group that is responsible for it can be targeted. This is accomplished by performing T-RFLP using PCR primers which amplify a functional gene encoding an enzyme important in the process. Shifts in functional gene T-RFLP profiles have been correlated with potential activities for denitrification (Wolsing and Prieme, 2004), nitrification (Mintie et al., 2003) and nitrogen fixation (Tan et al., 2003). In contrast, Yeager et al. (2004) found nitrogenase (nifH) T-RFLP profiles to be similar between mature and poorly developed desert soil crusts despite differences in potential nitrogenase activity. However, nitrogenase activity was correlated with the overall abundance of nifH measured by quantitative PCR using a SYBR Green assay. The results from Yeager et al. (2004) raise an important point, which is that T-RFLP provides information about community structure within a target group, but not a complete description of a community. The number or biomass of target organisms in a sample can be highly important in determining potential activity, and is best understood using other methods such as quantitative PCR or fluorescence in situ hybridization (FISH). Furthermore, most of the studies discussed so far have analysed communities at the DNA level, but the presence of DNA does not necessarily imply activity. In some cases, fine-scale predictions of activity may require use of mRNA assays or stable isotope probing of RNA or DNA (Wellington et al., 2003).




Materials and Methods Involved in T-RFLP Sample collection Replication in sampling is designed to assess the variability among experimental units, which determines confidence in results. Without replication of appropriate experimental units, results cannot be considered statistically significant. The purpose of an experiment determines the appropriate experimental units for replication. Normally, one wishes to generalize about the effects of a ‘treatment’ (experimental or natural) within an environment. In this case, the experimental units to be replicated are subsamples from the environment under each treatment, such as field plots, transects or microcosms (all referred to as ‘plots’ hereafter). Variability from one location to another within a plot is not of interest because a treatment is applied uniformly over the whole plot. Pooling multiple soil cores from a plot is a good idea in order to mask variability within a plot, but it is not a substitute for replication! Analysis of a single pooled sample, even with analytical laboratory replicates, does not provide any information about variability among plots, or even among soil cores. Other experimental units may be appropriate when the goal is not simply to determine differences between treatments. Intensive sampling within a plot is necessary to assess how variability itself is affected by treatments (Mummey and Stahl, 2003). Methodological variability is important in the evaluation of a new method, and can only be studied through use of analytical replicates (e.g. Blackwood et al., 2003). Analytical replicates are derived from the same sample and should theoretically be identical except for variability introduced through laboratory procedures.

DNA extraction DNA extraction and PCR are two steps that are known to introduce bias into

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community profiles. This occurs in the former due to differential lysis efficiencies of cells, and to protection of some cells within soil microsites. It is most important to treat all samples identically throughout all laboratory procedures so that, even with some bias, community profiles can still be compared. While many protocols have been described in the literature, DNA extraction from environmental samples is now often accomplished through commercial kits such as those available from Mo Bio Laboratories (Carlsbad, California, USA) or Qbiogene (Irvine, California, USA). PCR-amplifiable DNA has been isolated using such kits from a wide variety of soil, rhizosphere and plant litter samples. The kits involve bead beating, which can shear genomic DNA, but this is not a serious concern if the DNA fragments are larger than the gene to be targeted by PCR. In my experience, working with direct extract diluted 1 : 20 or 1 : 100 (direct extract : total volume, diluted with sterile H2O) has resulted in successful PCRs for most samples. Active cells have a higher RNA : DNA ratio than inactive cells, so it has been argued that analysis of rRNA amplicons reflects the active fraction of the community. T-RFLP can be performed on PCR amplicons derived from reverse-transcribed rRNA. RNA degrades easily and is more difficult to work with than DNA. Extraction of rRNA can be performed using a variety of methods described elsewhere (e.g. Sessitsch et al., 2002).

PCR primers Choice of PCR primers is a critical step in T-RFLP and other PCR-based community profiling methods. Primers define the gene to be targeted and the phylogenetic specificity of the analysis. Many previously published primers can be improved upon (Blackwood et al., 2005), in part because genetic databases are in a constant state of flux, with new sequences being added and taxonomy being revised. It is therefore important to check that the primers used



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discriminate against non-target sequences and comprehensively include all target sequences. While not all investigators will want to engage in primer design and testing, anyone using PCR should know what the limitations are for their chosen primers. Both primers must match as many sequences in the target group as possible, and at least one of the primers must also discriminate against all non-target sequences. Discrimination is achieved by locating a 1–4 bp region of the gene that has a sequence matching all target sequences and no other sequences. This region must be placed at the 3′ end of the oligonucleotide. Checking coverage of primers can be performed using a simple BLAST search in GenBank, but there are disadvantages to this approach including limited ability to modify output, define how searches are conducted and incorporate degenerate positions into the query sequence. Curated databases exist for ribosomal genes (Cole et al., 2003; Ludwig et al., 2004), while databases for other genes may have to be constructed by the investigator. Finding a discriminatory region of the gene is the first and most important step. After this, the primer annealing temperatures must be checked. The discriminatory primer should have a lower annealing temperature than the non-discriminatory primer, because the specificity of the primer set is determined by the primer with the lowest annealing temperature. Also, the primers should be checked to see if they will form strong homo- or heterodimers. These are molecules formed by primers binding to themselves or to each other. If primer dimers are extended by a DNA polymerase during the first few cycles of PCR, they can result in a greatly reduced yield of full-length target molecules.

PCR conditions PCR conditions need to be optimized for each unique set of primers. PCR is very sensitive to annealing temperature, with increased annealing temperature increasing the specificity of primer binding. On the other hand, annealing temperatures that are

too high result in weak or biased reactions because primer–template combinations are not stable. Other conditions which are often optimized include MgCl2 concentration, primer concentration and type of DNA polymerase. Other additives such as bovine serum albumin are commonly added to PCRs to reduce the effects of contaminants or alter primer binding efficiency. An optimized PCR should be tested by cloning and sequencing amplicons from typical samples. This is the best test to determine the specificity of the reaction under field conditions. Breadth of primers within the target group can also be tested using a collection of cultured organisms. These tests are important because primers do not always act as expected from the results of a computer analysis. Sequencing may also reveal PCR artefacts which need to be minimized (e.g. Egert and Friedrich, 2003; Osborne et al., 2005). Even with an optimized PCR, it is still often necessary to optimize the cycle number and/or dilution for each DNA extract. It would be ideal if all samples in a study could be PCR amplified under identical conditions, but this is not always possible due to differences in the amount of extracted DNA, target DNA and co-extractants which affect the efficiency of PCR. An excessive amount of template DNA or number of PCR cycles can lead to amplification of spurious non-target amplicons. These are often observed as a background smear of DNA above and below the DNA band of the correct size in an agarose gel (Blackwood et al., 2005). However, using a more concentrated sample can at times decrease PCR yield if co-extracted PCR inhibitors exceed a threshold, or if the genomic DNA concentration is so high that it reduces the concentration of Mg2+ ions. Multiple PCRs using the fluorescently tagged T-RFLP primer should be performed for each sample. These PCRs can then be analysed separately or pooled. PCR has been shown to be the source of some of the analytical variability in T-RFLP profiles, which can be partially overcome by pooling three or more PCR products (Blackwood et al., 2003).




Restriction enzymes and purification

Data Processing and Analysis

Choice of restriction enzyme has been shown to be important by Engebretsson and Moyer (2003). Group-specific primers result in a less diverse pool of amplicons, which makes the choice of restriction enzyme critical in order to obtain informative profiles (Blackwood and Buyer, 2006). In this case, computer simulations can be useful to test all restriction enzyme recognition sequences efficiently. It is common practice to purify PCR products using commercial PCR clean-up kits before digestion with restriction enzymes, although I have observed that many restriction enzymes function very well when added directly to a PCR product. Tests should be performed with amplicons from pure culture or a cloned amplicon whenever a new combination of restriction enzyme and incubation conditions is attempted. T-RFLP samples to be run on a capillary electrophoresis machine require further purification after digestion to remove all salts from the sample as these can interfere with uptake of the DNA into the capillary. If the samples will not be purified after digestion, deactivation of the restriction enzyme is necessary in order to keep the enzyme from degrading internal size standards.

Tables of fluorescence peaks

Electrophoresis Labelled T-RFs are separated by size using electrophoresis. A fluorescence detection sequencing apparatus is used to obtain high resolution separation and quantitative detection of fluorescently labelled T-RFs. Both polyacrylamide gel and capillary machines have been adapted to this purpose. An internal size standard labelled with a fluor, which emits at a different wavelength from the T-RFs, is included in each lane. Fragment analysis software accompanying these systems is used to call and size peaks in fluorescence. This software generates a table of peaks with the size of the DNA fragment in base pairs, and height and area of the peak.

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Data processing refers to steps taken to prepare tables of peaks for statistical analysis. A height threshold is used to exclude small peaks which are analytical noise. Using a threshold of 25 fluorescence units in Genescan (Applied Biosystems, Foster City, California, USA) has been reported to result in many unstable peaks (Dunbar et al., 2001), while using a threshold of 200 units has reduced sensitivity of an analysis (Blackwood et al., 2003). Therefore a threshold of 50–100 units is recommended. More peaks tend to be found in profiles where total fluorescence is stronger (measured by the sum of all peak heights), but total fluorescence is often an analytical artefact. To correct for this, all samples should be standardized by calculating the relative abundance of each peak (peak height divided by the sum of peak heights in that sample). Peaks with abundance below a minimum (such as 1%) are then deleted. Note that, to correct truly for the issue raised above, the minimum cut-off should be taken from the sample with the lowest total fluorescence (e.g. if the weakest sample had a total fluorescence of 1000 units and a minimum peak height of 50 units, all peaks with relative abundance < 5% should be deleted in other samples). Alignment of T-RFLP profiles is a step in data processing which involves deciding which peaks from different profiles represent the same T-RF, and which peaks are unique. The result of aligning profiles is a data matrix, with rows representing samples, and columns representing different T-RFs. Each cell in the matrix contains the peak height and area, or a presence–absence indicator for a particular T-RF in a sample. Alignment of profiles is primarily based on the lengths of T-RFs in base pairs, but even with internal lane standards there is noise in the size-calling of fragments (Clement et al., 1998; Kaplan and Kitts, 2003). Moeseneder et al. (1999) found a mean standard deviation in peak sizes of 2.6 bp (maximum 5 bp) using capillary electrophoresis,



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while Dunbar et al. (2001) stated that the error in determining fragment size was < 0.5 bp using polyacrylamide gel electrophoresis. Blackwood et al. (2003) found a mean range in peak size of 1.4 bp (maximum 4.4 bp), also using polyacrylamide gel electrophoresis. In complex communities, such as those found in soil, sizes of peaks in different samples often cannot be unambiguously categorized. It may be possible to use patterns within the electropherograms as guides for alignment, but the most important aspect of T-RFLP alignment is consistency across samples. Also, sample identities should be hidden during the alignment process to avoid systematic bias during the process. Creating bins with arbitrarily defined borders (e.g. all peaks with sizes between 100 and 102 bp) is not recommended because T-RF sizes determined by electrophoresis do not vary in this discrete fashion (there is temperature- and sequence-related variability which affects the electrophoretic mobility of fragments). A program such as GelcomparII (Applied Maths, Austin, Texas, USA) is convenient to use for data processing and will ensure that all samples are processed according to a single algorithm. GelComparII can import chromatograms and has a suite of semi-automated methods for calling and sizing peaks, and aligning profiles. Alternatively, tables of peaks can be imported for alignment into a matrix only (Blackwood and Buyer, 2006).

Community analysis Community analysis is performed to determine which communities have similar profiles. This information can be used to understand how environmental parameters affect the distributions of taxa, and how the taxa in a community co-vary. Multivariate methods should be used to analyse T-RFLP data unless there is a specific reason to focus an analysis on a particular T-RF. The first step is to choose a mathematical basis, known as a distance coefficient, upon which to compare profiles.

The distance coefficient used in standard statistics (Euclidean distance) has been shown to be a particularly bad choice for T-RFLP data (Blackwood et al., 2003). This problem is partly solved by analysis of relative fluorescence, although Blackwood et al. (2003) showed that use of Hellinger distance is more sensitive (lower probability of type II errors). Use of Hellinger distance also helps reduce the importance of large peaks relative to small peaks (see discussion in ‘Microbial communities in soil microsites: a case study’ section, this chapter). Transforming T-RFLP data for analysis by Hellinger distance is simple; it is the square-root of relative fluorescence. Transformed data can then be analysed directly by many standard statistical methods that had been criticized in the past because of their dependence on Euclidean distance (Legendre and Gallagher, 2001). Analysis of T-RF presence–absence through the Jaccard coefficient was even more sensitive, but only when total fluorescence was > 10,000 units for all samples (Blackwood et al., 2003). Other distances that have been used include the Dice and Bray–Curtis similarity coefficients, and χ2 distance (which is preserved through the procedure ‘correspondence analysis’). See Legendre and Legendre (1998) for a more complete discussion. Exploratory analyses should be used to look for patterns in data when there is no explicit hypothesis about community structure. Unfortunately, they are often used in the context of hypothesis testing in microbial ecology. This practice results in subjective determinations of the ‘significance’ of hypotheses, which may or may not be statistically valid. Ordination techniques (principal components analysis (PCA), principal coordinates analysis, non-metric multidimensional scaling) construct unique, independent variables (ordination axes) from community profiles. The goal is to capture the majority of the variance in the data set in the first few ordination axes so that these can be examined for major patterns. Cluster analysis groups samples into hierarchical relationships which are displayed in figures called dendrograms. Many algorithms for cluster analysis exist. The UPGMA method




and Ward’s method have been examined for T-RFLP data (Blackwood et al., 2003). The hypothesis that is commonly of interest when examining T-RFLP profiles is that the profiles are significantly related to experimental treatments or to environmental variables. Redundancy analysis (RDA) is a form of PCA where axes are constrained to summarize only that variability in the T-RFLP profiles that can be explained by external data. This can be used to determine the proportion of the total variability in a T-RFLP data set that is explained by the external variables, similar to an R2. Monte Carlo permutation methods can also be used to generate a P-value describing statistical confidence in the relationship detected. RDA has been shown to be a sensitive and flexible method of testing hypotheses in T-RFLP data (Blackwood et al., 2003). A wide variety of modifications can be used to accommodate distance coefficients, residual effects, interaction effects, non-linear relationships, spatial data, random effects, etc. In addition, RDA requires fewer unrealistic assumptions about the distributions of data sampled than many classical methods of multivariate hypothesis testing.

Phylogenetic inference and diversity statistics One of the major advantages of using molecular methods is that gene sequences are phylogenetically conserved, so it should be possible to fit experimental data into a phylogenetic framework. Experimental T-RFs can be related to sequences of known origin by comparing their size (in bp) with T-RF sizes generated by computer restriction of a database of target gene sequences. The web-based software programs Tap-T-RFLP (Marsh et al., 2000) and ISPaR (from MiCA, University of Idaho, USA; http://mica.ibest.uidaho.edu/) have been developed to perform virtual digestions of SSU ribosomal sequences from the Ribosomal Database Project (Cole et al., 2003). An SAS program available from the author can also be used to generate predicted T-RFs from any database of DNA sequences.

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One problem with this approach is that T-RFs are not phylogenetically specific (Dunbar et al., 2001; Engebretson and Moyer, 2003), and so comparisons with databases may not yield useful information. This is particularly true for analyses of very broad phylogenetic groups. Use of multiple profiles generated with different restriction enzymes may assist in narrowing down the number of possible database sequences. Several programs exist which automate the task of sorting through matches from multiple digests, including PAT (Kent et al., 2003), FragSort (from the University of Ohio, USA; http:// www.oardc.ohio-state.edu/trflpfragsort/ default.htm) and APLAUS+ (from the MiCA website noted above). These phylogenetic affiliations should still be viewed as hypotheses, at best. Observed T-RF length may be different from predicted T-RF length due to the effects of DNA sequence, dye label and ambient temperature during electrophoresis (Kaplan and Kitts, 2003). In addition, there is a large amount of unknown diversity present in natural systems, particularly in soils. Strategies for obtaining empirical identifications of organisms represented by T-RFs are presented in the ‘Advantages, Drawbacks and Extensions of T-RFLP’ section of this chapter. Another set of methods commonly employed to analyse T-RFLP data is ecological diversity statistics. This includes richness (the number of T-RFs present in a profile), various evenness indices calculated from T-RF peak height or area, and hybrid indices such as the Shannon–Weiner diversity index. There is interest stemming from ecological theory in quantifying these aspects of microbial communities. However, this practice should be avoided in the analysis of T-RFLP profiles, except in the case of very simple communities (< 20 species). One problem is that T-RFs are not necessarily phylogenetically specific. Also, differences in heights of peaks within one profile are used to calculate evenness indices. The lack of meaning in these differences has been discussed above. Finally, community profiling methods such as T-RFLP and denaturing gradient gel electrophoresis (DGGE) only



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detect dominant taxa in the community, typically those that comprise more than approximately 1% of community DNA (Dunbar et al., 2001; Muyzer et al., 1993). If it is determined that there are 15 dominant taxa in one system and 20 in another, this does not necessarily mean that the total number of taxa is different between these systems. In fact, the sample with 15 dominant taxa could include the same five additional taxa that are present in the other sample, except at lower abundances. Hence, richness indices applied to community profiles may primarily reflect evenness of the communities. At this point, it is unclear what diversity indices actually tell us about communities. There is a difference in structure between our hypothetical communities which has been successfully detected by T-RFLP, and would be revealed by multivariate community analysis. Use of diversity indices to portray this difference, however, would lead to severe misinterpretation of results.

Advantages, Drawbacks and Extensions of T-RFLP T-RFLP is comparable in many ways with other community profiling methods, and many of the comments above also apply to them. Alternative methods include DGGE (see also O’Callaghan et al., Chapter 6 this volume), length heterogeneity PCR (LHPCR), ribosomal intergenic spacer analysis (RISA) and single strand conformation polymorphism (SSCP). All PCR-based community profiling methods suffer from biases and artefacts associated with DNA extraction and PCR (for a review, see Wintzingerode et al., 1997). It is important to treat all samples identically in order to minimize spurious differences between samples. Profiling methods are a powerful approach to detecting differences between communities, but data must be interpreted with care as described above. Contrasting results have been obtained when different community profiling methods are compared directly (Moeseneder et al.,

1999; Mills et al., 2003; Nikolcheva et al., 2003; Hartmann et al., 2005). Different primers are used for different community profiling methods, so conclusions from empirical comparisons may not have general applicability. In addition, results will vary depending on the specific samples, primers and other experimental parameters that are chosen, as well as equipment and reagents used (e.g. Osborn et al., 2000; Ikeda et al., 2004). The ability to compare T-RF sizes with a database of predicted T-RFs is generally viewed as an advantage of T-RFLP. This can also be performed for LH-PCR and RISA, although these methods do not have as much software or database support. However, problems with this practice have been discussed above. Use of more specific primers to analyse more narrow phylogenetic groups will help increase the specificity of T-RFs as phylogenetic markers (Dunbar et al., 2001; Blackwood and Buyer, 2006). To make empirical identifications of T-RFs, several studies have used clone libraries to search for sequences that would yield particular T-RFs (e.g. Moeseneder et al., 2001; Scheid et al., 2004). This approach is acceptable, but in a complex community a large number of clones may need to be sequenced or screened by T-RFLP before finding any that produce the correct T-RF. Also, if there is a diverse population contributing to the signal of one T-RF, then this population may not be adequately sampled. Finally, a quicker and less expensive approach would be preferable. Grant and Ogilvie (2004) described a more efficient method of screening clone libraries which reduces the number of T-RFLPs that need to be run on clones by a factor of five. Mengoni et al. (2002) described a procedure to clone T-RFs, but the clone libraries included T-RFs of different sizes, so preliminary screening of the clones was still required. Their procedure also involved an additional nested PCR between T-RFLP and cloning steps, making it less than ideal. Blackwood and Buyer (2006) evaluate an alternative procedure which generates a clone library containing sequences from only the T-RF of interest. T-RFs are tagged using biotinylated primers instead of a




fluorescent molecule, and are then immobilized using paramagnetic beads so that nonterminal fragments can be removed. The T-RFs are released and run on a high resolution agarose gel. Bands of interest can then be excised, cloned and sequenced without contamination from other T-RFs or non-terminal fragments. T-RFLP has a variety of advantages which are responsible for its rapid gain in popularity: data are quantitative and comparable between laboratories, the final electrophoresis step can be performed with automated sequencing equipment at core sequencing facilities, the method is relatively straightforward to begin to use and it provides a picture of the community that incorporates diversity and phylogenetic detail. Placement of primer sites within a gene may require use of T-RFLP in preference to other profiling methods because PCR amplicons can be longer with this method. This is an advantage because full-length sequences are more useful than partial ones. A perceived advantage of DGGE may be that it is cheaper than T-RFLP, which requires expensive automated fluorescence sequencing equipment. T-RFLP profiles, however, can be generated without the use of such equipment using the procedure described above from Blackwood and Buyer (2006), a fluorescence scanner (Ikeda et al., 2004) or 32P-labelled primers (Dunfield and Germida, 2003).

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Conclusions T-RFLP is a highly effective method for comparing microbial communities based on composition of dominant taxa. Taxa can be identified by using modified versions of T-RFLP. Other information about the community, such as total diversity and size, can be obtained by other methods. The ability to obtain information about microbial communities efficiently will continue to be necessary as we seek to understand what determines species distributions and the links between community structure and ecosystem processes. The T-RFLP method can be focused on functional groups through the analysis of functional genes or welldefined taxonomic groups. Future studies targeting particular functional groups should lead to more rapid advances in our knowledge, as this will allow the integration of microbial ecology, community ecology and biogeochemistry (Zak et al., 2006).

Acknowledgements Kirsten Hofmockel, Deborah Hudleston and Jim Cooper provided useful comments during the preparation of this manuscript.

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6

Characterization of Phylloplane and Rhizosphere Microbial Populations Using PCR and Denaturing Gradient Gel Electrophoresis (DGGE) Maureen O’Callaghan1,*, Nicola Lorenz2 and Emily M. Gerard1 1AgResearch,

Biocontrol and Biosecurity Group, PO Box 60, Lincoln, Canterbury, New Zealand; 2The Ohio State University, School of Environment and Natural Resources, 2021 Coffey Road, Columbus, OH 43210, USA

Introduction The plant rhizosphere is a complex habitat, influenced by plant species, cultivar, season, soil type and nutrient supply, as well as by soil microbial community composition and fauna, especially protozoa and nematodes. Similarly, the plant phylloplane provides a diverse and changeable environment for a variety of microorganisms (Hirano and Upper, 2000). Historically, knowledge of the bacterial and fungal communities associated with plants has been based on traditional culture-based methods, in which microorganisms are typically plated onto various laboratory culture media. Other culture-based techniques, such as analysis of carbon source utilization patterns in BIOLOG® microplates, have also been used (e.g. Duineveld et al., 1998). However, it is now widely accepted that culture-based techniques significantly underestimate the diversity of microbial species present in environmental samples (Hugenholtz et al., 1998), and development of culture-independent methods has shed new light on the complexity of microbial

communities in a range of ecosystems (Torsvik et al., 1996; Yang et al., 2001b). For bacterial communities, many of these new insights have come about through the development of methods for the direct extraction of nucleic acids from soil and plant samples, followed by cloning and sequencing of 16S rRNA. The 16S rRNA gene has long been identified as uniquely suited to act as a molecular phylogenetic marker because of its structural and functional conservation and universal distribution (Pace et al., 1986). For many ecological studies, it is necessary to process large numbers of samples because the natural variability in a microbial community needs to be differentiated from effects of changing environmental conditions, season, plant type, etc. Cloning and sequencing strategies are both time-consuming and labour-intensive, and are therefore not well suited for monitoring large numbers of samples; genetic profiling techniques are needed for these types of studies. Muyzer et al. (1993) first used denaturing gradient gel electrophoresis (DGGE) to analyse 16S rDNA fragments amplified by polymerase chain

*Corresponding author; Phone: +64 3 325 9986, Fax: +64 3 325 9946, E-mail: [email protected] ©CAB International 2006. Molecular Approaches to Soil, Rhizosphere and Plant Microorganism Analysis (Cooper and Rao)


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reaction (PCR) from community DNA using bacteria-specific primers. Since then, this technique has been widely used for analysis of communities from diverse habitats including soil, plants and insects (Gelsomino et al., 1999; Smalla et al., 2001; O’Callaghan et al., 2003). DGGE allows the separation of small, diverse PCR-amplified products into a fingerprint or profile composed of bands. The separation of the DNA fragments is achieved on the basis of their differing GC content and distribution, which affects movement of the fragments through a linear denaturing gradient. The gradient can be produced by denaturing chemicals (DGGE) or heat (temperature gradient gel electrophoresis, TGGE). The profiles from different samples can then be compared between treatments to determine the level of similarity in community structure and to investigate shifts or changes in community composition. Numerous samples can be accommodated on a single gel, allowing rapid, simultaneous comparison between samples, and statistical analysis. A further advantage of the technique is the ability to excise bands from the gels, for subsequent cloning and sequencing, which can yield taxonomic information on populations in the community through database searches and phylogenetic analyses. To increase the specificity of the analysis, primers specific to various taxonomic or functional groups can be used, or community members can be identified by hybridization of blotted gels with group-specific oligonucleotide probes (Stephen et al., 1998; Heuer et al., 1999). This chapter reviews methods and recent applications of PCR-DGGE for fingerprinting or profiling of rhizosphere and phylloplane microbial communities.

PCR-DGGE Analysis Methods Extraction of DNA from soil Two main strategies have been developed for extraction of nucleic acids from environmental samples. The first requires cell extraction methods to separate microorganisms from soil prior to cell lysis and

recovery of DNA, and may be biased due to differential separation of various bacteria from soil particles. The second involves direct extraction of nucleic acids following in situ lysis of cells. Both have advantages and disadvantages relating to DNA yields and purity, and the unbiased representation of microbial community diversity (Robe et al., 2003). Direct in situ methods have been used most widely over the last decade, and numerous protocols have been published, most of which have been derived from the method first reported by Ogram et al. (1987) (e.g. More et al., 1994; Zhou et al., 1996; Krsek and Wellington, 1999; Martin-Laurent et al., 2001). In the first step of direct extraction methods, cells are lysed to allow release of nucleic acids. Various lysis methods have been used: physical (e.g. freezing–thawing cycles and bead beating), chemical (e.g. sodium dodecylsulphate (SDS)), enzymatic (e.g. lysozyme) and combinations of these. In the second step, nucleic acids are separated from soil particles and purified; soils contain many compounds such as humic acids and proteins that can inhibit or decrease the sensitivity of subsequent PCR analysis (Tsai and Olson, 1992; Krsek and Wellington, 1999). In most studies, the first step in the purification of DNA is organic solvent extraction (either phenol or chloroform) followed by ethanol, isopropanol or polyethylene glycol precipitation. Many different procedures have been used for further purification (e.g. CsCl–EtBr gradient centrifugation, hydroxyapatite columns and polyvinylpolypyrrolidone). However, purification strategies required to remove inhibitory compounds can significantly reduce yields, and their effectiveness varies with soil type (LloydJones and Hunter, 2001). Early DNA extraction methods were large scale (some using up to 100 g of soil), but in recent years smaller scale methods have been developed that allow high throughput of large numbers of small samples (typically 0.3–0.5 g of soil). The need to process many environmental samples has led to the widespread use of commercially available kits such as Mo Bio UltraClean™ soil DNA isolation kit (Mo Bio Laboratories, Carlsbad,



Characterization of Phylloplane and Rhizosphere Microbial Populations

California, USA), BIO101 FastDNA™ SPIN Kit for Soil (Qbiogene, Irvine, California, USA) and SoilMaster™ DNA Extraction Kit (Epicentre, Madison, Wisconsin, USA). The kits have been used in many analyses of soil bacterial communities and have some advantages over other methods. The simple standardized protocols are rapidly carried out, give reproducible results and yield pure DNA of a relatively high molecular weight. It should be noted, however, that soil sample size may influence the outcome of DNA fingerprinting analyses of fungal and bacterial communities (Ranjard et al., 2003).

PCR amplification of nucleic acids The 16S rRNA gene is an ideal target for PCR amplification in studies examining diversity in bacterial communities. Thus, primer sets annealing to the conserved regions of the 16S rRNA gene and spanning one or up to three variable regions have been developed and used on numerous occasions. Primer sequences and their associated PCR protocols can be found in many reviews and research publications (e.g. Heuer et al., 2001; Muyzer et al., 2004), but those used most commonly in analysis of plant-associated and soil bacterial populations are listed in Table 6.1. In comparison with prokaryotic microbial ecology, PCR-based culture-independent analysis of fungal community structure is still in its infancy (Kowalchuk, 1999; Smit et al., 1999). There are difficulties inherent in studying fungal communities, such as problematic identification via morphological and metabolic characteristics, inability to culture biotrophs (e.g. mycorrhizal fungi) and lack of clarity on the concept of a fungal individual. These challenges have driven the development of new molecular techniques to complement traditional mycological ones (Anderson and Cairney, 2004). The eukaryotic 18S rDNA sequence database, while being more limited than the prokaryotic 16S rRNA database, has yielded sufficient information to allow amplification of a range of fungi. However, for a variety of reasons (discussed

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further in Kowalchuk and Smit, 2004), it has proven difficult to engineer a single primer set suitable for fungal community analysis, and the primers chosen must be selected to suit particular research needs and the level of discrimination required. Characteristics, specificity and limitations of several primers used for PCR-DGGE analysis of general fungal communities have been compared by Kowalchuk and Smit (2004). Use of group-specific primers The sensitivity of DGGE analysis can be increased by using specific primers to target particular taxonomic groups that may not be dominant in the bacterial community. The PCR products obtained with group-specific primers can be analysed directly using DGGE, provided that one of the primers has a GC clamp. Alternatively, in a two-step process (a nested PCR approach), bacterial community DNA is amplified with groupspecific primers that also span the region of the 16S rDNA used for amplification with the universal DGGE primers. Products from the first PCR amplification are then used as a template for a second one using DGGE primers. Gomes et al. (2001) described primers for the analysis of Alpha- and Betaproteobacteria, and primers for Gammaproteobacteria have been developed more recently (Seghers et al., 2004). Actinobacterial populations have also been analysed using the nested PCR approach (Heuer et al., 1997). More recently, PCR-DGGE has been used to access particular bacterial genera, for example Pseudomonas spp. (Evans et al., 2004), Bacillus spp. (Garbeva et al., 2003) and Paenibacillus spp. (da Silva et al., 2003). Specific primers for arbuscular mycorrhizal fungi (Ma et al., 2005) and fungal genera, e.g. Gigaspora spp. (de Souza et al., 2004) have also been developed. The potential to examine the diversity of functional genes in microbial communities is currently limited by the availability of sequence information. However, functionally significant genes have been used to examine the diversity of organisms that play a role in the nitrogen cycle, such as ammonia-oxidizing bacteria (Kowalchuk et al., 1998; Briones



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Table 6.1. PCR primers used for DGGE analysis of rhizosphere bacterial and archaeal communities. Target

Primera

Reference

Bacteria

F27 PRBA338F + GC PRUN518R F968 + GC R1378 R1492 F243 F203 F948 MB10

Lane (1991) Øvreås et al. (1997) Øvreås et al. (1997) Nübel et al. (1996) Heuer et al. (1997) Lane (1991) Heuer et al. (1997) Gomes et al. (2001) Gomes et al. (2001) Seghers et al. (2004)

BacF PAEN515F Ps-F Ps-R F311Ps R1459Ps Burk3 + GC BurkR CTO189Fa/b/c + GC CTO654R AmoA-1F + GC AmoA-2R Primer 1F Primer 2R + GC FGPH19 PolR AQER PolF + GC

Garbeva et al. (2003) da Silva et al. (2003) Evans et al. (2004) Evans et al. (2004) Milling et al. (2004) Milling et al. (2004) Salles et al. (2002) Salles et al. (2002) Kowalchuk et al. (1997b) Kowalchuk et al. (1997b) Briones et al. (2003) Briones et al. (2003) Lovell et al. (2000) Lovell et al. (2000) Demba Diallo et al. (2004) Demba Diallo et al. (2004) Demba Diallo et al. (2004) Demba Diallo et al. (2004)

Ar3F Ar9R SAf(i)/SAf(ii) + GC PARCH519r

Nicol et al. (2003) Nicol et al. (2003) Nicol et al. (2003) Nicol et al. (2003)

Actinobacteria α-proteobacteria β-proteobacteria γ-proteobacteria (type I methanotrophs) Bacillus spp. Paenibacillus spp. Pseudomonas spp.

Burkholderia spp. Ammonia oxidising β-proteobacteria Ammonia monooxygenase gene (amoA) Diazotrophs (nitrogen fixers)

Archaea

aF,

forward primer; R, reverse primer; GC, G+C-rich sequence attached at the 5′ end (e.g. Muyzer et al., 1993; Nübel et al., 1996).

et al., 2003) and N2-fixing bacteria (Lovell et al., 2000; Demba Diallo et al., 2004).

Denaturing gradient gel electrophoresis Electrophoretic separation of PCR products is carried out using specialized equipment that is commercially available (e.g. DCode System

apparatus; Bio-Rad Laboratories, Hercules, California, USA). DNA fragments of the same length but with different sequences are separated according to their melting properties. The double-stranded DNA is electrophoresed through a linearly increasing gradient of the denaturants urea and formamide at approximately 60°C. The fragments remain double stranded until they reach the conditions




in the gel that cause melting and branching of the molecule. Partial melting sharply decreases the mobility of the DNA fragments through the gel. Complete melting of the PCR product is prevented by the presence of a GC clamp (40–45 base GC-rich sequence) attached to the 5′ end of the forward primer. The DNA fingerprint (or banding pattern), which becomes visible after staining, results from variations in the melting behaviour of the DNA sequences amplified from diverse communities (see Fig. 6.1). Gels can be stained with ethidium bromide, SYBR Green or silver nitrate, with silver staining being most commonly used

1

2

3

4

5

6

7

Fig. 6.1. DGGE profiles of 16S rDNA fragments amplified from community DNA extracted from tubers’ surfaces and rhizosphere of potatoes genetically modified to express the antimicrobial peptide magainin (O’Callaghan et al., 2004). Lane 1, GM tuber (line D5); lane 2, Iwa tuber (unmodified control line); lane 3, Karaka rhizosphere (unrelated cultivar); lane 4, GM rhizosphere (line D9); lane 5, Iwa rhizosphere; lane 6, GM rhizosphere (line D2); lane 7, GM rhizosphere (line D5).

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as it gives greatest sensitivity. However, it also stains single-stranded DNA, which can cause problems in subsequent analysis as single-stranded DNA often constitutes a strong band. Proteins are also stained by this method, so bovine serum albumin in the PCR mixture can cause a dark background smear on the gels. Detailed protocols and the limitations of the various methods of staining DGGE gels are presented in Muyzer et al. (2004).

Analysis of data In early studies, analysis of microbial community profiles generated by PCR-DGGE was restricted to visual interpretation. The recent development of specific software packages has significantly improved analysis of community fingerprints by allowing accurate comparison of both band position and the relative intensity of different bands within gels. This facilitates statistical analysis of the data and a more robust interpretation of the results. However, the accuracy of the interpretation remains dependent on the inclusion of appropriate internal standards, which are especially important when comparison between several gels is required. Various techniques have been applied for analysis of DGGE fingerprinting patterns including computation of similarity matrices, clustering techniques, and ordination methods such as principal component analysis. Community profiles can also be combined in a joint analysis with environmental data sets to determine whether the banding patterns are associated with measured environmental variables (e.g. McCaig et al., 2001). Fromin et al. (2002) reviewed statistical analysis of fingerprinting patterns, and alternative approaches have also appeared in the literature recently, e.g. representation of DNA fingerprints as high dimensional multivariate binary data (Wilbur et al., 2002) and significance testing using pairwise similarity measures (Kropf et al., 2004). Detailed explanations and instructions in the use of various computer programs for gel and statistical analyses have also been published (Dollhopf et al., 2004;



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Rademaker and de Bruijn, 2004; van Verseveld and Röling, 2004).

Bacterial and Fungal Community Composition of the Plant Phylloplane, Rhizosphere and Root Phylloplane populations Studies of leaf surface microorganisms using PCR-DGGE are scarce; to date, only a limited number of agricultural crops have been analysed. Yang et al. (2001b) used it to analyse bacterial populations of fieldgrown leaves from grapefruit (Citrus maxima Burm. × C. paradisi Macf.), oranges (C. sinensis L.), cotton (Gossypium hirsutum L.), maize (Zea mays L.), sugar beet (Beta vulgaris L.) and green bean (Phaseolus vulgaris L.), and found that bacterial populations differed between plant species. In addition, DGGE showed a much greater complexity of the bacterial population than culturedependent methods. Heuer and Smalla (1999) characterized the bacterial phyllosphere communities of potato plants (cultivars from Solanum tuberosum L.) and two T4lysozyme-producing transgenic variants. PCR-DGGE analysis revealed highly similar communities in the phyllosphere of T4lysozyme-producing plants and control plants. Differences in eubacterial community composition were greater between the potato varieties than between the transgenic and the wild-type lines. Kadivar and Stapelton (2003) investigated the effect of UV-B radiation on phylloplane bacteria of greenhouse- and field-grown maize (Z. mays) leaves; PCR-DGGE patterns showed a trend of increasing bacterial diversity on plants exposed to UV-B in the field. Phylloplane fungal community analysis by PCR-DGGE has been carried out infrequently. Fungi on leaves of water yam (Dioscorea alata L.) were investigated to identify key microorganisms causing disease (Fagbola et al., 2001; Abang et al., 2003). Traditional cultivation techniques were not suitable for tracking the disease-causing organism because Colletotrichum gloeosporioides was found to be highly variable

in morphology (Abang, 1997). PCR-DGGE fingerprints of 18S rDNA, on the other hand, have been shown to be useful for detection of and differentiation between Colletotrichum spp. (Fagbola et al., 2001). In a decomposition experiment, Nikolcheva et al. (2003) studied the community composition of freshwater fungi on decaying leaves (red maple (Acer rubrum L.), alder (Alnus glutinosa, L. Gaertn.), linden (Tilia cordata Mill.), beech (Fagus sylvatica L.) and oak (Quercus rubra L.)) and birch wood (Betula papyrifera Marsh.). PCRDGGE analysis of 18S rDNA fragments revealed that fungal species richness and community evenness decreased with time. In a second study, Nikolcheva and Bärlocher (2005) characterized the seasonal shifts and substrate preferences of fungi colonizing leaves (red maple, beech and linden) and birch wood in streams. The phyllosphere fungal communities differed with season and, when the DGGE band intensity was included in the analysis, the underlying leaf or wood was a significant factor. Rhizosphere and root populations DNA fingerprinting techniques such as PCR-DGGE are powerful tools to elucidate effects on microbial communities of different plant varieties, genetic modification of plants and infection of roots by pathogens or mycorrhizal fungi. Spatial and temporal variations of rhizosphere populations and plant endophytic populations have also been investigated. Other studies have focused on the effect of biological control agents or elevated CO2 concentration on the microbial rhizosphere population. Since most rhizosphere studies have investigated the diversity of bacteria, fungi or Archaea, all these groups are considered here. Rhizosphere bacterial community composition EFFECTS OF PLANT SPECIES AND GENETIC MODIFICATION OF PLANTS. Bacterial populations in the rhizosphere of various plant species have been compared in several




studies. In field crops studied by Smalla et al. (2001), DGGE fingerprints showed that bacterial populations in the rhizosphere were plant dependent. Oilseed rape (Brassica napus L.) and potato (S. tuberosum L.) had more similarities to each other than to strawberry (Fragaria ananassa Duch.), which may be explained by the fact that oilseed rape and potato are annual crops while strawberry is perennial. Also, the plant effect on the bacterial rhizosphere communities was more pronounced after two growing seasons. Similarly, Marschner et al. (2001, 2004) showed that bacterial community composition was dependent on plant species (chickpea (Cicer arietinum L.), canola (Brassica napus L. cv. Oscar) and Sudan grass (Sorghum bicolor L. Moench cv. Piper)), and proposed that root exudate amount and composition were key determinants of differences in community structure (Marschner et al., 2004). However, for some plants (cucumber (Cucumis sativus cv. Delicatess) and barley (Hordeum vulgare cv. Scarlett)), Marschner et al. (2004) showed that rhizosphere bacterial communities were not affected by plant species, but this may have been because of the short duration of the experiment (22 days). It should be noted that soil type, which was also a factor in the experiment with cucumber and barley, had a significant effect on the bacterial community composition in the rhizosphere (Marschner et al., 2004). In a field experiment, da Silva et al. (2003) found only slight differences in populations of Paenibacillus spp. associated with four maize cultivars; soil type was the main factor influencing community structure. Several studies have used PCR-DGGE analysis to examine potential impacts of genetic modification of plants on the indigenous rhizosphere community. Bacterial (including Alpha- and Betaproteobacteria) and actinobacterial communities associated with the roots of transgenic T4-lysozymeexpressing potatoes were analysed by Heuer et al. (2002). Bacterial communities were more affected by season, field site or year of sampling than by T4-lysozyme expression. Another field experiment examined the effects of T4-lysozyme-expressing potatoes

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on two antagonistic plant-associated bacteria and the whole bacterial community composition (Lottmann et al., 2000). PCR-DGGE revealed that neither of the introduced bacterial strains became dominant members in the bacterial community and the introduced microorganisms had no significant impact on the whole bacterial community composition of the rhizosphere. The diversity of bacterial and fungal communities associated with transgenic potatoes with an altered starch composition was compared with the non-transgenic parental cultivar and an additional unrelated cultivar (Milling et al., 2004). In most cases, patterns were similar when eubacterial-, alpha- and betaproteobacterial- and fungal-specific primers were used. When Pseudomonas-specific primers were used, differences were detected between the rhizosphere patterns of the transgenic potato line and the parental cultivar, and also between cultivars. PCR-DGGE techniques have been modified to analyse diversity in metabolically active populations, simply by carrying out the analysis on RNA (after reverse transcription to cDNA) extracted from the community, instead of DNA. Thus, Sessitsch et al. (2004) analysed the activity of bacterial populations in the rhizospheres of transgenic glufosinate-tolerant and wild-type oilseed rape by extracting community RNA and amplifying its 16S rRNA component by reverse transcription–PCR (RT–PCR). DGGE was then used to generate community fingerprints of metabolically active bacteria. Group-specific probes were also hybridized to the community RNA. Plant growth stage had the greatest effect on metabolically active populations in the rhizosphere, and dot-blot analysis showed altered activities and abundances of various phylogenetic groups. SEASONAL AND SPATIAL VARIATIONS IN RHIZOSPHERE BACTERIAL COMMUNITIES. In their previously described study, Smalla et al. (2001) showed that bacterial rhizosphere community composition of field-grown strawberry, oilseed rape and potato changed with time, with different bands becoming dominant over time and some bands no longer being detected.



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Some bands increased in intensity when plants were flowering. Field experiments performed in the tropics with maize cultivars (Gomes et al., 2001) revealed strong seasonal shifts of the bacterial communities in the rhizosphere, especially for Alphaand Betaproteobacteria. TGGE patterns revealed that young maize roots were associated with a less complex eubacterial community (Gomes et al., 2001). In addition, Marschner et al. (2004) showed that white lupin (Lupinus albus L.) cluster roots of different ages had distinct bacterial community structures. Marschner et al. (2002) had previously shown that changes in the bacterial community structure over time were correlated with cis-aconitic, citric and malic acid exudation of white lupin. In contrast, some studies found no seasonal variations of the rhizosphere bacterial community composition. For example, the bacterial community composition in the rhizosphere of chrysanthemum (Dendranthena grandiflora Tzvelev cv. Majoor Bosshardt) was stable over time and root parts (Duineveld et al., 1998). Similarly, bacterial rhizosphere communities of barley plants (H. vulgare) did not change with plant age (Normander and Prosser, 2000). Piceno et al. (1999) did not find any seasonal variation of nitrogenase genes (nifH sequences) in the rhizosphere of cordgrass (Spartina alterniflora) in a field study. Ammonium-oxidizing bacteria in the rhizosphere of Glyceria maxima, growing in a shallow lake, were investigated monthly (Kowalchuk et al., 1998); anoxic conditions periodically led to a simple population (2–6 DGGE bands per sample), which changed only slightly over time. Since rhizodeposition varies in different locations of the roots, efforts have been made to investigate microbial community composition associated with different parts of the root. In particular, the root tip is known to be the location with the highest rhizodeposition, and it has been suggested that the tip should be analysed separately from the root plane. Marschner et al. (2001) showed by PCR-DGGE that bacterial populations of the rhizosphere from rape and Sudan grass were strongly affected by the

root zone. Similarly, bacterial communities associated with the young root tips of chrysanthemum differed significantly from root base samples (Duineveld et al., 2001). In a small-scale microcosm experiment, Kandeler et al. (2002) cut thin slices of soil parallel to the rhizosphere, at distances of 0.2–5 mm from the root of maize. The bacterial community composition changed within the section from 0.2 to 2.2 mm from the root surface and these communities differed from communities located 2.5–5 mm from the root. In contrast, the composition of the diazotroph community associated with the root zone of field-grown cordgrass (S. alterniflora) was similar in different locations of the root (Piceno et al., 1999). IMPACT OF BIOLOGICAL AGENTS, FARMING PRACTICES AND INCREASED CO2 LEVELS. Many rhizosphere microorganisms have positive effects on plant health and growth; in particular, Pseudomonas fluorescens has been reported to promote plant growth (Reiter et al., 2003). The effect of artificially inoculated P. fluorescens on an indigenous rhizosphere microbial community was studied by Yang et al. (2001a), who characterized the bacterial community in the rhizosphere of healthy and Phytophthora cinnamomi-infected avocado (Persea americana Mill.) roots. Infection of avocado root by P. cinnamomi resulted in a change of the rhizosphere bacterial population. Plants with roots infected by P. cinnamomi and treated with P. fluorescens to control the disease showed the same rhizosphere bacterial community composition as uninfected avocado roots. Overall, infected avocado roots revealed a higher bacterial diversity than healthy or Pseudomonastreated roots. PCR-DGGE patterns of eubacteriaspecific and actinobacteria-specific PCR products showed a microbial succession in the rhizosphere and around decomposing leaves and barley roots resulting from seeds that had been coated with either the antagonistic strain P. fluorescens DR54-BN14 or the fungicide imazalil (Thirup et al., 2003). The bacterial community changed in response to root death, and significant differences were seen in communities associated with the




root tip and root base. However, no effect of either P. fluorescens DR54-BN14 or the fungicide treatment could be revealed by DGGE. P. fluorescens has been shown to protect crop plants from Pythium ultimum infections. To determine whether the use of biological control agents has any effect in the environment, a risk assessment using genetically engineered bacteria was carried out (Timms-Wilson et al., 2004). An introduced genetically modified biological control agent P. fluorescens 23.10R had no effect on microbial diversity (bacteria and fungi) in the rhizosphere of several crop plants (pea (Pisum satvium var. quincy), wheat (Triticum aestivum var. pena wawa) and sugar beet (B. vulgaris var. amythyst)), as assessed by PCR-DGGE. Farming practices can impact on soil bacterial communities (e.g. Crecchio et al., 2004). In the rhizosphere, the presence of Burkholderia species was studied under different crops and land uses. Maize monoculture, crop rotation and permanent grassland affected the diversity of Burkholderia spp. to a greater extent than plant species (Salles et al., 2004). Garbeva et al. (2003) studied the bacterial community composition under grassland, grassland recently turned to arable land, and arable land using Bacillus-specific PCR primers and DGGE. Banding patterns revealed a differentiation between the different management practices, Bacillus populations being more diverse under grassland and grassland recently turned to arable land than in arable land soil. The impact of elevated CO2 concentration, induced by 6 years of fumigation, on the microbial community structure in a grassland soil was characterized by Ebersberger et al. (2004) using PCR-DGGE. Soil samples were not strictly taken in the rhizosphere, although they could be regarded as rhizosphere samples because the soil was densely permeated by roots. PCR-DGGE fingerprints revealed that the bacterial community structure changed in response to elevation of the CO2 concentration in summer. This change may be linked to altered rhizodeposition: amounts deposited in control and fumigated plots were unchanged in

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summer, but the quality of the rhizodeposits was unknown. Endophytic populations and mycorrhizal infections Endophytic microorganisms are defined as those that can be isolated from surfacedisinfected plant tissues or extracted from within the plant and that, additionally, do not visibly harm the plant (Hallman et al., 1997). Some endophytes, for example certain Pseudomonas spp., have positive effects on plant health. As endophytic bacteria depend on the availability of nutrients provided by the plant or its associated pathogens, metabolites produced by pathogens such as Erwinia carotovora may affect endophytic pseudomonad populations. However, in a greenhouse experiment, endophytic Pseudomonas populations associated with potato plants showed only minor responses to the presence of E. carotovora; different plant varieties had a greater impact on Pseudomonas populations than the pathogen. PCR-DGGE analysis of 16S rDNA fragments showed a higher diversity than 16S rRNA, indicating that only a portion of the abundant population of Pseudomonas spp. was active (Reiter et al., 2003). Endophytic microorganisms may also be influenced by external factors such as farming practices. In a field experiment, the effects of long-term applications of herbicides and various fertilizers on Z. mays endophytic microbial communities were assessed (Seghers et al., 2004). Herbicide applications did not cause changes in any of the microbial groups examined (general bacteria, methanotrophs, actinomycetes and general fungi). Fertilizer treatment had a significant effect on methanotroph bacteria type I, separating mineral fertilizer from compost treatments, but general bacterial and fungal endophytic populations were unaffected by the various fertilizer treatments. Arbuscular mycorrhizal (AM) fungi alter root exudation significantly (Marschner et al., 1997) and will therefore impact on the microbial rhizosphere community. In a pot experiment, pea plants (P. sativum) were grown in previously γ-irradiated soil



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with and without addition of the AM fungus Glomus intraradices (Wamberg et al., 2003). PCR-DGGE patterns of the rhizosphere bacterial community were obtained at three sampling dates after sowing. The number of DGGE bands specific for the AM fungal treatment decreased over time, while DGGE patterns for the whole bacterial community of non-infected roots showed increasing diversity with time after sowing. A possible explanation could be a shift in carbon respiration of roots over time: accompanying measurements of soil respiration revealed that carbon limitation occurred as mature roots withered. Changes in the bacterial community structure induced by mycorrhizal colonization in split-root maize plants were examined in a pot experiment (Marschner and Baumann, 2003). Maize plants were inoculated with G. intraradices and Glomus mosseae, and treatments were chosen to examine the effect of phosphate availability and mycorrhizal colonization on the bacterial community. PCR-DGGE revealed that the bacterial community structure was different in the rhizosphere soil compared with the bulk soil and root surface 3 and 6 weeks after sowing. Phosphate availability had no effect on the rhizosphere communities in the non-mycorrhizal and mycorrhizal treatments, but the mycorrhiza changed the bacterial community on the root surface and the bulk soil. In another experiment, Marschner and Timonen (2005) analysed interactions between plant species (canola (B. napus L. cv. Mystic), clover (Trifolium subterraneum L. cv. Mount Barker), and two tomato genotypes (Lycopersicum esculentum L., WT 76R and reduced mycorrhizal colonisation mutant rmc)) and mycorrhizal colonization (G. intraradices and Glomus versiforme) on the bacterial community composition in the rhizosphere. Low levels of mycorrhizal colonization (8%) of canola resulted in a shift of the bacterial community structure. In contrast, clover was highly colonized by mycorrhiza (50%), but a change in bacterial community composition was not detected. Differences between the rhizosphere bacterial communities of the two tomato genotypes could be

revealed and were more pronounced at low light intensity compared with high light intensity (200–250 versus 550–650 µmol/m2/s). This may be related to an increased shoot– root ratio in both tomato genotypes under low light conditions. Also, differences in rhizosphere bacterial communities were more pronounced between the plant genotypes inoculated with G. intraradices. De Souza et al. (2004) showed in recent experiments that the PCR-DGGE profiling of inter- and intraspecies 18S rRNA gene sequence heterogeneity is an accurate and sensitive method to assess species diversity of AM fungi of the genus Gigaspora. Rhizosphere fungal and archaeal community composition In comparison with studies investigating the rhizosphere bacterial community structure, fewer studies have examined rhizosphere fungal communities using PCR-DGGE (Kowalchuk et al., 1997a; Kowalchuk, 1999; Smit et al., 1999; Gomes et al., 2003). Progress has been limited by difficulties in primer design (see above), and it is not yet possible to cover the whole fungal community using one primer combination. Kowalchuk et al. (1997a) analysed fungal infection of marram grass (Ammophila arenaria L.) and found a level of diversity in fungi associated with plant roots that was not detected in previous culture-based studies. DGGE could separate different fungal species but was unable to identify different strains. Addressing major concerns about reliable amplification of fungal DNA, Smit et al. (1999) published a nested PCR approach to characterize the soil fungal community in the rhizosphere of mesocosm-grown wheat. They estimated that 78% of all fungal 18S rDNA sequences would be amplified by the primer combination EF4–EF3 and EF4– fung5. Overall, PCR-TGGE revealed reproducible fingerprints that were distinctive for bulk soil and rhizosphere samples. Numbers of TGGE bands in bulk soil were higher than in the rhizosphere, indicating higher fungal diversity in the former type of sample. Similar results were obtained by Gomes et al. (2003) who were using 1.65 kb PCR




products for DGGE to study bulk and rhizosphere soil of two maize cultivars. Samples of bulk soil generated a high number of DGGE bands of similar intensity, while the rhizosphere samples produced fewer bands which were more intense, indicating a promotion of specific fungi in the rhizosphere. In addition, the DGGE fingerprints of 18S rDNA showed different fungal rhizosphere communities in all three stages of plant development, while similar fingerprints were detected for the two maize cultivars. To date, only one study has characterized the soil rhizosphere community of Archaea using PCR-DGGE. The impact of three different management techniques (unimproved, semi-improved and improved) on the archaeal community was studied by Nicol et al. (2003). Increased intensity of fertilizer amendment and grazing resulted in altered plant species composition and, based on PCR-DGGE of rDNA and rRNA and cloning and sequencing of amplified products, a shift in the archaeal community composition. Through sequencing, Nicol et al. (2003) showed that differences in community composition could be attributed to changes in the group of non-thermophilic crenarchaeotes. Previously, this kingdom was believed to consist solely of thermophilic organisms.

Limitations of PCR-DGGE As with any other technique used to study microbial communities, there are limitations that must be considered when using PCR-DGGE and interpreting the data it generates. Every effort must be made to ensure that the phyllosphere or rhizosphere community DNA used as template for PCR amplification is representative of the community of interest. This may require the development of sampling strategies that take into account spatial and seasonal variations in microbial communities. Heuer and Smalla (1999) used composite samples of potato foliage to reduce variability observed in single plants, which impeded comparison between plant lines. A comparison of

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fingerprints generated from single or composite plant samples is shown in Fig. 6.2. Pitfalls and limitations specific to PCRDGGE have been reviewed previously (von Wintzingerode et al., 1997). Complete cell lysis and efficient DNA extraction procedures, adapted if necessary for the soil type under study, are required. Several studies have evaluated the effect of DNA extraction techniques on soil DNA yield (More et al., 1994; Zhou et al., 1996; Krsek and Wellington, 1999; Lloyd-Jones and Hunter, 2001), but the quality and quantity of the nucleic acid extracted from soil can also impact on the outcome of phylogenetic diversity analysis of indigenous microbial communities in soil (Heuer et al., 1997; Kozdrój and van Elsas, 2000; Niemi et al., 2001; de Lipthay et al., 2004). Therefore, selection of an optimal DNA extraction technique is critical. While PCR is now a routine method, several problems can arise when it is applied to the complex communities found in soil. As discussed earlier, PCR can be inhibited by factors co-extracted with the nucleic acid, so specific purification steps may be necessary. Artefacts caused by PCR amplification of mixed templates include differential amplification of certain sequences and formation of chimeric and heteroduplex molecules (von Wintzingerode et al., 1997; Speksnijder et al., 2001; Muyzer et al., 2004). The separation of PCR products by DGGE is based on the premise that the nucleotide sequence is directly proportional to the melting properties of the fragments. The abundance and order of G–C and A–T pairs influence the melting temperature but, for closely related organisms, the relationship between nucleotide sequence, phylogenetic affiliation and melting point is not well established (Kisand and Wikner, 2003). 16S rDNA can contain multiple melting domains, which may result in ‘cloudy bands’ (Kisand and Wikner, 2003). DGGE patterns derived from habitats containing a large number of different bacterial populations, such as soil, might end up as a smear. It may be necessary to reduce the complexity of the patterns – this can be achieved by the use of more specific primers to target particular groups of microorganisms.



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Control S

S

S

GM C

C

S

C

C

S

S

Fig. 6.2. Comparison of alphaproteobacterial communities on the tubers of unmodified control potatoes (lanes 1–5) and potatoes modified to express the antimicrobial peptide magainin (lanes 6–10) as described in O’Callaghan et al. (2004). S = single plant sample; C = composite sample of tubers pooled from three plants.

A further inaccuracy in DGGE analysis may result from microheterogeneity in the DNA sequence, leading to a situation where a single band may be composed of several species (Sekiguchi et al., 2001) or that several bands are generated from a single species. Using PCR-single strand conformation polymorphism (SSCP) and Southern blot gene probe analysis, Schmalenberger and Tebbe (2003) showed that while similarity analysis of patterns could not detect differences between samples that exceeded variability between replicates, cloning and sequencing demonstrated that community

profiles contained many more sequences than were detectable by staining. As the direct cloning of PCR products is not feasible in studies where large numbers of samples need to be analysed, the authors suggested that gene probes and Southern hybridization could be used to provide necessary controls when comparing microbial communities.

Conclusions While there are a number of problems with regard to isolation of nucleic acids, PCR and




electrophoretic separation of PCR products, PCR-DGGE is nevertheless a relatively easy and inexpensive technique that allows reproducible analysis of large numbers of samples. Furthermore, the ability to excise, clone and sequence DGGE bands provides phylogenetic information about the most abundant species in a community. The analysis of metabolically active populations following extraction of community RNA is providing new information on factors impacting on activity of plant-associated communities, while the use of newly identified functional

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genes, either for PCR or as gene probes in combination with PCR-DGGE, will allow a rapid increase in understanding of the microbial ecology of plant-associated communities.

Acknowledgements We are grateful to Drs Jana Lottmann and Upali Sarathchandra for helpful comments on the manuscript, and to Sue Zydenbos for editorial assistance.

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Molecular Tools for Studying Plant Growth-promoting Rhizobacteria (PGPR) Ashley Franks, Robert P. Ryan, Abdelhamid Abbas, G. Louise Mark and Fergal O’Gara* The Biomerit Research Centre, Department of Microbiology, National University of Ireland (UCC), Cork, Ireland

Introduction Modern intensive agriculture is heavily dependent upon the application of chemical inputs, comprising mainly fertilizers, herbicides and pesticides, but, due to concerns for human health and environmental protection, viable alternatives to these chemicals are being sought. It has long been recognized that many naturally occurring rhizospheric bacteria and fungi are antagonistic towards crop pathogens and, as a consequence, they may offer an effective substitute for chemical control agents (Morrissey et al., 2004). The region of soil surrounding and including the plant root (the rhizosphere) is of crucial importance for plant health and nutrition; it harbours a large and diverse community of prokaryotic and eukaryotic microorganisms that interact with each other and also with plant roots. Growth and activity of individual community members affect the growth and the physiology of others, and also the physical and chemical properties of the soil (Cook et al., 1995). In this context, the soil-borne pseudomonads are of particular interest, since they can be utilized in low input sustainable agriculture applications, such as biocontrol,

on account of their ability to synthesize secondary metabolites with antibiotic properties. Bacteria that reside in the rhizosphere and have a beneficial effect on plants are termed ‘plant growth-promoting rhizobacteria’ or PGPR. For example, Pseudomonas strains that produce 2,4-diacetylphloroglucinol (DAPG) have been shown to induce systemic disease resistance in plants (Walsh et al., 2001). Production of DAPG is governed at the transcriptional and posttranscriptional levels, and analysis of the associated complex regulatory networks is ongoing (Abbas et al., 2002, 2004). Despite advances in fundamental science, problems remain in the development of biocontrol technology for widespread use in agriculture. Although many strains show good performance in specific trials, this is often not translated into consistent, effective biocontrol in diverse field situations. Microbes of course do not live in isolation, and very little is known about the complex interactions that occur between plant, fungal and bacterial communities in the rhizosphere. An understanding of these interactions and the associated exchange of signals, particularly between plant and microbial communities, could offer opportunities for the deployment of new strategies to combat disease or

*Corresponding author; Phone: +353 21 4902934, Fax: +353 21 4275934, E-mail: [email protected] 116




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to promote those interactions of benefit to the eukaryotic partner (Silby et al., 2004). It is generally accepted that extracellular signals from the plant can influence the behaviour and structure of bacterial communities in the rhizosphere. Classically established molecular methods such as denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE) can offer valuable insights into the genetic diversity of rhizospheric microbial populations (Kent and Triplett, 2002). The presence of a bacterial strain does not necessarily indicate that it is metabolically active, but this can now be determined by using stable isotope probing (SIP), a technique that is facilitating the task of elucidating the key functions performed by microorganisms in the rhizosphere. Even with such advances, little is still known about the influence of plant signals on most aspects of bacterial gene expression. In this chapter, we discuss techniques for elucidating biosynthetic pathways of plant growth-promoting secondary metabolites produced by PGPR, for studying the functional diversity of microorganisms in the rhizosphere and for identifying genes induced in it.

Techniques for Studying Biocontrol by PGPR Many PGPR produce secondary metabolites that are inhibitory to certain fungal and bacterial soil-borne phytopathogens. Members of the genus Pseudomonas are currently being studied for their potential as biocontrol agents, and evidence exists for a link between the production of secondary metabolites and colonization of rhizospheres. Secondary metabolites include DAPG, phenazine (Phz), pyrrolnitrin, oomycin A, viscosinamide, pyoluteorin and hydrogen cyanide (HCN) (Thomashow and Weller, 1988; Fenton et al., 1992; Keel et al., 1992; Pfender et al., 1993; Hill et al., 1994; Nielsen et al., 1999; Bloemberg and Lugtenberg, 2001; Kremer and Souissi, 2001; Raaijmakers et al., 2002; Haas and Keel, 2003). These compounds, except for the volatile HCN, are diffusible molecules that have been demonstrated to

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provide protection for several plant species from phytopathogens. Most Pseudomonas strains produce several antibiotics, suggesting that disease suppression occurs via more than one mechanism, and this could help to explain the very low incidence of resistant pathogens in the rhizosphere (Aarons et al., 2000; Schnider et al., 2000; Haas and Keel, 2003; Heeb et al., 2005).

Isolation of PGPR strains and their active compounds In order to study the production of antibiotics and its regulation by PGPR, it is first necessary to isolate bacterial strains that inhibit plant pathogens. Typically, a library of rhizobacterial isolates is screened for inhibitory activity against a plant pathogen in vitro. Screens for a particular antibiotic have been developed using known antibiotic-sensitive strains, an example being DAPG-sensitive Bacillus subtilis for identifying DAPGproducing fluorescent pseudomonads (Abbas et al., 2002). Inhibition zones in a soft agar overlay seeded with the sensitive strain identify isolates with antagonistic activity. This approach can be cumbersome and timeconsuming, especially when screening large libraries of strains. As well as having limited sensitivity, it lacks a quantitative or discriminative dimension and it suggests only a presumptive identity for the active compound. The only certain information it provides is that a rhizobacterial isolate produces compounds with inhibitory activity. Nevertheless, it is necessary to know this before embarking on the construction of non-producing mutants with disruptions in genes that are responsible for the biosynthesis of the antibiotic or the associated regulators. Therefore, this procedure is used as a necessary first step in the identification of plant growth-promoting biocontrol determinants. The isolated PGPR strain may be studied in an array of growth conditions with different carbon and nitrogen sources to determine the optimal combination for biocontrol compound production. The compound can then be purified and identified using various analytical techniques; as an example, high



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pressure liquid chromatography (HPLC) was extensively used in DAPG quantification (Abbas et al., 2002), and researchers at the Biomerit Research Centre have developed a protocol that is simple and highly reproducible. In brief, an overnight cell culture is centrifuged and the DAPG in the supernatant is purified by filtration through a SepPakC18 cartridge (Waters Corporation, Milford, Massachusetts, USA). The cartridge is washed twice with HPLC grade water and metabolites are eluted with 2 ml of methanol. The eluate is filtered (0.45 µm pore size) and analysed by HPLC. Samples are run at a flow rate of 1.0 ml/min and detected at a wavelength of 254 nm over 25 min. The detection limit of this protocol, as with most other reported DAPG purification techniques, is 10 ng/ml for both DAPG and monoacetylphloroglucinol (MAPG) (Shanahan et al., 1992; Schnider-Keel et al., 2000; Brodhagen et al., 2004, A. Abbas, unpublished data). Achkar et al. (2005) recently reported a gas chromatography-based protocol detecting and quantifying not only MAPG and DAPG, but also phloroglucinol, the direct precursor of MAPG. The detection and quantification of antibiotics and their precursors is essential for unravelling biosynthetic pathways at the molecular level.

Generating mutant PGPR strains The most common methods of creating bacterial mutants with reduced or abolished antibiotic activity are chemical and insertional transposon mutagenesis. Usually, transposons such as Tn3 or Tn5 bearing a resistance cassette and a reporter gene (e.g. lacZ) are introduced into the antibiotic-producing strain by transformation or tri-parental mating (Vincent et al., 1991; Fenton et al., 1992; Bangera and Thomashow, 1996, 1999; Hammer et al., 1997; Nowak-Thompson et al., 1999; Aarons et al., 2000; Delany et al., 2000, 2001; Schnider-Keel et al., 2000; Chin-A-Woeng et al., 2001; Heeb et al., 2005). In tri-parental mating, the recipient strain (the PGPR) is mixed with an Escherichia coli strain carrying a transposon-containing plasmid, such

as pSUP101::Tn5-lacZY (E. coli SM10) or pLG221 Tn5 (E. coli SW110), and a helper strain, a second E. coli strain containing plasmid pRK2013 bearing the transfer function not present on the transposon (Shanahan et al., 1992; Schnider et al., 1995a). Transconjugants can then be selected with appropriate antibiotics and phenotypes identified, such as loss of the ability to inhibit the growth of phytopathogens. Two categories of mutants can be differentiated by the extent of changes to their phenotype. Structural mutants usually display only one phenotype, such as the loss of production of a single antibiotic. Mutants in the second category harbour changes in regulatory genes, which may be either specific or global and can act either in a positive or in a negative fashion. Mutations of global regulators usually cause more pleiotropic effects on the phenotype (Sarniget et al., 1995; Whistler et al., 1998; Aarons et al., 2000; Heeb et al., 2005). Transposon mutagenesis is more convenient than chemical mutagenesis for many reasons, the most obvious ones being safety and efficiency. Chemical mutagens are usually carcinogenic and need to be handled with extreme care. An important limitation of transposon mutagenesis is the polar effect that a transposon may cause when integrated in a set of genes that are transcriptionally linked, leading to potential misidentification of some genes. The complementation of mutants is commonly used to confirm gene function and to test for polar effects.

Identifying antibiotic synthesis genes Identification of a locus responsible for the biosynthesis of a biocontrol determinant (such as DAPG or other secondary metabolites) can be undertaken in one of two ways, depending on whether the bacterium produces one or more antibiotics. If only one antibiotic is produced, it is possible that a single transposon insertion in a biosynthetic gene will prevent production of the compound. Genes responsible for the production of the antibiotic may be isolated from a transposon-generated non-inhibitory mutant through ligation of enzymatically




digested genomic DNA into an appropriate plasmid and introduction into E. coli. Screening for the marker present in the transposon selects clones containing the gene region of interest (Kraus and Loper, 1995). Sequencing of the transposon flanking regions will identify part of the disrupted gene, which constitutes a portion of the locus responsible for antibiotic biosynthesis. These sequences are used to produce gene-specific probes by PCR that then can be used to screen wild-type plasmid or cosmid libraries by colony hybridization. A number of plasmid or cosmid clones, identified by hybridization, can be further sequenced and used to restore antibiotic production in the generated mutants by complementation. It is not possible to use this method if the mutant library was made by chemical mutagenesis or if the strain in question produces more than one antibiotic compound. In the latter case, inactivation of the gene responsible for the biosynthesis of an antibiotic may not be perceptible because of the homeostatic regulatory mechanism which compensates for the loss of one antibiotic by overproducing another (Raaijmakers et al., 2002; Haas and Keel, 2003; Brodhagen et al., 2004). While offering direct access to the genes responsible for biosynthesis of the biocontrol determinant, transposon mutagenesis is necessarily limited to strains for which a transposon system is available. It has also been demonstrated that some transposons insert preferentially into particular sites within bacterial chromosomes. A different strategy is available for PGPR that produce more than one antibiotic. A genomic library of the inhibitory strain can be created in plasmid vectors and introduced into a strain known not to have biocontrol activity (Hill et al., 1994). Plasmids containing a locus responsible for antibiotic production can confer the ability to produce this antibiotic on the recipient strain. They can then be isolated and used to determine the sequence of structural genes involved in the production of the antibiotic. This method is reliant on the recipient strain being able to produce the antibiotic, the biosynthetic pathway being contained within an operon or within a relatively small section of the

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genome and the antibiotic compound being non-toxic to the recipient strain. Both the above-described methods have identified loci responsible for antibiotics biosynthesis in PGPR (Vincent et al., 1991; Fenton et al., 1992; Bangera and Thomashow, 1996, 1999; Hammer et al., 1997; NowakThompson et al., 1999; Delany et al., 2000, 2001; Schnider-Keel et al., 2000; Chin-AWoeng et al., 2001).

Antibiotic synthesis mechanisms In many cases, antibiotic biosynthetic genes are organized in operons that encode several open reading frames (ORFs), as in the case of the phl operon of Pseudomonas fluorescens F113. This operon contains four genes, phlACBD, responsible for the biosynthesis of DAPG, as well as phlF, a repressor of DAPG biosynthesis, and phlE, encoding resistance to DAPG. In an attempt to dissect the role that each gene in the phl operon plays in DAPG biosynthesis, site-directed mutagenesis was employed (Bangera and Thomashow, 1999). In this approach, the phlACBD operon was first placed under the control of a T7 promoter, then each gene within the ORF was individually disrupted. The recombinant plasmids were expressed in a heterologous system BL21(DE3) which carries a chromosomal copy of the T7 RNA polymerase gene under control of the lacUV5 promoter (Studier et al., 1990). This system was also used in combination with the His tag technology to purify PhlD. For this purpose, a 1.1 kb fragment of the phlD gene was amplified by PCR from P. fluorescens Pf-5 genomic DNA, digested with BamHI and ligated with BamHI-digested expression plasmid pHI8-3. The ligation fuses the six histidine codons to the N-terminal region of PhlD and puts it under the control of a T7 promoter. Purification of recombinant PhlD was greatly aided by the use of the 6×His/Ni-NTA system (Jez et al., 2000). Purified PhlD was further analysed for enzymatic activity and substrate utilization (Achkar et al., 2005). His-tagged purified PhlF, a DAPG pathwayspecific repressor, has also been used to



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demonstrate, by cross-linking, that PhlF forms a dimer and to show the physical interaction between PhlF and the phlA promoter region (Delany et al., 2000; Abbas et al., 2002). Several attempts have been made to explain the origin of DAPG and MAPG. Bangera and Thomashow (1999) suggested that condensation of acetoacetyl-CoA molecules produces triketooctanoate, which undergoes a cyclization to form MAPG. Achkar et al. (2005), using purified 6×HisPhlD, suggested more than one pathway for DAPG biosynthesis, involving malonylCoA as a sole substrate. All the proposed pathways involve sequential condensation and decarboxylation of three or four malonylCoAs to produce MAPG. Interestingly, one of these pathways identifies phloroglucinol as the precursor of MAPG (Achkar et al., 2005). This is the first time this monocyclic

phlE

phlD

phlB

phlC

phenolic has been identified as an intermediate in MAPG biosynthesis, not only in P. fluorescens and recombinant E. coli expressing the phl locus but also in vitro using purified PhlD (see Fig. 7.1). The data reported by Achkar et al. (2005) represent initial steps in understanding the biochemistry of MAPG biosynthesis. The T7 promoter with His tag system has several advantages: it allows a high expression level and a one-step purification of a recombinant protein, which may then be further analysed for enzymatic activity, putative substrates and DNA binding propensities.

Regulation of antibiotic synthesis Cloned and sequenced antibiotic genes offer the possibility to analyse the levels of

phlA

phlF

DAPG resistance Chalcone MAPG acetyltransferase Stress tolerance synthase

phlG

Repressor DAPG hydrolase

phlH Putative activator

External environment Carbon sources Cytoplasmic membrane

3HSCoA O

O

HSCoA

OH

O

HSCoA

OH

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OH

O

?

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SCoA

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3× MalonylCoA

OH

HO Malonyl-CoA

Phloroglucinol

MAPG

PhlD CHS

OH

HO Malonyl-CoA

OH

DAPG PhlACB

Cytoplasm

Fig. 7.1. The organization and the roles of genes within the Phl operon, as well as the biosynthesis of DAPG, were elucidated using a combination of site-directed mutagenesis, cloning, tagged fusions and genetic complementation. PhlD was shown to act as a chalcone synthase, producing phloroglucinol from three malonyl-CoA molecules, while PhlA, PhlB and PhlC act as MAPG acyltransferases. Transcriptional fusions also demonstrated that PhlF acts as a repressor of DAPG synthesis.




control exerted by global and dedicated regulators. The complexity of DAPG, Phz and HCN biosynthesis regulation has been dissected by using different types of fusion technology (Aarons et al., 2000; Delany et al., 2000; Schnider-Keel et al., 2000; Abbas et al., 2002). To analyse the role of PhlF in DAPG regulation, its production was compared in the wild-type and in a phlF mutant background. This demonstrated that overexpression of phlF abolished DAPG production (Bangera and Thomashow, 1999; Delany et al., 2000; Schnider-Keel et al., 2000; Abbas et al., 2002). To determine the level of regulation effected by PhlF, the phlA promoter was cloned upstream of a promoterless lacZ (pMP220) and its expression compared in both the presence and absence of expressed PhlF through measurement of β-galactosidase activity in a standard enzymatic assay (Miller et al., 1972). In this way, PhlF was shown to be a dedicated transcriptional repressor of phlA (Delany et al., 2000; Schnider-Keel et al., 2000; Abbas et al., 2002). Translational fusions identified another level of regulation of DAPG biosynthesis by GacA/GacS and RsmA/RsmB systems (Aarons et al., 2000; Heeb et al., 2002, 2005). The extent of the complexity of regulation of the biosynthesis of other secondary metabolites such as Phz and HCN was unravelled by the use of fusions expressed in different mutants (Blumer et al., 1999; Aarons et al., 2000; Heeb et al., 2002, 2005). The overexpression of PGPR antibiotic biosynthesis genes can improve biocontrol ability, but this is not the case for all determinants in all plant systems (Maurhofer et al., 1992, 1995; Schnider et al., 1995b; Delany et al., 2001).

Measuring PGPR secondary metabolite production Measuring the production of microbial plant growth-promoting secondary metabolites in situ can be problematic. Constraining factors may include: relatively low stability of the factors, irreversible binding to soil colloids, poor persistence in the soil and metabolism by other member of the microbial community (Thomashow et al., 1997).

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Reporter gene systems, together with adapted and optimized chemical analytical techniques including thin-layer chromatography (TLC), HPLC and mass spectrometry (MS) (Keel et al., 1992; Maurhofer et al., 1995; Bonsall et al., 1997; Glandorf et al., 2001) have been used to overcome such problems. β-Galactosidase (lacZ), ice nucleation protein (inaZ) and luciferase (luxAB) systems may be used to monitor the expression, in situ, of several genes involved in secondary metabolite production (Georgakopoulos et al., 1994; Kraus and Loper, 1995; Loper and Lindow, 1997; Chin-A-Woeng et al., 1998; Notz et al., 2001, 2002; Séveno et al., 2001; Baehler et al., 2005; Larrainzar et al., 2005). Pessi et al. (2001) described the important parameters that need to be considered when designing fusion experiments involving reporter genes and promoters of interest. For a variety of reasons, gene expression levels may not be closely correlated with the amounts of compounds produced; for example, the reporter system can present a relatively high level of promoter activity at the time when antibiotic production is low (Schnider-Keel et al., 2000). This is due to the long half-life of reporter proteins, which may not reflect environmental conditions at the moment of analysis. The development of unstable autofluorescent proteins, such as green fluorescent protein (GFP), with a much shorter half-life has overcome this limitation, and now even transient gene expression can be monitored (Baehler et al., 2005; Larrainzar et al., 2005). The main advantage of using fluorescent proteins resides in their ability to be detected by non-invasive means such as confocal microscopy. There is thus no requirement for external substrates that are essential for other reporter proteins such as luciferase and β-galactosidase. TLC, HPLC and MS provide more direct and accurate measurements of antibiotics produced in situ. Very small quantities of DAPG can be extracted from the rhizosphere and precisely measured (Bonsall et al., 1997; Glandorf et al., 2001; Keel et al., 1992; Maurhofer et al., 1995). In a root system, antibiotic production may, in some microsites, reach the concentration needed to control a pathogen or limit its metabolic



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activities, while in other places the antibiotic may remain below inhibitory levels. Modified GFP and it derivatives, such as DsRed, have provided important data on the location of both the biocontrol agent and the pathogen and also on the timing of antibiotic production (Bloemberg et al., 2000; Haas and Keel, 2003; Larrainzar et al., 2005) near to the root. To perform this type of analysis, the biocontrol agent and pathogen are differentially labelled with two fluorescent proteins and visualized in the same root system (Gage, 2002; Gau et al., 2002).

Antibiotic synthesis in soil pseudomonads – future research directions Unravelling the regulatory circuitry and biochemistry of antibiotic biosynthesis will improve our understanding of how and under what environmental conditions these compounds are produced. Important information on the role PhlD and PhlABC in DAPG biosynthesis and degradation was obtained by using His tag technology (Bangera and Thomashow, 1999; Achkar et al., 2005). Combining this methodology with site-directed mutagenesis would provide invaluable data on reaction mechanisms in the biosynthesis of important biocontrol molecules. Production of DAPG has been metabolically altered in several ways. P. fluorescens strains overproducing this metabolite were obtained by disrupting the phlF repressor gene. The mutant was constructed by cloning a truncated phlF gene in a suicide vector, pK18. The recombinant plasmid was introduced into P. fluorescens F113 by electroporation, and mutants with disrupted phlF were selected for kanamicyn resistance (Delany et al., 2000, 2001). The DAPG synthesis pathway was also engineered by increasing the gene dosage of phlACBD biosynthetic genes. A DNA fragment containing phlACBD was cloned into the broad host range vector, pME6010 (Heeb et al., 2000), and introduced into P. fluorescens to increase DAPG production considerably (Delany et al., 2001; Achkar et al., 2005). The same phenotype

was achieved by overexpression of the rpoD gene encoding the sigma-70 factor (Maurhofer et al., 1995; Schnider et al., 1995b). More detailed investigations into the molecular mechanisms of the production, survival and rhizosphere colonization of DAPGoverproducing strains would facilitate the construction of biocontrol strains with finely tuned persistence and biocontrol properties.

Functional Diversity of PGPR In recent years, advances in molecular methods and genomics have provided exciting opportunities to study and redefine the relationship that exists between plants and microbes occupying their rhizospheres (Delong, 2002). Bacteria do not exist in isolation in this localized soil environment but form part of dynamic, multispecies, functionally diverse communities. Several techniques have been developed to study the metabolic profile of bacteria directly in their natural environments, including PGPR (Delong, 2002; Wagner, 2004). As microbial ecology involves the study of both the structure and function of an ecosystem, meaningful assessments of microbial communities must consider not only the abundance and distribution of species but also their functional diversity. Plant species, plant age and different root zones on the same plant can support distinct bacterial communities (Yang and Crowley, 2000; Smalla et al., 2001). In addition, soil type plays an important role in determining the dominant bacteria in the rhizosphere (Marschner et al., 2001). As a consequence, the rhizosphere microbial populations of a single plant species growing in a particular field may exhibit both spatial and temporal variation. The diversity of metabolic functions possessed by microbial communities is often examined using BIOLOG™ GN substrate utilization assays (Isam, 1997), which assess the ability of the community as a whole to utilize select carbon substrates. This method has the inherent biases of other culturebased approaches, and the resulting metabolic fingerprint may not be an accurate




representation of the functional diversity of the natural microbial community (Isam, 1997). As many microorganisms, both in the soil and the rhizosphere, are difficult to grow or enumerate using traditional culturedependent methods, a variety of molecular alternatives, often based on rDNA or rRNA analyses, have been developed to assay for their presence in samples (Torsvik and Øvreås, 2002). Other molecular methods used for assessment of global diversity of different systems include phospholipid fatty acid (PLFA) analysis (Zumstein et al., 2000; Lipski, Chapter 8 this volume), terminal restriction fragment length polymorphism (T-RFLP) (Cottrell and Kirchman, 2000; Blackwood, Chapter 5 this volume), single strand conformation polymorphism (SSCP) (Smalla et al., 2001) and denaturing/temperature gradient gel electroresis (DGGE/TGGE) (Schmalenberger et al., 2002; O’Callaghan et al., Chapter 6 this volume). While they are useful for the detection of changes in diversity in the rhizosphere, these techniques can benefit from the addition of functional or numerical information when used in combination with others such as fluorescence in situ hybridization (FISH) (see Sharma et al., Chapter 1 this volume). Microautoradiography is another technique that has been successfully used in many ecological studies to measure activities of members of different autotrophic and heterotrophic prokaryotic groups (Lee et al., 1999). Combining FISH with microautoradiography (FISH–MAR) creates a powerful tool to link the uptake of radiochemicals to individual bacterial cells, thus giving an insight into biochemical interactions amongst members of the rhizosphere. This combination has been used successfully to detect and quantify the active bacterial populations utilizing specific substrates in soil ecosystems (Zhu and Miller, 2003). Another novel and promising approach for the analysis of bacterial functional diversity is SIP (Radajewski et al., 2000; Sharma et al., Chapter 1; Lipski, Chapter 8 this volume). This technology can now facilitate the measurement of metabolic activity in situ and help in unravelling PGPR–plant interactions.

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Discovering the Role of Novel Genes in PGPR The value of IVET One of the factors influencing soil microbial biodiversity is the type of plant cover on the soil surface. While it is accepted that extracellular signals from plants can directly influence the behaviour and structure of bacterial communities in the rhizosphere, with the exception of flavonoids in legume– rhizobia symbioses, little is known about the influence of plant signals on bacterial gene expression and the role of these genes in plant–microbe interactions. In vivo expression technology (IVET) permits the identification of genes whose expression is specifically upregulated in the rhizosphere (for a thorough consideration of IVET principles, methodology and applications, see Rediers and De Mot, Chapter 4 this volume). Many promoters are inactive under laboratory conditions, and IVET provides a means of identifying promoters that are active in a complex natural environment such as the rhizosphere (Silby et al., 2004). For example, 20 P. fluorescens SBW25 genes were shown to display elevated levels of expression during colonization of the sugar beet rhizosphere (Rainey, 1999), including those encoding proteins involved in type III secretion, oxidative stress and transport. Silby and Levy (2004) identified several genes in P. fluorescens Pf0-1 that may play roles in nutrient utilization, transport, detoxification and metabolism. Targeted mutagenesis was conducted on three of the genes predicted to be important for the survival of P. fluorescens Pf0-1 in soil. Mutant strains were found to be significantly defective in the ability to grow initially; however, by day 3, mutant populations colonized the rhizosphere to wild-type levels. These genes are therefore proposed to be involved in early establishment of the population on the plant root. IVET allows the study of ecologically important microbes in the rhizosphere, where the full complement of genes and associated physiological conditions necessary for survival and competition in vivo are expressed; however, since the system selects for genes



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that are upregulated in the rhizosphere, their detection is dependent on the relative level and timing of their transcription. Genes that are downregulated will not be detected by IVET.

PGPR and the ‘omic’ technologies The recent publication of complete genomic sequences for a number of Pseudomonas strains (P. aeruginosa PAO1, http://www. pseudomonas.com; P. putida KT2440, http:// www.tigr.org; P. syringae pv. tomato DC3000, http://www.tigr.org; P. fluorescens Pf0-1, http://genome.jgi-psf.org; P. fluorescens SBW-25, www.sanger.ac.uk) and advances in the other ‘omic’ technologies (transcriptomics, proteomics and metabolomics) offer possibilities for obtaining new insights into the molecular bases of beneficial plant– microbial ‘interactomes’ in the rhizosphere. Transcriptome profiling of PGPR using DNA microarrays previously has been limited by a lack of genome sequence for relevant bacteria and the high cost of generating the profiles. In general, commercially available microarrays and researchers employing them have focused on model organisms with sufficiently characterized genomes such as P. aeruginosa (Thomas and Klaper, 2004). Following the sequencing of the Sinorhizobium meliloti genome (Galibert et al., 2001), pilot microarrays became available for expression analysis under both symbiotic and free-living conditions (Ampe et al., 2003; Berges et al., 2003). A combined plant and bacterial array containing probe sets for the complete genome of S. meliloti has also been constructed (Barnett et al., 2004). For rhizosphere bacteria other than rhizobia, most interest has naturally focused on those Pseudomonas species that are efficient colonizers of plant roots and which possess species-specific plant growthpromoting properties (Pühler et al., 2004). Those for which full genome sequences are available (see above) are now potential candidates to act as models to unravel molecular signalling between PGPR and the plant host through transcriptome profiling, which measures gene expression as a function of

mRNA concentration. However, to use this method effectively, experiments must be designed to minimize undesirable artefacts. Where possible, experimental conditions should be chosen to highlight the genetic response of interest without impacting on unrelated systems. Arrays cannot be used to prove mechanisms of gene regulation, nor can they easily distinguish direct from indirect regulatory effects. Any given element that is differentially regulated or expressed in a microarray experiment may not necessarily have a functional significance (Smith and Greenfield, 2003). Despite these limitations, arrays have a potential to generate useful data on PGPR gene expression in the rhizosphere which has yet to be fully exploited. Microarray studies are also being enhanced by adherence to the minimum information about a microarray experiment (MIAME) standards (Brazma et al., 2001). For a full consideration of nucleic acid microarray applications in soil microbial ecology, see Loy et al., Chapter 2 this volume. Transcriptional analysis may be complemented by proteomics. The term ‘proteome’, originally coined in 1995 by Marc Wilkins, refers to the ‘protein complement of the genome’ (Wilkins et al., 1996). Proteomics is the large-scale analysis of proteins, a powerful tool for understanding the complex patterns of expression of genomes with respect to different environmental niches in which bacteria adapt and survive. Current methods of proteomic analysis involve the solubilization of protein samples and the subsequent separation of individual proteins by two-dimensional gel electrophoresis. They can then be stained, excised and identified using MS. The procedure for proteomic analysis is constantly being refined and improved with developments such as gel-free separation systems (Flory et al., 2002). Proteomics has been applied to rhizobia during their interaction with legume roots. Proteins in Rhizobium leguminosarum bv. trifolii whose expression levels are influenced by nod gene-inducing flavonoids from their prospective host plant were detected by Guerreiro et al. (1997). Morris and Djordjevic (2001) likewise identified some proteins that may be involved in the early




stages of clover nodulation by this bacterium. Proteomics has also been used for both ectomycorrhizal and endomycorrhizal symbioses (Bestel-Corre et al., 2002). This methology has the potential to generate important information when applied to other PGPR, but it has yet to be employed to determine the importance of individual microbial proteins during plant–microbe interactions in the rhizosphere. Proteomics has, on the other hand, already enhanced our knowledge of plant responses to microorganisms in the rhizosphere, an advance made possible by the availability of genome sequences for plants such as Arabidopsis thaliana and Oryza sativa. Peck et al. (2001) used a ‘directed proteomics’ strategy to identify Arabidopsis proteins that are phosphorylated rapidly in response to microbial elicitors. This involved focusing on a particular subset of proteins (phosphorylated proteins) which could be identified by radioisotope labelling, two-dimensional gel electrophoresis and MS. Ndimba et al. (2003) also studied the response of Arabidopsis to a variety of fungal elicitors. This work identified cell wall and extracellular proteins from an Arabidopsis suspension culture that were divergently expressed in the presence of both chitosan and a Fusarium elicitor. Kim et al. (2001) adapted the general two-dimensional gel electrophoresis protocol by introducing a polyethylene glycol (PEG)-mediated pre-fractionation step. This allowed the enrichment of rare proteins in the sample, thus increasing the sensitivity of the analysis. The main drawback of standard two-dimensional gel electrophoresis, with its ‘one extract–one gel’ approach, is its inability to detect low abundance proteins which may be important as regulators, signal transducers or receptors. Also, a number of overlapping proteins may not be separated unless a narrow pH gradient is used. To achieve separation, one would need to know which proteins are likely to overlap; an impossibility for the proteome of a mixed community. Another recent advance has allowed for the parallel analysis of plant–microbe interactions, using both transcriptomics and proteomics (Dumas-Gaudot et al., 2004).

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Previously, functional genomic analysis has been mainly performed at transcriptional (mRNA) and protein levels separately, with little correspondence between the two components (Gygi et al., 1999). This new protocol allows proteins and mRNA populations to be analysed from the same root sample, thus enabling proteomic and transcriptomic data to be linked for harmonized analysis. Developments such as this serve to confirm the value of proteomics as an analytical tool for dissecting plant–microbe interactions. Researchers working with data generated by high throughput technologies must cope with the practical reality that putative positive and negative results may not be biologically robust or relevant. Therefore, a form of functional genomic analysis is required that verifies the physiological significance of genes whose involvement in plant–microbe interactions is suggested from global profiling information. This has now become more feasible with the development of bacterial mutant gene libraries. These are available for P. aeruginosa PAO1 (www. genome.washington.edu) as well as various plant species including A. thaliana (http:// www-ijpb.versailles.inra.fr). These resources can be used to ascertain quickly the functions of a far larger number of genes displaying altered expression in the rhizosphere than was previously possible.

Conclusions PGPR have the potential to improve plant health and productivity, and they are particularly suitable for low input sustainable agriculture applications, such as biocontrol. Plant species can define the bacterial communities present in the rhizosphere via the selection of genetically diverse populations or by favouring specific dominant groups. However, despite the acceptance that plant extracellular signals influence microbial populations in the rhizosphere, for most microorganisms residing there, little is known about the genes involved and the roles they may play in plant–microbe interactions. Traditional molecular techniques, such as



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Stable isotope probing (SIP)

Microautoradiography-FISH BIOLOG, PLFA

Generation of root exudates

Rhizosphere

Isotopic microarrays (structural and functional genes)

DNA extraction

Functional/structural diversity by DGGE, T-RFLP, cloning

Heavy DNA separation by ultracentrifugation

Transcriptomics IVET Identification of novel genes

Functional genomics, Proteomics Sequencing

Fig. 7.2. A schematic diagram of different molecular methods that can be used to study plant growth-promoting bacteria. Functional and structural diversity of bacterial rhizosphere-associated communities can be investigated by techniques such as T-RFLP, DGGE, isotopic microarrays, BIOLOG and PLFA. SIP may be used in combination with these techniques to further our understanding of PGPR behaviour in mixed communities. Transcriptomics and IVET are techniques that are being employed to identify novel genes with roles in plant growth promotion by bacteria. Many complementary molecular techniques are now being used to gain information on PGPR.

transposon mutagenesis, provide researchers with important information about the production and regulation of biocontrol determinants. Modern techniques now allow molecular studies of PGPR not just in isolation but as a part of the complex interacting communities that occur in the rhizosphere. Yet our understanding of the links between microbial diversity and soil/rhizosphere functions remains poor; we have still to discover an easy and comprehensive method for measuring functional microbial diversity among individual community members. In addition, current measurements of microbial function determine only the overall rate of an entire metabolic process and are still unable to identify directly the microbial species involved. The characterization of novel genes involved in plant–microbe interactions is

vital for understanding the response of rhizospheric bacterial populations to plant signals at a molecular level. By incorporating techniques such as IVET with recent advances in transcriptome profiling, researchers can search for genes induced or repressed by plant signals. Our understanding of the rhizospheric function of these genes can be furthered by the application of ‘omic’ technologies, such as proteomics, metabolomics and metagenomics, while marker gene technology can provide spatial and temporal information on specific gene expression. To elucidate the role and function of PGPR in the rhizosphere, a number of complementary techniques may therefore be used to generate information (see Fig. 7.2) and further our overall understanding of bacteria–plant interactions.




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Detection of Autotrophic Sulphur- and Iron-oxidizing Bacteria Using Labelled Fatty Acid Methyl Esters (FAMEs) André Lipski

Universität Osnabrück, Abteilung Mikrobiologie, Fachbereich Biologie/Chemie, D-49069 Osnabrück, Germany

Introduction Since the pioneering work of Sergei Winogradsky more than a hundred years ago, chemolithoautotrophic processes have remained among the most fascinating reactions in microbiology, and the organisms associated with them continue to attract substantial research attention. Organisms which were successfully cultivated and isolated have subsequently revealed highly interesting insights into their physiology; however, the detection of chemolithoautotrophic bacteria by cultivation techniques is often a challenging task because of their low growth rates and fastidious requirements. Enrichment techniques tend to select for fast-growing microorganisms adapted to the culture medium and growth conditions used, and important taxa with low growth rates but high activities can easily be overlooked. For this reason, the cultivationindependent detection of microorganisms based on specific nucleic acid sequences or lipid biomarkers is now an important complement to culture-based approaches. These direct methods provide an impression of the quantitative composition of a microbial community or the size of a population of interest. Finally, direct methods are now

frequently coupled with stable isotope probing (SIP) techniques to combine the detection of a taxon with an analysis of its activity. Here, the application of stable isotope labelling by [13C]bicarbonate in combination with fatty acid methyl ester (FAME) analysis for the direct detection of autotrophic sulphur- and iron-oxidizing bacteria is presented.

Impacts of Sulphur and Iron Oxidation in Biogeochemical Systems Sulphur and iron oxidation are not only important reactions in global geochemical cycles but they can also greatly influence the character of a habitat. This can be impressively observed in habitats where iron sulphides such as pyrite (FeS2) can react with oxygen to give sulphuric acid and iron(II). Under mesophilic or moderate thermophilic conditions, iron(II), which is chemically stable under acidic conditions, is used as an electron donor in the presence of oxygen by iron-oxidizing chemolithoautotrophic bacteria. The product iron(III) acts as an oxidizing reagent and accelerates the further oxidation of pyrite according to the

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equation: FeS2 + 14 Fe3+ + 8 H2O → 15 Fe2+ + 2 SO42− + 16 H+. The iron(II) evolving from this reaction is then recycled by iron-oxidizing bacteria to iron(III), which leads to further solubilization of metal sulphides. Similarly, other metal sulphides such as molybdenite (MoS2), tungstenite (WS2), sphalerite (ZnS), galena (PbS), arsenopyrite (FeAsS), chalcopyrite (CuFeS2) and hauerite (MnS2) can be oxidized by the activity of microbially produced iron(III) (Rohwerder et al., 2003). The combined action of iron- and sulphide-oxidizing microorganisms leads to a highly acidic effluent commonly known as acid mine drainage. This is characterized by high concentrations of sulphuric acids, mobilized metal ions and a typical brownish precipitate of the iron mineral jarosite. The low pH and high concentrations of metal ions cause the toxic effect of acid mine drainage on aquatic and terrestrial life. On the other hand, the high activity of iron-oxidizing bacteria can be beneficial if low grade ores are treated in a process called microbial leaching (Rawlings, 2005). In this process, copper, uranium or even gold is mobilized from minerals such as chalcopyrite, chalcocite (CuFe2), uraninite (UO2) and gold-containing arsenopyrite (FeAsS[Au]). In a second step, the mobilized metals can be harvested from the leachate by precipitation. Microbially catalysed oxidation of sulphur and iron is not restricted to acidic environments: it is important at oxic–anoxic boundaries where reduced sulphur compounds and iron(II) are used by aerobic microorganisms for the autotrophic generation of biomass. Also elemental sulphur, which is chemically stable under aerobic conditions, is subjected to microbial oxidation leading to production of sulphuric acid and a consequent lowering of the pH.

Sulphur- and Iron-oxidizing Autotrophic Bacteria Diversity and properties The capability to oxidize sulphur and iron as sources of energy under mesophilic or

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moderate thermophilic conditions is scattered all over the phylogenetic tree of bacteria. Sulphur or sulphides as electron donors can be used by the genera Paracoccus, Starkeya and Acidiphilium (Alphaproteobacteria), Thiobacillus and Thiomonas (Betaproteobacteria), Acidithiobacillus, Halothiobacillus, Thermithiobacillus, Thiomicrospira and Beggiatoa (Gammaproteobacteria), Thiovulum (Epsilonproteobacteria) and Sulfobacillus (Firmicutes). Oxidation of iron is realized in the Nitrospira-phylum (genus Leptospirillum), the Firmicutes (genus Sulfobacillus), the Actinobacteria (genus Acidimicrobium), the Betaproteobacteria (genus Gallionella) and the Gammaproteobacteria (genus Acidithiobacillus). All of these taxa are obligate or facultative autotrophs. Depending on the local conditions, different taxa can dominate the oxidation process. Temperature, pH, oxygen concentration, availability of organic substrates or redox potential are important parameters which control the composition of the sulphur- or iron-oxidizing bacterial community (Robertson and Kuenen, 1999). Neutral and mesophilic conditions will favour autotrophic members of the genus Thiobacillus such as T. thioparus or T. denitrificans which grow between a pH of 4.5 and 7.8. Acidic conditions with a pH between 2.0 and 3.5 will support the growth of Acidithiobacillus species. Beggiatoa and Thiothrix are adapted to sulphide oxidation at neutral pH and low oxygen concentrations. The two iron-oxidizing species Acidithiobacillus ferrooxidans and Leptospirillum ferrooxidans are both adapted to the same habitat, which is characterized by pH 100 different compounds. A detailed description of this extended PLFA analysis, including a brief comparison with the other two methods, is provided by Zelles (1999). Which of these methods is the most useful depends on the intention of the study and the properties of the sample. A comparison of the total FAME method and the PLFA extraction by Drenovsky et al. (2004) showed that the former required significantly less sample mass than the PLFA extraction to give a reliable community fingerprint. In contrast, sample replicates were more consistent with the PLFA extraction than with the total FAME method. The PLFA methods gave higher amounts of bacterial fatty acids, whereas the fungal fatty acid pool was higher with the total FAME extraction. This effect is attributed to the higher amounts of neutral lipid fatty acids in eukaryotes, which are included in the total FAME extraction but not in the PLFA method.



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The fatty acid profiles of the sediment and soil samples presented in this chapter were obtained by the PLFA method. The PLFA extracts and the total FAME extracts of the reference cultures were analysed by gas chromatography–mass spectrometry (GC–MS) with a Hewlett-Packard model 5890 series II gas chromatograph equipped with a 5% phenyl methyl silicone capillary column and a model 5972 mass selective detector. Helium was used as the carrier gas, the injector temperature was 250°C and the injection volume 1 µl. A temperature gradient from 120 to 240°C at a rate of 5°C/min was used and the GC–MS transfer line temperature was 280°C. FAMEs were identified by their equivalent chain lengths (ECL) calculated from their retention times (Sasser, 1990) and their mass spectra. The positions of double bonds and cyclopropyl groups were determined by analysing dimethyl disulphide adducts (Nichols et al., 1986) and dimethyloxazoline derivates of the FAMEs (Fay and Richli, 1991).

Stable isotope labelling New aspects for the characterization of microbial communities by fatty acid analysis emerged with the introduction of SIP. The incubation of a sample with a stable isotope-labelled substrate allows the detection of active substrate-assimilating populations in the sample by MS analysis of the FAME profile of the sample without a cultivation requirement. Generally, gas chromatography coupled with combustion isotope ratio mass spectrometry (GC–c-IRMS) is used for a sensitive quantification of the isotope ratio of lipid biomarkers (Boschker et al., 1998; Hanson et al., 1999). With a lower sensitivity but higher availability for most laboratories, gas chromatography coupled with quadrupole mass spectrometry (GC–MS) can be used for detection and quantification of labelled fatty acids (Arao, 1999; Knief et al., 2003). Moreover, this method allows for the determination of the number and distribution of the label in the target molecule, which may provide important information on the assimilation pathways

of the stable isotope-labelled tracers (Sun, 2000). Labelling experiments with various autotrophic strains using [13C]bicarbonate showed that an incubation with 10 mM [13C]bicarbonate for 3 days produced a sufficient amount of labelled fatty acids in stationary phase cells (Knief et al., 2003). Also, in the presence of equimolar amounts of [12C]bicarbonate in addition to [13C]bicarbonate, reference strains were effectively labelled. Fatty acids of heterotrophic organisms were not labelled in detectable amounts although these organisms can fix minor amounts of CO2 by carboxylation reactions. Soil or sediment samples were incubated with [13C]bicarbonate in 500 ml screw-cap flasks. [13C]Bicarbonate was added to 20 or 40 g of sample to give a final concentration of 15–30 mM related to the water content of the samples. The samples were incubated at 25°C. Respiration and incorporation of 13CO2 were observed by analysing the gas phase by GC–MS for m/z 32 (O2), m/z 44 (CO2) and m/z 45 (13CO2). After a sufficient decline of 13CO , the soil samples were subjected to 2 PLFA extraction. All samples were also subjected to PLFA analyses before exposure to [13C]bicarbonate to control the stability of the community during incubation.

Detection of labelled compounds FAMEs labelled with 13C display identical retention times to those of their unlabelled counterparts and thus can be easily identified based on the standard ECL values. Chromatographic shifts, known as the isotope effect for deuterated lipids, are negligible for 13C-labelled compounds (Matucha et al., 1991). The incorporation of 13C can easily be detected in the scan mode of the mass spectrometer by the occurrence of isotopomers of the molecular ions of the biomarkers. However, the calculation of the portion of labelled fatty acids based on the molecular ions requires the quantification of individual masses for each fatty acid. To standardize the quantification using the same set of identical masses for each fatty acid, the analysis was restricted to the quantification




of the ions m/z 43, m/z 44, m/z 45 and m/z 46. These fragments represent an unspecific C3 fragment and its corresponding isotopomers, produced by almost all FAMEs. The labelled amount of each fatty acid was calculated by subtraction of the natural abundance of these masses from the total abundance. Natural abundances were acquired by the analysis of reference fatty acids from a broad range of bacterial strains or from the FAME profiles of the respective sample without labelling. Because of the small set of masses considered, this analysis can be performed in selective ion monitoring (SIM) mode, which reduces the background and increases the sensitivity of the mass selective detector (Knief et al., 2003). The analyses of pure cultures showed that labelling patterns were different between taxa. Whereas the fatty acids of Paracoccus versutus DSM 582T showed similar labelling intensities between 6 and 12% (Fig. 8.1B) for all fatty acids of this organism, large variations were found for Acidiphilium acidophilum DSM 700T. This strain showed variations between 3% for 8:0 and 14:0 3OH and 38% for 18:1 cis11 (Fig. 8.1F), suggesting different turnover rates for the different compounds. Generally, the labelling intensities reflect the fatty acid synthesis pathway. This is illustrated by the fatty acid 18:1 cis11, which showed a higher percentage in all reference strains in comparison with the fatty acid 19:0 cyclo11-12 (Table 8.1), which is a methylation product of the former (Law, 1971). This shows that the interpretation of the labelled fatty acids from complex samples should consider the different labelling efficiencies for the fatty acids of one taxon, particularly if samples were exposed for a short time to labelled bicarbonate.

Applications of PLFA Analyses and 13C Labelling in Soil and Sediment Studies Acid mining lake sediment A sample was taken from the upper 15 cm of the sediment of an acid mining lake (ML

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111, Lausitz, Germany) (supplied by Katrin Wendt-Potthoff, Environmental Research Centre, Magdeburg, Germany). The sediment sample was characterized by a low pH of 2.6 (determined in 1 M KCl) and high sulphate concentration of 23 mg SO42− /g dry weight (determined by high performance liquid chromatography (HPLC)). The acidity and the high sulphate concentrations originate from the oxidation of pyrite and marcasite at this site (Meier et al., 2004). An aliquot of the sediment was subjected to PLFA analysis directly after receiving the sample. For the incubation experiment, aliquots of the sample were supplemented with NaH13CO3 and NaH12CO3, respectively, to give a final concentration of 18.8 mM [13C]bicarbonate related to the water content of the sample. The exposure period for these samples was 14 days. The phospholipid fatty acid pattern showed the presence of several groups of microorganisms (Fig. 8.2). Representatives of at least three different fatty acid synthesis pathways were present in this sample. Oleic (18:1 cis9) and linoleic fatty acid (18:2 cis9,12) are typical products of the oxygen-dependent synthesis pathway (Erwin and Bloch, 1964). The anaerobic fatty acid synthesis pathway is represented by the characteristic products 18:1 cis11 and 19:0 cyclo11-12. A pathway favouring the synthesis of iso- and anteiso-branched chain compounds is also present (Kaneda, 1991). Neither the incubation time nor the presence of the stable isotope had an impact on the composition of the community. This was shown by comparison of the lipid profiles before and after incubation with [12C]bicarbonate as well as [13C]bicarbonate. Dominating lipids of the fatty acid profile were 16:1 cis9, 16:0 and 19:0 cyclo11-12. The high ratio of 19:0 cyclo11-12 to 18:1 cis11 is typical for adaptation to acidic conditions. Accordingly, this is also characteristic for the acidophilic reference cultures of the genera Acidiphilium and Acidithiobacillus (Tables 8.1 and 8.2). Strongly labelled fatty acids of the sediment sample were 16:1 cis7 and 16:0 11methyl, while 16:0 and 19:0 cyclo11-12 showed only weak but still significant labelling



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A. Lipski

10:0 3-OH

A

B

C

D

E

F

12:1 cis 5 14:0 3-OH 16:0 18:1 cis11 18:0 19:0 cyclo11-12 12:0 14:0 3-OH 16:1 cis 9 16:0 17:0 cyclo9-10 17:0 18:1 cis 9 18:1 cis11 18:1 trans11 18:0 18:1 cis11 11methyl 19:0 cyclo11-12 17:0 11methoxy 19:0 cyclo11-12 2-OH 8:0 14:0 3-OH 16:0 18:1 cis11 18:0 18:0 cis11 11methyl 19:0 cyclo11-12 18:0 3-OH 17:0 11methoxy 0

20

40

60

80 0

Total fatty acids (%)

10

20

30

40

Labelled amount of fatty acids (%)

Fig. 8.1. Labelling intensities of stationary phase reference cultures after exposure to 10 mM Na13CO3 for 3 days. In the left column (A, C and E), the total fatty acid profiles of the cultures are presented. In the right column (B, D and F), the labelled portion of each fatty acid is given. The analysed strains are: Paracoccus versutus DSM 582T (A and B), Acidithiobacillus thiooxidans DSM 504 (C and D) and Acidiphilium acidophilum DSM 700T (E and F). Data are adapted from Knief et al. (2003).

(Fig. 8.2). The weak labelling of 16:0 10methyl resulted from an overlap of the chromatographic peaks of 16:0 10methyl and the labelled 16:0 11methyl. Therefore, 16:0 10methyl is presumably not a

labelled fatty acid. In this sample, the fatty acids with the strongest labelling were 16:1 cis7 and 16:0 11methyl, in combination with 16:0 (Fig. 8.2). This indicates that L. ferrooxidans -like bacteria were the




141

14:0 15:0 iso 15:0 anteiso 16:0 iso 16:1 cis 7 16:1 cis 9 16:1 cis 11 16:0 17:1 iso cis 9 16:0 10 methyl 16:0 11 methyl 17:0 iso 17:0 anteiso 17:0 cyclo 9-10 ECL 17.463 18:2 cis 9,12 18:1 cis 9

Direct [13C]Carbonate

18:1 cis 11

[12C]Carbonate 18:0 19:0 cyclo 11-12 20:4 cis 5,8,11,14 20:5 cis 5,8,11,14,17 0

5

10

15

20 0


5

10

15

20

25

30


Fig. 8.2. Phospholipid fatty acid profile of a sediment sample from an acid mining lake. On the left, the total fatty acids are presented before exposure to [13C]bicarbonate (white bars) and after exposure for 14 days (dark grey bars). The light grey bars represent the lipid profile after 14 days exposure to [12C]bicarbonate. On the right, the labelled portion of each fatty acid was calculated for the [13C]bicarbonate-exposed sample. For fatty acids marked with X, labelled amounts were not calculated. Data are adapted from Knief et al. (2003).

dominant autotrophic organisms in this sample. One of the main fatty acids of L. ferrooxidans, 16:1 cis7, was only a minor component of the total fatty acid profile, which indicates that L. ferrooxidans-like

organisms make up only a small percentage of the total bacterial community. Nevertheless, the strong labelling of this fatty acid shows the importance of these organisms as primary producers in this ecosystem.



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14:0 iso 14:0 15:0 iso 15:0 anteiso 15:0 16:0 iso 16:1 cis 9 16:1 cis 11 16:0 16:0 10 methyl 17:0 iso 17:0 anteiso 17:0 cyclo 9-10 18:2 cis,cis 9,12 18:1 cis 9 18:1 cis 11 Direct 18:0

Thiosulphate Iron(ll)sulphate

18:1 cis 11 11methyl

Control 19:0 cyclo 11-12 0

10

20

30

40

50


0

5

10

15

20


Fig. 8.3. Phospholipid fatty acid profile of a Gleysol soil sample. The fatty acid profiles on the left represent the microbial community directly after sampling (white bars) and after exposure to [13C]bicarbonate (black bars). Additionally, [13C]bicarbonate-exposed samples were also supplemented with thiosulphate (dark grey bars) and iron(II)sulphate (light grey bars), respectively. On the right, the labelled portions of the fatty acids are given. For fatty acids marked with X, labelled amounts were not calculated.

Weak labelling of the fatty acid 19:0 cyclo11-12 indicates the presence of other autotrophic organisms, for example of the genera Acidithiobacillus or Acidiphilium. However, weakly labelled fatty acids can be

attributed to a secondary labelling effect, perhaps of a heterotrophic Acidiphilium strain, since it is known that these strains assimilate secondary metabolites of the primary producers (Islander et al., 1991).




Hydromorphic Gleysol soil Hydromorphic soils such as Gleysol are influenced by anaerobic groundwater, and therefore reduced sulphur compounds as well as reduced iron are available for microbial oxidation at the anoxic–oxic boundary. The soil sample was taken from this boundary layer of a Gleysol soil at a depth of about 40 cm. The sample had a water content of 34% and a pH of 5.9 (determined in 1 M KCl). An aliquot of the soil was subjected to PLFA analysis directly after sampling. For the incubation experiment, aliquots of the sample were supplemented with NaH13CO3 to give a final concentration of 25.7 mM [13C]bicarbonate related to the water content of the sample. Additionally, samples were supplemented with thiosulphate (40 mg/g soil) and iron(II)sulphate (8 mg/g soil), respectively. The exposure period for these samples was 13 days. The microbial community showed a shift during the exposure period as documented by the increase of 16:1 cis9 and 16:0 (Fig. 8.3). These fatty acids also showed the highest labelling rates. The detection of 13Clabelled fatty acids shows that autotrophic organisms are active and that this activity is high enough to produce detectable amounts of these compounds. No differences were found for thiosulphate- or iron(II)sulphatesupplemented aliquots. The labelling pattern suggested that Betaproteobacteria or Gammaproteobacteria rather than Alphaproteobacteria accounted for the autotrophic activity detected in this sample. Alphaproteobacteria

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are characterized by high proportions of 18:1 cis11 and/or 19:0 cyclo11-12, which were only labelled in minor amounts. Because the labelling intensities of the thiosulphate- and iron(II)sulphate-supplemented samples did not exceed those of the unsupplemented control, it is still not clear which is the most important or limiting electron donor for the autotrophic populations. For the oxidation of sulphur compounds, there are numerous candidates within the Beta- and Gammaproteobacteria, such as Thiomonas, Thiobacillus or Halothiobacillus. For iron-oxidizing organisms of the Betaproteobacteria such as Gallionella, the situation is unclear since no information about the lipid profile of these organisms is available so far. These examples show that fatty acid profiling not only provides a fingerprint of the microbial community of soils and sediments, but can also give important information about active organisms in these systems when the method is combined with the application of stable isotopes. An important requirement for this approach is the successive analysis of lipid profiles of known reference organisms to provide an extensive reference database for these analyses.

Acknowledgements I greatly appreciate the contribution of Claudia Knief and Sven Jechalke to the experimental work presented here.

References Arao, T. (1999) In situ detection of changes in soil bacterial and fungal activities by measuring 13C incorporation into soil phospholipid fatty acids from 13C acetate. Soil Biology and Biochemistry 31, 1015–1020. Boschker, H.T.S., Nold, S.C., Wellsbury, P., Bos, D., de Graaf, W., Pel, R., Parkes, R.J. and Cappenberg, T.E. (1998) Direct linking of microbial populations to specific biogeochemical processes by 13C-labelling of biomarkers. Nature 392, 801–805. Bossio, D.A., Scow, K.M., Gunapala, N. and Graham, K.J. (1998) Determinants of soil microbial communities: effects of agricultural management, season, and soil type on phospholipid fatty acid profiles. Microbial Ecology 36, 1–12. Cavigelli, M.A., Robertson, G.P. and Klug, M.J. (1995) Fatty acid methyl ester (FAME) profiles as measures of soil microbial community structure. Plant and Soil 170, 99–113. Clark, D.A. and Norris, P.R. (1996) Acidimicrobium ferrooxidans gen. nov., sp. nov.: mixed-culture ferrous iron oxidation with Sulfobacillus species. Microbiology 142, 785–790.



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Drenovsky, R.E., Elliott, G.N., Graham, K.J. and Scow, K.M. (2004) Comparison of phospholipid fatty acid (PLFA) and total soil fatty acid methyl esters (TSFAME) for characterizing soil microbial communities. Soil Biology and Biochemistry 36, 1793–1800. Erwin, J. and Bloch, K. (1964) Biosynthesis of unsaturated fatty acids in microorganisms. Science 143, 1006–1012. Fay, L. and Richli, U. (1991) Location of double bonds in polyunsaturated fatty acids by gas chromatography– mass spectrometry after 4,4-dimethyloxazoline derivatization. Journal of Chromatography 541, 89–98. Golovacheva, R.S. and Karavaiko, G.I. (1979) A new genus of thermophilic spore-forming bacteria, Sulfobacillus. Microbiology 47, 658–665. Hallbeck, L. and Pedersen, K. (1990) Culture parameters regulating stalk formation and growth rate of Gallionella ferruginea. Journal of General Microbiology 136, 1675–1680. Hanson, J.R., Macalady, J.L., Harris, D. and Scow, K.M. (1999) Linking toluene degradation with specific microbial populations in soil. Applied and Environmental Microbiology 65, 5403–5408. Ibekwe, A.M. and Kennedy, A.C. (1998) Fatty acid methyl ester (FAME) profiles as a tool to investigate community structure of two agricultural soils. Plant and Soil 206, 151–161. Islander, R.L., Devinny, J.S., Mansfeld, F., Postyn, A. and Shih, H. (1991) Microbial ecology of crown corrosion in sewers. Journal of Environmental Engineering 117, 751–770. Kaneda, T. (1991) Iso- and anteiso-fatty acids in bacteria: biosynthesis, function, and taxonomic significance. Microbiological Reviews 55, 288–302. Katayama-Fujimura, Y., Tsuzaki, N. and Kuraishi, H. (1982) Ubiquinone, fatty acid and DNA base composition determination as a guide to the taxonomy of the genus Thiobacillus. Journal of General Microbiology 128, 1599–1611. Knief, C., Altendorf, K. and Lipski, A. (2003) Linking autotrophic activity in environmental samples with specific bacterial taxa by detection of 13C-labelled fatty acids. Environmental Microbiology 5, 1155–1167. Law, J.H. (1971) Biosynthesis of cyclopropane rings. Accounts of Chemical Research 4, 199–203. Lawlor, K., Knight, B.P., Barbosa-Jefferson, V.L., Lane, P.W., Lilley, A.K., Paton, G.I., McGrath, S.P., O’Flaherty, S.M. and Hirsch, P.R. (2000) Comparison of methods to investigate microbial populations in soils under different agricultural management. FEMS Microbiology Ecology 33, 129–137. Lipski, A., Reichert, K., Reuter, B., Spröer, C. and Altendorf, K. (1998) Identification of bacterial isolates from biofilters as Paracoccus alkenifer sp. nov. and Paracoccus solventivorans with emended description of Paracoccus solventivorans. International Journal of Systematic Bacteriology 48, 529–536. Matucha, M., Jockisch, W., Verner, P. and Anders, G. (1991) Isotope effect in gas–liquid chromatography of labelled compounds. Journal of Chromatography 588, 251–258. Meier, J., Babenzien, H.-D. and Wendt-Potthoff, K. (2004) Microbial cycling of iron and sulfur in sediments of acidic and pH-neutral mining lakes in Lusatia (Brandenburg, Germany). Biogeochemistry 67, 135–156. Nichols, P.D., Guckert, J.B. and White, D.C. (1986) Determination of monounsaturated fatty acid double-bond position and geometry for microbial monocultures and complex consortia by capillary GC–MS of their dimethyl disulphide adducts. Journal of Microbiological Methods 5, 49–55. Norris, P.R., Clark, D.A., Owen, J.P. and Waterhouse, S. (1996) Characteristics of Sulfobacillus acidophilus sp. nov. and other moderately thermophilic mineral-sulphide-oxidizing bacteria. Microbiology 142, 775–783. Rawlings, D.E. (2005) Characteristics and adaptability of iron- and sulfur-oxidizing microorganisms used for the recovery of metals from minerals and their concentrates. Microbial Cell Factories 4, 13. Robertson, L.A. and Kuenen, J.G. (1999) The colorless sulfur bacteria. In: Dworkin, M., Falkow, S., Rosenberg, E., Schleifer, K.-H. and Stackebrandt, E. (eds), The Prokaryotes: an Evolving Electronic Resource for the Microbiological Community. Release 3.0, 5/21/1999, Springer Verlag, New York, USA, http://link. springer-ny.com/link/service/books/10125/. Rohwerder, T., Gehrke, T., Kinzler, K. and Sand, W. (2003) Bioleaching review part A: progress in bioleaching: fundamentals and mechanisms of bacterial metal sulfide oxidation. Applied Microbiology and Biotechnology 63, 239–248. Sasser, M. (1990) Identification of bacteria through fatty acid analysis. In: Klement, Z., Rudolph, K. and Sands, D.C. (eds), Methods in Phytobacteriology. Akademiai Kiado, Budapest, Hungary, pp. 199–204. Schrenk, M.O., Edwards, K.J., Goodman, R.M., Hamers, R.J. and Banfield, J.F. (1998) Distribution of Thiobacillus ferrooxidans and Leptospirillum ferrooxidans: implications for generation of acid mine drainage. Science 279, 1519–1522.




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Sun, M.-Y. (2000) Mass spectrometric characterization of 13C-labeled lipid tracers and their degradation products in microcosm sediments. Organic Geochemistry 31, 199–209. Tsaplina, I.A., Osipov, G.A., Bogdanova, T.I., Nedorezova, T.P. and Karavaiko, G.I. (1994) Fatty acid composition of lipids in thermoacidophilic bacteria of the genus Sulfobacillus. Microbiology 63, 459–464. Tunlid, A., Hoitink, H.A.J., Low, C. and White, D.C. (1989) Characterization of bacteria that suppress Rhizoctonia damping-off in bark compost media by analysis of fatty acid biomarkers. Applied and Environmental Microbiology 55, 1368–1374. White, D.C., Davis, W.M., Nickels, J.S., King, J.D. and Bobbie, R.J. (1979) Determination of the sedimentary microbial biomass by extractible lipid phosphate. Oecologia 40, 51–62. Zelles, L. (1999) Fatty acid patterns of phospholipids and lipopolysaccharides in the characterization of microbial communities in soil: a review. Biology and Fertility of Soils 29, 111–129. Zelles, L. and Bai, Q.Y. (1993) Fractionation of fatty acids derived from soil lipids by solid phase extraction and their quantitative analysis by GC–MS. Soil Biology and Biochemistry 25, 495–507.



9

Molecular Analyses of Soil Denitrifying Bacteria Laurent Philippot1,* and Sara Hallin2

1UMR

Microbiologie et Géochimie des Sols, INRA-Université de Bourgogne, CMSE, 17, rue Sully, BV 86510, 21065 Dijon Cedex, France; 2Department of Microbiology, Swedish University of Agricultural Sciences, Box 7025, SE 750 07 Uppsala, Sweden

Introduction Denitrification – process, enzymes and genes Microbial denitrification is a respiratory process in which nitrate (NO −3 ) or nitrite (NO −2 ) is reduced to nitric oxide (NO), nitrous oxide (N2O) or nitrogen gas (Fig. 9.1). These nitrogen oxides are used as electron acceptors instead of oxygen for generation of a transmembrane proton electrochemical potential across the cytoplasmic membrane. The energy sources of denitrifying bacteria include both organic and inorganic compounds, as well as light, although organic substrates are the most common. Denitrification is the main biological process responsible for returning fixed nitrogen to the atmosphere, thus completing the nitrogen cycle. This reduction of nitrate to gaseous nitrogen is negative for agriculture, since it can deplete the soil of an essential plant nutrient and the losses can be considerable (Hauck, 1981). On the other hand, the excessive use of nitrogen fertilizers in agriculture has led to nitrate accumulation in groundwater and surface water, and denitrification is useful for nitrogen

removal from these contaminated sources, as well as in industrial and municipal wastewater treatment plants. In addition, many denitrifiers can degrade toxic organic compounds such as benzene, toluene, chlorinated compounds, polycyclic aromatics and hydrocarbons, which makes them potentially very important for bioremediation (Casella and Payne, 1996). A more recent focus of research is incomplete denitrification, which leads to emissions of NO and N2O. Thus, denitrification contributes to the modification of global atmospheric chemistry, essentially through the greenhouse effect (Lashof and Ahuja, 1990) and destruction of the Earth’s ozone layer (Waibel et al., 1999). Microbial processes in soil contribute 70% of the total emissions of N2O (Conrad, 1996) and these are rising due to increased inputs of fertilizers (Skiba and Smith, 2000). The functioning of the denitrifying community is a crucial factor in regulating N2O emissions since denitrification is both a source and a sink for N2O. Denitrifiers are commonly found in soil, sediments, sewage, and marine and freshwater environments, but they have also been isolated from plants, animals and humans. The ability to denitrify has been observed

*Corresponding author; Phone: +33 3 80 69 33 46; Fax: +33 3 80 69 32 24; E-mail: [email protected] 146




Molecular Analyses of Soil Denitrifying Bacteria

NO2–

NO3–

Nar Nap

Fig. 9.1.

NO

Cd1-NirS Cu-NirK

N2O

cNor qNor

147

N2

Nos

The denitrification pathway including the different reductases.

in > 50 genera (Zumft, 1997) and therefore cannot be associated with specific taxonomic groups. Denitrification has been described in Firmicutes (Pichinoty et al., 1978), Actinobacteria (Shoun et al., 1998; Cheneby et al., 2000), Proteobacteria (Gamble et al., 1977), also in Archaea (Mancinelli and Hochstein, 1986) and even in fungi (Kobayashi et al., 1996). Thus, denitrifiers are involved in many other different functions of importance in ecosystems, e.g. nitrogen fixation (Rhizobium and Azospirillum) and ammonia oxidation (Nitrosomonas). Some denitrifiers are chemoautotrophic (Thiobacillus and Paracoccus) or phototrophic (Rhodobacter) bacteria, while some are pathogenic to plants, animals and humans (Agrobacterium, Alcaligenes and Campylobacter). Since denitrification is sporadically distributed within clades and closely related bacteria may have completely different denitrifying abilities (Clays-Josserand et al., 1995; Roussel-Delif et al., 2005), molecular biological techniques to study the ecology of denitrifiers are based on the use of the functional genes or enzymes involved in the denitrification pathway (Bothe et al., 2000; Philippot, 2005a). Genetic and biochemical studies have led to the identification of seven enzymes catalysing the four steps of the denitrifying pathway (Fig. 9.1). The first step of this sequential process is the reduction of NO −3 to NO −2 , which is carried out by bacteria that have one of the two types of nitrate reductase: the membrane-bound nitrate reductase (Nar) or the periplasmic nitrate reductase (Nap). The membrane-bound reductase is a complex with three subunits and the molybdopterin catalytic α subunit is encoded by the narG gene. The periplasmic reductase is a heterodimer and the large molybdopterin catalytic

subunit is encoded by the napA gene. Nap can be involved in either nitrate respiration or redox energy dissipation. Denitrifying bacteria can possess one or both of the nitrate reductases (Carter et al., 1995; Roussel-Delif et al., 2005). The next step of the denitrifying pathway, reduction of NO −2 to NO, is the key step, since soluble nitrogen oxides are reduced to gas. This reaction distinguishes denitrifiers from nitrate respirers and is catalysed by either a homotrimeric copper nitrite reductase (NirK) or a homodimeric cytochrome cd1 nitrite reductase (NirS) (Zumft, 1997). In general, denitrifiers possess only one of the two types of nitrite reductase, but a bacterial strain having both types has recently been isolated (P. Bonin, personal communication). The two enzymes are functionally and physiologically equivalent, as indicated by the finding that the nirK gene from Pseudomonas aureofaciens can be cloned and expressed, and be active in a Pseudomonas stutzeri mutant lacking the gene encoding NirS (Glockner et al., 1993). Reduction of the toxic intermediate NO is performed by two types of enzymes. One receives the electrons from cytochrome c or pseudoazurin (cNor) and the other from a quinol pool (qNor). cNor is a heterodimer encoded by the norB and norC genes. Finally, the last step of the denitrifying pathway, reduction of the greenhouse gas N2O to N2, is catalysed by the multicopper homodimeric nitrous oxide reductase (Nos) located in the periplasm. While Gram-positive denitrifiers have been shown to be able to reduce N2O, the purification of this reductase from a Gram-positive bacterium has never been reported. In Gram-negative bacteria, the nitrous oxide reductase catalytic subunit is encoded by the nosZ gene.



L. Philippot and S. Hallin

PCR-based Approaches to Assess Diversity and Density Most of the contemporary molecular methods available to assess the diversity and density of denitrifying communities follow a conventional approach commonly used to study bacteria in soil and other environments (Fig. 9.2) (Prosser, 2002; Kirk et al., 2004). Most often, DNA is extracted from environmental samples using various procedures based on mechanical lysis (Martin-Laurent et al., 2001), although in a few studies mRNA has been extracted instead in order to target denitrifiers actually expressing the

denitrification genes (Nogales et al., 2002; Sharma et al., 2005). The extracted nucleic acids (DNA or mRNA) are then amplified directly (DNA) or after reverse transcription (mRNA) in either a normal or a quantitative polymerase chain reaction (PCR) with selected primers. The amplification generates a mixed pool of amplicons that should reflect the composition or the abundance of the targeted denitrification genes in the studied environment. To reveal the polymorphism of the mixture of amplicons, different fingerprinting techniques are often used. Another way is to clone these amplicons and sequence the clone libraries.

Nucleic acid extraction

Environmental sample

Amplification using primers targeting denitrification genes

Fingerprint analysis

RFLP

T-RFLP

DGGE

Reassessing PCR primers

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Clone library analysis

Sequencing

Phylogenetic analysis

Fig. 9.2.

Procedure for denitrifying community analysis with a PCR-based approach.


RFLP



Development of primers for community diversity studies Since denitrification is not associated with any specific taxonomic group, as outlined above, 16S rRNA genes cannot be used to target denitrifying communities. Instead, functional genes encoding the denitrifying enzymes are the only way to target and describe the composition of denitrifying communities by PCR-based approaches. Ideally, the PCR primers should be both sensitive and specific, which means that they should amplify the target gene in all the bacteria carrying it, whatever the diversity of the functional community, but not other genes sharing conserved domains with the target gene. In addition, they should amplify fragments with enough information (length of 400 bp minimum) to be useful in environmental studies, but the size can be restricted by downstream analysis of the PCR fragments. Several research groups have developed different sets of primers for the denitrification genes; they are presented in Table 9.1. However, designing accurate primers for these genes is an ongoing process, and accumulation of complete sequences from microbial genome projects or from newly

149

isolated denitrifying bacteria is a good way to increase sensitivity of the primers. The validity of the primers can be tested against the denitrification genes using the Functional Gene Pipeline/Repository (FGRP) database: (http://flyingcloud.cme.msu.edu/fungene/ index.jsp). Fungene is an interactive functional gene database created using the Hidden Markov Model search program and protein models built from a set of different and well-characterized ‘training sequences’ to search for genes within the NCBI database (http://www.ncbi.nlm.nih.gov). Since the PCR-based approaches rely entirely on correct target sequences for primer hybridization, the choice of primers for assessing the diversity of denitrifiers is non-trivial and therefore it is important to go into the details for each gene. narG and napA Primers targeting the narG and napA genes encoding the two enzymes catalysing the first step of the denitrifying pathway have been developed (Table 9.1) (Flanagan et al., 1999; Gregory et al., 2000; Philippot et al., 2002). However, reduction of nitrate to nitrite by either the membrane-bound or the

Table 9.1. Commonly used PCR primers for narG, napA, nirS, nirK and nosZ genes in the denitrification pathway in community structure analysis. Primer

Positiona

Primer sequence (5′-3′)

Reference

narG narG1960f narG2650r T37

1741–1758 2377–2394 148–168

TA(C/T) GT(G/C) GG(G/C) CA(G/A) GA(G/A) AA TT(C/T) TC(G/A) TAC CA(G/C/T) GT(G/C/T) GC CA(C/T) GG(G/A/T/C) GT(G/A/T/C) AA(C/T) TG(C/T) AC(G/A/T/C) GG AC(G/T) TC(G/A/T/C) GT(C/T) TG(C/T) TC(G/A/T/C) CCC CA TA(G/A) TG(G/A/T/C) GGC CCA (G/A/T/C)CC (G/A/T/C) CC(G/A/T/C) CC (A/C)G(G/A/T/C) GG(G/A/T/C) TG(C/T) CC(G/A/T/C) (A/C)G(G/A/T/C) GG(G/A/T/C) GC

Philippot et al., 2002 Philippot et al., 2002 Gregory et al., 2000

GC(G/A/T/C) CC(G/A/T/C) TG(C/T) (A/C)G(G/A/T/C) TT(C/T) TG(C/T) GG (G/A)TG (C/T)TG (G/A)TT (G/A)AA (G/A/T/C)CC CAT (G/A/T/C)GT CCA

Flanagan et al., 1999

T38

690–710

T39

551–572

W9

270–290

napA V16

130–150

V17

1108–1133

Gregory et al., 2000 Gregory et al., 2000 Gregory et al., 2000


Continued



150

Table 9.1. Primer


Continued Positiona


Reference

TA(C/T) TT(C/T) (C/T)T(G/A/T/C) (A/C/T)(G/C) (G/A/T/C) AA(G/A) AT(A/C/T) ATG TA(C/T) GG (G/A/T)AT (G/A/T/C)GG (G/A)TG CAT (C/T)TC (G/A/T/C)GC CAT (G/A)TT


Braker et al., 1998 Hallin and Lindgren, 1999 Michotey et al., 2000

V66

253–279

V67

643–667

nirS nirS1F F1acd

763–780 856–871

CCT A(C/T)T GGC CGC C(A/G)C A(A/G)T TA(C/T) CAC CC(C/G) GA(A/G) CCG C

cd3F

916–935

cd3aF nirS4F nirS4R R3cd R4cd

916–935 1317–1336 1317–1336 1322–1341 1636–1654

GT(A/C/T/G) AA(C/T) GT(A/C/T/G) AA(A/G) GA(A/G) AC(A/C/T/G) GG GT(C/G) AAC GT(C/G) AAG GA(A/G) AC(C/G) GG TTC (A/G)TC AAG AC(C/G) CA(C/T) CCG AA TTC GG(G/A) TG(C/G) GTC TTG A(T/C)G AA GA(C/G) TTC GG(A/G) TG(C/G) GTC TTG A CGT TGA ACT T(G/A)C CGG T(C/G)G G CGT TGA ACT T(A/G)C CGG T

nirS6R

1638–1653

nirK Cunir3

504–521

CGT CTA (C/T)CA (C/T)TG CGC (A/C/G)CC

nirK1F F1aCu

526–542 568–584

GG(A/C) ATG GT(G/T) CC(C/G) TGG CA ATC ATG GT(C/G) CTG CCG CG

R3Cu

1021–1040

GCC TCG ATC AG(A/G) TTG TGG TT

nirK5R

1023–1040

GCC TCG ATC AG(A/G) TT(A/G) TGG

nosZ Nos661F nosZ-F Nos1773R

303–320 1169–1188 1396–1415

nosZ1622R 1603–1622 nosZ-R 1849–1869

CGG CTG GGG GCT GAC CAA CG(C/T) TGT TC(A/C) TCG ACA GCC AG AAC GA(A/C/G) CAG (T/C)TG ATC GA(T/C) AT CGS ACC TTS TTG CCS TYG CG CAT GTG CAG (A/C/G/T)GC (A/G)TG GCA GAA


Throbäck et al., 2004 Braker et al., 1998 Braker et al., 1998 Throbäck et al., 2004 Hallin and Lindgren, 1999 Braker et al., 1998 Casciotti and Ward, 2001 Braker et al., 1998 Hallin and Lindgren, 1999 Hallin and Lindgren, 1999 Braker et al., 1998 Scala and Kerkhof, 1999 Kloos et al., 2001 Scala and Kerkhof, 1999 Throbäck et al., 2004 Kloos et al., 2001

aPositions

in the narG gene of Pseudomonas fluorescens C7R12 (AF197465), in the napA gene of Escherichia coli K-12 (U00096), in the nirS gene of Pseudomonas stutzeri ZoBell ATCC 14405 (X56813), in the nirK gene of Alcaligenes faecalis S-6 (D13155), and in the nosZ gene of Pseudomonas aeruginosa DSM 50071 (X65277).

periplasmic nitrate reductase is not specific to denitrifiers and can be performed by bacteria such as Escherichia coli, which reduce the produced nitrite into ammonia. Therefore, the narG and napA genes can be considered as molecular markers not only of the denitrifying community, but also of the whole nitratereducing community (i.e. denitrifiers and bacteria reducing nitrate to ammonia).

The narG1960f–2650r primers designed by Philippot et al. (2002) have been shown to be capable of amplifying the narG gene in a direct PCR, in contrast to those designed by Gregory et al. (2000), which require a nested PCR to amplify a 366 bp fragment. Where possible, a direct PCR is the best choice since the nested approach increases the PCR bias. The narG1960f–2650r




primers were designed to amplify a 650 bp fragment in the last third of the narG gene containing a prokaryotic molybdopterin oxidoreductase motif. These primers were successfully used to amplify the narG gene in soils of different types and with different characteristics (Philippot, 2005a). No more than 7% of non-specific PCR products have been observed by sequencing the environmental narG gene clone libraries obtained. While the narG primers can amplify the narG gene from both Gram-positive and Gram-negative bacteria, the napA primers are restricted to Proteobacteriaceae since the periplasmic nitrate reductase is present only in Gram-negative bacteria. A nested PCR primed by four degenerate oligonucleotides was developed by Flanagan et al. (1999) to amplify a 196 bp fragment of the napA gene. Application of this primer set to sediment resulted in the amplification of tightly clustered sequences, which were specific for the periplasmic nitrate reductase. nirS and nirK Nitrite reductases are the key enzymes of denitrification since they catalyse the reduction of soluble nitrogen oxide into gas. This reaction distinguishes true denitrifiers from bacteria reducing nitrate to ammonia or nitrite-accumulating bacteria. Therefore, the first studies describing the diversity and composition of denitrifier communities using a molecular approach were performed using genes encoding the two types of nitrite reductase as molecular markers (Braker et al., 1998, 2000; Hallin and Lindgren, 1999). Several options for nirS primers have been published, and they are all located in the second half of the gene (Table 9.1). Because of this, the majority of the sequences in the databases are derived from this region and there are only a few complete nirS gene sequences available. The most commonly used primers so far, nirS1F and nirS6R designed by Braker et al. (1998), have been used to detect nirS genes in various environments, but have been shown to be less successful at targeting nirS in soils (Prieme et al., 2002; Throbäck et al., 2004; Wolsing and Prieme, 2004). The two nirS primers

151

F1acd and R4cd, originally published by Hallin and Lindgren, (1999), have been modified to determine diversity of denitrifying bacteria in groundwater (Yan et al., 2003) and sediments (Nogales et al., 2002). Recently, Throbäck et al. (2004) showed that the primer cd3aF, modified from Michotey et al. (2000), in combination with the new primer R3cd, readily detected nirS in different environmental samples without non-specific amplification. It was also demonstrated that agricultural soil harbours a substantial diversity of nirS denitrifiers. This new primer set therefore seems promising for future studies of nirS gene diversity in soil ecosystems. While the copper nitrite reductase is present in both Gram-positive and Gramnegative denitrifying bacteria (Coyne et al., 1989), the existing nirK primers are only able to amplify the nirK gene from Gram-negative denitrifiers (Table 9.1). This is because no nirK sequences from Gram-positive denitrifiers are yet available in the database. The nirK1F and nirK5R primers (Braker et al., 1998) have been the most widely used tools to survey the diversity of the NirK denitrifying bacteria in environmental samples. However, the nirK1F primer binds to a region of the nirK gene that has an insert of three bases in some sequences of denitrifying bacteria and these are not detected when using the nirK1F–nirK5R primer set. In addition, problems with non-specific amplification have been observed with these primers (Tuomainen et al., 2003; Wolsing and Prieme, 2004). The primer set F1aCu–R3Cu was originally designed to detect nirK in activated sludge (Hallin and Lindgren, 1999), but recent work has shown that they are able to target nirK genes in different environmental samples, including soil (Throbäck et al., 2004). So far, no non-specific amplification with these primers has been reported. norB The norB primers to amplify qNor and cNor are the most recently developed primers for denitrifiers (Braker and Tiedje, 2003). However, the norB gene encoding the quinol-oxidizing single subunit enzyme can be found in bacteria such as Synechosystis,



152


which are not denitrifiers. The presence of the nitric oxide reductase in nondenitrifying bacteria has been explained by its role in protecting the bacteria against exogenous or endogenous nitrosative stress (Philippot, 2005b). Three and four sets of degenerate primers have been designed to target the qNor and cNor enzymes. However, while PCR amplification with both qNor primer combinations resulted in a distinct band of the expected size, only the cnorB2F–6R combination was successfully used with environmental samples. Analysis of derived amino acid sequences confirmed the identity of the PCR products based on sequence similarity and iron-binding sites. nosZ Due to the complete lack of biochemical and genetic knowledge of the nitrous oxide reduction step in Gram-positive bacteria, the existing nosZ primers are restricted to nosZ from Proteobacteriaceae (Table 9.1). However, they amplify the nosZ gene from the Alpha-, Beta- and Gammaproteobacteria. Another limitation of primers targeting the nosZ gene is that they do not amplify denitrifiers lacking the last step in the denitrification pathway, the reduction of nitrous oxide to nitrogen gas. Moreover, these primers can amplify the nosZ gene in bacteria that reduce nitrous oxide without being denitrifiers in the original sense of the term (Tiedje, 1988; Mahne and Tiedje, 1995; Zumft, 1997). Nos661F and Nos1773R were the first primers targeting nosZ (Scala and Kerkhof, 1999). They were primarily used to amplify nosZ from marine sediments, but have also been used to survey this gene in soil and sediments (Nogales et al., 2002; Stres et al., 2004). Kloos et al. (2001) designed broadrange nosZ primers to survey different Azospirillum strains and other plant growthpromoting rhizobacteria, and these primers were later used to investigate nosZ in soil (Rösch et al., 2002; Rich et al., 2003; Mounier et al., 2004). Recently, the forward primer, nosZ-F, in this set was combined with a new reverse primer, nosZ1622R, and this worked well in soil and other

environments (Throbäck et al., 2004). All commonly used primer sets for nosZ seem promising for community surveys and the choice may depend on the intended downstream analyses of the amplicons. Some technical aspects to consider with regard to PCR primers Due to the high sequence polymorphism of the denitrifying genes and in order to achieve the most ‘universal’ primers possible, degeneracy was introduced, representing most wobble positions observed among known sequences, into the design of the primers described previously (Table 9.1). The primers targeting the denitrification genes are composed of a mix of one to 64 different primer combinations, resulting in a dilution of the combination that effectively matches a given sequence. The primer concentration used is therefore usually high, with a final concentration ranging between 0.5 and 6 µM. Moreover, to take into account the different melting temperatures (Tms) exhibited by the numerous primer combinations, the annealing step is usually performed using a temperature touchdown during the first 5–10 cycles. The subsequent steps are performed with an annealing temperature corresponding to the lowest Tm. In addition, for some primers such as narG1960F and narG2650R, a hot-start PCR is required to obtain PCR products. All attempts to increase ‘universality’ of the PCR for denitrification genes are likely to be accompanied by a trade-off in specificity. Several PCR protocols yield non-specific products of various sizes in addition to the expected one. Therefore, a gel extraction and purification step of the band of the expected size using commercial kits is sometimes required before analysis of the underlying polymorphism.

Revealing the polymorphism of the amplified denitrifying genes In the beginning, PCR-based approaches for ecological studies of bacteria in the environment relied on cloning of target genes; in




this way, the composition and diversity of the community can be assessed. Exploration of diversity is one issue, but the study of population changes is another. For the latter, multiple sample analysis is essential, and an approach based on sequencing thousands of clones is then too cumbersome, especially for terrestrial habitats of large denitrifying bacterial diversity. More appropriate are fingerprinting methods such as temperature or denaturing gradient gel electrophoresis (TGGE/DGGE), restriction fragment length polymorphism (RFLP) and terminal RFLP (T-RFLP). These techniques are useful for mapping complex dynamics such as successional population changes due to factors such as seasonal variations or environmental perturbations (Fig. 9.2).

153

been adapted for use in microbial ecology (Muyzer et al., 1993; Muyzer and Smalla, 1998). Today, these techniques are frequently used to study microbial community structure in different environments, but so far only DGGE has been used for denitrifying communities (Throbäck et al., 2004). DGGE separates gene fragments of the same size on a polyacrylamide gel with a gradient of increasing concentration of the denaturants formamide and urea, as shown for partial nosZ genes in Fig. 9.3. Compared with other fingerprinting techniques, DGGE makes it possible not only to fingerprint differences or shifts in the denitrifying communities, but also to sequence the bands directly, which should represent the dominant populations. The bands can be excised, purified, PCR-amplified again and sequenced. Thus,

Clone libraries Libraries of 50 to several hundred clones are generated from PCR amplification products. The sequence polymorphism of the plasmid-inserted PCR products is then analysed by fingerprinting, e.g. RFLP (see below), by sequencing or by a combination of these methods. Compared with fingerprint techniques, libraries are labour-intensive, timeconsuming and more expensive, but they allow for estimates of diversity. A large array of statistical methods is available for diversity assessment. For example, comparison of diversity between samples can be achieved using both rarefaction curves, thanks to software such as Analytic Rarefaction (Statigraphy Laboratory, University of Georgia, USA), and diversity indices such as that of Simpson or Shannon (Philippot et al., 2002). In addition, sequencing of the clones results in libraries of denitrification gene sequences that can be used to confirm the identity of the amplified PCR fragments and for detailed phylogenetic analysis of the denitrification genes. Denaturing gradient gel electrophoresis (DGGE) Gradient gel electrophoresis, with either a temperature or a chemical denaturant gradient, was originally developed to detect point mutations in DNA sequences, but has

Fig. 9.3. Examples of DGGE of partial nosZ genes amplified with primers nosZ-F–nosZ1622R from agricultural soil (picture by K. Enwall).



154


redundant sequencing or screening of hundreds of clones can be avoided when the aim is to provide information on differences in the predominant populations in the community. For successful DGGE analysis, the size of the fragment should not exceed 500 bp. This inevitably limits the amount of sequence information and restricts the possibilities of finding appropriate primers for the PCR. To avoid complete denaturation of the PCRamplified fragments, one of the primers should have a 30–40 bp GC clamp attached to it. The fragment then adopts a secondary structure that slows down migration through the gel. Depending on the design of the GC clamp, the PCR amplification can become less efficient. For optimal resolution, the DGGE methods for each gene must be optimized regarding both gradient concentration and running time. For this purpose, perpendicular gels can be very useful, as can parallel gels run as time-travel experiments (Muyzer and Smalla, 1998). The banding pattern is visualized after staining with ethidium bromide or SYBR Green, Gold or Silver (see also O’Callaghan et al., Chapter 6 this volume). Because DNA fragments with different sequences can have similar mobilities, one should be careful when drawing conclusions on denitrifying bacterial diversity from the DGGE patterns alone. Moreover, the intensity of the bands does not necessarily correspond to the relative abundance of a sequence type. DGGE is simplified for a given fragment when the region of interest lies in a single domain, but functional genes such as nirS, nirK and nosZ have great variation in sequence and melting profiles (Throbäck et al., 2004). Multiple melting domains typically result in fuzzy bands in the migration direction, hampering band resolution (Kisand and Wilkner, 2003). Different regions of the same gene might also result in different resolutions of separation and some fragments may be less useful for DGGE analysis (Nubel et al., 1996; Valleys et al., 1997; Throbäck et al., 2004). DGGE has been evaluated for nirS, nirK and nosZ fragments using the primer sets cd3aF–R3cd, F1aCu–R3Cu and

nosZ-F–nosZ1622R, respectively (Table 9.1) (Throbäck et al., 2004). To minimize the effects of multiple melting domains and to avoid complete denaturation of the PCRamplified fragments, a 33 bp GC clamp (5′ GGC GGC GCG CCG CCC GCC CCG CCC CCG TCG CCC 3′) was added to the three reverse primers R3cd, R3Cu and nosZ1622R, and this did not affect the amplification efficiency. DGGE of nirK and nosZ genes has proven to be a good tool for screening and comparing denitrifying communities in different types of environmental samples, but the resolution is insufficient to resolve nirS gene fragments. This is probably due to the multiple melting domains in this particular nirS fragment. Some bands harbour more than one sequence; these are in most cases closely related and are therefore difficult to separate. However, a few bands contain distantly related nirK or nosZ clones, indicating co-migration of DNA. To resolve these sequences, either cloning or running the fragment on a new DGGE can be employed. For DGGE of nirK and nosZ, 7% polyacrylamide gels were used with denaturing gradients of 50–70 and 40–70%, respectively. The electrophoresis was run for 13 and 17 h respectively at 130 V and 60°C. A mixture of 7 M urea and 40% formamide was defined as 100% denaturant (Muyzer et al., 1993). Restriction fragment length polymorphism (RFLP) RFLP is another tool to measure denitrifying community structure that relies on differences in the primary DNA structure. It has often been used to determine differences between various amplified 16S rDNA fragments and is then known as amplified rDNA restriction analysis (ARDRA). With this method, the sequence polymorphism of the mixed pool of amplicons is unravelled using the variation in the position of restriction sites within the amplified gene fragments. After cutting the fragments, the different fragment lengths are detected using non-denaturing gradient polyacrylamide gel analysis (Fig. 9.4). The method is useful for detecting changes in denitrifying communities




489 404 320

242 190

147 124 110

67

155

detectable or a very complex banding pattern in highly diverse communities, which is difficult to analyse. In silico restriction analysis of the existing sequences is the best way to select accurate enzymes. In contrast to DGGE, this approach requires longer PCR products ( > 500 bp) in order to facilitate both the choice of the restriction enzyme and the resolution of the gel analysis. For the narG region amplified with the narG1960f–narG2650r primers (Table 9.1), several studies have proven that the tetrameric enzyme AluI is a good choice (Philippot et al., 2002; Cheneby et al., 2003; Deiglmayr et al., 2004; Mounier et al., 2004; Patra et al., 2005). AluI has also been successfully used for the nosZ fragment obtained with the nosZ primers described by Kloos et al. (2001) (Table 9.1), while HhaI and HaeIII were used for the nosZ fragment amplified with 661F and 1773R primers (Table 9.1) (Stres et al., 2004). Approximately 500–1000 ng of purified PCR product is required for digestion with the selected restriction enzyme. After 2–3 h of digestion, the narG and nosZ PCR products are run on a 6% acrylamide gel for 12 h at 5 mA. The resulting gel can then be stained and analysed similarly to a DGGE gel. Terminal RFLP

Fig. 9.4. RFLP analysis of partial narG genes amplified with primers narG1960f–narG2650r from DNA extracted from four independent replicates of an agricultural soil (picture by C. Dambreville). Lane 1, DNA weight marker.

but not as a measure of diversity, since the number of bands does not truly reflect the number of different populations. The choice of restriction enzymes is crucial for the success of the analysis. By selecting an enzyme with restriction sites in conserved regions, the pattern underestimates the complexity of the sample. Ideally, the enzyme should cut in variable regions and result in fewer than five fragments larger than 50 bp to give a visual signal for each fragment. If the enzyme cuts too frequently, this could result in either a large number of small fragments that are not

In T-RFLP, the mixed pool of amplicons is resolved by restriction enzyme cutting similar to the RFLP method, but for T-RFLP only the terminal end of the digested PCR product is detected (see also Blackwood, Chapter 5 this volume). Usually the forward primer is labelled with a fluorescent tag and during the PCR the fluorophore is incorporated with each fragment. After digestion of the amplicons with a suitable restriction enzyme, the fluorescent terminal restriction fragments (T-RFs) are detected by automated gel or capillary electrophoresis commonly used for sequencing. This results in an electropherogram with peaks of different intensities (Fig. 9.5). Often, only the absence or presence of T-RFs is included in the analysis of the T-RFLP data, but sometimes the peak heights are also taken into account as a measure of relative abundance.



156


Dünnwald

35

50

75

100

139 150 160

200

250

300

340 350

400

450

490 500

Bad Sachsa

Fig. 9.5. T-RFLP electropherograms of partial nosZ genes amplified with primers nosZ-F–nosZ-R from an acid spruce forest soil (‘Dünnwald’) and a gypsum rock soil (‘Bad Sachsa’) in Germany (picture by C. Rösch).

The aim of T-RFLP is to limit the community restriction pattern to only one fragment per targeted sequence, but different sequences can give rise to the same peak. Therefore, it can be difficult to correlate a peak to a specific sequence when analysing complex samples such as soil. The choice of restriction enzyme becomes very important since the analysis relies on only 4 or 6 bp corresponding to the first restriction site in each fragment. To increase the chance of discriminating between different PCR fragments when fingerprinting complex samples, several restriction enzymes can be used or both primers can be tagged. In studies of denitrifying communities, T-RFLP has been applied to nosZ, nirK and nirS genes using the nosZ661–nosZ1773R, nirK1F–nirK5R and nirS1F–nirS6R primer sets, respectively (Table 9.1). 6-Carboxyfluorescein was used to label the 5′ end of the forward primers. The HinPI restriction enzyme was used to digest the nosZ PCR products (Scala and Kerkhof, 2000). For both nirS and nirK genes, the HhaI, TaqI and MspI enzymes were successfully used (Braker et al., 2001; Wolsing and Prieme, 2004), but for nirK HaeIII also worked well (Avrahami et al., 2002). The statistical analysis of similarities in T-RFLP patterns for nirK and nirS genes has been further elaborated by Wolsing and Prieme (2004), and these tools could be useful when analysing large sets of data. Recently, a database of TRFs from nosZ genes using multiple restriction enzymes was combined with a computer program (TReFID, www.trefid.net) to allow identification of different TRFs and to determine the

community structure of nosZ genes in environmental samples (Rösch and Bothe, 2005). The main problem with this approach is that the nosZ sequences do not necessarily correlate to the taxonomic phylogeny of the denitrifying bacteria, since horizontal gene transfer of denitrification genes most probably uncouples this link.

Quantification by PCR PCR can also be used for enumeration of denitrifying bacteria in soil. As in diversity studies, denitrifying genes are used as molecular markers of this functional community. The number of copies of the targeted gene in a sample is estimated using a control DNA of known concentration. The gene copy number is a rough estimate of the cell number in the studied environmental sample if the number of gene copies per organism is known and not highly variable between organisms. For the denitrification genes, only single copies per genome have been found so far, except for narG, which can be present in three copies or less (Philippot, 2002). Both real-time and competitive PCR have been used with success to quantify denitrification genes in the environment. Real-time PCR The size of amplified fragments should ideally be between 100 and 300 bp in realtime PCR applications, but fragments up to




400 bp have been quantified without problems. This makes it difficult to target the same fragments that are used for diversity studies since the primers that are used in those assays amplify larger fragments. In addition, most highly degenerate primers yield non-specific PCR products of various sizes. In diversity studies, these can easily be excluded by a gel extraction step, but since this is not possible for real-time PCR, new and highly specific primers are required. A first attempt to amplify a denitrification gene by real-time PCR was conducted by Gruntzig et al. (2001). To amplify the nirS gene, they used the TaqMan technology and designed a forward and reverse primer in addition to a probe that hybridizes between them (Table 9.2). Due to the high specificity of this technology, the primer–probe set was specific for nirS sequences that correspond only to P. stutzeri strains and was therefore not useful for quantification in environmental samples. The SYBR Green detection system does not require a probe and allows a universal quantification of the genes in the environment. SYBR Green is a fluorescent dye that

157

binds non-specifically to double-stranded DNA. During each annealing step, the fluorescence intensity is detected, resulting in a logarithmic increase in fluorescence emission until the reagents become limiting. The cycle number of the PCR at which the fluorescence rises above background is called the threshold cycle (Ct). There is a positive correlation between the log of the initial DNA template concentration and the corresponding Ct. Given a known starting amount of target DNA, a standard curve can be constructed by plotting the threshold cycle (Ct) as a function of the log of the copy number of the target DNA. The gene copy number in the sample DNA can then be determined based on its Ct. It is important to check the PCR efficiency, which is indicated by the slope of the curve, and to verify that the activity of the Taq polymerase is not inhibited by impurities in environmental samples. Dilution of extracted DNA or addition of a given amount of control DNA to environmental DNA can be used for verification. Real-time PCR assays have been developed recently to quantify the narG gene from an uncultivated group of nitrate-reducing

Table 9.2. Published primers for quantification of narG, nirS and nirK genes in the denitrification pathway by real-time PCR. Primer

Positiona


Reference

narG narG1960mod

1741–1762

TA(C/T) GT(G/C) GGG CAG GA(A/G) AAA CTG CGT AGA AGA AGC TGG TGC TGT T

(Lopez-Gutierrez et al., 2004) (Lopez-Gutierrez et al., 2004)

GT(A/C/T/G) AA(C/T) GT(A/C/T/G) AA(A/G) GA(A/G) AC(A/C/T/G) GG AC(A/G) TT(A/G) AA(C/T) TT(A/C/T/G) CC(A/C/T/G) GT(A/C/T/G) GG

(Michotey et al., 2000) (Michotey et al., 2000) (Henry et al., 2004) (Henry et al., 2004) (Qiu et al., 2004) (Qiu et al., 2004)

narG2050mod

Environmental clone

nirS cd3F

826–844

cd4R

1549–1565

nirK nirK876

883–1003

ATY GGC GGV CAY GGC GA

nirK1040

1026–1046

GCC TCG TCG ATC AGR TTR TGG TT

nirKF nirKR

575–595 904–925

TCA TGG TCC TGC CGC G(C/T) GAC GG GAA CTT GCC GGT (A/C/G/T)GC CCA GAC

aPositions

in the narG gene of Pseudomonas fluorescens C7R12 (AF197465), in the nirS gene of Pseudomonas stutzeri ZoBell ATCC 14405 (X56813) and in the nirK gene of Alcaligenes cycloclastes (Z48635).



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bacteria, and nirK from Gram-negative denitrifiers using the degenerate primer sets narG1960m2f–narG2050m2r (LopezGutierrez et al., 2004) and nirK876–nirK1040 (Henry et al., 2004), respectively (Table 9.2). So far, none of these methods has been applied to an ecological study of denitrifiers in soil. Competitive PCR (cPCR) In this technique, two different DNAs are amplified simultaneously. One is the target DNA from the sample and the other is the control DNA, the so-called competitor. The calculation of the initial amount of target DNA is based on the ratio of target to competitor PCR product by agarose gel electrophoresis. The competitor is targeted by the same primers that are used to amplify the sample DNA, but the resulting PCR products differ in size, making it easy to distinguish one from the other. Quantitative cPCR is associated with a number of drawbacks such as a limited dynamic range and the need to screen multiple dilutions. Due to these disadvantages and the requirement for a gel migration step, cPCR is more laborious and time-consuming than real-time PCR. On the other hand, the presence of the target and competitor DNA in the same mix levels out quantification problems caused by impurities in the samples. cPCR has been successfully used to quantify the nirS and nirK genes in marine, stream sediment and biofilm samples, but has not been tested for soil (Michotey et al., 2000; Cole et al., 2004; Qiu et al., 2004). Comparison of cPCR and most probable number (MPN)PCR resulted in approximately the same count for the nirS gene in marine samples (Michotey et al., 2000).

PCR-independent Approaches to Assess Diversity and Density DNA arrays to analyse and quantify denitrifying communities DNA microarray technology is a new approach for the characterization of microbial

communities in soil that is based on the well-established DNA–DNA hybridization principle (see also Loy et al., Chapter 2 this volume). Both the composition and quantification of either the total gene pool or the expressed genes can be determined with this technology. For denitrifying bacteria, Wu et al. (2001) were the first to develop a gene array for assessing nirS and nirK diversity and distribution, and shortly thereafter an array for quantitative detection of nirS was published (Wu et al., 2001; Cho and Tiedje, 2002). To allow for similar hybridization conditions across the array, Taroncher-Oldenburg et al. (2003) designed 70-mer oligonucleotide microarrays for nirK and nirS, which also minimized the inconsistency among probe–target Tms. Tiquia et al. (2004) made a 50-mer oligonucleotide array that was even better for quantification of denitrification genes with a resolution at the population level. DNA microarrays have potential in environmental studies of denitrifiers, although more development is needed to improve sensitivity. The shorter oligonucleotide arrays seem to be more promising than the DNA arrays, but sensitivity still needs to be improved for accurate quantification.

Immunological approaches to analyse and quantify denitrifying communities By targeting proteins instead of nucleic acids, it is possible to determine the denitrifying community structure and abundance of active denitrifiers that are actually producing the enzymes, since proteins are at the end of the regulatory cascade. Although antibodies were used in the early days by both Coyne et al. (1989) and Ward et al. (1993) to detect NirK and NirS reductases in isolated denitrifiers, there are only a few reports on the application of immunological techniques to study denitrification in the environment. Metz et al. (2003) designed an antibody specific for the copper nitrite reductase to study denitrifying populations expressing copper nitrite reductase in situ. By sorting out the antibody-labelled cells




using flow cytometry, the phylogenetic affiliation of the populations could be determined with 16S rRNA oligonucleotide probes. For quantification, Maron et al. (2004) proposed targeting the membranebound nitrate reductase in cells extracted from soil. Detection of a denitrifying enzyme indicates the presence of the corresponding activity, but a direct correlation between enzyme concentration and activity could be disturbed by the fact that the specific activity of a protein can vary depending on the strain that possesses it (Carter et al., 1995; Afshar et al., 2001). Other potential problems arise from the lack of information on the stability of the denitrifying enzymes and on how long an enzyme can be detected in the cell after the disappearance of its substrate.

Examples of Ecological Studies in Soil and Rhizosphere The ecology of denitrifier populations in soil is still largely unknown despite the essential role of denitrification in the nitrogen cycle and the economic and environmental problems that are associated with it. An understanding of the forces that shape denitrifying communities is critical for linking them to ecosystem-scale processes. So far, only a few studies using a molecular approach have been conducted on denitrifier community composition in soils. Denitrifiers have been targeted by either nirK or nosZ in different soil ecosystems, since the primers readily detecting nirS in soil were not available until 2004. In many studies, narG has also been used as a molecular marker for the intermixed group of denitrifiers and nitrate reducers. The denitrification gene sequences retrieved from different soil ecosystems show a great undiscovered diversity of denitrifiers that relates to as yet uncultivated environmental clones and only occasionally to the genes from known denitrifying strains. However, the ecological significance of the diversity of all these functional genes remains undetermined.

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Diversity of denitrifiers in different soil ecosystems The diversity and composition of denitrifying communities in various soil ecosystems have been estimated by different research groups. These studies are so few and such different approaches have been used that it is difficult to draw any general conclusions. By cloning and sequencing a few nirK, nirS and nosZ genes, Rösch et al. (2002) found a high diversity of unknown sequences in a German oak/hornbeam forest soil. In another forest soil, the diversity of nirK was lower than that observed in a marsh soil and all the nirK sequences found in the forest soil were closely related to those found in the marsh soil (Prieme et al., 2002). The high organic carbon content and the fluctuating water level support denitrifying activity and could explain why the marsh soil was more diverse with respect to denitrifying bacteria. T-RFLP analysis of nosZ in transects from an Abies spp. forest to a meadow with grasses, perennial herbs and ferns indicated large differences between the sites, and the community composition appeared to be linked to function (Rich et al., 2003). The opposite was found when an agricultural field planted with ryegrass, a riparian soil and a creek sediment were compared by RFLP analysis of nosZ, although significant differences in the nosZ distribution among habitats were observed (Rich and Myrold, 2004). In both these studies, the dominant nosZ sequences related to nosZ from Rhizobiaceae.

Influence of agricultural practice on denitrifying communities Fertilization is known to promote denitrification activity (Mulvaney et al., 1997), but knowledge of how the composition of the denitrifying community is affected is limited. One study reported that high ammonium concentrations induced changes in both the denitrifying activity and the nirK community structure in an incubation experiment with arable soil (Avrahami et al., 2002). In samples from a field experiment,



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Wolsing and Prieme (2004) mainly concluded that seasonal variations in the community structure of nirK denitrifiers occurred but that the small variations observed might be explained by the type of fertilizer applied. However, the amount of fertilizer did not seem to have any effect. Recent work has confirmed that different organic and mineral fertilizers in long-term field experiments affect the denitrification activity and modify the composition of both the nosZ and narG communities (Enwall et al., 2005; C. Dambreville, unpublished data). The effect of cultivation on denitrifying communities was examined by Stres et al. (2004). Intensive agricultural practices seemed to increase denitrifier diversity compared with native non-cultivated soil as determined by RFLP analysis of soil nosZ fragments, together with their cloning and subsequent sequencing. Moreover, nitrous oxide emissions were more than twice as high from the cultivated field plots.

Influence of rhizosphere on denitrifying communities The main factors regulating the denitrification respiratory process – carbon, nitrate and oxygen – are affected in the rhizosphere. Roots of plants excrete metabolizable carbon compounds, while both oxygen partial pressure and nitrate availability are decreased in this region via respiration by roots and associated microorganisms and by nitrate assimilation, respectively. It has been suggested that the rhizosphere not only stimulates denitrifying activity but also modifies the composition of this functional community. To test this hypothesis, the narG diversity of nitrate-reducing communities in maize-planted and non-planted pots was analysed by both RFLP fingerprinting and sequencing clone libraries (Philippot et al., 2002). A shift in the composition of the nitrate-reducing community between bulk and rhizosphere soils was observed, but the diversity indices were similar. Sequencing revealed that most of the dominant clones in the rhizosphere were related to narG from actinomycetes,

suggesting a specific selection of nitratereducing actinobacteria by the maize roots. A similar study was performed with another soil type (Cheneby et al., 2003) and, in contrast to the results obtained by Philippot et al. (2002), both planted and non-planted soil exhibited a very low number of narG clones related to narG from actinobacteria. A reduction of the reciprocal Simpson’s diversity index was observed in the rhizosphere soil compared with the non-planted soil, but without any major modification of the composition of the nitrate-reducing community. The results from these two studies suggest that the composition of the nitrate-reducing community differs more between different soil types than between bulk and rhizosphere soil. The dependency of the narG community on soil type was later confirmed by comparison of narG fingerprints from seven different French soils (Philippot, 2005a). Comparison of the nitrate-reducing community structure in rhizosphere soil from fields planted with Lolium perenne and Trifolium repens did not show significant differences, suggesting that the rhizosphere effect possibly depends on the plant species (Deiglmayr et al., 2004). Actually, recent work by Sharma et al. (2005) targeting the transcripts for the nirK genes in the rhizosphere of three grain–legume crops demonstrated that plant species can affect expression of the denitrification genes (see also Sharma et al., Chapter 1 this volume). For a better understanding of the rhizosphere effect, a recent study dissects the effect of the regulating factors by investigating the significance of root-derived carbon on the activity and diversity of denitrifiers, targeted by both narG and nosZ. This work revealed that addition of maize mucilage to soil results in a strong impact on the activity of the denitrifying community but only in minor changes in its diversity (Mounier et al., 2004).

Conclusions and Outlook As with other functional bacterial communities involved in the nitrogen cycle, such




as nitrifiers (Kowalchuk and Stephen, 2001) or diazotrophs (Zehr et al., 2003), numerous molecular tools have been developed since 1998 to target the denitrifying bacteria using cultivation-independent approaches. Difficulties in studying this community using functional genes as markers are associated with the fact that denitrification is a fourstep reduction process and that most of these steps are catalysed by at least two different enzymes (Philippot and Hallin, 2005). In addition, some bacteria can possess enzymes catalysing one, two or three steps in the denitrification pathway. Nevertheless, tools are now available to target bacteria that are genetically capable of performing the different steps, although no method can capture the complete pathway. Identifying and counting bacteria with denitrification genes is the first step in understanding the ecology of denitrifiers, but identification of the bacteria without knowing their contribution to denitrifying activity is of limited value. Therefore, the emphasis in microbial ecology is now on targeting the active microorganisms in order

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to understand the relationship between bacterial diversity and soil functioning. To achieve this aim, technologies based either on mRNA or proteins are currently being developed. These methods are still in their infancy and not yet suitable for routine analysis. Among them, the use of immunological approaches in combination with other techniques could be the best way to identify active denitrifiers in the environment and to estimate their denitrifying activity.

Acknowledgements The images in Figs 9.3–9.5 were kindly provided by Karin Enwall, SLU, Sweden; Christophe Dambreville, INRA Dijon, France and Christopher Rösch, University of Cologne, Germany, respectively. We thank COST 856 for financially supporting L.P.’s stay in Uppsala, and The Royal Swedish Academy of Agriculture and Forestry (KSLA) for providing a grant for S.H. to visit Dijon to complete the writing of the manuscript.

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10

Ecology of Streptomyces in Soil and Rhizosphere Janice L. Strap1 and Don L. Crawford2,*

1Environmental

Biotechnology Institute, Food Research Center 103, University of Idaho, Moscow, ID 83844-1052, USA; 2Environmental Science Program, 216 Morrill Hall, University of Idaho, Moscow, ID 83844-3006, USA

Introduction The importance of streptomycetes in soil and rhizosphere Streptomyces is a well studied genus of saprophytic actinomycetes that decompose organic matter, especially biopolymers such as lignocellulose, starch and chitin, in soil (Goodfellow et al., 1988; Crawford et al., 1993). Members of this genus are mycelial, spore-forming Gram-positive eubacteria with a high GC genomic DNA content (Goodfellow and Ke, 1987). Characteristically, this genus produces a vegetative mycelium as well as chains of spores within an aerial mycelium when growing on solid surfaces. The filamentous mode of growth of Streptomyces gives them a competitive advantage in colonizing not only solid substrates such as decomposing plant residues, but also the rhizosphere. Streptomycetes are both qualitatively and quantitatively important in the rhizosphere, although their activities in that location have not been extensively studied. Specific rhizospherecolonizing Streptomyces are known to grow in close association with plant root systems, where their presence and activity benefit plant growth and protect plant roots against

invasion by fungal pathogens (Crawford et al., 1993; Doumbou et al., 2002; Tokala et al., 2002; Coombs and Franco, 2003; Conn and Franco, 2004).

The rhizosphere as a microbial niche The rhizosphere, the zone of soil directly influenced by plant roots, represents a unique biological niche within the soil environment (Lechevalier, 1989b) supporting an abundance of diverse, saprophytic microorganisms able to decompose polymeric organic matter such as starch, lignocellulose and chitin in the soil (Lynch, 1990; Lynch and Whipps, 1990). The influence exerted by the plant is highly variable and depends on the amount and composition of the organic material released by it (Griffiths et al., 1999). These root exudates selectively influence the growth of bacterial and fungal populations that colonize the rhizosphere by altering the chemistry of the soil in the vicinity of plant roots and by serving as selective growth substrates for soil microorganisms (Grayston et al., 1996; Yang and Crowley, 2000; Whipps, 2001). The highly localized and unique nutrient source provided by root exudates in the soil environment

*Corresponding author; Phone: +1 208 885 6113, Fax: +1 208 885 4674, E-mail: [email protected] 166




Ecology of Streptomyces in Soil and Rhizosphere

selectively enriches plant rhizospheres for microorganisms that are well adapted to the utilization of the specific organic exudate material (Lynch and Whipps, 1990). Therefore, some bacterial populations, including those that antagonize pathogen development, may be more abundant in the rhizosphere and may be different in association with different plant species (Neal et al., 1970). Plant root exudates contain sugars, amino acids, organic acids, fatty acids, sterols, vitamins and nucleotides (Rovira, 1956; Smucker, 1993; Jaeger et al., 1999). It is likely that this variety of nutritional compounds is plant specific and therefore can influence the diversity of microbes that inhabit the rhizosphere of particular plant species (Grayston et al., 1996; Buyer et al., 2002; Doumbou et al., 2002). In addition to supplying a localized nutrient source, plant roots influence soilborne microbial communities by providing a solid surface for attachment and by encouraging competition for nutrients (Rovira, 1956; Brown, 1975).

Functions of Streptomycetes in Soils Actinomycetes as producers of bioactive metabolites Streptomyces are prolific synthesizers of antibiotics and other bioactive metabolites, some of which are produced during primary growth phase, while others are generated during a secondary metabolic phase associated with morphological and chemical differentiation which includes aerial mycelium production and spore formation (Challis and Hopwood, 2003). Of the many bioactive metabolites produced by Streptomyces, the ones most important for their functions as biodegradative soil saprophytes and rootcolonizing bacteria include extracellular hydrolytic and oxidative enzymes, antibacterial and antifungal antibiotics, phytohormones and siderophores (Doumbou et al., 2002). Plant diseases caused by soil-dwelling microorganisms are responsible for severe losses of agricultural crops each year. The traditional approach to control the spread of

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these diseases and to prevent losses in harvest yield is the use of pesticides. However, chemical pesticides and fertilizers are costly, and can pose hazards to the environment and to individual consumers. Thus, there is a high demand for alternative approaches to pest control and fertilization. The use of plant growth-promoting and biocontrol microbes provides a valuable, less invasive alternative to these traditional agricultural approaches.

Plant growth promotion by Streptomyces Some Streptomyces strains are used commercially as inoculant biocontrol agents primarily to control fungal root diseases (Lechevalier, 1989b; Lynch and Whipps, 1990; Crawford et al., 1993; Milus and Rothrock, 1993; Yuan and Crawford, 1995; Tokala et al., 2002); they are also known to function as plant growth-promoting rhizobacteria (PGPR) in the absence of plant pathogen pressure (Aldesequy et al., 1998; Doumbou et al., 2002; Hamby-Salove, 2002; Tokala et al., 2002). Promotion of plant growth by PGPR occurs when the plant is supplied with a compound that is synthesized by the bacteria, or when a PGPR otherwise facilitates plant uptake of soil nutrients (Lynch and Whipps, 1990; Doumbou et al., 2002). Possible mechanisms include nitrogen fixation, siderophore synthesis, phytohormone synthesis and/or solubilization of minerals to make them available for plant uptake (Glick, 1995). Streptomyces are clearly involved in such beneficial activities. For example, siderophore production by root-colonizing Streptomyces species is one confirmed mechanism for plant growth enhancement (Hamby-Salove, 2002; Tokala et al., 2002; Tokala, 2004). In legumes, Streptomyces assimilate iron from the surrounding soil and transfer it into root nodules where it is assimilated by bacteroids (Tokala et al., 2002; Tokala, 2004). For many other PGPR, there is also a positive correlation between siderophore production and observed enhancement of plant growth (Becker and Cook, 1988; Hofte et al., 1991; Aronson and



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J.L. Strap and D.L. Crawford

Boyer, 1994). There are multiple proposed mechanisms by which this occurs. First, by sequestering the iron in the rhizosphere, PGPR render the iron less available to potential pathogens in the rhizosphere (Kloepper et al., 1980; Pannuti et al., 1992). Secondly, the PGPR’s own growth and its ability to promote host plant growth could be enhanced if it is able to utilize the bound, bioavailable iron through specific membrane receptors and uptake transport systems. These receptors and transporters, and their corresponding genes, have been described for several rhizobacteria (Marugg et al., 1985; de Weger et al., 1986; Reigh and O’Connell, 1993; LeVier and Guerinot, 1996). Thirdly, the PGPR may help the host plant assimilate iron when bioavailability is low in soil. Finally, certain root-colonizing species of rhizobacteria are effective biocontrol agents. Microbial biocontrol is typically achieved through competitive antagonism or parasitism of the plant pathogen, and the capacity of some biocontrol agents to antagonize a plant pathogen appears to be closely related to siderophore production (Xu and Gross, 1986; Simeoni et al., 1987; Becker and Cook, 1988; Loper, 1988; Hofte et al., 1991; Mondal and Sen, 1999).

Biological control of plant diseases The control of plant diseases is an urgent need for sustainable agriculture. The application of agrochemicals for this purpose, while still an important method in agricultural practices, is not without its problems, such as environmental pollution and detrimental effects on non-target organisms (Kunoh, 2002). Biological control offers a much needed alternative to the use of synthetic agrochemicals (Emmert and Handelsman, 1999; Bloemberg and Lugtenberg, 2001; Kunoh, 2002). For example, natural antibiotics produced within the microhabitat of the rhizosphere by organisms such as Streptomyces are considered to be less polluting and less stressful on indigenous microbes compared with chemical fungicides (Tonaka and Omura, 1993).

The genus Streptomyces has been studied extensively for potential biological control agents against fungal phytopathogens such as Pythium ultimum (Crawford et al., 1993; Chamberlain and Crawford, 1999), Sclerotinia homeocarpa (Trejo-Estrada et al., 1998; Chamberlain and Crawford, 1999), Fusarium oxysporum (Chamberlain and Crawford, 1999), Gaeumannomyces graminis (Chamberlain and Crawford, 1999) and Phytophthora fragariae (Valois et al., 1996). In situ, biocontrol by Streptomyces spp. has been demonstrated using the organisms themselves as well as the isolated, bioactive products produced by them (Smith et al., 1990). The unique growth strategy of Streptomyces makes them ideally suited for use as biocontrol agents: they have the ability to colonize plant root surfaces (Barakate et al., 2002; Tokala et al., 2002; Basil et al., 2004), they exhibit antibiosis against plant root pathogens (Chamberlain and Crawford, 1999), they synthesize extracellular enzymes (Chamberlain and Crawford, 2000), they have the ability to degrade many phytotoxins (Trejo-Estrada et al., 1998; Tokala et al., 2002) and, importantly, they have the ability to form desiccant-resistant spores which can be formulated as dry, stable, powdered products (Emmert and Handelsman, 1999). As one can see, the mechanisms of plant growth promotion and biocontrol are not mutually exclusive but interdependent. Many of the properties exhibited by PGPR are also those exhibited by effective biocontrol organisms, which makes their use in agriculture an attractive alternative to agrochemicals (see also Franks et al., Chapter 7 this volume).

Enumeration, Isolation and Screening of Actinomycetes for Potential Biocontrol Activity Studies of rhizosphere-associated actinomycetes often include a comparison of actinomycete populations within the rhizosphere with those in the surrounding bulk soil (Basil et al., 2004). Soils are collected from an area of an established plant community




of interest. Multiple soil samples are obtained, and care is taken to avoid crosscontamination between samples. Plant communities sampled can vary in terms of age, vitality, disease pressure, etc., depending upon the interest of the investigator. Rhizosphere soil samples are taken by collecting only that soil that is closely adhering to the plant roots (Lynch and Whipps, 1990; Basil et al., 2004). Nearby non-rhizosphere soil samples, sometimes called the bulk soil samples, are taken a metre or two away from a plant being sampled, from an area underneath the root zone of any grasses or other plants growing on the surface of the site (Basil et al., 2004). The textural class and pH of the soils should also be determined after bringing the samples back to the laboratory. There are a variety of approaches to handling the soils once they reach the laboratory, and to enumerating and isolating actinomycetes in them. For example, freshly collected soils can be passed through a sieve (e.g. 2 mm) before being plated onto agar media using the dilution plating method (Okazaki et al., 1983). Some investigators prefer to air-dry the soils for varying amounts of time to reduce the numbers of eubacteria relative to actinomycetes. A variety of media can be used to enumerate and isolate actinomycetes from the rhizosphere and bulk soils, and to compare culturable actinomycete population numbers between soil samples and/or to enumerate the total numbers of culturable actinomycetes, eubacteria and fungi in the soils (Basil et al., 2004). In a typical investigation for enumeration and isolation of actinomycetes, serial dilutions can be made onto a medium such as potato dextrose agar (PDA) supplemented with cycloheximide (100 µg/ml to inhibit fungal growth) (Goodfellow and Williams, 1983; Hu and van Bruggen, 1995). Culture plates are then incubated at an appropriate temperature (e.g. 28–30°C) for 10–15 days. Colonies morphologically characteristic of actinomycetes (e.g. leathery with sporulated aerial mycelium) are first counted, and selected colonies are restreaked on an appropriate agar medium for isolation and purification. In addition, examination by light microscopy can be used to corroborate

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identification of isolates as actinomycetes (Lechevalier, 1989a). Total eubacterial and actinomycete counts can also be determined on media such as trypticase soy agar (TSA) and PDA, respectively, both supplemented with cycloheximide (100 µg/l), while total culturable fungi can be enumerated on PDA without cycloheximide. The media available for enumeration and isolation are many, and actinomycetes are best enumerated on those containing low levels of nutrients (Goodfellow and Ke, 1987; Lechevalier, 1989a; Crawford et al., 1993). For example, an effective low nutrient medium for actinomycete enumeration and isolation is water yeast extract agar (WYE) (Reddi and Rao, 1971; Crawford et al., 1993). A diversity of actinomycetes grow, sporulate well and out-compete other bacteria on this medium, which contains a low level of yeast extract (0.25 g/l) as the sole carbon and nitrogen source. Casamino acids yeast extract dextrose agar (YCED) is a medium somewhat higher in nutrient diversity and concentration, but is also a good medium for isolation of actinomycetes (Crawford et al., 1993). In addition to yeast extract, this medium is supplemented with low amounts of casamino acids (0.3 g/l) and glucose (0.3 g/l). In contrast to actinobacteria, total eubacteria are typically enumerated on rich organic media such as TSA, and fungi on PDA supplemented with an antibacterial antibiotic such as carbenicillin (100 µg/l) (Basil et al., 2004). Whatever media are chosen, it is important to define their composition and the reasons for employing them, in addition to keeping the chosen media consistent when making comparisons between results obtained for different soils. Also, one must remember that these techniques only enumerate culturable microbes from the soils; many very slow-growing and non-culturable actinomycetes are also present in most, if not all, soils, and therefore these dilution plate techniques underestimate the true microbial numbers. Studies of soils from desert habitats, such as those where the shrub big sagebrush grows, are one example of recent comparisons of both culturable and non-culturable actinomycete populations (Basil et al., 2004;



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Gonzalez-Franco, 2004). Basil et al. (2004) showed that the rhizosphere of desert shrubs is a source of actinomycetes producing a broad spectrum of antifungal compounds. However, the culturable numbers of actinomycetes, eubacteria and fungi present within the sagebrush rhizosphere were not statistically different from those in the surrounding bulk soil. Despite this, polymerase chain reaction (PCR) amplification of the 16S rRNA gene sequences of total microbial genomic DNA and denaturing gradient gel electrophoresis (DGGE) showed that the actinomycete community structures differed between the rhizosphere and bulk soils. In this research, total actinomycete counts were enumerated on WYE and YCED, while total eubacterial and fungal counts were enumerated on TSA and PDA, respectively. The PDA was supplemented with carbenicillin (100 µg/ml) to eliminate bacterial growth. Gonzalez-Franco (2004) showed that the sagebrush rhizosphere contained culturable Streptomyces, some of which appeared to be novel species, that were sources of potent antifungal enzymes and antibiotics. In contrast, these soils were not as good a source of antibacterial actinomycetes. In this research, antimicrobial susceptibility tests were performed in vitro using a panel of plant pathogen strains including: fungi (Rhizoctonia solani, Fusarium oxysporum and Pythium ultimum), Gram-positive bacteria (Bacillus subtilis and Rathayibacter tritici) and Gram-negative bacteria (Xanthomonas campestris pv. campestris and Burkholderia cepacea). The potential of an actinomycete to control fungal diseases within the rhizosphere may also be shown by examining its ability to control not only known pathogens, but also native fungi from the habitat under study, as was reported by Basil et al. (2004). Antibacterial activity assays were performed by streak inoculation of the actinomycete on one side of multiple PDA plates followed by incubation at 30°C for 10 days to allow the production and diffusion of metabolites and extracellular hydrolytic enzymes. Test bacteria were then inoculated as lines perpendicular to the actinomycete growth, and the plates were incubated at 30°C. Bacteria were also streaked on non-inoculated

plates as controls. Bacterial growth inhibition was recorded at different time intervals for 5 days. Antifungal activity was determined similarly by inoculating agar blocks of actively growing test fungi on the medium directly across from the actinomycete growth (Gonzalez-Franco et al., 2003). Examination of the antifungal enzyme chitinase was tested on colloidal chitin (CC) plates as described by Gonzalez-Franco et al. (2003). Chitinases were produced abundantly by most of the antifungal cultures investigated in this study. A rhizosphere effect was observed in this work, where there was a greater diversity of actinomycetes observed within the sagebrush rhizosphere soils as compared with the surrounding bulk soils. For these experiments, total bacterial and actinomycete counts were determined for each soil using TSA and PDA, respectively; both media were supplemented with 100 µg/ml cycloheximide. In both of the reports cited above, a combination of culture-based and molecular genetic techniques was used to assess these actinomycete populations. In comparison with the culturable enumeration techniques, the ability of the molecular techniques to demonstrate better the population diversity differences between samples was clear. Yet, studies of individual isolates required that they be cultured and isolated. In a final example, a sagebrush rhizosphere Streptomyces isolate was found to exhibit antagonism against a broad spectrum of fungi and yeasts (Fig. 10.1) when examined by in vitro plate bioassays (J.L. Strap, unpublished). Culture supernatants of this Streptomyces isolate grown in liquid culture were able to inhibit Candida albicans and Aspergillus niger (Fig. 10.2) (J.L. Strap, unpublished). A. niger was even inhibited upon three, twofold serial dilutions of the culture supernatant with sterile water.

Molecular Characterization of Actinomycete Communities The demand for new methods to augment existing disease control strategies, coupled




171

Sp Sc Ca A

B

C

D

E

Fig. 10.1. Streptomyces isolate exhibiting antagonism against (A) yeasts, (B) A. niger, (C) F. oxysporum, (D) R. solani, (E) P. ultimum. Abbreviations: S. pastorianus (Sp), S. cerevisiae (Sc), C. albicans (Ca). (Photo courtesy of Janice L. Strap.)

Undil

N=1

N=2

N=3

Control

A. niger

C. albicans

Fig. 10.2. Culture supernatant of a Streptomyces isolate grown in liquid production culture exhibiting antagonism against A. niger and C. albicans (7). Serial twofold dilutions of culture supernatant were used in the in vitro assay with A. niger. Assays with C. albicans were performed with sterile paper disks impregnated with undiluted culture supernatant. Culture supernatants devoid of activity (8 and 9) and uninoculated culture broth served as controls. (Photo courtesy of Janice L. Strap.)

with the need to achieve more efficient disease control, requires a deep understanding of plant–microbe interactions in addition to the complex relationships between plant pathogens and potential biological control agents. The use of both culture-dependent and culture-independent techniques is vital to such investigations. Many methods can be used to differentiate between closely related species and strains of Streptomyces isolated from the environment. These include the use of chemotaxonomic traits such as diagnostic isomers of diaminopimelic acid (DAP) (Lechevalier and Lechevalier, 1970; Hasegawa et al., 1983), cell wall sugars (Lechevalier and Lechevalier, 1970), fatty acids (Minnikin et al., 1980) and phospholipids (Lechevalier et al., 1977; Collins, 1985), or DNA–DNA relatedness studies (De Ley et al., 1970; Huß et al.,

1983) and the molecular ecology techniques of comparing 16S rDNA genes and housekeeping genes. Strategies for the molecular characterization of pure culture isolates as well as characterization of actinobacterial community diversity are described below.

16S rDNA and housekeeping gene sequence analysis The ribosome consists of two subunits each containing RNA and protein molecules for protein synthesis. The prokaryotic small subunit (SSU) rRNA molecule is a fragment with a sedimentation coefficient of 16S; the large subunit (LSU) rRNA contains 23S and 5S molecules. The 16S rRNA gene is most often used for bacterial characterization (Kolbert and Persing, 1999; Monciardini



172


et al., 2002). It is amenable for this purpose because it contains alternating regions of sequence conservation and heterogeneity (Woese, 1987). In addition to 16S rRNA genes, other targets including 23S rDNA, 16S–23S rDNA internal transcribed sequences, and housekeeping genes such as gyrB, rpoB and groEL have been used to characterize isolates to the genus or species level (Hunt et al., 1994; Yamamoto and Harayama, 1998; Hinrikson et al., 2000a, b; Kasai et al., 2000a; Sumner et al., 2000; Tang et al., 2000; Hatano et al., 2003; Kim et al., 2004; Lee et al., 2005). Members of the latter category are particularly useful in that nucleotide conservation is often lower than in rRNA genes, making them valuable for determination of taxonomic relationships among closely related organisms such as Streptomyces. DNA extraction for PCR studies METHOD 1. A modified procedure of Oho et al. (2000) is routinely used in our laboratory. Briefly, isolated Streptomyces colonies are aseptically excised from culture plates and resuspended in 100–200 µl of lysis solution consisting of 10 mM Tris–HCl buffer, 1 mM EDTA, 1% Triton X-100, pH 8.0 in a microcentrifuge tube and boiled for 15 min. Boiling the samples prevents nuclease activity from inhibiting the PCR. The reaction tube is then flash-frozen in a dry ice–ethanol bath, boiled for an additional 15 min and placed on ice for 2 min prior to centrifugation at 13,000 r.p.m. for 5 min. The supernatant obtained after centrifugation is used as template for PCR. METHOD 2. Total genomic or metagenomic DNA is isolated from pure cultures or directly from soil, respectively, using the UltraClean Soil DNA kit (Mo Bio Laboratories Inc., Carlsbad, California, USA) with the addition of a 1.5 min heat treatment at 65°C and a cooling step on ice for 2 min to increase the efficiency of cell lysis. It is important to realize that the cell lysis and DNA extraction methods chosen can profoundly affect the diversity of genomic DNA recovered for community analysis (von Wintzingerode et al., 1997).

PCR amplification PRIMERS. Characterization of isolates or metagenomic samples can be achieved by amplification of 16S rRNA genes (Table 10.1) or other housekeeping genes (Table 10.2). REACTION CONDITIONS. Typical reactions for 16S rDNA amplification consist of 50 µl PCR volumes containing 1 × PCR buffer (Sigma-Aldrich, St Louis, Missouri, USA), 2.5 mM MgCl2, 200 µM (each) deoxyribonucleoside triphosphate (dTTP, dATP, dGTP and dCTP), 1.0 µM (each) primer, 20–50 ng of template DNA and JumpStart™ Taq DNA polymerase (0.05 U; Sigma-Aldrich). PCR amplification is performed on an MBS Thermal Cycler (Thermo Electron Corporation, Milford, Massachusetts, USA) or equivalent, under the following conditions: initial denaturation of the template DNA at 95°C for 5 min, 25 cycles of 95°C for 30 s, 55°C for 30 s and 72°C for 60 s, and a final elongation step at 72°C for 10 min. The PCR products are visualized by electrophoresis on 1% (w/v) agarose gels. Positive controls include Escherichia coli HB101 and Streptomyces coelicolor M145 genomic DNA. Negative controls consist of DNA from C. albicans ATCC 90027 and reactions with no template DNA addition. Reaction products are purified with MO BIO Ultraclean™ PCR Clean-Up™ kit (Mo Bio Laboratories Inc.) according to the manufacturer’s instructions. PCR products can be sequenced directly (for pure isolates) or the amplicons can be cloned (metagenomic studies) using the TOPO TA Cloning® Kit for Sequencing (version k) (Invitrogen Corp., Carlsbad, California, USA). It is recommended that for metagenomic studies, multiple PCRs be pooled to reduce PCR bias.

Characterization of actinomycete community diversity Despite considerable interest in the secondary metabolite production, plant growth promotion and biocontrol capabilities of cultured Streptomyces spp., very little is known about the diversity and ecology of




173

Table 10.1.

Primer sequences for characterization of actinomycete 16S rRNA genes.

Primer

Primer Sequence (5′-3′)a

Comment

Reference

27fb

AGAGTTTGATCMTGGCTCAG GCTGCCTCCCGTAGGAGT CCGTCAATTCMTTTRAGTTT TACGGYTACCTTGTTACGACTT AAGGAGGTGWTCCARCC GGATGAGCCCGCGGCCTA gc-CGGCCGCGGCTGCTGGCACGTA gc-AACGCGAAGAACCTTAC CGGTGTGTACAAGGCCCGGGAACG CCAGCCCCACCTTCGAC GGTGGCGAAGGCGGA GAACTGAGACCGGCTTTTTGA CGCCCGCCGCGCCCCGCGCCCGTCCCGCCGCCCCCGCCCG

Bacteria (8-27)

Lane, 1991

Bacteria (338-355)

Amann et al., 1990; Amann et al., 1995 Lane, 1991

338fIb 907rc 1492rc 1525rc F243b R513GCc F984GCb R1378c A3Rc Sm6Fb Sm5Rc gc-

Bacteria, eukarya, archaea (907-926) Bacteria, archaea (1492-1513)

Lane, 1991

Bacteria, archaea (1541-1525) Actinomycetes (226-243)

Lane, 1991 Heuer et al., 1997

Actinomycetes (513-528)

Heuer et al., 1997

Bacteria (968-984)

Heuer et al., 1997

Bacteria (1378-1401)

Heuer et al., 1997

Actinomycetes (1414-1430) Streptomycetes (721-735) Streptomycetes (1283-1303)

Monciardini et al., 2002 Monciardini et al., 2002 Monciardini et al., 2002

GC-clamp for DGGE

Muyzer et al., 1993

aM

= C:A; R = A:G; W = A:T; Y = C:T. forward primer. cr, reverse primer. bf,

the unculturable representatives of this genus in natural environments. Much of the information we have on rhizosphere microflora has been obtained primarily through cultivation and isolation on laboratory media, a method which is now recognized to focus on only a very small percentage of the actual rhizosphere community members. Detection of greater diversity within the microbial populations associated with roots is possible through currently available cultureindependent techniques such as repetitive elements-PCR (rep-PCR) (Schneider and De Brujin, 1996; Davelos et al., 2004), BOX-PCR (Lanoot et al., 2004; Davelos et al., 2004), restriction fragment length polymorphism (RFLP) and DGGE (Muyzer and Smalla, 1998; Williamson et al., 2000;

Basil et al., 2004). The latter two techniques are described below. Restriction fragment length polymorphism (RFLP) analysis Actinomycete communities in soil can be investigated by RFLP analysis (Fig. 10.3). For these investigations, metagenomic DNA is amplified using nearly full-length 16S rDNA primers (Table 10.1) as described above. PCR products of the correct size are purified with a MO BIO Ultraclean™ PCR Clean-Up™ kit (Mo Bio Laboratories Inc.) according to the manufacturer’s instructions. The amplicons are cloned using the TOPO TA Cloning® Kit for Sequencing (version k) (Invitrogen Corp.). Clones are



174

Table 10.2.


Primer sequences for characterization of streptomycete housekeeping genes.

Primer

Primer Sequence (5′-3′)a

Comment

Reference

gyrBC-1b

GCCCGAAAGAGCTCGAGTGTGACTAC

Schmutz et al., 2004

gyrBC-2c

CGGTGTCCAAGCTTAGATGTCGAGGA

UP1TLb

CAYGCNGGNGGNAARTTYGA

gyrB; Xho I site underlined gyrB HindIII site underlined gyrB

UP2Rtlc

TCNACRTCNGCRTCNGTCAT

gyrB

UP1b

gyrB

SRPOF1b

GAAGTCATCATGACCGTTCTGCAYGCNGGNGGNAARTTYGA AGCAGGGTACGGATGTGCGAGCCRTCNACRTCNGCRTCNGTCAT GAGGTCGTGCTGACCGTGCTGCACGCGGGCGGCAAGTTCGGC GTTGATGTGCTGGCCGTCGACGTCGGCGTCCGCCAT TCGACCACTTCGGCAACCGC

SRPOR1c

TCGATCGGGCACATGCGGCC

rpoB

GRO-NCb GRORCc

GACCGCCGCAAGGCGATG GCCCTCCTCGACCGCGGC

groEL groEL

UP2rc PF-1b PR-2c

Schmutz et al., 2004

gyrB

Kasai et al., 2000a; Kasai et al., 2000b Kasai et al., 2000a; Kasai et al., 2000b Yamamoto and Harayama, 1995 Yamamoto and Harayama, 1995 Hatano et al., 2003

gyrB

Hatano et al., 2003

rpoB

Kim et al., 2004; Lee et al., 2005 Kim et al., 2004; Lee et al., 2005 de Leon et al., 1997 de Leon et al., 1997

gyrB

aN

= A:C:G:T; R = A:G; Y = C:T. primer. creverse primer. bforward

grown in 96-well format plates with appropriate antibiotic selection, and plasmid DNA is purified using the Montage Plasmid Miniprep96 Kit (Millipore, Billerica, Massachusetts, USA) or equivalent. RFLP patterns are produced for each clone by digestion of 1 µg of plasmid DNA with 5 U of HaeIII (Invitrogen), MspI (New England Biolabs, Ipswich, Massachusetts, USA) or other suitable restriction enzyme, in a final reaction volume of 50 µl. Reactions are typically incubated at 37°C for 2 h followed by enzyme inactivation at 65°C for 10 min. Fragmentation patterns are analysed by agarose gel electrophoresis on 3.5% NuSieve 3 : 1 agarose (Cambrex Bio Science, Baltimore, Maryland, USA). Gels are stained with ethidium bromide and visualized under UV transillumination. Kodak 1D Image analysis software (Kodak Eastman, New Haven, Connecticut, USA) facilitates grouping of clones. At least one representative

from each RFLP group is sequenced in both directions. Contiguous sequences are obtained using ContigExpress software (Vector NTI suite version 9). Prior to comparative sequence analysis, vector sequences flanking the 16S rDNA inserts must be removed. Sequences obtained from community studies must be analysed for chimeras using the Ribosomal Database Project II CHECK_CHIMERA program (Maidak et al., 1996; Maidak et al., 2001) and/or Bellerophon (Huber et al., 2004). Nucleotide– nucleotide BLAST (Basic Local Alignment Search Tool) (Altschul et al., 1990) can be used to search GenBank for nearest relative sequences. BLAST results and RFLP representative clones are aligned using ClustalX (Thompson et al., 1997) or equivalent software. Phylogenetic trees are inferred using PAUP* (version 4.0b10; Sinauer Associates, Inc., Sutherland, Massachusetts, USA). Confidence estimates for the nodes within




175

Plasmid

Metagenomic DNA

Restriction digest

PCR

Clone/transform E. coli

Sequence

Fig. 10.3. Strategy for restriction fragment length polymorphism (RFLP) analysis of Streptomyces spp. from soil environments. Metagenomic DNA is used as template for PCRs. Resultant amplicons are cloned into an appropriate vector, transformed into E. coli and picked into a 96-well format plate for culturing and archival purposes. Plasmid DNA is purified from each clone and subjected to digestion by restriction enzyme(s). Products of restriction enzyme digests are resolved by agarose gel electrophoresis. Clones exhibiting the same banding patterns are grouped together and representatives of each group are sequenced.

phylogenetic trees are performed by bootstrap analysis (500–1000 replicates). Trees are visualized using TreeView software version 1.6.6 (Page, 1996).

Denaturing gradient gel electrophoresis (DGGE) Actinomycete diversity in soils can be assessed by the use of DGGE. For DGGE analysis, metagenomic DNA (50–100 ng) extracted from soil samples (as described above; see also Basil et al., 2004; O’Callaghan et al., Chapter 6 this volume) is used as template for amplification with 16S rRNA gene primers. The primers used are 338F-gc and 907R (Table 10.1). A GC clamp (Muyzer et al., 1993) is added to the forward primer. PCR conditions are as described above. Once products have been confirmed by agarose gel electrophoresis, the PCR amplicons generated are purified using MO BIO Ultraclean™ PCR Clean-Up™ kit (Mo Bio Laboratories Inc.) and separated via DGGE using the Bio-Rad D CODE apparatus (Bio-Rad Laboratories, Hercules,

California, USA). Polyacrylamide (6% w/v; 0.75 mm) gels with denaturing gradients ranging between 40 and 80% are used (Basil et al., 2004). A 100% denaturant concentration is defined as 7 M urea and 40% deionized formamide. Gradients vary with the primers used for amplification, the nucleotide composition of the amplicons and apparatus used for DGGE. DGGE gels are loaded with 20 µl of PCR product mixed with loading dye and are run in a 0.5× Tris acetate EDTA (TAE) buffer system (40 mM Tris, 40 mM acetic acid, 1 M Na2EDTA, pH 7.4) maintained at 65°C and 65 V for 20 h. To visualize gels, they are either silver stained (Riesner et al., 1989) or stained with ethidium bromide and visualized under UV transillumination (Fig. 10.4).

Root Colonization Studies of Streptomyces Isolates from the Rhizosphere Before embarking upon detailed studies of its biocontrol properties, it is important to



176


1

2

3

4

5

6

40%

a

b c

80%

Fig. 10.4. Denaturing gradient gel electrophoresis (DGGE) of rhizosphere and bulk soils from three different sampling sites using a denaturant gradient of 40–80% (Basil et al., 2004). Lanes 1–3: bulk soil 3; bulk soil 2; bulk soil 1. Lanes 4–6: rhizosphere soil 3; rhizosphere soil 2; rhizosphere soil 1. Bacteria found only in the rhizosphere soil (a); bacteria found only in bulk soil (b); bacteria found in both rhizosphere and bulk soil (c). (Photo courtesy of Antony J. Basil.)

confirm that an isolate obtained from a rhizosphere soil is actually rhizosphere competent, i.e. capable of colonizing the roots of the plants from which it was isolated, and possibly those of other plant species as well. Some isolates that are not root colonizers may be inadvertently isolated with typically used isolation and culture techniques. Confirmation can be achieved by carrying out in vitro root colonization studies in the laboratory or greenhouse. Also, as exemplified by the research of Basil et al. (2004) and others (Doumbou et al., 2002),

some rhizosphere-competent actinomycete strains may preferentially colonize the roots of specific plants. Examples of this specificity include Streptomyces lydicus WYEC108, an isolate from the rhizosphere of linseed (Crawford et al., 1993), and Streptomyces strain RG, an isolate from the rhizosphere of sagebrush (Basil et al., 2004; Tokala, 2004). S. lydicus readily colonizes the roots of a variety of plants (Yuan and Crawford, 1995; Crawford et al., 2005), but is a poor colonizer of sagebrush (Basil et al., 2004). This difference in specificity (Basil et al., 2004) is exemplified in Fig. 10.5. In this case, S. lydicus WYEC108 was a poor colonizer of sagebrush roots, in comparison with Streptomyces strain RG, which aggressively colonized them. Procedures for examining root colonization involve seed inoculation, followed by monitoring of colonization using scanning electron microscopy (SEM). Typically, surface-sterilized seeds are treated with an actinomycete spore-containing dry powder formulation. First, spores of the actinomycete strain being studied are suspended in the carrier (e.g. sterile talc) to about 108 c.f.u./g. Sterile carrier alone is used for treating control seeds. Surface-sterilized seeds are planted into soil and then treated with carrier using a method such as that described by Basil et al. (2004). In that work, researchers planted the seeds in sterile sandy soil placed into ‘cone-tainers’™ (Stuewe and Sons, Inc., Corvallis, Oregon, USA) (13 cm × 4 cm), which were filled with sterile sandy soil to a 0.5 cm gap from the top. A cavity of about 2.0 cm depth was created in the soil, and 1.0 g of talc containing spores (or sterile talc) was placed into the cavity containing the seeds. This was followed immediately by watering. The ‘cone-tainers’™ were incubated in a growth chamber under conditions appropriate for seed germination and plant growth. In the above-mentioned study, plants were harvested after 30 days, although this time will vary with the plant, and it is always possible to harvest plants at various time intervals to examine root colonization temporally. After harvest, the roots are washed thoroughly with sterile distilled water, aseptically cut




A

177

B

C

100 µm 100 µm

100 µm

Fig. 10.5. Scanning electron micrographs of root colonization by a streptomycete on 30-day-old sagebrush roots. (A) Streptomyces sp. strain RG, a sagebrush isolate, exhibited very good colonization. (B) A selected portion of the RG-colonized sage roots showing the hyphae of Streptomyces. (C) Streptomyces lydicus WYEC 108, a non-sagebrush rhizosphere isolate, showed very poor colonization of the sage root. (Photo courtesy of Antony J. Basil.)

Fungus

Actinomycete

Fig. 10.6. Scanning electron micrograph showing a microbe–microbe interaction between an actinomycete species and a fungus on the surface of a plant root. (Photo courtesy of Ranjeet K. Tokala.)

into 1 mm pieces using a sterile scalpel, and immediately processed for SEM. For SEM (for example, see Basil et al., 2004), the washed root samples are fixed in 2–3 ml of glutaraldehyde in cacodylate buffer and then rinsed with the same buffer for three 10 min intervals. Osmium tetroxide is next added to the samples and incubated for 12–16 h at 4°C. The samples are then rinsed with cacodylate buffer three times

for 10 min each, and dehydrated in a graded series of ethanol. Dehydrated samples are critical point dried, washed with gold, and observed under the scanning electron microscope. These SEM methods are well described (Bozolla and Russell, 1992). Root colonization studies can be expanded into examining a variety of plant– microbe and microbe–microbe interactions that are important to the symbioses that



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develop between actinomycetes and their plant hosts, and to examining the biocontrol activities of the actinomycetes within the rhizosphere. Often, one can visualize microbe–microbe interactions that might not otherwise be obvious (Fig. 10.6).

Concluding Remarks A combination of microbiological, biochemical, enzymological, molecular and

microscopic visualization techniques is necessary for examination of the properties and activities of actinomycetes in the rhizosphere. While streptomycetes have been actively studied for the usefulness of their bioactive secondary metabolites, little is known of their ecology in situ. Investigations into their diversity, and their interactions with other microbes and with plant roots within the rhizosphere will be important for efficient soil management strategies in agriculture.

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Molecular Ecology of Ectomycorrhizal Fungal Communities: New Frontiers Ian C. Anderson The Macaulay Institute, Craigiebuckler, Aberdeen AB15 8QH, UK

Introduction Ectomycorrhizal (ECM) fungi are ubiquitous in forest ecosystems and form symbiotic associations with roots of woody plants. They have important roles in both carbon and nutrient cycling processes, particularly those associated with host plant nutrition (Smith and Read, 1997). Significant advances have been made in ECM community ecology during the last decade (Horton and Bruns, 2001). This has been driven largely by the development and implementation of molecular biological techniques which have been used alongside traditional morphological and anatomical-based approaches. Undoubtedly this ‘molecular revolution’ (Horton and Bruns, 2001) has, at least in part, been due to the utility and informative nature of the internal transcribed spacer (ITS) regions of fungal rRNA that is targeted in most ECM ecological studies, and the fact that the costs associated with the generation of molecular data have decreased considerably over the last few years. Traditional molecular approaches such as ITS-restriction fragment length polymorphism (RFLP) and sequencing are now more routine in ecological studies of ECM fungi; nevertheless, their use continues to generate exciting

new data, furthering our understanding of the diversity and ecology of ECM communities. For example, recent work using ITS-polymerase chain reaction (PCR) and sequencing has shown that ECM communities can be vertically stratified, with some species only being present in the deeper mineral soil layers (Rosling et al., 2003), and it has been used to describe spatial and temporal variation in ECM communities (Izzo et al., 2005). As powerful as the molecular approaches of ITS-PCR/RFLP and sequencing have been for ECM ecology, they are still sometimes limited by the number of samples that can be processed in a realistic time frame. The below-ground nature of ECM fungi makes sampling their communities difficult, and often large numbers of samples need to be taken to describe a community adequately (Taylor, 2002). Inevitably there is a trade-off between the time it takes to process the samples and ensuring there is enough replication in the sampling design for the data to be meaningful. Often, time is the limiting factor as it is not uncommon for ECM community studies to involve the extraction and analysis of thousands of root tips. The adoption of molecular community profiling techniques in studies of ECM

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ecology may alleviate some of the difficulties associated with sampling as they are generally more rapid and can handle higher sample numbers, including bulk root or soil samples. These approaches have been widely used in bacterial ecology and are beginning to be used in soil fungal ecology, including some studies on ECM fungi (Anderson and Cairney, 2004). The ability of these techniques to take us below ground opens up exciting new opportunities and challenges for ECM community ecology. Particularly important is the ability of the techniques to detect ECM mycelium in soil, which allows hypotheses about ECM mycelial communities to be tested. The impact molecular approaches such as ITS-RFLP and sequencing have had on advancing our understanding of ECM communities over the last decade is summarized in an excellent review by Horton and Bruns (2001). My main aim here is to concentrate on the recent advances that have been made since that review and to introduce new techniques that have been developed to investigate soil fungal communities. I will briefly discuss their limitations and, where possible, give examples of how they allow us to address new and exciting ecological questions to further our understanding of ECM fungal communities. Finally, I will introduce some emerging molecular technologies that demonstrate significant potential for ECM ecological research in the future.

To Morphotype or Not to Morphotype? Many species of ECM fungi produce above-ground fruiting structures that can be collected, identified and used in investigations of their community structure. Some ECM fungi, however, produce inconspicuous resupinate or hypogeous fruiting bodies which are difficult to detect and are often overlooked in field studies, while others produce above-ground fruiting structures infrequently. As a result, it is now widely accepted that the analysis of above-ground fruiting bodies provides only a glimpse of

the diversity of ECM fungi that may be present at any site, hence the need for both above- and below-ground analysis when investigating ECM community structure (see Horton and Bruns, 2001). This is perhaps unsurprising given the fact that ECM fungi are below-ground organisms and spend most of their life as vegetative structures in soil, only producing above-ground reproductive structures when conditions, which are likely to vary between species, are favourable. Advances in below-ground studies of ECM community structure have relied on the presence of symbiotic ECM root tips formed between compatible ECM fungi and host root systems. The ECM root tip is often the sampling unit of choice for many ECM ecologists as they are discrete structures that can be collected, counted, weighed and analysed. The morphological and anatomical characteristics of ECM root tips formed by different ECM fungal species are often unique, and allow their classification into ‘morphotypes’ which can be used as a guide to identify the colonizing fungal species (Agerer, 1991). Although morphotyping has been successfully used in many ecological studies, the approach has several drawbacks. These include the fact that it is extremely time-consuming to morphotype properly, and it is a skill that requires significant training and practice. In addition, many morphotypes often remain unidentified in ecological studies due to the lack of distinguishable characteristics or the time it would take to morphotype more precisely. These limitations, particularly the trade-off between time and level of information gained, have led to the question ‘How much to morphotype?’ being asked by Horton and Bruns (2001). Given the difficulties associated with morphotyping, do we still need to morphotype at all? In reality, it still often provides useful and novel data and, importantly, enables the ECM samples that require molecular analysis to be reduced to a manageable number. Morphotyping also characterizes morpho-anatomical features of mycorrhizas, and these can provide useful insights into the functional roles of the fungi in the community in relation to nutrient and water capture (Agerer, 2001).



Molecular Ecology of Ectomycorrhizal Fungal Communities

In fact, morphotyping followed by ITS-PCR and RFLP is still the most widely adopted approach in ECM ecology (Horton and Bruns, 2001). None the less, there are cases where morphotyping may not be the most appropriate first step, particularly in substantial field-based ecological studies where the number of samples that require processing is large. Such studies can benefit from the application of recently developed molecular community profiling techniques that allow the separation, and often identification, of individuals in a mixed community DNA sample (Anderson and Cairney, 2004). The use of these approaches circumvents the need for morphotyping and allows ECM community structure to be determined directly from DNA extracted from pooled ECM root tip samples (Burke et al., 2005; Johnson et al., 2005; Landeweert et al., 2005), thereby enabling a larger number of samples to be processed than would be possible with traditional methods. However, if species identification is required, a robust database of the species present at the study site is essential, and obtaining this would require the use of morphotyping and ITS analysis.

Community Profiling Techniques and Their Application to ECM Ecology A suite of new community profiling techniques has been developed for the detection of microorganisms in environmental samples, and although their use in the field of fungal ecology is still in its infancy (for a review, see Anderson and Cairney, 2004), they have revolutionized bacterial ecology and have significant potential for furthering our understanding of ECM fungal communities. The most promising techniques for ECM ecology include terminal restriction fragment length polymorphism (T-RFLP), denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE) and cloning. One major advantage of these techniques is that they allow high throughput community analysis of DNA extracted from soil or symbiotic

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root tissue samples, thereby enabling large, well-replicated field experimentation. The inability of conventional, individual root tip ITS-RFLP analyses to cope with large sample numbers has limited ECM field studies in the past (Horton and Bruns, 2001; Taylor et al., 2002). Perhaps the greatest advantage of these new techniques is the direct detection of ECM mycelium in soil, making it possible to investigate the distribution, dynamics and abundance of ECM mycelial communities in the field, which until now has been extremely difficult. This is a major leap forward, given the known significance of ECM mycelia in forest soil (Cairney, 2005).

Primer selection As with all PCR-based methods, the specificity of the analysis is determined by the choice of primers used in the initial PCR (Anderson et al., 2003a). PCR primers used for the detection and identification of ECM and other fungi in soil fungal communities are reviewed in Anderson and Cairney (2004). In community studies of ECM fungi, ITS primers are the most useful due to the amount of reference ITS sequence information that is available for these organisms. Primers to 18S rDNA have also been used in molecular studies of general soil fungi (Anderson and Cairney, 2004); however, they are presently of more limited value for ECM fungi. This is predominantly due to the lower taxonomic resolution of 18S rRNA gene sequences and to the limited amount of reference database information that is currently available for many ECM groups, although there are some exceptions (see Horton and Bruns, 2001). Due to the fact that soil fungal communities are extremely diverse and that primers for the specific PCR amplification of ECM fungal DNA are not available, all analyses that use environmental samples as the DNA source will inevitably co-detect ECM and non-ECM fungi. The most common PCR primers used to detect ECM fungi in environmental samples are the ITS fungal primers ITS1F (Gardes and Bruns, 1993) and



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ITS4 (White et al., 1990), which will detect both asco- and basidiomycete ECM fungi. The basidiomycete-specific primers ITS1F and ITS4B (Gardes and Bruns, 1993) can also be used to investigate ECM community structure in soil DNA (Edwards et al., 2004), avoiding the amplification of general soil ascomycetes. However, it is important to remember that these primers will also fail to amplify DNA from ascomycete ECM taxa, which we know are very common and which are often dominant in many ecosystems (e.g. Izzo et al., 2005; Richard et al., 2005; Saari et al., 2005). In addition, the potential functional importance of ascomycete ECM fungi has been highlighted, for example, by recent work demonstrating that an ECM ascomycete isolate belonging to the Hymenoscyphus ericae aggregate (Vrålstad et al., 2002) can form both ECM with Scots pine and what appear to be ericoid mycorrhizas with ericaceous plant species in vitro (VillarrealRuiz et al., 2004). Such work demonstrates that some ascomycete ECM fungi may have the potential to interact with the roots of both over- and understorey vegetation in the field. While such a hypothesis remains to be tested, the findings demonstrate that ECM ascomycetes are important components of boreal forest ECM communities and, in these cases, should not be overlooked.

Terminal restriction fragment length polymorphism (T-RFLP) T-RFLP is perhaps the most promising profiling technique for ECM communities, and so far has provided the most significant insights into ECM ecology. The use of automated DNA sequencer technology for T-RFLP not only allows increased sample throughput compared with other community profiling techniques, it is also extremely accurate in sizing terminal fragments through reference to an internal size standard included in every run. This applies, however, only if the running conditions of the sequencer are kept constant for all samples within a single experiment. Another advantage of T-RFLP is that it is a slight modification of the traditional RFLP approach which is one of

the most widely used molecular techniques in ECM community ecology (Horton and Bruns, 2001) and for which abundant existing database information is available. The technique consists of a PCR using fluorescently labelled primers (either forward, reverse or both), a restriction digest of the resulting amplicons, and size detection of the fluorescently labelled terminal fragments using a DNA sequencer. The main difference from RFLP, apart from the detection method, is that only the terminal fragments are detected in T-RFLP as opposed to the detection of all fragments generated in a traditional RFLP analysis. Further applications of T-RFLP in soil microbial ecology are considered by Blackwood (Chapter 5 this volume). The strength of this technique for ECM community ecology lies in its ability to detect and possibly identify particular fungal species by comparing the community T-RFLP data generated with a database containing terminal fragment sizes of known species. This, however, requires an initial investment of time in the development of a robust T-RFLP database. The inability to generate sequence information from T-RFLP peaks makes identification of unknown species in a sample difficult without linking the data back to a database generated from the same field site. Identification based on virtual restriction digests performed on public database sequences is potentially dangerous as it assumes that only a single species can have a peak of a particular size in a sample and that the public database sequence is accurately identified, complete (i.e. includes the whole ITS region) and of good quality. Problems associated with inter- and intraspecific variation in ITS sequences (see Horton, 2002) are not circumvented by the use of community profiling techniques such as T-RFLP, and careful consideration of this problem is still required when interpreting the data. For example, it has been shown that different species of the ECM genus Cortinarius can have the same ITS-RFLP profiles with multiple restriction enzymes (Kårén et al., 1997), and that isolates of the same species of Pisolithus (Hitchcock et al., 2003) and Tricholoma (Horton, 2002) can




have different ITS-RFLP profiles. For this reason, and because two different restriction enzymes used on a single soil DNA sample can give a contrasting view of the level of diversity in that sample (e.g. Klamer et al., 2002), it is wise to use data from at least two restriction enzymes in the identification of ECM species from field samples (Dickie et al., 2002; Edwards and Turco, 2005; Koide et al., 2005a). In addition, those restriction enzymes traditionally favoured for RFLP may not be the best for T-RFLP as only the terminal fragment is detected. This is particularly true for T-RFLP analysis of rDNA ITS regions as it is possible that some enzymes may have a common restriction site in the 3′ end of the 18S rRNA gene or in the 5′ end of the 28S rRNA. These areas are more conserved among different species than the ITS regions and fall within the region amplified by the primers ITS1F and ITS4/ITS4B. As a result, the same small terminal fragment may be produced for many different species in the community. Therefore, careful selection of appropriate restriction endonucleases is clearly important. The detection of diversity in a sample using T-RFLP is limited by the detection threshold of the sequencer and the background cut-off limits that are applied, similar to the way in which gel-based techniques are limited by the sensitivity of the stains used. Therefore, the absence of a peak does not necessarily confirm the absence of a species (Dickie et al., 2002). Conversely, pseudo-terminal restriction fragments resulting from the presence of single-stranded amplicons are also sometimes formed, resulting in peaks which do not represent fungal species in the community (Egert and Friedrich, 2003). Minor limitations aside, T-RFLP is a very powerful technique, particularly for the identification of target organisms within a sample, and it has been used successfully for assessing the diversity and ecology of general soil (e.g. Klamer et al., 2002; Lord et al., 2002) and ECM (e.g. Dickie et al., 2002) fungal communities. T-RFLP has enabled below-ground field investigations to begin to unravel the finescale interactions between the mycelium of ECM fungi in forest soil. Recent research has

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shown that hyphae of different ECM fungi in a Pinus resinosa forest appear to occupy different niches based on vertical distribution patterns in the soil profile (Dickie et al., 2002), as well as some evidence of species interactions (Koide et al., 2005b). Koide et al. (2005a) have demonstrated that belowground views of ECM community structure can be significantly different depending on whether the analysis is based on root tips or mycelium. These data, together with our understanding of the differences between above- and below-ground views of ECM community structure, suggest that analyses of ECM community diversity require consideration of mycelial, ECM root tip and fruiting body communities if a complete picture is to be obtained. These advances have stemmed from the ability of T-RFLP methodology to detect mycelial communities of ECM fungi in field samples.

Denaturing/temperature gradient gel electrophoresis (DGGE/TGGE) DGGE and TGGE are community profiling techniques that separate DNA fragments of the same size but different base composition, based on the melting behaviour of DNA. DNA sequences of different base composition melt at different positions in a polyacrylamide gel containing either a linear gradient of denaturants (DGGE) or temperature (TGGE) based on GC base content. Single strand conformation polymorphism (SSCP) is a similar technique, although the samples are denatured prior to loading the gel. DGGE/TGGE have been the most widely adopted community fingerprinting techniques in microbial ecology since the introduction of DGGE by Muyzer et al. (1993). They provide a rapid, visual means of investigating soil fungal communities, particularly where the aim is to investigate shifts or changes in community composition (e.g. Anderson et al., 2003b). DGGE has been used to assess the diversity of fungal communities in forest soils dominated by the mycelium of ECM fungi (Pennanen et al., 2001; Anderson et al., 2003b; Smit et al., 2003; Landeweert et al., 2005) as well as of



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pooled ECM root tip samples (Johnson et al., 2005; Landeweert et al., 2005). Due to the extensive use of DGGE/ TGGE in bacterial ecology, the potential and pitfalls of the techniques for the analysis of soil microbial communities are well documented (Muyzer, 1999; O’Callaghan et al., Chapter 6 this volume). Numerous samples can be run on a single gel, allowing a rapid, simultaneous visual comparison between samples. The development of specialist software packages has substantially improved the way in which community fingerprints can be analysed by allowing comparison of both the position and relative intensity of different bands within gels. This enables statistical analyses to be performed, thereby facilitating a more robust ecological interpretation of the data (Fromin et al., 2002). The accuracy of the comparison between samples is heavily dependent upon the inclusion of suitable internal standards and assumes that the resolution and quality of gels have been standardized. This is particularly crucial where large sample numbers mean that comparison across several different gels is required, since reproducibility between different gels has been highlighted as a pitfall of DGGE (Fromin et al., 2002). While the way in which gels are prepared can be optimized to reduce gel to gel variability, comparison of data across numerous different gels can still be difficult even when aided by computer software packages, and care should be taken to distribute samples (treatments or replicates) randomly across gels. One of the main advantages of gel-based community profiling techniques is that bands of interest can be excised and sequenced to yield taxonomic information on selected members of the community via database searches and/or phylogenetic analysis. This is a particular advantage over T-RFLP where a T-RFLP reference database does not exist for the study site. It is also powerful when bulked ECM root tip samples are analysed without prior morphotyping or knowledge of the species composition. DGGE analysis of short fragments (< 500 bp), such as partial fungal ITS-PCR products (Anderson et al., 2003b), results in better resolution

between bands in a profile but this limits the amount of taxonomic information that can be obtained from sequenced bands. Larger (> 500 bp) PCR products have also been analysed using DGGE, including full-length ITS sequences of ECM fungi (Landeweert et al., 2005), although the resolution of the profile depends on many factors, including the complexity and melting properties of the community DNA being analysed. Other factors that require consideration are that single bands on a DGGE gel have been shown to comprise more than a single sequence type (e.g. Schmalenberger and Tebbe, 2003) and that even the most sensitive staining methods are often not sensitive enough to detect all the diversity present within a sample.

Cloning PCR products generated from environmental DNA Different DNA sequences amplified from community DNA samples can be separated by cloning all the products into a plasmid vector and subsequently screening the clones using PCR, restriction digest, sequencing or a combination of these options. Clone screening has been used to demonstrate vertical distribution of ECM mycelium in the soil profile (Landeweert et al., 2003a), to investigate the effects of prescribed burning on ECM and other soil fungi in Australian sclerophyll forest sites (Chen and Cairney, 2002) and to demonstrate that ITS sequences with affinities for ECM taxa can be detected in soil collected from the forefront of a receding glacier (Jumpponen, 2003). Cloning is a rapid way of determining the diversity and identity of species present in a sample; however, if sample numbers in an experimental design are high, it can be prohibitively costly and time-consuming. Initial screening of clones using RFLP to group them into operational taxonomic units (OTUs) can reduce sample numbers, but it is difficult to determine how many clones require analysis in order to describe fully the diversity contained within a single sample. The production of species or OTU area curves can aid in this (e.g. Anderson et al., 2003a), in




the same way that species area curves can be produced to determine how many cores require analysis to sample fully the community in ECM root tip studies (e.g. Taylor, 2002). Despite being laborious and potentially costly, the analysis of clone libraries produced from environmental DNA is extremely valuable. Cloning is complementary to the other community profiling techniques such as DGGE and T-RFLP, as it can be used to generate T-RFLP libraries for the identification of T-RFLP peaks detected in samples or full-length ITS sequences from DGGE bands generated by nested PCR. Indeed, it is now frequently used in conjunction with other community profiling techniques in bacterial ecology (e.g. Freitag and Prosser, 2003). Community profiling techniques separate different DNA sequence types from a mixed community DNA sample regardless of biological origin. Therefore, an additional challenge is to be able to relate the molecular signals (including bands on a gel or peaks in an electropherogram) back to the original biological source. This is particularly important given that ECM fungi can be present in soil as spores, mycelium or colonized ECM root tips, but interpretation is not necessarily straightforward. This notwithstanding, the use of molecular profiling techniques will undoubtedly lead to significant advances in ECM community ecology. These will derive mainly from the larger sample numbers that can be processed by these methods and which are often required in order to address the fundamental ecological questions that are often asked but are difficult to answer in the case of below-ground organisms.

The Power of Sequencing and Phylogenies Traditional ITS-RFLP analysis and the community profiling approaches described above are, in themselves, powerful molecular tools. However, their use in combination with sequencing and phylogenetics makes the information gained from the presence of

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bands on gels or peaks on electropherograms much more valuable. ITS-RFLP for grouping ECM root tips is frequently used in ECM community ecology (Horton and Bruns, 2001), followed by validation of ITS-RFLP types and subsequent species identification using DNA sequencing. The use of sequence analysis in this way should be a common feature of ECM community studies. It is a rapid way of identifying species colonizing root tips or species present in a community DNA sample and the costs associated with DNA sequencing have diminished considerably over the last few years, making the generation of sequence information less problematic. In addition, sequence analysis is a useful way of identifying and eliminating PCR-generated artefacts (e.g. Qiu et al., 2001; Jumpponen, 2003) from a data set. The production of ITS sequences from field material does not always guarantee to resolve all ‘unknown’ taxa or RFLP types to species, but it often enables them to be allocated to broader taxonomic groups. Comparing sequences with those available in public databases such as EMBL or GenBank is the most common way of obtaining some level of species identification. The level of confidence in species identification is often based on the percentage similarity that sequences of unknown origin share with database sequences. Care needs to be taken with this approach, including consideration of levels of inter- and intraspecific variation in ITS within species groups (e.g. see Horton, 2002) along with careful consideration of more than just the top database match to avoid false identifications. It is important to recognize that databases are public, and that the responsibility for identifying the biological origin of deposited sequences lies with the sequence authors. It has been claimed that as many as 20% of rDNA sequences available for some fungal groups, including ECM taxa, may be unreliable due to poor sequence quality or species misidentifications (Bridge et al., 2003). This reinforces the importance of depositing voucher specimens whenever possible (Agerer et al., 2000) and emphasizes the particular value of wellannotated sequence databases that are linked to described herbarium specimens such as



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UNITE (http://unite.ut.ee) for the identification of ECM fungi (Kõljalg et al., 2005). Phylogenetic analysis can be a useful way of allocating unknown sequence types to broad taxa, particularly when they do not have a high similarity to any reference database sequences but where there is good coverage of potentially related taxa that can be included in the analysis. While identification to the species level is still often difficult, a higher level taxonomic identification is often better than simply allocating an ‘unknown’ designation. Target regions other than the ITS, including 18S and 28S rRNA and the mitochondrial large subunit gene, have also been used in phylogenetic analysis of ECM fungi (Horton and Bruns, 2001). Phylogenies constructed using these genes are more reliable than those produced using ITS data due to the difficulties in aligning highly variable ITS sequences. However, the lack of database sequence information restricts the usefulness of these genes for identification to certain ECM groups at present.

Quantification Traditionally, ECM community studies have relied on actual counts of collected fruiting bodies or colonized ECM root tips to assess the relative abundance of different species. Such information identifies which species are represented in the greatest numbers in those structures and provides an indication of the dominant members of the community, although problems associated with estimating species richness and community evenness have been discussed previously (Taylor, 2002). However, we do know that the mycelial component of ECM communities is, at least for certain species, functionally and ecologically more relevant, because it relates directly to the potential ability of the fungus to forage and explore the surrounding soil volume in search of nutrients. We also know that mycelia of different ECM fungal species explore the root and soil environment in different ways. For example, some species produce little emanating

mycelium while others produce highly differentiated, long-ranging rhizomorphs (Agerer, 2001). Hence, if the most abundant species, based on the presence of colonized ECM root tips, is one that produces little external mycelium, it may be functionally less important for the host plant than a less abundant species that is capable of producing an extensive network of external mycelium, assuming similar levels of enzyme and transport activity. Therefore, methods that allow the quantification of individual species in a mixed environmental sample are extremely important, particularly where the data can be related to mycelial biomass. Phospholipid fatty acid (PLFA) analysis has been used to estimate fungal biomass in soil, including forest soils dominated by ECM fungi (e.g. Bååth et al., 2004) and sand-filled in-growth mesh bags colonized by ECM fungi (e.g. Wallander et al., 2001; Nilsson, et al., 2005). The main fungal PFLA marker 18:2ω6,9, however, does not discriminate between different taxonomic or functional groups. The technique is generally used for the estimation of total fungal biomass, although a method of estimating the biomass of forest mycorrhizal fungi has been suggested (Bååth et al., 2004) and the PLFA marker 16:1ω5 can be used for estimating the biomass of arbuscular mycorrhizal fungi (Olsson et al., 1995). Molecular community profiling approaches, such as those outlined earlier, can be used to estimate the relative abundance of species in a mixed community sample. This can be achieved by the comparison of band intensities on DGGE/TGGE gels or peak height/area in T-RFLP outputs, with the most abundant species having the most intense band or largest peak. However, these techniques rely on standard PCR for the generation of products prior to the analysis and so suffer from PCR-associated limitations (see Anderson and Cairney, 2004). As a result, community profiling is semiquantitative at best, the methods being more useful for detecting the presence and/or absence of species rather than for quantification of abundance. An alternative, which does not rely on PCR amplification, is direct detection and quantification




of rRNA using probes. This has been used to determine the proportion of Pisolithus tinctorius and Eucalyptus globulus 5.8S rRNA in total rRNA pools extracted from symbiotic ECM tissue generated in a microcosm experiment using probes specific to fungal and plant 5.8S rRNA, respectively (Diaz et al., 1997). Unfortunately there is insufficient variation in 5.8S rRNA genes between different fungal species for this method to be used in a mixed community. Therefore, the biggest challenge for its application to ECM community ecology is to develop probes targeting genes of sufficient variability that they do not hybridize with RNA of more than one species. Two other PCR-based techniques have been used successfully for the quantification of ECM fungi: competitive PCR (cPCR), which uses a known concentration of a competitor DNA sequence in the PCR mix, and real-time PCR, which detects the amplification of DNA using fluorescent dyes or probes and quantifies the DNA by comparison with a standard. Both techniques avoid the limitations associated with standard PCR, but are at present largely limited to quantification of target DNA in soil rather than quantification of biomass. This is due to a general lack of understanding of how amounts of DNA equate with biomass in filamentous fungi. Guidot et al. (2002) used cPCR to show that mycelium of Hebeloma cylindrosporum only occurred directly underneath fruiting bodies and that DNA of the species could not be detected at distances > 50 cm from the base of a fruiting body. Further use of this method demonstrated that DNA of H. cylindrosporum could not be detected at a site 1 year after the disappearance of a fruiting body even though the technique was sensitive enough to detect 102 basidiospores of H. cylindrosporum in 0.5 g of soil (Guidot et al., 2004). This work highlights the significant spatial and temporal variability that can occur in ECM fungal populations in the field. Real-time PCR is yet to be used in fieldbased ECM investigations; however, it has been developed for the detection and quantification of Piloderma croceum mycelium

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from pure cultures and non-sterile microcosms (Schubert et al., 2003). This was achieved by the application of specific ITS primers and probes for P. croceum and comparing the real-time PCR results with hyphal length counts to validate the data (Schubert et al., 2003). While it is sensible to standardize the real-time PCR data with hyphal counts, particularly as a multicopy region was targeted, it will be difficult to do this for ECM fungi that are not readily culturable. A similar approach has been taken for quantification of mycelium of Suillus bovinus and Paxillus involutus grown in dual microcosms with Pinus sylvestris, although no direct comparison was made between the hyphal length and real-time PCR data (Landeweert et al., 2003b). Therefore, while the potential of real-time PCR for the detection and quantification of ECM mycelium has been demonstrated, its true power for ECM field investigations is yet to be realized. To date, research conducted on ECM fungi has targeted multicopy regions of the genome (Guidot et al., 2002, 2004; Landeweert et al., 2003b; Schubert et al., 2003), although single-copy genes have also been tested with much less success (Guidot et al., 2002). Differences in copy number will undoubtedly influence the quantitative results that are obtained, particularly if different ECM species have significantly different copies of a particular target gene. The extent of variation in copy number of common target regions between different ECM species is, however, currently unknown. Therefore, while copy number needs to be taken into consideration, this should not be an over-riding constraint on the application of these techniques as, even with multicopy genes, they offer opportunities for making real advances in below-ground ECM ecology.

Linking ECM Community Structure Back to the Host Host plant age, composition and species richness can influence the diversity and



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community structure of mycorrhizal fungi (Johnson et al., 2005). In addition, host specificity has been demonstrated between certain ECM fungal and plant host species (e.g. Kennedy et al., 2003; den Bakker et al., 2004). Therefore, the host plant community itself can play an important role in determining the structure of below-ground mycorrhizal communities. While plant species composition and richness effects are not confounding factors in below-ground ECM studies conducted in pure stands, they are in a mixed forest where the roots of more than one mycorrhizal host species may occupy a single soil core. Untangling these host–fungal species interactions has been made possible by the application of plant molecular markers in ECM community ecology. Izzo et al. (2005) used PCRRFLP analysis of a chloroplast tRNA spacer region amplified from colonized ECM root tips to identify the plant host. They identified four different host plant species in the ECM root tips analysed from a field experiment (Izzo et al., 2005). A similar procedure has also been followed in other studies of ECM (Kennedy et al., 2003) and arbuscular mycorrhizal community structure (Vandenkoornhuyse et al., 2003). An alternative is to use plant-specific ITS primers. These have been used for identification of plant roots back to the host (Linder et al., 2000), but they have not yet been applied to ECM community studies, and care would need to be taken to ensure they did not cross-amplify ECM fungal DNA in the way that the fungal primers ITS1 and ITS4 can occasionally amplify plant DNA (Gardes and Bruns, 1993). The use of plant molecular markers allows hypotheses about host–fungal species interactions to be tested; however, finer scale interactions at the genotype level also have the potential to play a role in influencing the structure of below-ground ECM communities. Genotypic variation within individual plant species can result in significant changes in phenotypic characters, some of which may have the potential to influence colonization patterns of ECM fungi. This may result in interactions between certain ECM species and host plant

genotypes based on compatibility. Saari et al. (2005) developed a rapid way of distinguishing between ECM root tips originating from different genotypes of P. sylvestris by amplification of pine microsatellite markers from individual ECM DNA extracts. The power of this procedure derives from the fact that the microsatellite primers are highly specific for P. sylvestris DNA, so that they can be used in conjunction with traditional ITS-RFLP and sequencing for identification of the colonizing ECM fungus from a single ECM root tip DNA extract (Saari et al., 2005). Moreover, microsatellite primers have already been developed for many different ECM host plant species for use in plant population genetics, so the methodology could be easily adapted to a wide range of different ecosystems and ECM hosts. ECM community ecology research based on colonized root tips has shown that communities often comprise a few very abundant species as well as a number of less dominant ones. These studies thus provide information on species richness and community evenness (Taylor, 2002). However, it cannot be assumed that all ECM tips of the same species are colonized by the mycelium of different individuals (genets) of that species. It is highly likely that many root tips collected from the same field site are colonized by the mycelium of the same genetic individual, particularly when they are collected from the same or spatially close soil cores. Microsatellite markers have been developed for a number of ECM fungi that allow the genetic diversity within populations of an individual species to be determined. If applied below ground, the number of genets of a particular ECM species colonizing the root tips can be determined (e.g. Kretzer et al., 2005). Therefore, if microsatellite markers for ECM fungi are used in tandem with plant microsatellites, it will enable important hypotheses about individual fungal–plant genotype interactions in the field to be tested. This would represent a substantial advance given our understanding of the functional consequences of genotypic variation in ECM species (Cairney, 1999).




Molecular Technologies of the Future Molecular technology continues to develop at a rapid pace and many recent advances have been made in bacterial molecular ecology. While these are too numerous to mention individually here, macro- and microarray technology (see Loy et al., Chapter 2 this volume), RNA-based field studies and stable isotope probing (Radajewski et al., 2000; Sharma et al., Chapter 1 this volume) are techniques that offer significant potential for future ECM ecological research. These, along with others including the ‘omics’ (genomics, proteomics, transcriptomics and metabolomics), are likely to become extremely valuable tools in the future. Phylogenetic macro- and microarrays for the rapid identification of microbial species present in a complex DNA sample offer levels of throughput that were previously unattainable, making even the community profiling techniques described earlier appear laborious. However, the effort involved in the generation of the arrays should not be underestimated and neither should the difficulties associated with using the ITS region for identification of species in some ECM taxonomic groups. Although we are not restricted to using the ITS as the target region, it is the obvious choice at present because most existing database sequences are available for it. Microarrays have been developed for the identification of bacteria in environmental samples (e.g. Wu et al., 2004; Loy et al., Chapter 2 this volume), but the technology has yet to be developed for fungi. The presence of a species’ DNA does not necessarily mean that species is a current, functionally active member of the community but rather may reflect the historical presence of that species or one that is metabolically dormant. This is possible due to the potential of DNA molecules to persist in the environment, particularly for eukaryotic organisms such as ECM fungi. RNA, however, is only produced if the organism is metabolically active and, in the case of rRNA, synthesizing ribosomes for protein synthesis. For this reason, rRNA is now often the target molecule in bacterial ecology studies

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through detection of 16S rRNA molecules using reverse transcription–PCR (RT–PCR) as opposed to conventional DNA-based PCR of 16S rRNA genes. Species that are detected in the amplified 16S rRNA pool are deemed to be metabolically active members of the community. A similar approach has yet to be reported for the mycelium of ECM fungi, but it has been used for studying general soil fungal communities by targeting 18S rRNA (e.g. Rangel-Castro et al., 2005). RNA could also be used in hybridization experiments with macro-/microarrays or individual oligonucleotide probes (Amann and Ludwig, 2000) for the detection of active ECM species. Determining the species composition of a microbial community in an environmental sample is now relatively straightforward using any of the approaches mentioned earlier. However, determining the functional importance of those species is significantly more challenging and, until recently, extremely difficult in field-scale experimentation. Consequently, our knowledge of the functional role of ECM fungi is largely limited to those species that can be easily maintained and manipulated in laboratory microcosm experiments. While it is possible to detect genes involved in key ecosystem processes, such as basidiomycete laccases (Luis et al., 2004), from a whole soil fungal community, it is currently impossible to link the detection of these genes back to individual species. Perhaps the most promising technique for linking the taxonomic structure of ECM communities with function is stable isotope probing (SIP) (Radajewski et al., 2000), although other approaches, including metagenomics, are also being used in bacterial ecology (Nelson, 2003). SIP separates nucleic acids of different organisms according to their abilities to utilize particular substrates labelled with stable isotopes, principally 13C. Community profiling techniques can then be applied to the labelled and non-labelled nucleic acid pools to determine which species are most active in utilizing the substrate. The level of isotope incorporation into nucleic acid pools is crucial. Therefore, RNA-SIP has been developed (Manefield et al., 2002) as



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the rate of isotope incorporation into cellular RNA pools is likely to be higher than for DNA due to higher rates of synthesis. SIP has significant potential for ECM ecology and particularly for investigating transfers of carbon in the field, given that vast quantities of assimilated carbon are passed from the host to the mycelium of their ECM partners (Söderström, 2002) and that 13C is the most commonly used isotope in SIP studies. SIP has been used to investigate the ecology of various functional groups of soil bacteria and, more recently, soil fungi (e.g. Lueders et al., 2004; Rangel-Castro et al., 2005). Importantly, RNA-SIP has been used to investigate the movement of carbon through plants to the rhizosphere in a grassland 13CO2 pulse labelling experiment (Rangel-Castro et al., 2005). This study was able to detect 13C from photosynthetically fixed 13CO2 in the RNA of rhizosphere microorganisms, including fungi. This approach is potentially extremely powerful for future functional ecology studies of ECM communities.

technologies (both those described here and others) can only further facilitate continued growth in our understanding of ECM communities by enabling some of the hurdles that are insurmountable with traditional methodologies to be overcome. While the benefits are numerous, the potential of the techniques to facilitate field investigations into the community and functional ecology of ECM mycelia is particularly exciting. In addition, the implementation of careful sampling designs, along with the exploitation of advances in statistical techniques, will allow fundamental ecological theories to be tested on below-ground ECM communities, a task that until now has been difficult and often constrained by too small a sample size. This said, the ITS-RFLP and sequencing approach that has become the reliable ‘work horse’ for ECM community ecology, driving the significant advances we have seen over recent years, will continue to be an extremely important and widely used procedure and, in many cases, will still be the most appropriate one to use.

Conclusions

Acknowledgements

The application of molecular techniques has been instrumental in the advances that have been made in the field of ECM community ecology over the last decade. Embracing and developing the emerging molecular

Thanks to I.J. Alexander, D. Johnson and D.R. Genney for comments on an earlier draft. The Macaulay Institute receives funding from the Scottish Executive Environment and Rural Affairs Department.

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12

Molecular Ecology of Arbuscular Mycorrhizal Fungi: a Review of PCR-based Techniques Dirk Redecker

Institute of Botany, University of Basel, Hebelstrasse 11, 4056 Basel, Switzerland

Introduction Arbuscular mycorrhiza (AM) is an ancient symbiosis between the majority of land plants and fungi from the phylum Glomeromycota. Fossil spores from the Ordovician 460 million years ago (Redecker et al., 2000a), and fossilized symbiotic structures (arbuscules) from the Devonian (Remy et al., 1994) suggest that this association has been present throughout the evolutionary history of terrestrial plants. As in other mycorrhizal symbioses, the fungi are allowed to colonize the plant roots and provide improved mineral nutrition to the plant in exchange for carbohydrates. The hallmark of AM is the arbuscule, a tree-like structure formed by the fungus within root cells. Some fungi also form storage organs, the so-called vesicles. Outside the root, an extensive extraradical fungal mycelium proliferates into the surrounding soil. As the intraradical symbiotic structures of the different AM fungal taxa are rather similar, their large (40–800 µm) multinucleate spores have been used for identification purposes and taxonomy. However, spore production is dependent on the physiological status of the fungus and may not occur at all in some fungal species. Strong discrepancies have been reported between

the taxa present as spores in the field, which may have formed years ago, and the symbiotically active fungal community in roots. AM fungi were previously placed in the order Glomales of the Zygomycota. As it became clear that AM fungi do not share as many characteristics with Zygomycetes as previously thought, the phylum Glomeromycota was established (Schüßler et al., 2001b). To date, < 200 species of AM fungi have been described, mainly on the basis of their spore morphology. As the Glomeromycota cannot be cultivated without their plants hosts, it is not always straightforward to obtain pure fungal biomass for appropriate molecular biological analyses. The spores harvested from open pot cultures harbour a wide range of other microorganisms and, if the genes of those contaminants are mistaken for the mycorrhizal fungi, the results can be misleading (Redecker et al., 1999). Only a subset of all species can be cultivated in open pot cultures, and only very few grow in root organ cultures (Fortin et al., 2002), which currently are the only way to harvest fungal tissue free from other microorganisms. These problems explain why relatively few genes are available from a broad range of AM fungal taxa.

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It has become increasingly clear that not all AM fungal taxa are equivalent in their symbiotic function. Some combinations of plant and fungal symbionts apparently were more beneficial to the plants than others (Klironomos, 2003). It was even shown that AM fungi co-determine the composition of plant communities, apparently by favouring certain plant species over others (van der Heijden et al., 1998). Molecular identification methods have revolutionized our views of the ecology of AM fungi, because they have allowed new insights into the diversity and dynamics of AM fungal communities in the field. In the following, some methodological approaches used in this context will be reviewed and discussed. Although molecules other than DNA have been used to identify AM fungal taxa (e.g. lipids: Bentivenga and Morton, 1996), the polymerase chain reaction (PCR)based nucleic acid methods have had the largest impact and will therefore be the focus of this chapter.

PCR-based Methodologies for AM Fungi

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In the Glomeromycota, the sequencing of new genes is very much complicated by the inability to cultivate these fungi. The possibility of contamination when using fungal biomass not harvested in root organ cultures makes it necessary to verify the glomeromycotan origin of the sequences in question; this requires ‘safe’ reference data. Among the rRNA genes, the ribosomal small subunit (SSU) was the first to be made available (Simon et al., 1992, 1993). A central long, variable region was used in later studies (Helgason et al., 1998), as well as the 3′ end of the subunit (Bidartondo et al., 2002). The internal transcribed spacers 1 and 2 (ITS1 and 2) are situated between the SSU and the large ribosomal subunit (LSU), are highly variable and frame the tiny 5.8S subunit (Fig. 12.1). Some glomeromycotan protein-coding genes have recently been made available from a limited number of taxa (Helgason et al., 2003; Corradi et al., 2004). They were used for molecular phylogenetics, but molecular identification tools based on these data have yet to be developed. The use of non-coding regions for population studies will be discussed separately below.

Genes used for community studies Most efforts to identify AM fungi by molecular characters have focused on the nuclear-encoded rRNA genes (rDNA). Due to the presence of conserved regions, these genes are relatively easy to access in organisms when no other sequence information is yet available. They are present in all living things, usually in multiple copies, which facilitates enzymatic amplification. At the same time, they have interspersed regions that are variable among species and are useful for identification purposes. On the other hand, it is more problematic to design group-specific PCR primers for protein genes, due to their triplet codon structure. The design of specific PCR primers, however, is a crucial prerequisite for studying the molecular ecology of AM fungi, because they usually have to be detected and identified from mixed DNAs, e.g. colonized roots.

Sequence heterogeneity within spores and species It was shown by several authors (Sanders et al., 1995; Lanfranco et al., 1999; Jansa et al., 2002b) that different variants of rDNA genes co-exist within glomeromycotan spores, which are the smallest units of these organisms that can be discerned. Potentially, these variants could be different copies of the ribosomal tandem repeats within the same genome. These copies may have escaped the process known as concerted evolution, which normally keeps them identical. Different rRNA variants have been reported from many other organisms (Buckler et al., 1997), but in the Glomeromycota the phenomenon appears to be more pronounced. It is most obvious in the variable regions, such as the ITS, with up to 24.1% variation among sequences within a single spore (Jansa et al., 2002b), but it also



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SSU-Glom1> ITS1F> ITS1> ITS5>

a

VANS1>

SSU

LSU

FLR3> (LSU0061) 5.8R 5.8R LSUAca1f> LSUcla1f>

c

60 shown) and posterior probabilities from Bayesian analysis, respectively.




satisfactory when using full-length SSU. Interestingly, the tree topology is almost identical between the neighbour-joining and the Bayesian tree. Therefore, although distance analyses are sometimes claimed to be inferior to other methods, they can provide valuable phylogenetic hypotheses and have the advantage of higher speed. Compared with ITS sequences, this region has the advantage that all glomeromycotan groups can be compared in one data set. Within most subgroups of the SSU tree indicated in Fig. 12.2, the ITS would be unalignable across all taxa. Examples for this are the G. mosseae and G. intraradices clades within Glomus group A, as well as many other deeply divergent lineages within Glomus group A that are emerging in large numbers from environmental samples. On the other hand, the resolution of this region of the SSU is limited at the lower taxonomic range. The ITS or variable

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regions of the LSU are better suited for distinguishing between closely related species, for instance G. mosseae/coronatum or G. caledonium/geosporum. An ITS-based tree of the G. mosseae subclade within Glomus group A is shown in Fig. 12.3. G. mosseae and G. coronatum can be clearly differentiated. G. dimorphicum groups within G. mosseae sequences, and the two have been suggested to be conspecific (Walker, 1992). G. caledonium and G. geosporum can also be separated, although the former does not result in a monophyletic group.

Acknowledgements The author would like to thank Dr Stefanie Possekel for critically reading the manuscript, and the Botanical Institute for continuing support.

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Transcriptomics for Determining Gene Expression in Symbiotic Root–Fungus Interactions Philipp Franken1,* and Franziska Krajinski2 1Institute

for Vegetables and Ornamental Crops, Theodor-Echtermeyer-Weg, D-14979 Grossbeeren, Germany; 2Department of Molecular Genetics, University Hannover, Herrenhäuser Str. 2, D-30419 Hannover, Germany

Introduction Plant roots in natural or man-made environments are usually colonized by numerous different fungi. One can call such forms of coexistence symbioses in a broad sense, which would include all parasitic, neutral, commensalistic and mutualistic interactions. However, this chapter will give an overview of transcriptomics for those which benefit both partners. The most important class of such interactions is summarized by the term ‘mycorrhiza’ which can be found in 90% of all vascular land plants (Smith and Read, 1997). The main basis for the mutual beneficial effects of these interactions is the bidirectional transfer of nutrients (Smith et al., 2001; Chalot et al., 2002; Read and Perez-Moreno, 2003). Products of plant photosynthesis (presumably monosaccharides) are exchanged against mineral nutrients provided by the fungus. Most investigated in this respect are phosphate (Karandashov and Bucher, 2005), nitrogen (Botton and Chalot, 1998) and carbohydrates (Nehls et al., 2001a; Pfeffer et al., 2001). A second important feature is the differential response to various stress

conditions; mycorrhizal plants are more resistant to root pathogens (Azcón-Aguilar and Barea, 1996) and show an enhanced capability to survive in adverse climate conditions such as drought (Auge, 2004) or to colonize soils that are contaminated with material such as heavy metals (Meharg, 2003). Due to their worldwide distribution and their broad influence on plant growth and physiology, the mycorrhizal fungi exert a significant impact on ecosystems (Allen et al., 2003). Moreover, they have the potential to be applied for sustainable plant production systems with low inputs of mineral fertilizers and pesticides, and also for the revegetation and bioremediation of polluted environments (Jeffries et al., 2003; Gaur and Adholeya, 2004). In order to understand the ecological significance and to exploit the potential for application of mycorrhiza, it is necessary to investigate the basic mechanisms of their symbioses. One approach to this task is the identification of the sum of genes being transcribed during the process of interaction, the symbiotic transcriptome, and to compare this with the plant and the fungal transcriptome under non-symbiotic conditions. The outcomes

*Corresponding author; Phone: +49 33701 78215, Fax: +49 33701 55391, E-mail: [email protected] ©CAB International 2006. Molecular Approaches to Soil, Rhizosphere and Plant Microorganism Analysis (Cooper and Rao)


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of such comparisons are hypotheses concerning the physiology and the regulation of the plant and the fungus during their interaction, which can be confirmed subsequently by other methodologies. Depending on the morphology and the group of fungi and plants involved, mycorrhizal symbioses are further subdivided into ectotrophic (ectomycorrhiza) and endotrophic (arbuscular, ericoid, orchidoid, arbutoid and monotropoid endomycorrhiza) types (Smith and Read, 1997). The most widespread of these are the ectomycorrhiza and the arbuscular endomycorrhiza. It is therefore not surprising to find that nearly all gene expression analyses of beneficial root–fungus interactions are concerned with those two classes. In the following sections, we provide introductions to transcriptomic methodologies and the biology of ectomycorrhizas and arbuscular endomycorrhizas, together with a review of the attempts which have been made to describe their transcriptomes.

Available Methodologies The transcriptome of a cell comprises the entirety of its RNA transcripts synthesized under defined conditions. Since the development of mycorrhizal symbioses leads to significant changes in cell morphology and physiology of either the plant or fungal cells involved, one can expect that the transcriptome of mycorrhizal roots or symbiotic hyphae will differ dramatically compared with non-mycorrhizal roots or free-living mycelium, respectively. Genes appearing with different frequencies in transcriptome populations of mycorrhizal and non-mycorrhizal tissues are considered to be differentially expressed in response to mycorrhiza development. The identification of these differentially expressed genes is the aim of transcriptome approaches in mycorrhiza research, since the collection of genes, up- or downregulated upon mycorrhiza development, provides insights into the molecular mechanisms of these plant– fungus interactions. Furthermore, genes

differentially expressed in response to mycorrhiza development serve as starting points for investigating the molecular regulation of the symbioses (e.g. by using promoter constructs), or the role of particular cellular functions by the RNA interference (RNAi) technology. The classical procedure for comparing transcript frequencies of single genes within two RNA populations is northern blot hybridization. Because candidate genes have to be pre-selected, this is regarded as a targeted approach. The technique allows the comparison of transcript accumulation for single genes only, in one hybridization step. Moreover, it needs relatively high amounts of RNA, which, especially for the analysis of arbuscular mycorrhizal (AM) fungal transcript accumulation, are nearly impossible to obtain. This problem can, however, be overcome by using quantitative reverse transcription–polymerase chain reaction (RT–PCR) methods, where transcript amounts are compared after amplification steps using gene-specific primers. Targeted approaches have mainly been used in the analysis of genes involved in transport processes and metabolism of nutrients or in plant interaction with other microorganisms (Lapopin and Franken, 2001; Tagu et al., 2002a; Ferrol et al., 2004). In comparison with northern hybridization, the generation and screening of complementary DNA (cDNA) libraries represents an untargeted approach. This procedure comprises the preparation of mRNA, cDNA synthesis and cloning of these cDNAs in appropriate vector systems to yield a library. Subsequently, clones or cDNA inserts are hybridized to labelled complex cDNA probes generated from RNA populations. Differences in hybridization intensities of a certain clone indicate that the corresponding gene is regulated under the selected conditions, for example in mycorrhizal- and non-mycorrhizal roots (Tahiri-Alaoui et al., 1996) or free-living hyphae versus symbiotic mycelium (e.g. Tagu et al., 1993). Such cDNA libraries can be enriched for clones of differentially expressed genes before screening by subtracting one cDNA population from another (van Buuren et al., 1999).



Transcriptomics for Determining Gene Expression

In contrast to screening whole cDNA libraries, differential RNA displays or cDNA AFLPs (amplified fragment length polymorphisms) follow a strategy of first detecting differences in transcript accumulation followed in a second step by cloning and identification of the corresponding genes. Both techniques are based on amplification of cDNA fragments using either short random primers (differential RNA display; e.g. Martin-Laurent et al., 1997; Kim et al., 1999a) or a combination of adaptor fragments and primer sequences after digestion with restriction enzymes (cDNA-AFLP; Kistner et al., 2005). After amplification, generated PCR patterns are compared by polyacrylamide gel electrophoresis with the aim of searching for differentially appearing cDNA fragments which can be subsequently cloned and analysed. The amount of RNA needed is relatively low due to the amplification steps applied, making these approaches useful for analysing AM fungal transcriptomes (e.g. Requena et al., 1999). A combination of PCR-based detection of transcript differences and random cDNA libraries can generate subtractive cDNA libraries by suppressive subtraction hybridization (SSH; Diatchenko et al., 1996). Using both amplification and hybridization steps, this method results in a cDNA library which is enriched for transcripts that are over-represented in the selected RNA population. Within the last few years, this technology has been successfully applied to identify a number of fungal (Voiblet et al., 2001; Requena et al., 2002) and plant (Wulf et al., 2003) genes regulated during mycorrhiza development. Especially in combination with the tools of modern genome research (e.g. high throughput sequencing), SSH libraries provide an efficient means of identifying key genes of mycorrhiza regulation (Frenzel et al., 2005). Recent developments in the field of functional genomics open up new and promising possibilities for analysing the molecular biology of mycorrhizal symbioses. Recently, a large number of random and SSH-cDNA libraries have been completely sequenced, leading to a survey of total mycorrhiza transcriptomes. The resulting raw

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sequences are further processed, remaining vector sequences are cut and a quality clipping is carried out, which finally leads to collections of expressed sequence tags (ESTs). Each EST represents a single gene present in the analysed transcriptome; thus the corresponding gene is expressed in the analysed tissue. Such EST collections have been generated for various mycorrhiza tissues (Tables 13.1a and 13.1b), and they represent a rich source of material for subsequent investigations involving cDNA arrays or in silico analysis. The first step for both of these applications is the so-called clustering of ESTs. This means that ESTs corresponding to a single transcript are aligned and a tentative consensus (TC) sequence is created. This is a prerequisite for the construction of non-redundant cDNA arrays, where inserts of cDNA clones or oligonucleotides are spotted on nylon membranes (macroarrays) or on glass slides (microarrays) and hybridized to complex cDNA probes in a manner similar to the above-described procedure for screening cDNA libraries (Voiblet et al., 2001; Liu et al., 2003; Hohnjec et al., 2005). Besides providing the basis for cDNA array production, EST collections constitute an efficient tool to identify differentially expressed genes by in silico approaches (Journet et al., 2002).

Ectomycorrhiza The main areas of distribution of ectomycorrhizas are the boreal conifer and the temperate angiosperm forests in the northern hemisphere (Smith and Read, 1997). The number of plant species is relatively low, while approximately 5000 fungal species are capable of being involved in the symbiotic interaction, mainly from the basidiomycetes (Smith and Read, 1997). Developmental changes in both partners already start before any physical contact is made (Herrmann et al., 1998). Obvious changes of fungal morphology, however, occur when the fungus reaches the root surface (Fig. 13.1). Hyphae start to swell and to branch heavily, resulting later in one



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Free-living hyphae

Sterile roots

Pre-infection

Increased rhizogenesis Decreased root hair formation

Hyphal branching

Physical contact

Hyphal tip swelling Aggregation of hyphae

Colonization

Formation of the fungal sheath or mantle on the root surface

Symbiosis

Intradical hyphae form Hartig net

Extradical hyphae

Fig. 13.1. Ectomycorrhizal development. Free-living hyphae and sterile roots change their growth patterns if they come close to each other. During this pre-infection stage, plant root exudates induce branching of fungal hyphae, while auxin-like compounds exuded by the fungus lead to increased lateral root formation and decay of root hairs. Upon physical contact, swelling and aggregation of hyphae can be observed on the surface of the root, which is increasingly covered by the fungal mycelium during this colonization stage. In the actual symbiosis, the root is totally covered by the fungal sheath or mantle, intraradical hyphae have formed the Hartig net between the cells of the root cortex, while extraradical hyphae are exploring the surrounding soil.

of the distinct structures, the fungal sheath or mantle (Cairney and Burke, 1996). This sheath is imbedded in a hydrophobic matrix and totally surrounds the plant root, which has also undergone a morphological change due to auxin-like compounds exuded by the fungus (Gay and Gea, 1994). Nutrients and water do not pass the sheath and only reach the root via the fungal hyphae. The fungus therefore totally controls the nutrition of the plant. From this sheath, hyphae grow on the one side into the root between the cells of the cortex, forming the so-called Hartig net. This represents the interface between the ectomycorrhizal fungus and the plant, and is the location of nutrient exchange between the partners. On the other side, extraradical hyphae protrude into the soil and absorb mineral nutrients much more efficiently than the plant root due to the large surface of the extraradical mycelium, which reaches even the finest soil capillaries.

The first cDNA library of RNA from ectomycorrhizal tissue was established from the interaction between Eucalyptus globulus and Pisolithus tinctorius (Tagu et al., 1993). This library was exploited by hybridization of randomly chosen clones to complex cDNA probes obtained from free-living mycelium or mycorrhizal tissue. Six genes were sequenced whereof one of plant origin showed homology to a PR (pathogenesisrelated) protein-encoding gene, indicating the onset of defence reactions during early ectomycorrhizal interactions (Tagu and Martin, 1994). Later, such cDNA libraries were used to build up the first collection of ESTs from mycorrhiza, a first step towards the description of a symbiotic transcriptome (Tagu and Martin, 1995; Martin and Voiblet, 1998). Some of these clones revealed similarity to genes encoding hydrophobins. These genes show strong RNA accumulation not only during aerial hyphae formation as in




other fungi, but also during the early steps of sheath formation, indicating the importance of cysteine-rich proteins for the function of the sheath as a barrier (Tagu and Martin, 1996; Tagu et al., 1996). A second group encoded a novel class of fungal cell wall polypeptides, which are probably responsible for the aggregation of hyphae forming the sheath (Laurent et al., 1999). Other identified genes could be allocated to the plant. One mycorrhiza-induced α-tubulin gene supported the role of cytoskeleton elements during the morphological changes of ectomycorrhizal root development (Diaz et al., 1996). The physiological changes were mirrored by one EST corresponding to the earliest induced genes encoding a cytosolic NADP-dependent isocitrate dehydrogenase (Boiffin et al., 1998). A third gene for a glutathione S-transferase was upregulated by hypaphorine, an auxin derivative produced by the fungal partner (Nehls et al., 1998a). This demonstrated for the first time the control of plant gene expression by the fungus during formation of the symbiosis. Since the first cDNA library was limited in size, larger scale EST collections of this ectomycorrhizal system were established by Voiblet et al. (2001) and Peter et al. (2003). They sequenced and annotated 2531 cDNA clones from symbiotic tissue or from free-living hyphae. These ESTs were subsequently screened by differential hybridization. In a first attempt, hybridizing probes from a 4-day-old ectomycorrhiza to 850 symbiotic ESTs, 33 fungal and 12 plant genes could be shown as upregulated, while 12 fungal and eight plant genes were repressed (Voiblet et al., 2001). Later on, all ESTs were clustered to 1345 TCs from Pisolithus microcarpus (formerly P. tinctorius) and 193 TCs from Eucalyptus globulus (Duplessis et al., 2005). In order to obtain an insight into the coordination of gene expression, TC-representative clones were hybridized to cDNA from 4-, 7-, 12and 21-day-old symbiotic tissue against a mixed cDNA from free-living hyphae and uncolonized roots (Duplessis et al., 2005). Clustering the genes for their putative function and their expression revealed five major patterns: (i) early induction (4 days) of plant

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genes involved in defence and stress reactions and fungal genes for hydrophobins; (ii) genes involved in carbon and hormone metabolism most induced at 7 days; (iii) genes involved in amino acid metabolism and protein fate most induced at 12 days; (iv) fungal genes repressed early during ectomycorrhizal formation; and (v) fungal genes repressed late during ectomycorrhizal formation. A second ectomycorrhizal fungus, which has been intensely studied is Amanita muscaria. The interaction of this fungus with Populus tremula and Populus tremula × tremuloides was chosen by the German Research Council (DFG) focusing project ‘MolMyk’ as the model system for random sequencing of cDNA libraries from ectomycorrhizal tissue. The first gene, however, was not cloned for its putative role in symbiotic development, but for being involved in the biosynthesis of betalain, the red colour of the cap of the fungus (Hinz et al., 1997). Molecular analysis of symbiotic development with its host Picea abies was mainly concentrated on carbon and nitrogen metabolism. A gene encoding a monosaccharide transporter was identified by PCR and shown to be induced in free-living hyphae by high concentrations of glucose and fructose, but also during mycorrhiza development (Nehls et al., 1998b, 2001b). In contrast, the homologous gene in P. abies was slightly downregulated in the symbiosis (Nehls et al., 2000). In a more comprehensive analysis of poplar, a monosaccharide transporter gene family could be identified (Grunze et al., 2004). One member was induced during mycorrhiza development, indicating that the plant is able to compete for hexoses present in the common apoplast. Random sequencing and differential screening of a cDNA library of ectomycorrhiza was a further source for the identification of genes (Nehls et al., 1999a). Among those, one encoded a phenylalanine ammonium lyase which is a key enzyme in the biosynthesis of phenolic compounds (Nehls et al., 1999a). Because it is strongly expressed in the fungal mantle, it could play a role in the bioprotection of plant roots against pathogens by ectomycorrhizal fungi. Another gene, which is induced by nitrogen starvation,



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was shown by heterologous expression in yeast to encode an amino acid permease (Nehls et al., 1999b). Strong expression when nitrate is the sole nitrogen source indicated that A. muscaria cannot use this form of nitrogen. As already mentioned, most of the ectomycorrhizal fungi are basidiomycetes. Tuber borchii, however, is an ascomycete forming a symbiosis with the fine roots of a number of gymnosperms and angiosperms. The fruit bodies known as truffles are of particular commercial interest in Mediterranean countries. Its development has therefore also been analysed by molecular tools. Using differential display and hybridization of arrayed cDNAs, RNA accumulation patterns were compared between the vegetative phase and the fructification phase of development (Zeppa et al., 2000, 2002; Lacourt et al., 2002). Numerous genes could be identified as being upregulated, and annotation suggested that amino acid biosynthesis, the glyoxalate cycle and cell wall synthesis are involved in morphogenesis (Lacourt et al., 2002). In parallel, targeted approaches were carried out and several genes were shown to be regulated during development (Balestrini et al., 2000; Zeppa et al., 2001) or by nitrogen or carbon sources (Montanini et al., 2002, 2003; Vallorani et al., 2002; Guescini et al., 2003; Polidori et al., 2004). For some of the nitrogen-regulated genes, it transpired that they are also differentially expressed during ectomycorrhiza formation: a glutamate dehydrogenase (Vallorani et al., 2002), a glutamine synthetase (Montanini et al., 2003) or a nitrate reductase (Guescini et al., 2003), here also pointing to the important role of fungal nitrogen metabolism during the symbiosis. Although the life cycle of T. borchii is hypogeous, light can influence its mycelial growth, and a gene for a blue light photoreceptor has been cloned (Ambra et al., 2004). One gene of special interest encoded a phospholipase A which seems to act as a central regulator not only of adaptations to starvation conditions, but also of mycorrhiza formation (Soragni et al., 2001; Miozzi et al., 2004). Two analyses were directly targeted to the interaction with the

plant host basswood. In one case, an ectomycorrhizal cDNA library was constructed and clones were differentially screened with probes from the two partners alone or in symbiosis (Polidori et al., 2002). A number of fungal and plant genes could be identified as being mainly upregulated during the symbiosis. A second study was concentrated on the pre-infection stage where plant and fungus interact without physical contact (Menotta et al., 2004). A subtractive cDNA library was analysed and 58 fungal genes were found to be induced in the vicinity of the plant partner. Annotation of these genes showed that they are involved in secretion, in apical growth, in detoxification and also in general metabolism. Preinfection stages have also been analysed for Piloderma croceum and Quercus robur, but here plant genes were identified (Krüger et al., 2004). Interestingly, underlying the plant defence response, which has also been seen in other pre-infection stages, several upregulated genes encoded proteins for elements in signal transduction and perception. The experimental systems described up to now have proven to be very useful for characterizing the transcriptome of an ectomycorrhiza. The identification of genes that are differentially expressed during the interaction indicates that they play a distinct role in the symbiosis. In order to prove this role, it is necessary to switch off their expression and to analyse the resulting phenotype, as has been undertaken for many other fungi (Ruiz-Diez, 2002). However, the mycorrhizal fungi analysed up to now have been found to be very difficult to transform. The function of certain fungal genes can therefore only be tested by heterologous expression in other organisms (Tagu et al., 2002b), but their putative role in the interaction with host plants cannot be confirmed. Nevertheless, the analysis of the transcriptome has given impressive insights into symbiotic functioning and has fomented many ideas concerning the molecular cross-talk of the two partners. Facing the problem that it is necessary to have a transformation system to study the role of genes from a certain organism, researchers have chosen the strategy of first




looking for efficient transformation systems before they start to analyse the molecular basis of symbiotic functioning. This has been the case for Hebeloma cylindrosporum (Marmeisse et al., 1992; Combier et al., 2003). Besides allowing analysis of the roles of differentially expressed genes, this system has also led to the establishment of a population of ectomycorrhizal mutants by insertional mutagenesis. These mutants are now the starting point for the direct cloning of fungal genes required for symbiotic development with the host Pinus pinaster (Combier et al., 2004). Gene expression analyses were mainly targeted to the nitrogen metabolism of the fungus. A nitrate reductase gene was isolated (Marmeisse et al., 1998), which was found, in contrast to many other nitrophilic organisms, not to require nitrate for RNA accumulation (Jargeat et al., 2000). However, genes for two out of three ammonium transporters and for a glutamate dehydrogenase (Javelle et al., 2003), as well as for a nitrate transporter (Jargeat et al., 2003), could be shown to be regulated by ammonium or nitrate, respectively. On the plant side, an auxin-induced gene for a transcription factor was identified (Charvet-Candela et al., 2002). This gene also showed enhanced RNA accumulation after inoculation of P. pinaster roots with a non-mycorrhizal mutant of the fungus, indicating that it belongs to the defence response of the plant (Reddy et al., 2003). Induction of genes belonging to the defence response has already been shown in a different host plant, Betula pendula, and it was suggested that such responses control the spread of the Hartig net of H. cylindrosporum (Feugey et al., 1999). A first cDNA library was constructed in a yeast mutant (Wipf et al., 2003) and this was further used for cloning by functional complementation, as well as for large-scale sequencing in order to establish an EST collection of the fungus (Lambilliotte et al., 2004). Altogether, it is surely correct to name H. cylindrosporum as a model species for research on ectomycorrhiza (Marmeisse et al., 2004), the more so because two of its hosts, P. pinaster and B. pendula, can also be transformed (Valjakka et al., 2000; Trontin et al., 2002).

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A similar strategy was chosen for Paxillus involutus, a common fungus in temperate forests which forms ectomycorrhiza with many different hosts including pine, birch, elder and poplar. A transformation system was established (Bills et al., 1995), before the first RNA accumulation analyses were carried out. A targeted approach for cloning certain genes involved in the symbiosis was only followed in one case, where hexose transporter genes from B. pendula were investigated (Wright et al., 2000). Instead, P. involutus became a prime subject for the analysis of global expression patterns. An EST collection was generated from ectomycorrhiza with B. pendula and from free-living hyphae or non-colonized roots (Johansson et al., 2004). EST clustering revealed a non-redundant set of 2284 transcripts, of which half derived from the fungus and half from the plant. Analysis of the corresponding genes for their expression patterns showed that approximately 10% of the identified genes were > 2-fold regulated during mycorrhiza formation, with most of them being repressed and encoding enzymes of the carbon metabolism of the two partners. Other encoded proteins were involved in stress response and synthesis of proteins, in transport processes and regulation of development, or showed homologies to cytoskeleton elements. Further experiments were carried out in order to correlate the regulation of certain genes to particular developmental processes. This showed, for example, that mantle formation induced defence-related plant genes, which were repressed later in development (Le Quéré et al., 2005), as has been observed in the other interactions (see above). On the fungal side, the analyses indicated the involvement of urea and polyamine transporters in the translocation of nitrogenous compounds in the extraradical mycelium (Morel et al., 2005). Furthermore, by using microcosms as an experimental system, it was shown that genes involved in the glutamine/glutamate and urea cycles, as well as in the biosynthesis of carbon skeletons for ammonium assimilation, are highly induced in the part of the extraradical mycelium that grows into nutrient-enriched patches (Wright



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et al., 2005). During the early stages of mantle and Hartig net formation, genes involved in mitochondrial respiration were upregulated (Le Quéré et al., 2005). Interestingly, this parallels a finding in the AM fungus Gigaspora rosea (see below), where such genes were induced during pre-symbiotic development (Tamasloukht et al., 2003). The collection of fungal genes was also used to analyse the difference between P. involutus strains which are either compatible or incompatible with B. pendula (Le Quéré et al., 2004). The strains showed a high sequence identity and also the gene copy number was very similar. Nevertheless, 66 out of 1075 fungal genes showed a differential expression pattern during interaction with the plant. One important aspect of the ectomycorrhizal symbiosis is the tolerance of plants to heavy metal contamination, which is induced by the interaction with the fungal partner (Godbold et al., 1998). In order to investigate the molecular basis for this phenomenon, suppressive subtractive hybridization and screening of microarrays were carried out (Jacob et al., 2001, 2004). These studies identified a number of P. involutus genes which could be key elements in the response of the fungus to heavy metals and hence in the induced tolerance of the plant partner. A very widespread ectomycorrhizal fungus is Laccaria bicolor. It can be found all over the world and possesses a large host range including important timber-producing trees (Smith and Read, 1997). A transformation system was established following the desire to improve its biocontrol activities (Bills et al., 1999). Symbiosis-related genes were identified in an experimental system with Pinus resinosa (Kim et al., 1998, 1999a, b). One of these genes encoded a protein which was shown by heterologous expression in yeast to be involved in autophagocytosis (Kim et al., 1999b). It probably plays a role in vesicle turnover and the recycling of material at the beginning of the plant–fungus interaction. A second gene, which was regulated during mycorrhiza formation, showed homology to Ras, a key element of signal transduction in other fungi. This gene could complement a

corresponding yeast mutant and is associated with signalling pathways during hyphal growth and development (Sundaram et al., 2001). Using a yeast two-hybrid system, a number of interacting proteins were identified, of which one was localized near the doliporus of Hartig net hyphae, where it is probably involved in the transport of signalling molecules (Sundaram et al., 2004). Another gene identified by differential display encoded a malate synthase (Balasubramaniam et al., 2002). In order to broaden the small collection of ESTs derived from the RNA display analysis, cDNA libraries were established and analysed from early symbiotic interactions (Podila et al., 2002) and from free-living hyphae (Peter et al., 2003). Interestingly, 11% of the unique transcripts of L. bicolor showed similarities to sequences from P. microcarpus, but the number of genes common to non-mycorrhizal fungi was very low (Peter et al., 2003).

Current status of ectomycorrhizal transcriptomics Taken together, the analyses of the transcriptomes of different ectomycorrhizal interactions have given a good insight into symbiotic functioning and have yielded many ideas about the molecular cross-talk of the two partners. In order to make more progress, it is now necessary to concentrate on model systems, where the organisms will be subjected to whole-genome sequencing projects to allow future analysis of the entire transcriptome during symbiotic development. As the plant partner, poplar was chosen because of its relatively small genome size, rapid early growth and an available routine transformation protocol (Martin et al., 2004). The sequencing was completed in 2004 and results can be obtained from the home page of the consortium (www.ornl.gov/sci/ipgc/ home.htm). Poplar interacts with a number of ectomycorrhizal fungi, but L. bicolor was selected for whole genomic sequencing, because it is very aggressive in ectomycorrhiza formation and therefore easy to handle in experimental systems (Martin et al., 2004).




Table 13.1a.

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EST collections of ectomycorrhizal tissues.

EM-system

Eucalyptus globulus Pisolithus tinctorius

Tissue or EM stage (dai1)

Selection method Number

Symbiosis (4)

Random 400 sequences Random and SSH2 634 non-redundant Random 754 non-redundant Random 550 non-redundant

Tagu et al., 1995; Martin and Voiblet, 1998 Voiblet et al., 2001

Symbiosis (4, 12, 21) Free-living hyphae

Reference

Peter et al., 2003

Amanita muscaria Populus tremula ¥ tremuloides

Symbiosis

Random 3837 non-redundant

Nehls et al., unpublished

Populus trichocarpa ¥ deltoides Tuber borchii

Sterile roots

Random 4874 non-redundant

Kohler et al., 2003

Free-living hyphae

Lacourt et al., 2002

Tilia americana Tuber borchii Quercum robur Piloderma croceum Hebeloma cylindrosporum

Preinfection

Random 214 sequences SSH2 58 sequences SSH2 50 sequences Random in yeast 4596 sequences

Laccaria bicolor

Free-living hyphae

Random 1244 non redundant

Peter et al., 2003

Pinus resinosa Laccaria bicolor

Symbiosis

SSH2 98 sequences

Podila et al., 2002

Betula pendula Paxillus involutus

Symbiosis (25)


Johansson et al., 2004

Free-living hyphae


Sterile roots


Free-living hyphae

SSH2 623 non redundant

Paxillus involutus

1dai:

Preinfection Free-living hyphae

Menotta et al., 2004 Krüger et al., 2004 Wipf et al., 2003; Lambilliotte et al., 2004

Morel et al., 2005

days after inoculation. suppressive subtractive hybridization.

2SSH:

It usually grows as a heterokaryon, but haploid strains can be derived from spores and used for transformation. Sexual development can be induced and completed in the laboratory. It is therefore an ideal tool for genetic and molecular analyses. Genomic data are not yet available, but a BLAST search of ESTs from Hebeloma, Laccaria, Paxillus,

Pisolithus and Tuber can be conducted at mycor.nancy.inra.fr/BLAST/blastmycor.php.

Arbuscular Mycorrhiza AM fungi interact with the roots of 80% of all vascular land plants and are common to



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Table 13.1b.

EST collections of arbuscular mycorrhizal tissues.

AM system

Tissue or AM stage

Selection method Number

Reference

Pisum sativum Glomus mosseae

Symbiosis (late)

SSH1

Grunwald et al., 2004

Medicago truncatula Glomus intraradices

Sterile root/nodules/AM (late)

Random 6 359 non-redundant

Journet et al., 2002

Symbiosis (late)

SSH1 1 805 sequences

Wulf et al., 2003

Symbiosis (late)

Random 3 182 sequences

Frenzel et al., 2005

Medicago truncatula Glomus versiforme

Symbiosis (all stages)

Random 7 351 sequences

Liu et al., 2003

Medicago truncatula Glomus intraradices Glomus mosseae

Only plant

16 0862

Hohnjecr et al., 2005

Oryza sativa Glomus intraradices

Only plant

ca. 50 0002

Güimil et al., 2005

Gigaspora rosea

Spores

Random 50 sequences

Stommel et al., 2001

Glomus intraradices

Germinating spores

Random 303 sequences

Lammers et al., 2001; Jun et al., 2002

Extraradical hyphae

Random 150 sequences

Sawaki and Saito, 2001

Appressoria

SSH1 200 non redundant

Breuninger and Requena, 2004

Glomus mosseae

1SSH:

67 non-redundant

suppressive subtractive hybridization. EST collection represented by oligomers.

2Virtual

nearly all terrestrial ecosystems (Smith and Read, 1997; see also Redecker, Chapter 12 this volume). In contrast to the ectomycorrhizal fungi, they form a special phylum, the Glomeromycota, with only approximately 160 species (Schüssler et al., 2001). AM fungi are asexual obligate biotrophs and cannot be grown in pure culture. Their life cycle (Franken et al., 2002) starts with the germination of the spores (Fig. 13.2). If the germ tubes come into contact with root exudates, they cease apical growth and start to branch. Upon physical contact with the host root, appressoria develop from which the fungus is able to penetrate the epidermis and to colonize the cortex of the root. Two forms of further development exist from this stage onwards, the Arum type and the Paris type of colonization (Smith and

Smith, 1997). In the former type, the fungi grow first intercellularly before they enter root cells and develop single large arbuscules. In the latter, the fungus omits the extracellular phase and directly enters and spreads through the root from cell to cell, where it forms large coils with small intercalated arbuscules. The arbuscules are assumed to be the site of general transfer of nutrients from the fungus to the plant, as was shown for phosphate exchange (Rausch et al., 2001; Harrison et al., 2002). Exactly where plant carbohydrates are delivered to the fungus is still the subject of debate (Harrison, 1999). A second typical structure, the vesicle, is only formed by certain species and is thought to be a storage organ. In parallel to the colonization of the root, the AM fungi spread into the soil with




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Dormant spores Asymbiosis Spore germination Pre-symbiosis Sterile roots

Hyphal branching

Appressorium formation upon physical contact Symbiosis (early) Colonization of the root cortex

Symbiosis (late)

Extraradical hyphae spore formation

Development, functioning and degradation of arbuscules

Fig. 13.2. Arbuscular endomycorrhizal development. During the asymbiotic stage, dormant spores are germinating due to an increase in temperature and humidity. If the germ tubes come into contact with root exudates, they change to pre-symbiotic development, where they forego apical growth and show intense hyphal branching. The early symbiotic stages are characterized by appressorium development on the surface of the root and colonization of the cortex cells. At late symbiosis, fungal hyphae penetrate plant cell walls and build up the intracellular arbuscules, which are degraded after approximately 10 days. In parallel, extraradical hyphae grow into the soil, where the next generation of spores is formed at the end of the fungal life cycle.

their extraradical mycelium, where they take up nutrients and complete the life cycle by forming the next generation of chlamydospores. The transcriptome analysis of an AM is markedly different from that of ectomycorrhiza. While RNA extracted from the ectotrophic symbiosis contains approximately 50% ectomycorrhiza fungal transcripts (Diaz et al., 1997), AM fungal transcripts account for only 1–5% of the symbiotic transcriptome (Maldonado-Mendoza et al., 2002). In addition, pure fungal material is difficult to obtain due to the obligate biotrophic nature of AM fungi. This has resulted in a bias of knowledge. The number of characterized AM fungal genes is very limited, but numerous plant genes have so far been identified which are regulated during AM development. These investigations were mainly targeted at plant genes involved in defence reactions, in nutrient transport or otherwise

known from root nodule symbioses with rhizobia (Gianinazzi-Pearson et al., 1996; Harrison, 1999; Lapopin and Franken, 2001). An overview of these studies would be beyond the scope of this chapter, which therefore concentrates on non-targeted approaches.

Plant gene expression in AM symbioses The first non-targeted attempt to identify mycorrhiza-regulated plant genes was published in 1996. A tomato mycorrhiza cDNA library was differentially screened and several genes could be identified with a mycorrhizarelated expression profile (Tahiri-Alaoui et al., 1996). One of them showed similarity to a cullin gene and might be involved in cell cycle control (Tahiri-Alaoui et al., 2002). Subsequently, seven symbiosis-regulated genes were identified by differential RNA



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display of mycorrhizal roots from Pisum sativum (Martin-Laurent et al., 1997). Psam1, 2 and 3 were further analysed for their expression patterns, cellular localization or their structure (Krajinski et al., 1998; MartinLaurent et al., 1998, 2001). Differential screening of a cDNA library from barley mycorrhizal roots resulted in the identification of four plant genes, one of which putatively encoded an H+-ATPase (Murphy et al., 1997). This correlated with the finding of increased activity at the periarbuscular membrane, which totally enwraps the intracellular fungal arbuscule (GianinazziPearson et al., 1991). This was later confirmed in other plants, where homologous mycorrhiza-induced genes were also detected (Gianinazzi-Pearson et al., 2000; Krajinski et al., 2002). Screening of a library from Medicago truncatula mycorrhiza with cDNA probes resulted in the cloning of the repressed gene Mt4 (Burleigh and Harrison, 1997), which is systemically downregulated by phosphate (Burleigh and Harrison, 1999). A second clone derived from differential screening encoded a plasma membranelocated zinc transporter (Burleigh et al., 2003). Repressed expression of this gene in mycorrhizal roots correlates with the reduced concentration of zinc in plants inoculated with the symbiont. Three mycorrhiza-induced genes have been identified in a subtractive library (van Buuren et al., 1999) and two of them were characterized further (van Buuren et al., 1999; Maldonado-Mendoza et al., 2005). They encoded enzymes for cell wall modifications and could therefore be involved in the formation of the matrix between the plant periarbuscular membrane and the fungal arbuscule. In order to identify genes which are particularly induced during certain stages of mycorrhizal development, early and late pea mutants have been employed. Using on the one hand the line RisNod24, which allows normal cortex colonization but harbours only a very few aborted arbuscules, numerous genes could be identified, with enhanced RNA accumulation during late stages of the symbiosis (Lapopin et al., 1999; Grunwald et al., 2004). One of them showed similarity to a family of trypsin inhibitor-encoding genes

from M. truncatula, where all members were mycorrhiza induced and at least one was activated in the vicinity of fungal arbuscules (Grunwald et al., 2004). On the other hand, differential display analysis of the early mutant P2 resulted in the identification of a Clp serine proteinase which could be involved in recognition of the two partners (Roussel et al., 2001). As already discussed for the ectomycorrhiza, it was also important for the arbuscular endomycorrhiza to have an agreed model system for further research. Nowadays, two systems are mainly used, Lotus japonicum and M. truncatula, where the mycorrhiza can be compared with the root nodule symbiosis on the same plants (Cook et al., 1997). L. japonicum has, up to now, been used more for the analysis of mutant lines. Just recently, a transcriptomic approach was applied in order to characterize such mutants (Kistner et al., 2005). This analysis indicated the presence of two independent pathways of signalling, because the mycorrhizal-specific induction of genes was abolished by the mutations, while the repression was not. The analysis of the M. truncatula transcriptome has been started by a French consortium which sequenced cDNA clones derived from control, mycorrhizal and nodulated roots of this model legume (Journet et al., 2002). More than 20,000 sequences were clustered and 6359 ESTs obtained. Analysis of the redundancy of certain ESTs in distinct cDNA libraries (electronic northern blots) resulted in predictions of expression patterns for many putative genes. In order to obtain genes which are specifically induced by colonization with the AM fungus Glomus intraradices, cDNAs from M. truncatula mycorrhizal roots were subtracted by cDNAs obtained from roots inoculated with a pathogen or with a nodule-forming bacterium, or fertilized with phosphate (Wulf et al., 2003). A first qualitative analysis revealed that about two-thirds of the library clones represent differentially expressed genes. Selected genes from this first screening were further characterized for their expression and localization (Doll et al., 2003). Within the DFG focusing project ‘MolMyk’, two large-scale




EST collections of AM tissue were generated: the above-mentioned SSH-cDNA library and one random cDNA library of mycorrhizal M. truncatula roots. Large-scale screenings of all clustered TCs deriving from both libraries by in silico and experimental approaches revealed a large set of novel, not previously listed mycorrhiza-specific genes of M. truncatula including a number of transporters, genes involved in signalling processes and a family of AM-specific lectins (Frenzel et al., 2005). Another subtractive library was targeted at the early stages of the symbiosis (Weidmann et al., 2004). Twentynine genes were identified as already upregulated 5 days after inoculation. A more detailed analysis of 11 genes showed that they were even induced before physical contact and the formation of fungal appressoria. This indicates that the fungus exudes compounds which are recognized by the plant. Interestingly, the genes were not induced in the mutant host plant, which seems to be unable to respond to the putative fungal signal. An overview of the whole development of the symbiosis was achieved by preparing a mixed cDNA library from mycorrhizal roots at different stages of colonization (Liu et al., 2003). A total of 2268 clones were arrayed on nylon membranes and hybridized to cDNA probes from roots at five time points after inoculation with Glomus versiforme or fertilization with low and high amounts of phosphate. Ninety-two plant genes were regulated during mycorrhiza development, and they could be grouped into different clusters according to their expression patterns: (i) induced at early and repressed at late stages; (ii) mainly encoding elements of the plant defence response; (iii) expression level correlated to colonization level (including many new unknown genes); and (iv) genes regulated by phosphate alone or by phosphate and mycorrhiza. In a subsequent experiment, 4702 plasmid clones of the same mycorrhizal cDNA library instead of the corresponding ESTs were used for hybridization (Liu et al., 2004). Low abundance transcripts could not be detected with this method but, nevertheless, a number of genes were identified which are probably involved in plant–fungus signalling during the interaction.

225

The first array representing a part of the M. truncatula root transcriptome that was not directly targeted to the mycorrhizal symbiosis was produced by spotting the ESTs from the French consortium (see above) on nylon membranes and glass slides (Küster et al., 2004). These arrays were validated by hybridizations to roots inoculated with an AM fungus or with the root nodule-forming Sinorhizobium meliloti. Several well known marker genes for both symbioses were identified, but new genes could also be detected. A detailed analysis was carried out, and several hundred genes that are regulated by root symbioses were identified; 75 of these showed an overlapping expression pattern between mycorrhiza and root nodules (Manthey et al., 2004). A comparison of expression patterns in mycorrhiza and nodules with those of roots inoculated with the plant growth-promoting Pseudomonas fluorescens interestingly revealed a higher number of commonly regulated genes between roots colonized with the AM fungus and P. fluorescens than between mycorrhizal and nodulated roots (Sanchez et al., 2004). The most comprehensive array was produced by spotting > 16,000 oligonucleotides on glass slides representing all identified genes from M. truncatula deposited in the TIGR database (www.tigr. org/tdb/mtgi). These arrays were hybridized with cDNA probes from roots colonized with two different AM fungi: G. mosseae and G. intraradices (Hohnjec et al., 2005). About 1100 and 1400 regulated genes, respectively, were identified and 400 genes showed co-regulation by both fungi. As noted already by Liu et al. (2003), only a very few genes were also regulated by phosphate. This suggests that the amount of phosphate delivered by the fungus is below the threshold for gene induction or that mycorrhiza-delivered phosphate regulates a different set of genes from those regulated by phosphate taken up by the plant itself.

Fungal gene expression in AM symbioses Despite the low amount of fungal RNA in AM symbiotic tissues, as discussed above,



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RNA display or screening of cDNA libraries also resulted in the identification of genes from AM fungi (Harrison and van Buuren, 1995; Harrier et al., 1998; Franken et al., 2000; Delp et al., 2000; Ruiz-Lozano et al., 2002). The highest number was obtained in a subtractive library, where 680 clones were analysed (Brechenmacher et al., 2004). Six genes from Glomus mosseae could be identified, of which three showed constitutive RNA accumulation throughout the fungal life cycle, two were only expressed in intraradical hyphae and one was restricted to the late stages of symbiotic development. More AM fungal genes were obtained from genomic DNA in spores by PCR with degenerate primers or by screening of libraries (for a review, see Ferrol et al., 2004). The establishment of cDNA libraries, however, needed RNA extraction from pure fungal material, which was first obtained from sterilized spores (Franken et al., 1997). This allowed the application of the differential RNA display technique to AM fungal germ tubes showing increased branching in response to certain rhizobacteria (Requena et al., 1999) or to plant factors from root exudates (Franken et al., 2002; Tamasloukht et al., 2003). After involving root organ cultures as an experimental system to study gene expression, EST collections could be obtained from three different developmental stages: dormant spores (Stommel et al., 2001), asymbiotic spores with germ tubes (Lammers et al., 2001; Jun et al., 2002; Lanfranco et al., 2002) or symbiotic extraradical hyphae (Sawaki and Saito, 2001; Aono et al., 2004). RNAs from these stages were also used for subtractive hybridization in order to enrich such libraries for ESTs from genes induced during asymbiosis (Requena et al., 2002) or presymbiosis (Tamasloukht et al., 2003). Another developmental stage outside the root is the fungal appressorium. This structure is very difficult to isolate and to collect in sufficient amounts but, nevertheless, ESTs could also be obtained from this material (Breuninger and Requena, 2004). The cDNA fragments of the different collections were on the one hand used to choose single genes based on their sequence annotation

for analysis of their expression and function. These genes encoded enzymes of carbohydrate metabolism (Lammers et al., 2001; Bago et al., 2002, 2003), and proteins involved in stress responses (Stommel et al., 2001; Lanfranco et al., 2002, 2005) and in the metabolism of mineral nutrients (Aono et al., 2004; Breuninger et al., 2004; GonzálezGuerrero et al., 2005; Govindarajulu et al., 2005). On the other hand, ESTs were subjected to hybridizations with complex cDNA probes. This resulted in the identification of fungal genes which are regulated by the plant at different stages of the fungal life cycle: asymbiosis (Requena et al., 2002), presymbiosis (Tamasloukht et al., 2003), during appressorium development (Breuninger and Requena, 2004) or at late symbiotic stages (Requena et al., 2003).

Current status of AM transcriptomics Currently, AM researchers are looking forward to the genomic sequences of their models. This will allow the construction of arrays which carry probes for all genes of an organism, thus representing its total transcriptome. On the fungal side of the AM symbiosis, G. intraradices has been chosen on account of its small genome size (Hijri and Sanders, 2004) and its good performance in root organ cultures where massive amounts of biological material can be produced. Data about this project are published at darwin.nmsu.edu/∼fungi/index.php. On the plant side, the progress of sequencing the genomes of M. truncatula and L. japonicum can be followed at mtgenome. ucdavis.edu and www.kazusa.or.jp/lotus/ (Young et al., 2005). Beside these two legumes, Oryza sativa (rice) represents a plant with a completely sequenced genome and the ability to form AM. However, it was introduced very late as a model system for the transcriptome analysis of arbuscular mycorrhiza. A comprehensive analysis used an array with approximately 50,000 identified genes, which presumably covers the whole transcriptome of rice (Güimil et al., 2005). This array was hybridized to complex cDNA probes derived not only from roots




inoculated with G. intraradices, but also from material infected with two pathogens, Magnaporthe grisea and Fusarium monoliforme. Two hundred and twenty-four genes were identified as symbiosis regulated, of which a high number (40%) showed an overlapping expression pattern with at least one of the two pathogens. These genes probably play a role in compatible interactions between the plant and fungal root colonizers. It will be interesting to compare these data with those which will be obtained from other plants. Already, one-third of the rice genes show homology to mycorrhizarelated EST sequences of different dicots (Güimil et al., 2005).

Outlook Although the analysis of RNA accumulation patterns in mycorrhiza started relatively late compared with other subjects in plant or fungal biology, many data have been amassed, from the first northern blot with RNA from an AM (Gianinazzi-Pearson et al., 1992), through the first cDNA library of an ectomycorrhiza (Tagu et al., 1993) to the mycorrhizal transcriptome analysis of a whole plant genome (Güimil et al., 2005). The next steps will be analysing the whole mycorrhizal transcriptome of more model organisms. These transcriptomes can be compared with each other and with those of other symbiotic and pathogenic interactions; tasks that have already been started (Manthey et al., 2004; Güimil et al., 2005). Mycorrhizal interactions show a certain level of functional diversity (Smith et al., 2004), and it will be interesting therefore to extend the study of Hohnjec et al. (2005) to more

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plant–fungus combinations. One interesting point is surely the comparison of an ectoand an endomycorrhiza, which will be possible in poplar, because the plant is able to form both types of symbioses. If all such studies, in addition, include different naturally occurring conditions, they will make important contributions to understanding the ecological context of the symbiosis. In parallel, it is necessary to obtain more knowledge about the molecular basis of the beneficial effects, which requires the analysis of mycorrhizal transcriptomes in defined stress situations. This should include specific nutritional conditions as they occur in certain agricultural soils and inoculation with pathogens or application of pollutants (e.g. heavy metals) to the soil substrate. Moreover, it will not be sufficient to analyse only the plant root. As agricultural research demands a more holistic view, it is necessary to include the photosynthetic parts of the plant as well as the developing fruits in any transcriptome analyses, as has been performed in one case for the leaves of mycorrhizal tomato plants (Taylor and Harrier, 2003). The identification of genes involved in yield and quality of plant products may be used subsequently for experimental systems in order to compare different mycorrhizal inocula. Such genes could also be introduced as functional markers in crop breeding programmes or as elements of constructs for creating transgenic plants with improved traits. These are complex and demanding tasks, but their fulfilment will finally aid a much more efficient exploitation of mycorrhizal symbioses in sustainable plant production systems than is the case at present, where the application of AM fungi is mainly based on empirically obtained data.

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Differentiation of Nitrogen-fixing Legume Root Nodule Bacteria (Rhizobia) Kristina Lindström*, Paula Kokko-Gonzales, Zewdu Terefework and Leena A. Räsänen Department of Applied Chemistry and Microbiology, Biocenter 1, PO Box 56, FIN-00014 University of Helsinki, Finland

Introduction Nitrogen-fixing root nodule bacteria or rhizobia (rhizobium) induce root or stem nodules on and provide fixed N2 to their legume hosts, allowing these plants to thrive in nitrogen-deficient soils. The term rhizobia is derived from the Greek words Riza = roots and Bios = life. Although nitrogen fixation is widely distributed among bacteria, rhizobia are distinguished from the rest because they elicit the development of a specialized organ, the nodule, and engage in a symbiotic relationship with their hosts. Rhizobia enter their host either through intracellular infection of the root hairs or through cracks created in the root area. A comprehensive overview of currently recognized symbiotic interactions with rhizobia is given by Sprent (2001). In the early days of rhizobium taxonomy, there was only one genus, Rhizobium, which was divided into species according to the host plants the bacteria nodulated. This classification was abandoned when bacterial taxonomy in general became more streamlined and polyphasic. However, for understanding the biology of rhizobia, the nodulation phenotype is still very important.

The reason we study rhizobia is mainly on account of their nodulation and nitrogen fixation capability, which have implications for the global nitrogen cycle and for agriculture, agroforestry and forestry. From a practical point of view, rhizobia that nodulate but do not fix nitrogen are especially challenging (see Sessitsch, Chapter 15 this volume). Thus, any newly discovered rhizobia should be studied for their ability to induce nodules on and fix nitrogen in symbiosis with various plants. Sprent (2005) has reinforced the need for careful sampling, identification and characterization of nodules, as well as the importance of performing nodulation tests with putative rhizobia. With the development of taxonomic tools and theory, the classification of rhizobia has undergone changes that can sometimes be difficult to follow. Very early on it was recognized that there are two main types of rhizobia – fast growing and slow growing. The slow-growing rhizobia were assigned their own genus, Bradyrhizobium, in 1982. The bradyrhizobia are phenotypically distinguished from other rhizobia by their slow growth rate on standard media, e.g. yeastmannitoe (YM); colonies with a diameter of 1 mm are only formed after 7 days at 28°C,





Differentiation of Nitrogen-fixing Rhizobia

whereas the fastest growing rhizobia form larger colonies in about 2 days. In this chapter, we use the name ‘Bradyrhizobium’ when we specifically mean rhizobia with a slow growth rate on YM. Bradyrhizobia are also different from other rhizobial genera in various other criteria used to distinguish bacterial taxa, such as small subunit (SSU) and large subunit (LSU) ribosomal gene sequences and DNA–DNA homology. Today, the following bacterial genera contain root nodule bacteria: the alphaproteobacterial genera Rhizobium, Mesorhizobium, Sinorhizobium, Bradyrhizobium, Allorhizobium and Azorhizobium (for a review, see Sawada et al., 2003), which were initially designated for root nodule bacteria; the Betaproteobacteria Burkholderia and Cupriavidus (Ralstonia) (Chen et al., 2001; Moulin et al., 2001); and occasionally the Alphaproteobacteria Agrobacterium, Devosia, Methylobacterium, Ochrobactrum and Blastobacter. Thus, the genera with ‘rhizobium’ in their name are those in which nodulating bacteria were first discovered, even though they may also contain non-nodulating members. The other genera harbouring root nodule bacteria were first described for other taxa and have only recently been found also to contain root nodule bacteria. Thus, the genus name of a bacterium will not necessarily tell us whether it is nodulating or not. A good source of information about rhizobial taxa is the ‘List of bacterial names with standing in nomenclature’, a website maintained by J.P. Euzéby (2006). The list is continuously updated and it contains all validly published names of bacterial taxa. The exciting new discoveries of nodulating bacteria in such a diversity of genera have partly been facilitated by the development of new methods for the identification of symbiosis-related genes and gene products in combination with molecular phylogenies of bacterial genes. Whereas the traditional rhizobiologist would consider everything that ‘smells like pseudomonas’ to be contamination by bacteria on the nodule surface or by saprophytic bacteria thriving on decaying nodule material, the modern rhizobiologist should also be able

237

to pick the unusual variants for further identification and testing. Good microbiological practice is, however, called upon as much as before – Koch’s postulates should be fulfilled. In order to claim that a bacterial strain is nodulating, it should be demonstrated that a pure culture of it forms nodules, preferably on its original host. The nodulation tests were more easily undertaken when the known rhizobial host plants were few – generally those used and inoculated in agriculture. With the increasing exploration of biodiversity resources, rhizobia have been isolated from exotic plants, the seeds of which are not always available. In such cases, demonstration of the presence of symbiosis-related genes can be a temporary substitute for plant testing. We are often asked the question: ‘What shall I do to identify and classify my strains?’ The intention with this chapter is to guide the reader through some of the various methods that are available for rhizobial differentiation and to communicate experience from our laboratory. Different laboratories have different facilities and others might employ a different selection of equally useful procedures. Before moving on to reviewing methods, we shall give a brief introduction to the conceptual framework within which bacterial differentiation, classification and identification are taking place – taxonomy.

Species Concepts and Definitions Applicable to Rhizobia The evolutionary species concept (Simpson, 1961; Mayden, 1997) is an excellent theoretical framework for taxonomic, systematic and diversity studies with root nodule bacteria. It implies that the description of bacterial species should, as far as possible, be based on attempts to track their evolution. During evolution, bacteria on the one hand diversify and on the other form cohesive clusters; this process is called speciation. The aim of taxonomic work is to detect and demonstrate this phenomenon. The species would thus be defined as a group whose recognition is based on



238

K. Lindström et al.

significant biological discontinuities among diverse populations. Depending on the organisms and the circumstances, the speciation process may move along diverse routes. With sexually reproducing organisms, speciation is the formation of reproductive barriers. The barriers arise when geographically isolated populations of a single species undergo independent evolutionary divergence; reproductive barriers are formed as by-products of the differentiation (allopatric speciation) (Mayr, 1957). Among rhizobia, recombination-restricting barriers can also arise without geographic isolation (sympatric speciation) (Vinuesa et al., 2005b). For those readers who are interested in species concepts and definitions, we refer to, for example, Roselló Mora and Amann (2001), Cohan (2002), Vinuesa and Silva (2004), Eardley and van Berkum (2005) and Vinuesa et al. (2005a).

Rhizobia Have Two Lives, and Genes to Cope with Both of Them Prokaryotes inhabit an immense range of habitats and display an extremely wide repertoire of genetic and phenotypic features. It is therefore convenient to consider the ecology and taxonomy of diverse prokaryotic groups separately. In many cases, bacteria are adapted to inhabiting a certain ecological niche. Rhizobia, however, populate at least two niches – the soil and the leguminous root nodule. Consequently, they need to have genes that enable them to function in these very disparate conditions. For a long time it has been known that genes involved in symbiosis often reside on plasmids. With the sequencing of several rhizobial genomes or parts of them, this has been confirmed, as has the existence of genomic islands carrying symbiotic genes in some species (e.g. Sullivan and Ronson, 1998; Galibert et al., 2001). On a conceptual level, it is therefore convenient to envisage the rhizobial genome as consisting of the core and the accessory elements (the latter also being known as the flexible genome). The core genome is

comprised of housekeeping genes, i.e. ribosomal and other genes necessary for the maintenance and basic metabolism of the bacteria. These are found in all rhizobia and are useful for determining gene phylogenies and for deducing organismal evolution. The genes involved in symbiotic interaction with the host are part of the accessory genome of rhizobia. Whereas this component of the accessory genome is fairly well known, other parts are less known or completely unknown. The genomes of rhizobia are large, about 5–8 megabases, compared with < 1 megabase for obligate parasites. The symbiotic genes of rhizobia account for only about 0.5 megabases (Galibert et al., 2001), so there is evidently much more to discover. The Sinorhizobium meliloti genome is organized in three replicons: the circular chromosome and two plasmids, pSymA and pSymB. The plasmid pSymA carries many genes necessary for symbiosis, whereas pSymB has both symbiotic and other genes, some of them essential. So even though this plasmid has a plasmid-type mode of replication, it is essential for the survival of the bacterium. On pSymB there were nine new loci found to be involved in the biosynthesis of polysaccharides, in addition to two new chromosomal loci. Why do the bacteria need so many polysaccharide genes? Are they housekeeping or accessory genes? So, although the sequencing of a rhizobial genome can provide new information of this type, it is clearly more a starting point than an end-point. Based on whole genome sequences of other bacteria, it has been discovered that related species of obligate symbionts have very similar genomes (Tamas et al., 2002) whereas, for example, in Legionella species (bacteria that live as endoparasites of amoebae and, as pathogens, infect human macrophages), strains belonging to the same species have about 10% of their genes different, two species differ in about 50% of their gene content (Chien et al., 2004). This information and similar studies with other bacteria let us anticipate that rhizobial genomes are diverse and that there is a lot more to learn about their structure




and function in relation to the environment and in the scope of evolution. Depending on their lifestyle, different bacterial species are genomically diverse in a range of different magnitudes (Joyce et al., 2002).

Approaches to Describing Rhizobial Diversity When working with prokaryotes, the basic unit is not an individual but a clone. In theory, a clone is static and consists of identical cells, but in practice we are dealing with mixtures of clones: populations. When we attempt to describe rhizobial diversity, we are looking for the diversification of assemblages of clones (populations) or speciation. Even though each described bacterial species must be represented by a type culture (type strain), it is becoming increasingly evident that the description of a new species cannot be based on studies of just one clone. In our laboratory, we have for a long time been fascinated by the diversity that exists among rhizobia (e.g. Lindström et al., 1983; Zhang et al., 1991; Gao et al., 2001; Wolde-meskel et al., 2005), and the description of new species has mainly arisen as a by-product of the diversity studies, with the aim of giving proper names to the organisms under study, to facilitate communication (Lindström, 1989; De Lajudie et al., 1998; Nick et al., 1999b; Gao et al., 2004). The polyphasic approach (Gillis et al., 2001) nowadays has become a favoured choice for assessing diversity of rhizobia. Thus, a range of phenotypic and genotypic characterizations are established as requirements in describing rhizobial species.

Choice of methods for initial screening of unknown isolates Even though every clone or strain is interesting per se and perhaps worth studying for its own interesting properties, we can gain much more by sampling extensively and making a comprehensive study when setting out to investigate rhizobial diversity.

239

In this way, we get not only a perspective on the organisms, but also a more robust basis for the treatment of the data. This is especially important when dealing with completely new rhizobial biodiversity. The more we have explored the rhizobial world, the more complex it seems to become. In the 1980s, starting with the description of rhizobia that nodulate Galega species, we found that the bacteria in question, though infecting two plant species, G. orientalis and G. officinalis, belonged to the same bacterial species which we described as Rhizobium galegae (Lindström, 1989). In the case of R. galegae, the DNA–DNA hybridization experiments performed were decisive for the species delineation, but phage typing was also a powerful tool for identifying the species (Lindström et al., 1983). The next decade saw a much greater diversity surfacing. Tree-nodulating isolates from Africa were first investigated by phenotypic testing. Numerical taxonomy grouped them into several clusters containing putative new species (Zhang et al., 1991). More new species that nodulate the tropical Acacia and Prosopis tree species, such as Mesorhizobium plurifarium, Sinorhizobium arboris and S. kostiense, were described by De Lajudie et al. (1998) and Nick et al. (1999a). Already in 1991 it seemed that the same plant could be nodulated by divergent rhizobial species and the same rhizobial species could nodulate different host plants. By using the molecular fingerprinting methods that became available at the beginning of the 1990s, we were able to demonstrate that the phenotypic diversity did indeed reflect a genetic diversity, extending from 16S rDNA to the whole genome (Nick et al., 1999a, b), and that it also showed a good correspondence with pulsed-field gel electrophoresis (PFGE, CHEF) (Haukka et al., 1994), soluble cellular protein profiles (sodium dodecyl sulphate–polyacrilamide gel electrophoresis (SDS–PAGE)) (Nick et al., 1999a) as well as total fatty acid profiles (FAME) (Tighe et al., 2000). From these exercises, polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP) of 16S rDNA, first



240


applied to rhizobia by Laguerre et al. (1996), and repetitive sequence-based (rep)PCR genomic fingerprinting (Nick and Lindström, 1994; Versalovic et al., 1994) became our favoured methods. We used them to show that Mesorhizobium populations isolated from root nodules of Astragalus sinicus growing on many different sites in China were genomically very diverse (X.-X. Zhang et al., 1999). We could also tie groundnut bradyrhizobial strains to their site of isolation by applying rep-PCR fingerprinting (X.P. Zhang et al., 1999). However, since rep-PCR proved to be very sensitive to practically all components involved in the application of the method (reagents, PCR conditions and thermal cycler, the DNA and the performer of the experiments), we sought a more stable replacement. This appeared in the form of AFLP (amplified fragment length polymorphism), first described for bacteria by Vos et al. (1995). We successfully applied it to all known rhizobial species and evaluated various ways of displaying and analysing the data (Terefework et al., 2001). The AFLP method is useful because, with appropriate adjustments, it can be used for fingerprinting any type of DNA. We used the method to type groundnut bradyrhizobia and groundnut plant cultivars genomically when looking for co-evolution (Chen et al., 2001). We also applied it to our ‘Caucasian collection’ of Rhizobium galegae isolates coming from the gene centre for the Galega orientalis species in the Caucasus. For every isolate, we have data for the site of isolation and plant seed samples from the host plants, either G. orientalis or G. officinalis, which also grow in the Caucasus but presumably have their gene centre in Turkey. The first study of this collection was published by Andronov et al. (2003). We were working from the hypothesis that the diversity of the rhizobia is largest in their centre of origin, which is presumably the same as the gene centre for the host plant. Indeed we found this to be the case. By AFLP fingerprinting as well as ITS (the internal transcribed spacer between the 16S and the 23S ribosomal operons) typing, it

was shown that the diversity of R. galegae bv. orientalis (this biovar forms an effective, nitrogen-fixing symbiosis with G. orientalis) was greater than that of R. galegae bv. officinalis (effective symbiosis with G. officinalis). This was surprising, since our previous studies had shown the reverse to be the case; the diversity of bv. officinalis strains in our culture collection, which comprised isolates from different parts of the world, was surpassed by the diversity of the Caucasian bv. orientalis collection. The diversity studies were extended to the symbiotic regions of the isolates, and the findings were similar: bv. orientalis also displayed a greater diversity in this region of the genome.

Initial characterization of unknown isolates Taking nodulation as a primary prerequisite in identifying isolates as rhizobia or nodule bacteria, once the nodulation capacity of isolates is established, one can then take the polyphasic approach and explore their phenotypic and genotypic diversity. However, it is worth noting that not all noduleoccupying symbionts are infective; at times, a nodule may harbour more than a single clonal population whose concerted effect is to induce nodulation and, subsequently, nitrogen fixation. Arrays of phenotypic and genotypic methods are both available and employed for analysis. Although most laboratories are free to screen their isolates as they wish, certain methods are becoming widely accepted. Of course, not all laboratories are equipped with the necessary facilities required to perform them; researchers in developing countries are at a particular disadvantage in having to choose a method which is easy to perform, is cost efficient and requires inexpensive equipment. PCR-RFLP and sequencing of ribosomal genes We use 16S PCR-RFLP for the initial fingerprinting of unknown isolates. Amplification is performed with the universal bacterial




primers rD1 and fD1 (Table 14.1), and we normally digest the amplification products with four-cutter restriction enzymes MspI, AluI and HaeIII. In case the patterns look unknown and do not compare with the references used, we sequence part of the 16S ribosomal gene. Amplification with primers pA′ and pF′ (Table 14.1) gives a product suitable for one sequencing reaction, yielding about 900 bp of sequence (Terefework et al., 1998). ‘Blasting’ the obtained sequence against the EMBL database usually yields a preliminary identification. When dealing with collections comprising many strains (tens to hundreds), we use the ribosomal PCR-RFLP fingerprints to group the strains initially, followed by sequencing representatives of all the different patterns obtained (e.g. X.-X. Zhang et al., 1999). PCR-RFLP of 23S ribosomal genes is also very useful. We use primers 3 and 4 designed by Terefework et al. (1998) (Table 14.1). They were designed to exclude the B8 loop of the molecules. Especially with Bradyrhizobium species, which have very similar 16S genes, the fingerprinting of genes encoding the LSU has been worthwhile (unpublished results from our laboratory). In a study of 95 fast-growing rhizobial strains from Astragalus adsurgens growing in China, Gao et al. (2001) found 26 rDNA genotypes when combining typing results of the 16S and the 23S genes. The products were in this case digested with AluI and Hinf I in addition to MspI and HaeIII. Comparison with patterns from reference species suggested that the A. adsurgens rhizobia belonged to the genera Agrobacterium, Mesorhizobium, Rhizobium and Sinorhizobium. Other approaches NUMERICAL TAXONOMY. Other approaches for undertaking initial typing of unknown isolates are in use. As mentioned previously, we used numerical taxonomy of phenotypic traits to group African tree rhizobia (Zhang et al., 1991). The grouping was later found to correlate well with genomic methods

241

(e.g. Nick et al., 1999b). Professor Wen-Xin Chen’s group at the China Agricultural University created a battery of 120 phenotypic tests that they have used for initial grouping of rhizobia belonging to several new species (e.g. Gao et al., 1994; Gao et al., 2004). BIOLOG® is a commercial identification system which currently provides 95 carbon sources for testing strains of either Gram-negative or Gram-positive bacteria in a microtitre format. The ability of bacterial isolates to reduce the substrates is indicated by a colour reaction. A database for comparison of reduction patterns for unknown strains is commercially available, but it does not contain rhizobia. Therefore, some authors have created their own databases (McInroy et al., 1999; Wolde-meskel et al., 2004a, b). However, Wolde-meskel (2004a, b), when using the system for initial characterization of a diverse collection of Ethiopian nodule isolates, pointed out that phenotypic testing is very prone to variation depending on growth conditions, amount of inoculum, etc. FAME. Analysis of total fatty acid methyl ester (FAME) composition is widely used with other bacteria (see also Lipski, Chapter 8 this volume). To be reliable, this method requires an identification database. A database for fast-growing rhizobial strains was created by Tighe et al. (2000), and X.P. Zhang et al. (1999) used the Bradyrhizobium database created by Graham et al. (1995) to demonstrate that bradyrhizobia isolated from groundnut root nodules in China differed from other bradyrhizobia by the high proportion of a specific fatty acid. Otherwise, the method seems not to be widely used among rhizobiologists.

Total soluble protein patterns in SDS–PAGE are used by microbiologists at the University of Gent, Belgium, who have created a large database for the identification of new isolates (Nick et al., 1999a). The protein patterns of tropical tree rhizobia (mainly Sinorhizobium species) were, for example, used to delineate the new species S. arboris and S. kostiense. PROTEIN PATTERNS.



FGPL-132′ FGPS1490-72

ITS R

~ 900–1400

5′-CCG GGT TTC CCC ATT CGG-3′ 5′-TGC GGC TGG ATC CCC TCC TT-3′

See reference

5′-CCG TGA GGG AAA GGT GAA AAG TAC C-3′ 5′-CCC GCT TAG ATG CTT TCA GC-3′

Consult a sequencing laboratory

5′-AAG GAG GTG ATC CAG CC-3′ 5′-AGA GTT TGA TCC TGG CTC AG-3′ 5′-AGA GTT TGA TCC TGG CTC AG-3′ 5′-ACG AGC TGA CGA CAG CCA TG-3′

Sequences

*The purpose of the amplification is either R = RFLP or S = sequencing.

Several

23S rDNA S

4

~ 2300

3

23S rDNA R

~ 900 (first part)

∼ 1500

~ 1500

rD1 fD1 pA′ pF′

Primers

Size of amplified fragment (bp)

Sequences of primers for RFLP and sequencing of ribosomal regions in rhizobia.

Normand et al. (1996)

Van Berkum et al. (2003)

Terefework et al. (1998)

X.-X. Zhang et al. (1999)

Weisburg et al. (1991)

References

Work with all rhizobia we tested. Work with all rhizobia we tested. These primers can also serve as sequencing primers. The procedure is nowadays so common that methods and primers are abundant and work well. These primers have worked well with all rhizobia we tested, including bradyrhizobia. This primer set might not work with all rhizobia. The length and copy number varies with genus and species, even strains.

Comments

242

16S rDNA S

16S rDNA S

16S rDNA R

Gene and purpose*

Table 14.1





In the case of R. galegae, the patterns were very similar at the species level (Lipsanen and Lindström, 1989). PHAGE TYPING. Before the advent of the DNAbased methods, phage typing was a fairly convenient method for identifying rhizobia, although it does require the isolation of suitable phage and can be laborious to set up. This method worked very well for the typing and identification of R. galegae (Lindström et al., 1983), and it also proved to be easy to isolate specific phages from soil that had been inoculated with the rhizobia only a few years earlier (Lindström et al., 1990; Lindström and Kaijalainen, 1991). A battery of phages for typing R. leguminosarum bv. trifolii was also created with isolates from Finnish soils by Kankila and Lindström (1994). INTRINSIC ANTIBIOTIC RESISTANCE. Intrinsic antibiotic resistance (IAR) patterns have been used as a complement to other phenotypic methods and included in numerical taxonomy (Lindström and Lehtomäki, 1988; Zhang et al., 1991). When performing phenotypic tests, it is important to standardize the methods carefully. The protein and fatty acid composition of the bacterial cells are dependent on growth medium and growth conditions, age of the culture, etc. For growth tests, the size and quality of the inoculum will also influence the results.

Grouping of strains at different taxonomic levels Fingerprinting and partial sequencing of ribosomal genes help to allocate isolates to a genus. For species designation, even sequencing the full length of the 16S gene is not always sufficient. For classification of the bacteria at lower taxonomic ranks, we thus need to consider additional methods. Sequencing the 23S gene might be advantageous on account of its large size (Van Berkum et al., 2003), but this brings its own problems – at 2.3 kb it requires several sequencing primers to be designed and they are not always universal enough.

243

ITS fingerprinting by PCR-RFLP Fingerprinting the 16S–23S variable ITS sequences of rhizobia has been used by an increasing number of scientists. ITS is particularly suited to outlining groups of closely related strains that have limited divergence in their ribosomal genes sequences. Because these sequences contain non-coding DNA, they are more variable than the coding ribosomal genes. ITS has been used for phylogeny estimation (van Berkum and Fuhrmann, 2000; Tan et al., 2001; Willems et al., 2001a, 2003; Kwon et al., 2005; Vinuesa et al., 2005b). This can be problematic, since the size of this sequence varies even within a species. Fast-growing rhizobia, which often accommodate several copies of ribosomal genes, have divergent ITS sequences even within the same strain. This polymorphism of ITS fragments, however, can be used for an initial screening of a large number of diverse isolates. ITS fingerprinting by PCR-RFLP holds much promise. If we do not aim at creating phylogenies, the difficulties mentioned above will just be treated as strain differences. ITS fingerprinting distinguished the R. galegae bvs orientalis and officinalis (Andronov et al., 2003), and recently we applied this method for grouping isolates belonging to R. leguminosarum bv. trifolii. These experiments are now described in more detail below. RESEARCH QUESTION. The strains included in the study are mentioned in Figs 14.3–14.5. The collection consists of R. leguminosarum bv. trifolii strains which were isolated from root nodules of red clover (Trifolium pratense) in Finland and Norway, strains isolated from white clover (T. repens) growing in Sichuan, China, and some reference strains. The Finnish red clover rhizobium collection was a result of intensive screening and research in the 1980s (Lindström, 1984; Lipsanen and Lindström, 1986; Lindström and Myllyniemi, 1987). The Norwegian strains were isolated during a national programme, and the Chinese strains as part of a project promoting the cultivation of forage legumes in Sichuan.



244


FGPS 1490-72 and FGPS 132′ (Table 14.1). For the amplification, any commercially available polymerase should work well. PCR was performed in an MJ Research, PTC-200, Peltier Thermal Cycler (Bio-Rad Laboratories Inc., Waltham, Massachusetts, USA), and the cycles were (1) 94°C for 1 min 35 s, (2) 94°C for 35 s, (3) 52°C for 1 min, (4) 72°C for 2 min and (5) 72°C for 10 min. Cycles 2–4 were repeated 34 times. After checking the quality of the PCR products by agarose

After confirming by 16S PCR-RFLP (DNA isolation and amplification procedures as for ITS) that all the strains gave patterns similar to R. leguminosarum (Fig. 14.1A), we asked whether ITS-RFLP fingerprinting would be capable of differentiating the strains. PROCEDURE. DNA was isolated according to the procedure outlined in Ausubel et al. (1987). ITS was amplified with primers

A

B

1

1

2

2

3

3

4

4

5

5

6

6

7

7

8

8

9

10 11 12 13 14 15 16 17

9 10 11 12 13 14 15 16 17 18 19

Fig. 14.1 (A) 16S rDNA-RFLP profiles of some representative Rhizobium leguminosarum strains digested with HaeIII (lanes 2–8) and MspI (lanes 10–16). The pGEM marker is in lanes 1, 9 and 17. The profiles in lanes 7 and 15 represent the Norwegian strain B4, which differs from the others by one site in both digestions. (B) ITS profiles obtained with HaeIII (lanes 2–9) and MspI (lanes 11–18). The pGEM marker is in lanes 1, 10 and 19.




gel electrophoresis, the restriction digestions were performed in the following way: 5 µl of amplified PCR product was digested with 5 µl of diluted restriction enzymes HaeIII or MspI (dilution 2 U/µl in sterilized ultra pure water, Milli-Q, and the dilutions could be stored at –20°C for 2 weeks), giving a final enzyme concentration of 1 U/µl (10 U/10 µl). The digestion was incubated overnight at 37°C. For gel electrophoresis 10 µl of digested PCR product +1 µl of undiluted dye buffer was run in a 3% agarose gel (0.5× TAE, 2–3 h, 100 V). RESULTS. As can be seen in Fig. 14.1B, ITSRFLP fingerprinting allowed discrimination between several strains. The gels were further analysed with the BioNumerics Version 4.0 program (Applied Maths BVBA, Sint-Martens-Latem, Belgium) and a dendrogram was constructed using the Dice index for distance calculation and the UPGMA for tree construction (Fig. 14.2). It should be pointed out that this tree is not a phylogenetic presentation but a means of displaying the data to see how they cluster. Principal component analysis is another method that is suitable for revealing underlying structures in multivariate data. CONCLUSIONS. ITS-RFLP fingerprinting is a convenient method for differentiation at the species to strain level. Since the primers used for amplification are very universal, little time needs to be spent on optimization of the procedure. Also, since ITS sequencing has been used for many different species, there is an extensive sequence database available (see references cited above).

Other housekeeping genes Sequencing of a range of protein-coding housekeeping genes is a recommended method for taxonomic characterization of bacteria in general (Stackebrandt et al., 2002), and it is nowadays especially recommended for rhizobial species description. So far, there are reports on the use of the following protein-coding genes for phylogenetic inference and species delineation in rhizobia: atpD, dnaK, glnA, glnII and recA

245

(Turner and Young, 2000; Gaunt et al., 2001; Moulin et al., 2001; Stepkowski et al., 2003; Parker, 2004; Eardley et al., 2005; Vinuesa et al., 2005a, b, c). PROCEDURE AND RESULTS. DNA isolation and PCR amplification can initially be as described for ITS. However, different genes and species might require different conditions, and these need to be optimized in each case. Primers reported in the literature are listed in Table 14.2 together with the appropriate references. We have tested several of them in order to be able to screen larger collections by RFLP, and our experience is communicated in the comment column of the table. Primers that have not worked with our strains are not mentioned. When screening diverse collections of unclassified rhizobia, it is to be expected that the same sets of primers may not amplify certain genes even within a species. The accumulation of more sequence data in the databases, however, will improve the possibility of designing suitable primers for isolates from diverse sources. CONCLUSIONS. In theory, the RFLP and sequencing of protein-coding housekeeping genes is one of the most promising approaches to detect speciation in rhizobia. They are therefore also ideal for additional studies of diverse collections. The advantage of using gene sequences for strain comparisons lies in the ability to make use of published sequences online, minimizing the need for collecting, handling and analysing many reference strains. In future, one can hope that the databases with sequences for rhizobia will be expanded and made freely available to the scientific community.

Studies of symbiotic genes The following symbiotic genes have been used for the differentiation of strains on the basis of their polymorphism: nodA, nodB, nodC, nodD, regions between the nod boxes and nifH (Haukka et al., 1998; Laguerre et al., 2001; Moulin et al., 2001; Parker et al., 2002; Vinuesa et al., 2005a, b, c). Andronov et al. (2003) designed primers



100

90

80

70

60

50


40

30

246

R. leguminosarum bv. trifolii

TH 174

Yaan

China

Trifolium repens

.R. leguminosarum bv. trifolii

XC 11

Xichang

China

Trifolium repens


XC 22

Xichang

China

Trifolium repens


TH 172

Yaan

China

Trifolium repens


XC 21

Xichang

China

Trifolium repens


B1

Norway

Trifolium repens


B3

Norway

Trifolium repens


B9

Norway

Trifolium repens

R. leguminosarum bv. viciae

Pisum sativum

LMG 14904/ Hambi 12*


Hambi 1119

Finland

Pisum sativum


B2

Norway

Trifolium repens


Hambi 1210

Finland

Trifolium pratense


Hambi 537

Finland

Trifolium pratense


Hambi 1211


Hambi 1407

R. leguminosarum bv. trifolii R. leguminosarum bv. trifolii

Seinäjoki

Finland

Trifolium pratense

Kemiö

Finland

Trifolium pratense

Hambi 518

Nuijamaa

Finland

Trifolium pratense

Hambi 460

Pielavesi

Finland

Trifolium pratense


Hambi 547

Kuopio

Finland

Trifolium pratense


Hambi 1127/NZP547/TA1


Hambi 461

Kankaanpää Finland

Trifolium pratense


Hambi 1343

Kettula

Finland

Trifolium pratense


Hambi 531

Joensuu

Finland

Trifolium pratense


Hambi 539

Turku

Finland

Trifolium pratense


Hambi 528

Oulu

Finland

Trifolium pratense


Hambi 459

Rautavaara Finland

Trifolium pratense


Hambi 13/LMG6122**

Australia Trifolium repens


Hambi 458

Finland

Trifolium pratense


Hambi 14/LMG8820***


Hambi 548

Häme

Finland

Trifolium pratense


Hambi 1344

Viikki

Finland

Trifolium pratense


Hambi 1402

Kärkkä

Finland

Trifolium pratense


Hambi 1401

Jokioinen

Finland

Trifolium pratense


Hambi 515

Valio

Finland

Trifolium pratense


Hambi 517

Virolahti

Finland

Trifolium pratense


TH 162

Yaan

China

Trifolium repens


TH 171

Yaan

China

Trifolium repens


TH 165

Yaan

China

Trifolium repens


TH 123

Yaan

China

Trifolium repens


TH 122

Yaan

China

Trifolium repens


TH 144

Yaan

China

Trifolium repens


TH 163

Yaan

China

Trifolium repens


TH 143

Yaan

China

Trifolium repens


TH 232

Yaan

China

Trifolium repens


TH 121

Yaan

China

Trifolium repens


TH 124

Yaan

China

Trifolium repens


TH 134

Yaan

China

Trifolium repens


TH 164

Yaan

China

Trifolium repens


TH 111

Yaan

China

Trifolium repens


HY 11

Hongyuan

China

Trifolium repens


HY 21

Hongyuan

China

Trifolium repens

Sysmä

Australia Trifolium subterraneum

Trifolium sp.

Fig. 14.2 UPGMA dendrogram of Rhizobium leguminosarum strains based on ITS profiles obtained by using restriction enzymes MspI and HaeIII and BioNumerics software (distance calculation with the Dice index). *Hambi is the Culture Collection at the Department of Applied Chemistry, University of Helsinki, Finland. **Type strain for R. leguminosarum, belongs to bv. viciae. ***‘Type strain’ for R. leguminosarum bv. trifolii (formerly a separate species).



recA

glnII

glnA

dnaK

Housekeeping atpD

Sequence to be amplified

GSI-1 GSI-2 GSI-3 GSI-4 GSI-5 GSII-1 GSII-2 GSII-3 GSII-4 glnII-12F glnII-689R recA-41F recA-640R

atpD-273F atpD 771R atpD-255F atpD 782R Several

Primers

800

650

450

600

500

500


5′-AAG GGC GGC TAY TTC CCGGT-3′ (532–551) 5′-GTC GAG ACC GGC CATCAG CA-3′ (1143–1124) 5′-GAY CTG CGY TTY ACC GAC C-3′ (58–76) 5′-CTT CRT GGT GRT GCT TTT C-3′ (643–625) 5′-GCA AGC TGC AGC AYG TGA CG-3′ (83–102) 5′-AAC GCA GAT CAA GGA ATT CG-3′ 5′-ATG CCC GAG CCG TTC CAG TC-3′ 5′-AGR TYT TCG GCA AGG GYT C-3′ 5′-GCG AAC GAT CTG GTA GGG GT-3′ 5′-YAA GCT CGA CTA CAT YTC-3′ 5′-TGC ATG CCS GAG CCG TTC CA-3′ 5′-TTC GGC AAG GGM TCG RTS ATG-3′ 5′-ACA TSA CRC CGA TCT TCA TGC-3′

5′-SCT GGG SCG YAT CMT GAA CGT-3′ 5′-GCC GAC ACT TCC GAA CCN GCC TG-3′ 5′-GCT SGG CCG CAT CMT SAA CGT C-3′ 5′-GCC GAC ACT TCM GAA CCN GCC TG-3′ See reference

Sequence

Vinuesa et al. (2005a) Vinuesa et al. (2005a)

Turner and Young (2000)

Turner and Young (2000)

Eardley et al. (2005)

Vinuesa et al. (2005a)

Gaunt et al. (2001)

References

Continued

Work well with various bradyrhizobia.

These worked well with different bradyrhizobia.

Used for sequencing. Different rhiozbia require different sets. We have had difficulties in finding primers universal enough for PCR screening. Consult the article to find out more.

Variable results with different bradyrhizobia.

Comments

Table 14.2. Results from PCR amplification tests done in our laboratory with primers designed for the amplification of housekeeping and symbiotic protein-coding genes.


Differentiation of Nitrogen-fixing Rhizobia 247


5′-GGA GGA YAH CCR TCG TGC ARV AG-3′ 5′-AAG TGC GTG GAG TCC GGT GG-3′ 5′-AAG TGC GTG GAG TCC GGT GG-3′

5′-TAC GGN AAR GGS GGN ATC GGC AA-3′ 5′-AGC ATG TCY TCS AGY TCN TCC A-3′

780

800

nifH-F nifH-I

nifH 40F nifH 817R

5′-GGN ATC GGC AAG TCS ACS AC-3′ 5′-TCR AMC AGC ATG TCC TCS AGC TC-3′

5′-TTC CGA CYT CRT GCC CTT CBG C-3′

5′-AGG ATA YCC GTC GTG CAG GAG CA-3′

5′-ATG CGK TTY ARR GGM CTN GAT CT-3′ 5′-CGC AWC CAN ATR TTY CCN GGR TC-3′ 5′-CAG ATC NAG DCC BTT GAA RCG CA-3′

Symbols used: N = A, C, G or T; R = A or G; S = C or G; W = A or T, Y = C or T.

nifH

nodB-nodD

Y5 900 Y6 TSnodD1 1a 1800–2000 with TS nodB1 or TS nodB2 or TS nodB3 nifH-1 640 nifH-2

Vinuesa et al. (2005a)

Laguerre et al. (2001)

Eardley et al. (1992) Haukka et al. (1998)

Moulin et al. (2001)

Haukka et al. (1998)

Laguerre et al. (2001)

Zhang et al. (2000)

Haukka et al. (1998)

References

Designed for S. meliloti, work with many other Sinorhizobium, Mesorhizobium and Rhizobium species. Designed for R. leguminosarum bv. trifolii, work with many rhizobia and bradyrhizobia. Work with different kinds of bradyrhizobia.

Work well with rhizobia with nodA adjacent to nodB; did not work with bradyrhizobia. Try these in combination with nodA-1 for rhizobia with nodA separated from nodB, e.g. some mesorhizobia. Designed for Rhizobium leguminosarum, work also with different bradyrhizobia. Work well with fast-growing rhizobia. Work with bradyrhizhobia but with varying specificity.

Comments

248

5′-AYG THG TYG ACG GAT C-3′ 5′-CGY GAC AGC CAN TCK CTA TTG-3′

nodD

900

nodCF4 nodCI

5′-TCA TAG CTC YGR ACC GTTCCG-3′ 5′-ATC ATC KYN CCG GNN GGC CA-3′

5′-TGC RGT GGA ARN TRN NCT GGG AAA-3′ 5′-GGN CCG TCR TCR AAW GTC ARG TA-3′

nodC

660

Sequence

nodA-3 nodA-4

nodA-1 nodA-2


nodA

Symbiotic nodA

Primers

Continued

Sequence to be amplified

Table 14.2.





primarily based on the regulatory nod box sequences for population studies of R. galegae biovars. We have not tested those on other species but, since the nod box sequence is quite conserved, it forms a suitable basis for primer design. PROCEDURE.

We amplified the nodA, nodC and nodD sequence from the R. legminosarum strains under study (Table 14.2), using the primers nodA-1 and nodA-2 designed by Haukka et al. (1998). The procedures followed those outlined above for ITS.

RESULTS.

nodA, nodC and nodD sequence was amplified from all strains. A selection of RFLP profiles of nodA is shown in Fig. 14.3 and a nodA dendrogram including all strains in Fig. 14.4. The results from nodC and nodD PCR-RFLP are not shown, but the dendrograms are fairly congruent with that of nodA. R. leguminosarum bv. viciae differs in its nod genes in comparison with bv. trifolii, which means that the 1

2

3

4

5

6

7

8

249

genotype reflects the nodulation phenotype (Fig. 14.4, data on nodC and nodD not shown). CONCLUSIONS. The nodA primers primarily designed for Sinorhizobium species worked well for R. leguminosarum. The function of these primers is dependent on the organization of nodulation genes in the investigated strains, since one of the primers for nodA extends into nodB, which in these species is adjacent to nodA. If there is no amplification, other primers should be tested and the presence of nodA checked by Southern hybridization. The nodC and nodD primers used (Table 14.2) seem quite universal: for instance, the nodCF4–nodC1 primer pair also amplified several bradyrhizobial nodC sequences.

Problems encountered when amplifying protein-coding genes In all RFLP experiments, a representative product from the initial PCR amplification 9

10

11

12

13

14

15

16

Fig. 14.3 Rhizobium leguminosarum nodA-RFLP profiles as digested with HaeIII (lanes 2–8) and MspI (lanes 9–15). The pGEM marker is in lanes 1 and 16.



100

90

70

80

50

60

30


40

20

250


TH 123

Yaan

China

Trifolium repens

TH 134

Yaan

China

Trifolium repens


TH 124

Yaan

China

Trifolium repens


Hambi 1344

Viikki

Finland

Hambi 1401

Jokioinen

Finland

Trifolium pratense Trifolium pratense


Hambi 1402

Kärkkä

Finland

Trifolium pratense


Hambi 518

Nuijamaa

Finland

Trifolium pratense Trifolium repens

R. leguminosarum bv. trifolii R. leguminosarum bv. trifolii R. leguminosarum bv. trifolii

Hambi 548

Häme

Finland

Trifolium pratense

HY 21

Hongyuan

China

Hambi 1407

Kemiö

Finland

Trifolium repens Trifolium pratense


Hambi 1127/NZP 574/TA1

Australia

Trifolium subterraneum

Hambi 1211

Finland

Trifolium pratense


Hambi 1210

Finland

B2

Norway



B4

Norway

B9

Norway

Trifolium repens

Hambi 1343

Kettula

Finland

Hambi 515

Valio

Finland



Hambi 517

Virolahti

Finland

Trifolium pratense

TH 164

Yaan

China

TH 165

Yaan

China

Trifolium repens Trifolium repens


Hambi 14/LMG 8820*** Hambi 458

Sysmä

Finland

Trifolium pratense


Hambi 461

Kankaanpää Finland

Hambi 539

Turku

Finland



Hambi 528

Oulu

Finland

TH 121

Yaan

China



TH 122

Yaan

China

Trifolium repens


TH 143

Yaan

China

TH 144

Yaan

China



TH 163

Yaan

China

Trifolium repens

Hambi 459

Rautavaara

Finland

Trifolium pratense


TH 111

Yaan

China

HY 11

Hongyuan

China



Hambi 531

Joensuu

Finland

Trifolium pratense

Hambi 547

Kuopio

Finland

Hambi 460

Pielavesi

Finland



XC 11

Xichang

China

Trifolium repens

TH 171

Yaan

China Australia



TH 174

Yaan

China

Trifolium repens

XC 22

Xichang

China

Trifolium repens


TH 232

Yaan

China

TH 162

Yaan

China



TH 172

Yaan

China

Trifolium repens

Norway


R. leguminosarum bv. trifolii R. leguminosarum bv. trifolii R. leguminosarum bv. viciae

XC 21

Xichang

China

Trifolium repens

Hambi 537

Seinäjoki

Finland

Trifolium pratense Pisum sativum


Hambi 1119

Finland

Pisum sativum

Hambi 13/LMG 6122

B1

Trifolium sp.

Norway

B3

Hambi 12/LMG 14904**

Fig. 14.4. UPGMA dendrogram of Rhizobium leguminosarum strains based on nodA profiles obtained by using restriction enzymes MspI and HaeIII and BioNumerics software (distance calculation with the Dice index). See legend of Fig. 14.2 for explanations.

must be sequenced to confirm its identity. In PCR, we try to achieve the amplification of one product, i.e. one strong and clear band displayed in the gel. If there are several bands, the PCR conditions must be optimized. The same is true if there is no product. If background bands interfere with

the interpretation of the results, the main band must be cloned and sequenced and PCR-RFLP cannot be used. Alternatively, the major band can be extracted from the gel and re-amplified. If no product is produced, the PCR conditions can be altered or the primers changed.




Displaying genomic polymorphisms rep-PCR The rep-PCR fingerprinting method employs primers designed on the basis of repetitive sequences in bacterial genomes. The primer pairs amplify DNA regions located between the primers. Depending on the complementarities of the primers and the sizes of the amplified fragments, banding patterns obtained consist of stronger and weaker bands. The gel pictures are amenable to analysis by software such as BioNumerics, and the differentiating power of band intensity can be accounted for by using the Pearson product moment correlation coefficient for distance estimation. Rhizobia can be analysed by REP-, ERIC-, BOX- and (GTG)5-PCR. In the literature, there are numerous examples of procedures and profiles for rhizobia and other bacteria (e.g. Nick and Lindström, 1994; Versalovic et al., 1994; Nick et al., 1999a; Vinuesa et al., 2005a; Jussila et al., 2006).

AFLP AFLP is a genomic fingerprinting method suitable for analysis of DNA from various sources. It has three steps: a restriction digestion of total genomic DNA, ligation to adaptors containing the primer sequences, and PCR amplification. The digestion is normally done with one six-cutting and one four-cutting enzyme, and the PCR uses primers complementary to adaptors ligated to the ends of the DNA fragments formed during digestion. The adaptors are selective so that the bases binding to the fragments generated during digestion have two or three selective bases which pair with the protruding ends of the fragments. Terefework et al. (2001) have thoroughly described the application of the method to rhizobia, adapting the procedures originally described by Vos et al. (1995), and focusing on different ways to display and analyse the data. For each set of strains, the method needs to be optimized. We describe here the AFLP fingerprinting of the R. leguminosarum strains referred to previously.

251

PROCEDURE. A 60–600 ng aliquot of DNA was digested with EcoRI and Tru1I (MseI) restriction enzymes and ligated with doublestranded restriction half-site-specific adaptors in a 25 µl reaction. Optimal results were achieved with Eco-AC6-FAM* and Mse-GC adaptors (*denotes fluorescent label). The PCR amplification was done separately from the digestion–ligation, using a 2 µl sample. The reader is advised to consult the appropriate papers for detailed descriptions of procedures (e.g. Gao et al., 2001; Terefework et al., 2001; Andronov et al., 2003; Qiang et al., 2003). After MicroSpin purification, the fragments were separated by capillary electrophoresis with ABI Prism 310 (Applied Biosystems, Foster City, California, USA), according to Dresler-Nurmi et al. (2000). RESULTS. All strains produced distinct patterns, which were displayed as either electropherograms or banding patterns, which can be converted into dendrograms by, for example, BioNumerics (Fig. 14.5). CONCLUSIONS. AFLP performed as outlined is practical and reproducible. The separation of fluorescently labelled fragments by capillary electrophoresis is a very good choice for fragment separation, if available. Alternatively, though cumbersome, silver staining of products separated on polyacrylamide gels also produces legible fingerprints.

Other methods DNA fingerprinting Other fingerprinting methods, such as TP-RAPD (two primers random amplified polymorphic DNA) (Rivas et al., 2001) or low molecular weight RNA profiling (Rivas et al., 2002) are claimed to group strains at the species level according to identity of patterns, but we have no experience of them. RAPD, arbitrary primer (AP)-PCR, DNA amplification fingerprinting (DAF) and pulsed-field gel electrophoresis (PFGE) are other fingerprinting techniques that have been used (Welsh and McClelland, 1990; Williams et al., 1990; Caetano-Anolles



252


100

80

60

40

20

Pearson correlation [0.0–100.0%]

R. leguminosarum bv. trifolii Hambi 1344

Viikki

Finland

Trifolium pratense


Jokioinen

Finland

Trifolium pratense


Valio

Finland

Trifolium pratense


Kärkkä

Finland

Trifolium pratense

R. leguminosarum bv. trifolii Hambi 14/LMG 8820*** R. leguminosarum bv. trifolii Hambi 548

Häme

R. leguminosarum bv. trifolii B 3 R. leguminosarum bv. trifolii B 9

Trifolium sp. Finland

Trifolium pratense

Norway

Trifolium repens

Norway

Trifolium repens


Pielavesi

Finland

Trifolium pratense


Joensuu

Finland

Trifolium pratense

R. leguminosarum bv. trifolii Hambi 13/LMG 6122

Australia Trifolium repens


Kuopio

Finland

Trifolium pratense


Oulu

Finland

Trifolium pratense


Seinäjoki

Finland

Trifolium pratense


Rautavaara Finland

Trifolium pratense


Turku

Finland

Trifolium pratense

R. leguminosarum bv. trifolii TH 162

Yaan

China

Trifolium repens Trifolium pratense

Pisum sativum

R. leguminosarum bv. viciae Hambi 12/LMG 14904** R. leguminosarum bv. trifolii Hambi 517

Virolahti

Finland


Sysmä

Finland

Trifolium pratense


Kettula

Finland

Trifolium pratense


Kemiö

Finland

Trifolium pratense


Kankaanp.

Finland

Trifolium pratense

R. leguminosarum bv. trifolii Hambi 1127NZP 574/TA1

Australia Trifolium subterraneum


Finland

Trifolium pratense

R. leguminosarum bv. viciae Hambi 1119

Finland

Pisum sativum


Finland

Trifolium pratense

R. leguminosarum bv. trifolii B 2

Norway

Trifolium repens


Norway

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens

R. leguminosarum bv. trifolii HY 11

Hongyuan

China

Trifolium repens

R. leguminosarum bv. trifolii HY 21

Hongyuan

China

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens


Yaan

China

Trifolium repens

R. leguminosarum bv. trifolii XC 11

Xichang

China

Trifolium repens


Xichang

China

Trifolium repens


Xichang

China

Trifolium repens

Norway

Trifolium repens


Fig. 14.5. AFLP profiles obtained by fragment analysis with ABI Prism 340 and the corresponding UPGMA dendrogram of Rhizobium leguminosarum strains by using BioNumerics software (distance calculation with the Pearson product moment correlation coefficient). See legend of Fig. 14.2 for explanations.

et al., 1991; Haukka and Lindström, 1994; Laranjo et al., 2002). Fingerprinting using insertion sequences (IS) can be appropriate for strains or species with suitable elements in their genomes. It has been applied to Sinorhizobium meliloti (Roumiantseva et al.,

2002) and Rhizobium galegae (Andronov et al., 2003). Plasmid profiling Many rhizobia contain plasmids, the sizes ranging from a few kilobases to > 1 megabase.




Often, the plasmids carry symbiotic genes. Thus, determining plasmid patterns is a good way of obtaining additional, useful information about rhizobia. Multilocus enzyme electrophoresis Multilocus enzyme electrophoresis (MLEE) takes advantage of polyphormism in housekeeping enzymes produced by bacteria. Different electrophoretic types can be distinguished, and the results used to group the isolates numerically. It has recently been used on rhizobia by Vinuesa et al. (2005a, b) and Eardley and van Berkum (2005). We used MLEE as part of the description of the new species Sinorhizobium arboris (Nick et al., 1999a), but we do not have the method working in our laboratory. It is powerful but laborious, and MLEE data are not portable. Phenotypic methods

three to four restriction enzymes is a good preliminary method for sorting rhizobia into genera. Reference patterns must be available, though. PCR-RFLP of 23S rDNA is a good complement, and in the future PCR-RFLP of protein-coding genes should also become routine. Protein profiling, rep-PCR and AFLP fingerprinting normally work well at the species level, but with completely new isolates rep-PCR and AFLP give too high a differentiation at the strain level to be usable for genus assignment. After initial sorting of new isolates by RFLP or grouping by genomic fingerprinting, representatives of all occurring types should have diagnostic genes sequenced and analysed by appropriate phylogenetic methods. This part of the work will be reviewed by the Subcommittee on Agrobacterium and Rhizobium of the International Committee for the Systematics of Prokaryotes in their guidelines for the description of new species of root-nodule bacteria.

Most of the phenotypic methods in use for rhizobia have been listed under other approaches.

Diagnostics and description of new species Diagnostics is the identification of a certain strain as belonging to a species or at least a genus. Each new, validly described species should be diagnosable by a phenotypic test (Gillis et al., 2001). However, rhizobia are not generally reliably identified by simple phenotypic testing, even though the presence of nodulation and nitrogen fixation capacity can be readily ascertained by inoculation of a permissible host. For taxonomic diagnostics, we rely on genotypic methods. PCR-RFLP of 16S rDNA using

253

Acknowledgements In this chapter, we mainly cited our own published work when describing methods we have used. We apologize to all those authors whose work is not mentioned. Warm thanks to our students Iiris Mattila and Tuomas Olkkonen, who tested primers for Bradyrhizobium. Mette M. Svenning, Tromsö, Norway, is sincerely acknowledged for the Norwegian reference strains. The work reported here was funded by the Academy of Finland with a grant awarded for collaboration with Sichuan Agricultural University, China. The Chinese strains were collected by our collaborator Zhang Xiaoping.

References Andronov, E.E., Terefework, Z., Roumiantseva, M.L., Kurchak, O., Onychuk, O., Dzubenko, N.I., Dresler-Nurmi, A., Young, J.P.W., Simarov, B.V. and Lindström, K. (2003) Symbiotic and genetic diversity of Rhizobium galegae isolates collected from the Galega orientalis gene center in the Caucasus in relation to their host plants. Applied and Environmental Microbiology 69, 1067–1074. Ausubel, F.M., Brent, R., Moore, J.G., Seidman, J.G., Smith, J.A. and Struh, K. (1987) Current Protocols in Molecular Biology. Wiley Interscience, Sunderland, Massachusetts, USA.



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15

Molecular Markers for Studying the Ecology of Rhizobia Angela Sessitsch

ARC Seibersdorf Research GmbH, Department of Bioresources, A-2444 Seibersdorf, Austria

Introduction Symbiotic nitrogen fixation by rhizobia is an important source of nitrogen for various legume crops and pasture species, which often fix as much as 200–300 kg/ha of this essential nutrient element annually (Peoples et al., 1995). Globally, symbiotic nitrogen fixation has been estimated to capture at least 70 mt/year of nitrogen (Brockwell et al., 1995). Due to the fact that the production of nitrogen fertilizers is an extremely energy-demanding process and because nitrogen fertilizers in many cases escape the root zone and thereby cause environmental pollution, biological nitrogen fixation is an integral component of sustainable agricultural systems. The use of legumes in crop rotations offers flexible management of nitrogen compounds (Peoples et al., 1995) and, in addition, control of crop diseases and pests (Graham and Vance, 2000). Rhizobia colonize many soils saprophytically, but strains often show drastically different characteristics regarding survival and persistence, competitive ability for nodulation or nitrogen fixation efficiency. Inoculation of legumes with rhizobial strains selected for high nitrogen capacity can

improve nitrogen fixation, particularly when local rhizobial strains are absent from soils or are ineffective. However, newly introduced strains often fail to compete with well-adapted indigenous populations. Tremendous efforts have been made to improve nodulation by trying to understand the factors that affect the interactions between rhizobia and soil fauna, between macro- and microsymbionts, as well as between these components and the edaphic environment. Apart from their interaction with legumes, rhizobia are also frequently found in less intimate association with non-leguminous plants, colonizing either the rhizosphere or the plant apoplast. In these habitats, they may exhibit several plant growth-promoting effects such as hormone production, phosphate solubilization and the suppression of pathogens (Sessitsch et al., 2002). For these traits to be successfully exploited, rhizobia have to be able to persist in soils and colonize plants well. Although rhizobia are widely distributed in soils, inoculation of legume seed is often required, since suitable strains may be absent or population densities too low to sustain legume growth. Out-competition of inoculant strains and nodulation by unwanted strains has been a problem for

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decades (Ireland and Vincent, 1968; Brockwell et al., 1982; Denton et al., 2000) and is particularly serious where regenerating legumes must nodulate from within the soil population of rhizobia after the year of establishment. Numerous rhizobial strains have been identified that show high nitrogen-fixing ability with their target host legume (e.g. Howieson et al., 2000). Nevertheless, attempts to increase legume yields in agricultural fields by inoculation with superior strains have often failed (Brockwell and Bottomley, 1995). In some cases, this may be due to inappropriate inoculation technology, but it is frequently the result of the inability of inoculant strains to compete with indigenous rhizobia for nodule formation on the plant host. Considerable effort has been expended to understand rhizobial competition, and the various factors contributing to inoculation success have been reviewed (Dowling and Broughton, 1986; Streeter, 1994). Apart from the ability to compete for nodulation, rhizobia can only colonize and nodulate plants successfully if they have the ability to persist in soil, to tolerate environmental stresses and to compete with microbes other than rhizobia. Reporter or marker genes are of particular interest to researchers who wish to track the performance of a single strain of a bacterium in a population containing other strains of the same organism. This chapter considers the value of this technology for investigating rhizobial ecology. The genes available and the assays required for their detection are reviewed, the advantages and limitations of various marker gene systems are analysed and examples of their applications are provided.

Available Reporter Genes Reporter genes are powerful molecular tools for various applications in rhizobium research. Through creation of gene fusions, they may be used to replace a gene of interest, leading to information on expression and regulation of that gene (e.g. Mazur et al., 2002; Janczarek and Skorupska, 2004) under conditions partly mimicking the

symbiotic interaction in planta. They are also useful for permitting identification of novel genes required for symbiosis (Xi et al., 2001), and random genomic libraries have been constructed with reporters such as the green fluorescent protein (GFP). Clones of a library that did not express GFP under laboratory (culture medium) conditions were inoculated into the environment in order to identify environmental regulation of bacterial gene expression and to obtain more information on spatial, temporal and physical effects (Allaway et al., 2001). Apart from studies looking at the genetic level of the interaction of rhizobium with its host and macroenvironment, reporter genes have been used to detect directly bacterial/ rhizobial strains in the environment. They have also been employed for reporting on various properties of the surrounding environment, e.g. bioavailability of phosphate (de Weger et al., 1994), naphthalene (Heitzer et al., 1992) or sucrose (Miller et al., 2001). Strains genetically modified with various marker genes have also been investigated in risk assessment studies on horizontal gene transfer of transgenes in genetically engineered microorganisms (Selbitschka et al., 1995; Corich et al., 2001). The key advantage of reporter genes as tools in microbial ecology is that they enable closely related strains of bacteria to be readily distinguished from each other and they provide a rapid means of identifying any strain of interest (Wilson, 1995). In rhizobium ecology, the application of reporter genes has greatly facilitated competition and persistence studies, where a particular gene has been introduced into a strain of interest, thus permitting its subsequent unequivocal detection (e.g. Sessitsch et al., 1997, 1998; Hallmann et al., 2001). Various reporter genes as well as marker gene systems are available, and each of them has different advantages and limitations (partly reviewed in Drahos, 1991; Wilson, 1995; Sessitsch et al., 1998). One of the first reporter genes to become available was lacZ, encoding β-galactosidase, which has been used to study root colonization by Azospirillum (Katupitiya et al., 1995), but also to assess



Molecular Markers for Studying the Ecology of Rhizobia

rhizobial competition for the nodulation of soybean (Krishnan and Pueppke, 1992). However, although several substrates are commercially available for the simple detection of its expression, lacZ has been replaced by other markers due to its high background expression levels in both bacteria and plants. Similarly, the phoA gene encoding alkaline phosphatase has been used as a reporter of gene expression in Rhizobium (Reuber et al., 1991), but it too displays high background activity in both plants and bacteria, and this restricts its potential for application in microbial ecology studies. The gene encoding catechol-2,3dioxygenase, xylE, is derived from TOL plasmids and is involved in the degradation of toluenes, benzoates and their methyl derivatives (Saunders et al., 1996). Bacteria can be grown on appropriate agar plates and sprayed with catechol in order to detect catechol-2,3-dioxygenase. Enzyme activity should only indicate intact and viable cells as the enzyme is rapidly inactivated in the presence of oxygen. This reporter has been introduced into bacteria in order to monitor the survival of Pseudomonas strains in lake water (Winstantley et al., 1991) as well as to study the impact of Pseudomonas fluorescens, genetically modified and marked with xylE, on the soil microflora (de Leij et al., 1995). Due to the solubility of the product of the assay, xylE activity is not a suitable marker for the detection of rhizobia within nodules on intact roots. Bioluminescence-based marker systems have been widely used in microbial ecology research. Biological production of light is achieved by both prokaryotic and eukaryotic organisms, including the marine bacteria Vibrio fischeri and Vibrio harveyi among the former and firefly (Photinus pyralis) in the latter group. Firefly luciferase (encoded by the luc gene) has been used as a marker in Rhizobium (Cebolla et al., 1991; 1993; Damann-Kalinowski et al., 1996; Räsänen et al., 2001), as well as in a range of other soil bacteria (Möller et al., 1994; Shao et al., 2002; Björklof et al., 2003; Backman et al., 2004). Although eukaryotic luminescence is very effective, its use is

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restricted by the high cost of the substrate luciferin as well as by the fact that it is not freely permeable across bacterial cell walls (Prosser et al., 1996a, b). Therefore, the prokaryotic luminescence system based on the luxAB genes, which encode the structural genes for the luciferase enzyme, has been widely applied in Rhizobium (Chabot et al., 1996; Paton et al., 1997) as well as in other soil bacteria (de Weger et al., 1997; Unge et al., 1999; Mishra et al., 2004). Detection of expression requires only a long chain fatty acid aldehyde, which is inexpensive and freely permeable across the bacterial cell wall. Addition of lux C, D and E, which encode a fatty acid reductase responsible for the synthesis of the aldehyde substrate, altogether eliminates the need to add a substrate to visualize enzyme activity (Prosser et al., 1996a). Bacteria tagged with luc or lux genes can be easily detected as luminescent colonies on agar plates, but the main advantage is that light development can be monitored in situ with a luminometer without the need for cultivation of cells (reviewed by Jansson et al., 2000). Luciferases are dependent on cellular energy reservoirs such as ATP for eukaryotic and FMNH2 for prokaryotic luciferase activity, and therefore enzyme activity indicates metabolically active cells. Unge et al. (1999) applied a dual gfp–luxAB marker system making use of the FMNH2 dependence of luciferase activity in order to detect only active cells, whereas cells irrespective of their metabolic status were detected by gfp. Luminescence markers are not suited to detecting cells under conditions where they are stressed or become starved (Jansson et al., 2000). The gusA gene derived from Escherichia coli and coding for β-glucuronidase (GUS) has been a widely used reporter gene in plant molecular biology because there is no background activity in plants. For this reason, it has also proved to be a highly suitable marker for studying plant–microbe interactions as GUS activity is also absent from those bacteria frequently studied on account of their agricultural importance, such as Rhizobium, Bradyrhizobium, Agrobacterium, Azospirillum, Pseudomonas or



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Streptomyces (Wilson et al., 1992; Sessitsch et al., 1998). GUS cleaves β-glucuronide substrates such as X-glcA (5-bromo-4-chloro3-indolyl-β-D-glucuronide) or Magenta-glcA (5-bromo-6-chloro-3-indolyl-β-D -glucuronide), releasing an indigo or magenta coloured precipitate, by which the marked strain can be visualized (Figs 15.1 and 15.2). In addition, several other substrates are available and a large number of possible assays exist (Wilson, 1996). Assays have been devised for the quantification of marked cells based on the substrates described above and on the chromogenic and fluorogenic substrates p-nitrophenol glucuronide and 4methylumbeliferyl glucuronide, respectively (Wilson, 1996). The gusA marker has been shown to be particularly useful for studying plant colonization and rhizobial competition (Sessitsch et al., 1998; Pitkäjärvi et al., 2003; Compant et al., 2005) since the assay to detect the marked strain within nodules or on the root system is extremely easy to perform. gusA-marked cells turn blue when the washed root is incubated in a phosphate buffer containing a GUS substrate such as X-glcA (Wilson et al., 1995). This procedure

eliminates the time-consuming steps of picking nodules and preparing bacterial isolates from them that are requirements for other detection techniques. The celB marker gene system is based on a thermostable β-galactosidase activity that allows detection of a marked strain after heat inactivation of the endogenous enzyme. The celB gene was isolated from the hyperthermophilic archaeon Pyrococcus furiosus, an organism that has an optimum growth temperature above 85°C (Voorhorst et al., 1995). The enzyme encoded by this gene is a β-glucosidase that also shows a high β-galactosidase activity. It is one of the most thermostable enzymes known at the present time, with a half-life of 85 h at 100°C (Kengen et al., 1993). The enzyme β-galactosidase cleaves β-galactopyranosides and, as with the detection of lacZ and gusA activity, several histochemical and chromogenic substrates are available. A widely used histochemical compound allowing the detection of spatial localization of a marked microbe is 5-bromo-4-chloro-3-indolyl-β-Dgalactopyranoside (X-gal) that forms an indigo precipitate due to β-galactosidase

Fig. 15.1. Phaseolus vulgaris root nodules occupied by either R. tropici strain CIAT899 (unstained nodule, right) or by the gusA-marked derivative CIAT899::gusA10 (blue nodule, left) after staining with X-glcA.




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Fig. 15.2. Phaseolus vulgaris root nodules occupied by either R. tropici strain CIAT899::celB10 (blue nodule, left) or by strain CIAT899::gusA10 (red nodule, right) after staining with Magenta-glcA and X-gal.

activity. celB-marked bacteria can be visualized as blue spots or blue areas on roots or within root nodules after heat denaturation of endogenous enzymes (Fig. 15.2). The celB marker gene has been used to determine rhizobial nodule occupancy by incubating the roots in a phosphate buffer at 70°C followed by incubation in the same buffer amended with X-gal at 37°C (Sessitsch et al., 1996a, b). Enzyme activity has been quantified by using o-nitrophenyl-β-Dgalactopyranoside (oNPG) and by measuring spectrophotometrically the amount of p-nitrophenol (pNP) produced (Sessitsch et al., 1996a, b). Initially, the celB marker gene system was developed for use in combination with the gusA marker in order to detect more than two rhizobial strains on or in plant tissue. The similar staining procedures using different histochemical reagents for gusA- and celB-marked cells, giving rise to different colours, allow their simultaneous localization, for example on roots, by using a combined gusA/celB assay (Sessitsch et al., 1996a; de Oliveira et al., 1998). As the histochemical substrates used for glycosides are substantially cheaper than

the corresponding glucuronide substrates, the celB marker may find additional applications. Another promising marker is the GFP of jellyfish (Aequorea victoria), encoded by the gfp gene. It has the advantage that it fluoresces upon illumination without the need to add any external substrate other than O2, which is required for the production of the chromophore (Jansson et al., 2000). Furthermore, tagged cells can be detected without significant sample disturbance, and the intrinsic fluorescence and stability of GFP permit the non-destructive visualization of gfp-marked cells. Additionally, several GFP variants exist, which show altered spectral properties and/or optimized codon use for specific applications (Delagrave et al., 1995; Ehrig et al., 1995; Crameri et al., 1996). gfptagged cells can be visualized and tracked by a variety of methods including epifluorescence microscopy, confocal laser microscopy and flow cytometry (Unge et al., 1996). GFP has been used to visualize interactions between rhizobia (Gage et al., 1996; Hallmann et al., 2001) or other bacteria (Unge et al., 1999; Unge and Jansson, 2001)



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and plants, as well as for the localization of soil microbes (Tresse et al., 1998).

Choice of Marker Gene System In early studies, bacteria were tagged by adding a plasmid carrying the marker gene. However, for ecological experiments, it is advantageous to insert foreign genes into the chromosome of a bacterial strain. In this location, they are not likely to be overexpressed and are as stable as other chromosomal genes. The location of marker genes on plasmids may result in overexpression due to high copy number, which might be an advantage, particularly where a few cells can only be detected by high marker gene activity. Nevertheless, for ecological studies, it is highly important that the marker gene itself and its product elicit minimal interference with the physiological properties of the host strain. Furthermore, the maintenance of plasmids is in most cases dependent on the presence of a selective pressure, which is a limitation for experiments carried out under ‘natural’ conditions. However, several studies have demonstrated satisfactory stability of plasmids carrying marker genes (Stuurman et al., 2000; Ramos et al., 2002). Most marker gene systems available for Gram-negative bacteria, and especially those used for tagging rhizobia, have been based on the Tn5 transposable element (Wilson et al., 1995; Sessitsch et al., 1996a; Unge et al., 1999; Xi et al., 1999; see also Table 15.1). Mini-transposons have been constructed (Herrero et al., 1990; de Lorenzo et al., 1990), which are located on suicide plasmids allowing simple integration of the marker genes. The delivery plasmid carrying the marker can be transferred from a donor E. coli strain to Rhizobium through bacterial conjugation, a process that requires only basic microbiological techniques. Alternatively, the plasmid can be transferred to the recipient strain by electroporation. As specific proteins from E. coli are required for plasmid replication, the plasmid itself cannot be maintained in the recipient strain,

whereas the marker is moved by transposition to a new location in the genome. The transposase gene required for transposition is located outside the mini-transposon, reducing the probability of further transposition and hence increasing the stability of the marker. With most available chromosomal marker systems, the insertion site is random, leading sometimes to deleterious mutations (Dennis and Zylstra, 1998). Therefore, before using marked bacteria in ecological experiments, preliminary screening is highly recommended to ensure that there are no major changes in relevant properties. Depending on the objective of a particular study and the experimental set-up, different marker gene cassettes, in which the introduced tag is subjected to different regulation systems, might be preferred. A gene cassette consists of the marker gene itself and sequences that regulate gene expression, primarily promoters, terminators and regulating sequences. Promoters may be regulated, generally either by gene products of other regulating sequences or by environmental signals. Constitutive expression has been chosen for various constructs, and the promoters used include the neomycin phosphotransferase II (nptII) or kanamycin resistance gene (aph) promoter of Tn5, the Salmonella typhimurium trp promoter, or the tac promoter originally derived from the lac operon (Selbitschka et al., 1995; Wilson et al., 1995; Sessitsch et al., 1996b; Xi et al., 1999; Miller et al., 2000; Ramos et al., 2002). These promoters operate in a broad range of Gram-negative bacteria, but may induce different levels of expression. In order to reduce possible effects on ecological fitness, regulated systems have been developed, in which the marker gene is not induced at all or only at very low levels until the experimental assay is initiated (Wilson et al., 1995; Sessitsch et al., 1996b). The tac promoter in combination with the lacI repressor gene has been applied, as well as various symbiotic promoters which might be advantageous in long-term nodule occupancy experiments. Finally, various promoterless marker gene constructs have been developed mainly for molecular genetic studies, but they are also highly useful for



miniTn5PpsbA-gfp luxAB Kmr

mTn5gusA-gfp11

mTn5SScelB31

mTn5SScelB10

mTn5SSgusA40

mTn5SSgusA31

mTn5SSgusA10

mTn5SSgusA11

Name of construct

nptII

I

PnifH

PnifH

gfp PpsbA

celB

celB

gusA

Ptac

luxAB

PnptII

Sm/Sp

I

nptII

Sm/Sp

I

gfp

gusA

Sm/Sp

I

I

gusA

Sm/Sp

gusA

I

Ptac

Sm/Sp

I

Ptac

gusA

Sm/Sp

I

Construct details

lacI

lacI

Constitutive

Constitutive/no promoter

Symbiotic

Regulated (lacI repressor)

No promoter

Symbiotic

Regulated (lacI repressor)

Constitutive

Promoter type

Table 15.1. Recommended marker gene constructs for labelling rhizobia to be used in ecological experiments.

Unge et al., 1999

Xi et al., 1999

Sessitsch et al., 1996a

Sessitsch et al., 1996a

Wilson et al., 1995

Wilson et al., 1995

Wilson et al., 1995

Wilson et al., 1995

Reference


Molecular Markers for Studying the Ecology of Rhizobia 265


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screening bacteria for promoters that respond to specific environmental signals (Wilson et al., 1995; Xi et al., 1999; Izallalen et al., 2002; He et al., 2005). Dual marker gene systems are also available. Some of them include a constitutively expressed marker gene and another marker without a promoter (Xi et al., 1999; Ramos et al., 2002), allowing both the localization of a particular strain and the study of gene expression. As mentioned above, one construct allows simultaneous quantification of bacterial cell numbers and their activity by combining the gfp gene, as a highly expressed biomarker indicating the presence/colonization of a particular strain, with the luxAB genes, which depend on cellular metabolic activity (Unge et al., 1999).

Examples of Marker Gene Applications In Rhizobium ecology, the main objective has been to understand the factors influencing the interaction of rhizobia with the plant (and consequently the success or otherwise of nodulation) as well as with the biotic and abiotic environment. Marker gene technology has been particularly useful for studying the fitness and persistence of rhizobial strains in soils and on plants, competition between rhizobial strains and how important genes respond to environmental stimuli. As rhizobia can also colonize non-legumes and frequently exhibit plant growth-promoting abilities, marked rhizobia have been applied in order for them to be visualized on roots and to study their colonization behaviour and efficiency. Chabot et al. (1996) studied root colonization of maize and lettuce by bioluminescent, phosphate-solubilizing R. leguminosarum bv. phaseoli strains and compared their colonization efficiency with that of other plant growth-promoting rhizobacteria. This screening process identified rhizobia that could colonize roots of monocotyledonous (maize) and dicotyledonous (lettuce) plants very efficiently. The biocontrol agent R. etli G12 has been tagged with gfp in order to study rhizoplane and endophytic colonization of

potato and Arabidopsis in relation to infection with the root-knot nematode Meloidogyne incognita (Hallmann et al., 2001). Tagged bacteria were found over the entire rhizoplane, but they preferentially colonized root tips, the emerging lateral roots, and galled tissue caused by Meloidogyne infestation. In the presence of the pathogen, R. etli colonized the galled tissue in large numbers. Photosynthetic Bradyrhizobium isolates obtained from African wild rice and showing plant growth-promoting as well as nitrogen-fixing potential were marked with lacZ and inoculated onto rice plants (Chaintreuil et al., 2000). Marked bacteria efficiently colonized the rhizoplane, followed by intercellular and, rarely, intracellular colonization of rice roots. Marker genes have been successfully used to monitor the persistence of rhizobia in field releases or field lysimeters. Sinorhizobium meliloti strains were tagged with the luciferase gene and released as part of a risk assessment programme on genetically modified organisms (Schwieger et al., 2000). Throughout the whole monitoring period of 2 years, inoculated strains were exclusively found in the A(p) horizon but not below it or in the flow-through rainwater. Soil chemical properties and quantities of microbial populations were not affected. Pitkäjärvi et al. (2003) conducted field lysimeter studies with the perennial goat’s rue (Galega orientalis), which was inoculated with marker gene-tagged R. galegae strains. Persistence, population dynamics and competitiveness of three different strains were investigated, as well as the effect of hydrocarbon pollution on these parameters. Marker genes have been repeatedly applied in rhizobial nodulation and competition tests as they confer a number of advantages. Wilson et al. (1991) used the gusA gene as a marker for detection of nodule occupancy by S. meliloti on Medicago sativa and of Bradyrhizobium sp. on Macroptilium atropurpureum, and suggested its general use in rhizobial competition studies. Early competition experiments with gene tags were carried out by Krishnan and Pueppke (1992), in which S. fredii was tagged with a constitutively expressed lacZ gene. The use of




this marker gene made it necessary to pick a representative number of individual nodules and to screen them for bacterial β-galactosidase activity. The gusA gene, on the other hand, has the advantage that its assay can be performed with the whole root (i.e. with all nodules on a complete root system) without removing nodules or isolating rhizobia from them. Streit et al. (1992) compared the capacities of 17 R. leguminosarum bv. phaseoli and three R. tropici strains at different soil pH values to compete for nodulation by co-inoculating them with a gusA-marked derivative of R. leguminosarum bv. phaseoli strain KIM5s. Competitive abilities ranged between 4 and 96% depending on the strain genotype and pH value. This study, as well as a related screening for highly competitive strains (Streit et al., 1995), clearly demonstrated the potential of marker genes for high throughput screening of nodulation efficiency and competitiveness. As it is a key requirement that the marker gene should have no intrinsic effect on the ecological property studied, the impact of introducing the gusA gene on rhizobial nodulation and competition was examined thoroughly (Sessitsch et al., 1997, 1998). It was shown that chromosomal insertion of a regulated gusA gene construct

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did not affect nitrogen fixation, nodulation efficiency or competitiveness per se, but that individual transconjugants may have significantly altered competitive abilities under specific conditions. It is advisable to test the competitive ability of a marked strain by co-inoculating it in a 1 : 1 ratio with the parent strain before applying it in further competition studies. The potential of applying differently marked strains has been demonstrated (Sessitsch et al., 1996a; de Oliveira et al., 1998).

Concluding Remarks Marker genes have a broad potential for application in studies which aim at a better understanding of the interaction between plant and microbe as well as of the ways in which environmental conditions affect this interaction. In this context, promoter–probe marker gene cassettes, eventually in combination with other techniques such as in vivo expression technology (IVET), differential fluorescence induction or microarray analysis, are particularly useful for increasing our understanding of how specific genes respond to environmental signals.

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Kengen, S.W.M., Luesink, E.J., Stams, A.J.M. and Zehnder, A.J.B. (1993) Purification and characterization of an extremely stable β-glucosidase from the hyperthermophilic archaeon Pyrococcus furiosus. European Journal of Biochemistry 213, 305–312. Krishnan, H.B. and Pueppke, S.G. (1992) A nolC–lacZ gene fusion in Rhizobium fredii facilitates direct assessment of competition for nodulation of soybean. Canadian Journal of Microbiology 38, 515–519. Mazur, A., Krol, J.E., Wielbo, J., Urbanik-Sypniewska, T. and Skorupska, A. (2002) Rhizobium leguminosarum bv. trifolii PssP protein is required for exopolysaccharide biosynthesis and polymerization. Molecular Plant-Microbe Interactions 15, 388–397. Miller, W.G., Leveau, J.H.J. and Lindow, S.E. (2000) Improved gfp and inaZ broad-host range promoter–probe vectors. Molecular Plant-Microbe Interactions 13, 577–582. Miller, W.G., Brandl, M.T., Quiñones, B. and Lindow, S.E. (2001) Biological sensor for sucrose availability: relative sensitivities of various reporter genes. Applied and Environmental Microbiology 67, 1308–1317. Mishra, S., Sarma, P.M. and Lal, P. (2004) Crude oil degradation efficiency of a recombinant Acinetobacter baumannii strain and its survival in crude oil-contaminated soil microcosm. FEMS Microbiology Letters 235, 323–331. Möller, A., Gustafsson, K. and Jansson, J.K. (1994) Specific detection of the firefly luciferase marker gene in environmental samples by bioluminescence and PCR amplification. FEMS Microbiology Ecology 15, 193–206. Paton, G.I., Palmer, G., Burton, M., Rattray, E.A., McGrath, S.P., Glover, L.A. and Killham, K. (1997) Development of an acute and chronic ecotoxicity assay using lux-marked Rhizobium leguminosarum biovar trifolii. Letters in Applied Microbiology 24, 296–300. Peoples, M.B., Herridge, D.F. and Lahda, J.K. (1995) Biological nitrogen fixation: an efficient source of nitrogen for sustainable agricultural production? Plant and Soil 174, 3–28. Pitkäjärvi, J., Räsänen, L.A., Langenskiöld, J., Wallenius, K., Niemi, M. and Lindström, K. (2003) Persistence, population dynamics and competitiveness for nodulation of marker gene-tagged Rhizobium galegae strains in field lysimeters in the boreal climatic zone. FEMS Microbiology Ecology 46, 91–104. Prosser, J.I., Rattray, E.A.S., Killham, K. and Glover, L.A. (1996a) Lux as a marker gene to track microbes. In: Akkermans, A.D.L., van Elsas, J.D. and de Bruijn, F.J. (eds), Molecular Microbial Ecology Manual, Section 6.1.1. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 1–17. Prosser, J.I., Killham, K., Glover, L.A. and Rattray, E.A.S. (1996b) Luminescence-based systems for detection of bacteria in the environment. Critical Reviews in Biotechnology 16, 157–183. 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16

Molecular Characterization of Bacterial Plant Pathogens Scott A. Godfrey1,* and Robert W. Jackson2

1School

of Biological and Molecular Sciences, Oxford Brookes University, Oxford OX3 0BP, UK; 2Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, UK

Introduction Horticultural and forest plants are continually threatened by native bacterial pathogens and/or invasive species from other countries. It is important that research continues to establish effective characterization methods for pathogenic bacteria to allow the correct implementation of control strategies when a particular pathogen is identified. Effective characterization of a given bacterial isolate will provide specific identification (detecting genera, species, subspecies and pathovars of a bacterial pathogen); distinguish an isolate from other bacteria; and establish methods to investigate a given bacterial isolate in an environmental population. Characterization further provides valuable information on the epidemiology of virulence (the disease severity) and pathogenicity (the ability to cause disease) during disease processes. The use of new methods, principally of the molecular type, in characterization has been particularly important in the reclassification of several plant root pathogens including Ralstonia (formerly classified as Burkholderia and Pseudomonas), Burkholderia cenocepacia (formerly Pseudomonas cepacia) and Leifsonia (formerly Clavibacter and Corynebacterium). These methods have

also been used extensively to characterize Pseudomonas, Erwinia, Xanthomonas, Clavibacter and Streptomyces pathogens, which are mentioned throughout this chapter. We first describe the common methodologies used for the characterization of bacterial isolates and then focus on targets that can identify pathogens. We then comment on the comparative benefits and shortcomings of different techniques and conclude by assessing the potential of relatively new technologies for characterization.

Known versus unknown plant pathogens Requirements for characterization of a bacterial plant pathogen can be categorized within two major headings: (i) characterization of an unknown bacterial pathogen (i.e. when a new disease symptom is observed and the causative agent needs to be isolated and identified); or (ii) verification of a known bacterial pathogen (i.e. where a disease symptom is observed and the causative agent needs verification using preestablished data). Diagnosis of a new unknown disease and characterizing a novel bacterial plant pathogen is a more complicated procedure requiring far more experience and is often a





Molecular Characterization of Bacterial Plant Pathogens

process of integrating many diagnostic principles, using both phenotypic and genotypic tests (Wallace, 1978; Grogan, 1981). Initial characterization of an unknown isolate generally uses presumptive tests (Schaad et al., 2002) of ‘likely’ bacterial agent(s) based on comparison with previous disease symptoms, and the results of these are used to eliminate known pathogens from consideration. Characterization of an unknown is often time-consuming, as many techniques must be applied before unequivocally accepting a bacterial designation. In comparison, characterization of a known pathogen is generally the process of confirmation of a suspected pathogen in infected tissue. Many bacterial diseases can be diagnosed quickly and efficiently using established methods and materials that are already available either from commercial companies or from previously published literature.

Methods of Characterization When presented with a plant disease symptom(s), the putative bacteria are: (i) isolated and determined to be the causal organism(s); (ii) taxonomically assigned by defining unique morphological, phenotypic, biochemical and molecular markers for future reference and comparison; and (iii) characterized by identifying virulence factors involved in bacterial pathogenicity. These characterization processes can be loosely classified into two types: phenotypic and molecular – the former involving methods that do not require molecular analyses and that encompass a variety of techniques based on cellular and colony morphology, cellular composition, phenotypic expression of extracellular compounds and biochemical analysis of nutritional and physiological properties. Despite the popularity of molecular approaches (described below), phenotypic analyses are still essential for both an accurate characterization of an unknown isolate and a validation of established molecular characterization methodologies of known organisms. Key phenotypic stages that need to be addressed are: initial assessment of unknown disease symptom(s)

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(Wallace, 1978; Grogan, 1981), comparison with known diseases (Bradbury, 1986), isolation and purification of putative candidate(s) on non-selective and semi-selective agar (Schaad et al., 2002), establishment of biological assays (bioassays) to confirm the ability to cause disease (pathogenicity) and assess disease severity (virulence), and morphological and biochemical analysis (Holt, 1989; Holt et al., 1994). However, with the constraints of time and increasing expectations of high productivity, there is a need for rapid, reliable characterization tests. Phenotypic characterization that involves isolation, subculturing, morphological and biochemical identification may require from 1 week to several weeks to perform, and often the degree of discrimination is limited (especially within species, subspecies and pathovars). An example was the numerical analysis of 295 phenotypic features that could not reliably differentiate one pathovar from 100 different Xanthomonas pathovars (Vandermooter and Swings, 1990).

Molecular methods In contrast, molecular methods are considered more efficient because they form the basis of technologies that enable detailed examination of the variability of DNA nucleotide sequence, mRNA expression rates, enzyme activity and composition of cellular physiology. Polyphasic approaches for efficient characterization Although we outline many individual methodologies, it is important to stress that any given bacterial characterization will only be accurately achieved by the use of many complementary techniques, in what is known as a polyphasic approach. This allows each technique to validate the others so that a wide range of evidence is obtained to make an accurate identification; a good example is the polyphasic analysis of Ralstonia solanacearum (van Overbeek et al., 2002). Furthermore, effective characterization is



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only achieved by comprehensive comparison of many reference strains (often from international strain collections) within any given technique. Polymerase chain reaction (PCR) A technical revolution occurred in 1985 with the development of the PCR (Saiki et al., 1985), and the employment of PCR has had arguably the greatest impact on advancing bacterial characterization. The majority of reviews on molecular detection of plant pathogenic bacteria (Martin et al., 2000; Van Sluys et al., 2002; Schaad et al., 2003; Alvarez, 2004) report the extensive use of PCR (in some form) for detection and characterization (usually in combination with one or more other techniques), and the PCR oligonucleotide primer sequences used for these various studies have been comprehensively described (Louws et al., 1999). Comparison of the intrinsic variability amongst DNA provides high discriminatory power for characterization of bacterial species using PCR. DNA targets may be loosely defined as: (i) small conserved noncoding regions that are distributed within a genome (e.g. rep-PCR); (ii) conserved coding regions that are required for basic bacterial growth (the so-called housekeeping genes); or (iii) regions of DNA that are unique within a given target organism (e.g. virulence genes). Modifications of PCR for enhanced characterization Modifications of standard PCR have been developed (Erlich et al., 1991) and adapted to enable specific application for enhanced bacterial characterization including reverse transcription–PCR (RT–PCR), nested PCR, multiplex PCR and quantitative PCR. RT–PCR was developed to amplify RNA targets which are first converted to complementary DNA (cDNA) by reverse transcription and then amplified by PCR. Although reverse transcription reactions may be fastidious, RT–PCR is a powerful technique that enables detection and comparison of

transcription that indicates actively expressed products rather than non-coding DNA regions (which is especially important in characterization of virulence factors). One example of the power of this technique was analysis of environmental conditions affecting cblA expression, which encodes the major pilin of the pathogenicity factor, cable pilus, in Burkholderia cenocepacia (Tomich and Mohr, 2004). Nested PCR is essentially a method used to increase PCR sensitivity and uses two sets of amplification primers in a twostage process. The first set produces an amplicon and the second set is specific for an internal sequence from this amplicon. Nested PCR is extremely useful for amplifying non-enriched environmental samples in which smaller quantities of the target are available (e.g. identification of the potato ring rot pathogen, Clavibacter michiganensis subsp. sepedonicus (Lee et al., 1997)) and, additionally, detection using this method is more accurate because the second primer set verifies the specificity of the first round product. Multiplex PCR is a reaction in which two or more sets of primer pairs, specific for different targets, are used in the same reaction to amplify multiple targets at the same time. Such techniques need much validation and attention paid to similar annealing temperatures of primers, specificity of amplicons and reproducibility within heterogeneous samples of varying target concentration. Multiplex PCR is used in diagnostics for multiple pathogens from a single specimen (e.g. R. solanacearum cells have been distinguished within a mixed population (Weller et al., 2000)). Quantitative PCR can be used to quantify the amount of target DNA or RNA in a given sample. It is a variant of multiplex PCR in which labelled probes bind to a specific target within two primer-binding sites. Amplification of the target region will involve oligonucleotide primer extension, which removes a labelled probe, thus emitting a fluorescent signal. The detected intensity of the fluorescent signal can be correlated to the number of copies of template DNA.




As bacterial thresholds are important in the establishment of many diseases, quantification of a given pathogen may provide valuable insight into disease epidemiology (e.g. R. solanacearum (Weller et al., 2000)) as well as allowing the monitoring of gene expression in vivo (e.g. Thwaites et al., 2004). Quantitative PCR is a method that is in its infancy due to limitations such as high cost of the machinery and labelled probes, and difficulties in obtaining consistent template quality, reproducible results and having sufficient internal controls. However, it continues to be refined and has found an application in diagnostics (Cubero et al., 2001). Broad range PCR ‘Broad range PCR’ is a term that encompasses PCR approaches that use universal primer sets to amplify rapidly target regions that may then be characterized further by various methods including: direct sequence analysis of conserved coding regions (such as 16S rDNA comparisons); electrophoresis comparison of banding patterns (fingerprinting); analysis of fragments after restriction endonuclease digestion (restriction fragment length polymorphism (RFLP)); or nucleic acid probe hybridization. 16S rRNA gene analysis – an example of a conserved genomic region Bacterial progeny that have evolved from a common ancestor usually have highly conserved DNA coding regions critical for survival (encoding housekeeping enzymes). There is strong selection pressure to maintain these essential genes, otherwise mutations may lead to loss of function, and cell death (extinction). Therefore, variation in such genes accumulates very slowly within a given population, and mutations are usually selectively neutral. Of the housekeeping genes, nucleotide sequence analysis and comparison of the 16S rRNA gene has long been considered an effective method for defining prokaryotic genotypic relatedness and resolving taxonomic identities (Head et al., 1998).

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Critical to characterization are regions in the 16S rRNA genes that evolve more rapidly than the surrounding rDNA because they are subject to less functional selective pressures, and these positions may vary in nucleotide composition (hypervariable regions). Not only are hypervariable regions especially useful for reflecting the inferred intrageneric lineages and discerning strains within a given genus, but they also define a unique DNA sequence that many studies have utilized as targets for species-specific PCR detection. Analysis of multiple coding regions for increased discrimination Many other housekeeping genes have been targeted including rpoD and gyrB (Yamamoto et al., 2000), which have allowed a polyphasic analysis and new levels of phylogenetic resolution. This study highlights the fact that phylogenetic assignment based on data from one housekeeping gene alone can be misleading and, consequently, multilocus sequence typing (MLST) has been developed to allow the cumulative comparison of multiple DNA nucleotide sequences for various loci (usually seven genes). This technique has been used to examine human pathogens, and it is also being applied to plant pathogens (e.g. Sarkar and Guttman, 2004) as well as potential pathogens of plants and humans (Curran et al., 2004). Denaturing gradient gel electrophoresis (DGGE) DGGE is a powerful PCR-based technique targeting the 16S rRNA gene used for analysing samples that contain multiple species of bacteria; it is reviewed elsewhere (see O’Callaghan et al., Chapter 6 this volume). Although DGGE has attracted the greatest interest in its application to show changes in the composition of bacterial species within environmental samples, it may also allow isolation of selected amplicons that can be sequenced to obtain species identification (especially for analysis of non-culturable or fastidious bacterial species) and, depending on the novelty of the



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nucleotide sequence, the design of speciesspecific DNA hybridization methods. PCR-based genomic fingerprinting Using universal primers with multiple binding sites, PCR can generate an array of DNA amplicons frequently referred to as a genomic fingerprint, which is of great assistance in bacterial taxonomy (Vaneechoutte, 1996). PCR-based genomic fingerprinting enables comparison of unknown with known isolates by assessment of global variation of genomes (rather than focused targets) by amplification of either specific or arbitrary nucleotide sequences. Genomic fingerprinting continues to be applied with the confidence that results complement phenotype data (Toth et al., 1999). Additionally, selected bands in genomic fingerprinting profiles can be used to obtain unique targets for PCR primers or hybridization probes. REP/ERIC/BOX PCR. REP elements are short intergenic repeated sequences located in distinct positions around the genome of most Gram-negative and several Gram-positive bacteria. They contain highly conserved central inverted repeats and can be divided into three classes: repetitive extragenic palindromic (REP) sequences, enterobacterial repetitive intergenic consensus (ERIC) sequences and BOX elements (Versalovic et al., 1998). PCR oligonucleotide primers designed to target the inverted repeats provide selective amplification of distinct genomic regions distributed between REP, ERIC or BOX elements (rep-PCR genomic fingerprinting) that are separated by electrophoresis. Computer-assisted fingerprint analysis of high quality data can be performed using commercial software packages for resolution of closely related (sub) species, pathovars or strains (Rademaker and De Bruijn, 1997). However, between genera, it is virtually impossible to obtain information on phylogenetic relationships using rep-PCR, and therefore characterization is required, at least to the genus level, before applying this technique. Many previous studies have used rep-PCR genomic fingerprints generated from bacterial plant pathogens to

permit differentiation to the species, subspecies and strain level (e.g. Erwinia amylovora (McManus and Jones, 1995)). RANDOM AMPLIFIED POLYMORPHIC DNA ANALYSIS (RAPD) AND ARBITRARILY PRIMED PCR (AP-PCR).

RAPD and AP-PCR both involve the use of short (usually 10–15 bp), arbitrarily chosen oligonucleotide primers to generate variable sized PCR fingerprints under low stringency conditions (Welsh and McClelland, 1990). RAPD is considered useful for determination of epidemiological relatedness between two isolates of the same species, such as E. amylovora (Momol et al., 1997). Like rep-PCR, its efficacy in identifying an unknown bacterial isolate is questionable because of reproducibility concerns, and comparison with known isolates is essential. AMPLIFIED FRAGMENT LENGTH POLYMORPHISM (AFLP). AFLP enables analysis of genetic relationships by detecting restriction site polymorphisms within (and between) populations. AFLP involves: (i) the digestion of DNA with selected restriction endonucleases; (ii) subsequent ligation of corresponding site-specific adaptors that function as priming sites; and (iii) amplification of fragments using primers complementary to the ligated adaptors. Amplified products are electrophoretically separated by (generally) polyacrylamide gel electrophoresis, which provides a high degree of data resolution for effective genomic characterization for organisms such as Xanthomonas axonopodis pv. manihotis (Restrepo et al., 1999) and Erwinia carotovora (Avrova et al., 2002). However, AFLP analysis cannot be performed on mixed cultures; its robustness as a multiple locus fingerprinting technique has been shown to compare with wellestablished genotypic (DNA–DNA hybridization) and chemotaxonomic (cellular fatty acid analysis) methods (Janssen et al., 1996).

Non-PCR-based molecular analyses Many non-PCR-based techniques exist that can detect genetic differences between various bacterial strains and are therefore




equally useful tools for characterization and epidemiological studies. However, most usually take significantly longer to complete than PCR-based methods and are less sensitive. Restriction fragment length polymorphism (RFLP) RFLP generates DNA profiles resulting from the digestion of target DNA fragments with restriction endonucleases. It detects restriction site polymorphisms between targeted DNA from different bacterial strains, resulting in different banding profiles when visualized by electrophoresis. This technique is highly reproducible and accurate when analysing short, defined genomic regions (Hartung and Civerolo, 1989); however, as direct nucleotide sequencing has become routine, smaller regions of DNA can be analysed by nucleotide sequence comparison more economically, and in most cases more accurately, than by RFLP. Amplified rDNA restriction analysis (ARDRA) combines 16S rDNA PCR amplification and RFLP to classify bacterial isolates to the genus and (sometimes) species level. ARDRA has been used in the analysis of mixed bacterial populations from different environments. Although it gives little or no information about the type of microorganisms present in the sample, it can be used for a quick assessment of diversity and genotypic relatedness between isolates, as in the case of its application to xanthomonads (Nesme et al., 1995).

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nomenclature are used. A limitation of PFGE is the time it takes to perform (up to 8 days), and accuracy of analysis is only as good as the number of closely related bacterial species available for comparison. However, once methodologies are established, PFGE provides highly definitive data that enable effective bacterial strain characterization and has the added benefit that it may be combined with Southern transfer and hybridization for identification of targeted DNA regions (such as virulence factors).

Multilocus enzyme electrophoresis (MLEE) MLEE involves the electrophoretic separation, under non-denaturing conditions, of functional housekeeping enzymes. Nonsilent nucleotide substitutions in genes encoding functionally similar enzymes may alter enzyme structure (molecular mass, net charge, or both), thereby affecting electrophoretic migration, which can be assessed by MLEE. MLEE has been applied with success in previous studies of fluorescent pseudomonad populations within an evolutionary framework (Haubold and Rainey, 1996) and may be used to exclude or infer species relatedness. Although only a small number of alleles can be identified within a bacterial population by using this type of variation, high levels of discrimination are achieved by analysing many different loci (Maiden et al., 1998) even though many mutations that do not affect migration or enzyme activity may exist.

Pulsed-field gel electrophoresis (PFGE)

Plasmid analysis

RFLP is of less use when attempting to resolve and analyse complex banding profiles consisting of multiple fragments from larger DNA elements (such as chromosomes). For such analyses, PFGE is used for efficient separation of very large DNA fragments to provide accurate comparison of purified isolates with reference strains. In order to compare the epidemiological data from different laboratories and obtain a global perspective, it is imperative that a standardized procedure and appropriate

Plasmids often encode genes related to certain virulence factors (Dreier et al., 1997) and, because they are mobile, extrachromosomal elements can be spontaneously lost or readily acquired by a host strain. The presence/ absence of plasmids and subsequent analysis of plasmid profiling (such as RFLP) can prove a useful characterization tool in distinguishing pathovars (Pruvost et al., 1992). Additionally, plasmids often encode virulence factors involved in pathogenicity, and detailed analysis may aid epidemiology



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studies in determining virulence transfer amongst populations. DNA–DNA hybridization DNA–DNA homology (or DNA–DNA reassociation kinetics) has been used to compare pairs of bacterial isolates, and total genomic DNA–DNA homology was considered to be a major determinant of bacterial species (Wayne et al., 1987).

Other molecular methods Although the above molecular methods are commonly applied to bacterial characterization, there exist many other techniques with varying efficacy. A potentially rapid technique for identifying gene differences between microbial strains is subtractive hybridization (Brown and Curtiss, 1996); however, it is technically demanding and of limited application unless it incorporates a PCR amplification capability, which can endow it with the power to identify small differences between bacteria with highly homologous genomes (Cooper et al., 1998). Another PCR-based subtraction method, called suppression subtractive hybridization (SSH), has also been used for rapid identification of differences amongst bacterial strains (Akopyants et al., 1998), although its application to plant pathogens has been limited (e.g. Xylella fastidiosa (Harakava and Gabriel, 2003)). Internally transcribed spacer region PCR (ITS-PCR) uses conserved primers targeting the 16S and 23S ribosomal genes to amplify the ITS region that includes several tRNA genes and non-coding regions, which are relatively variable because they are under less selection pressure than 16S and 23S themselves. Enhanced levels of discrimination can be achieved using RFLP (Schmidt, 1994). Furthermore, automated ribosomal intergenic spacer analysis (ARISA) fingerprints also target the 16S–23S intergenic spacer region and have been generated from soil samples to provide a highly reproducible and robust method for discriminating

between microbial communities (Ranjard et al., 2001). Terminal RFLP (T-RFLP) analysis is a variant of ARDRA that exploits the fact that the length of the terminal restriction fragment of the 16S rRNA gene is specific to different phylogenetic groups (Liu et al., 1997). For further information on applications of T-RFLP, see Blackwood, Chapter 5 this volume. tRNAs are encoded by conserved genes that are frequently located in the ITS region between 16S and 23S rRNA genes, and primers designed to anneal to the tRNA sequences provide fingerprints that are genus or species specific (Seal et al., 1992). Other techniques used for molecular screens and strain or gene characterization are frequently described, but often not utilized by other research laboratories; some of these are described below. Differential display has been used to recover known and unknown RNA molecules that are differentially expressed, from bacterial and soil samples (Fleming et al., 1998). Another method developed for detection of RNA molecules is AmpliDet, which has been used, for example, to detect as few as 100 Clavibacter michiganensis subsp. sepedonicus cells in complex samples (van Beckhoven et al., 2002). Stender et al. (2001) used peptide nucleic acid (PNA) probes in chemiluminescent in situ hybridization (CISH) to identify species-specific RNA molecules within bacterial mixtures and to identify Pseudomonas aeruginosa and Escherichia coli cells within mixed populations. Artificial neural networks were developed by Chun et al. (1993) to analyse pyrolysis mass spectrum data from three Streptomyces species and aid identification of these bacteria. Malloff et al. (2001) describe a twodimensional DNA electrophoresiscomparative hybridization method to compare the genomes of bacterial strains and determine regions of the genome that have been acquired by horizontal transfer. An interesting DNA analysis method was described by Wong et al. (1996) who used random hexamer oligonucleotides to generate tagged molecules for PCR amplification and contig production.




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Molecular Targeting of Virulence Factors

often responsible for broad host ranges (Tan, 2002).

This section considers the important tasks of identifying the molecular virulence factor(s) involved in bacterial pathogenicity and then utilizing them as the target criteria for characterization. Many of the methods discussed previously in this chapter involve analysis of genetic regions not directly involved with the disease process. To characterize a bacterial isolate as a pathogen correctly, its virulence factors (determinants of pathogenicity) need to be confirmed. Once the methodology is established for their identification, they are commonly used for the definitive characterization of a given bacterial pathogen.

Bacterial pathogenicity is often multifactorial

Determinants of bacterial pathogenicity It is well established that any given disease is a complex interaction between pathogen and host (Alfano and Collmer, 1996), and the mechanisms of bacterial pathogenicity continue to be elucidated. Virulence and pathogenicity genes may be harboured in different replicons (independent replicating units); these may be spread throughout the chromosome(s) or on one or more extrachromosomal elements (plasmids (Jackson et al., 1999)), in specialized areas termed genomic or pathogenicity islands (Arnold et al., 2003) and/or in bacterial viruses integrated in the chromosome. Bacterial diseases of plants involve many phases, each one being important in the ultimate formation and severity of disease, including: proximity to host, motility (Harshey, 2003); chemotaxis (Brencic and Winans, 2005); epiphytic growth (survival in the given environment – stress resistance and competitiveness); adhesion; presence of wounds (created by physical abrasion or a primary colonizing pathogen); vectors (insects, nematodes, water or farmers’ implements); production of virulence factors (Salmond, 1994; Alfano and Collmer, 2004); and complex genetic regulation (Cao et al., 2001; Genin and Boucher, 2004),

For a bacterium to be pathogenic, it must overcome many host defence systems, which usually requires an arsenal of virulence factors that cumulatively contribute to pathogenicity. A good example of the multifactorial nature of virulence is provided by R. solanacearum where the genomic sequence (R. solanacearum GMI1000) has revealed similarity to approximately 200 known or potential virulence factor genes (Salanoubat et al., 2002). R. solanacearum is not only an efficient colonizer of an unusually wide range of host plants (Hayward, 1991) with an ability to adapt to strikingly different nutritional environments, but it also possesses an array of virulence factors that are quantitative, each one adding a degree of pathogen aggressiveness. Many of the known virulence factors are secreted, including an extracellular polysaccharide (EPS I), an endoglucanase and three polygalacturonases (Roberts et al., 1988; Schell et al., 1988; Denny and Baek, 1991; Huang and Allen, 1997). A type III secretion system translocates effector proteins of unknown function into host cells in response to pathogen–plant cell contact (Van Gijsegem et al., 1995; Aldon et al., 2000). Bacterial twitching motility (mediated by type-IV pili) and flagellar swimming motility also contribute to virulence (Liu et al., 2001). Additionally, expression of these factors is controlled by complex regulatory cascades that respond to bacterial cell density (Schell, 2000).

Characterization of bacterial virulence factors Analysis and comparison of virulence genes provide an overall understanding of the epidemiology of a given disease and its genetic regulation, as well as furnishing a means of identifying a subset of important parameters when characterizing plant pathogenic bacteria.



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Bioassays for determining pathogenicity and virulence Essential to understanding bacterial virulence and pathogenicity is the establishment of a bioassay(s) that allows accurate discrimination between pathogenic and non-pathogenic isolates and (preferentially) determination of virulence levels. Ultimately, the best bioassay is one that replicates in planta conditions from which the bacterial pathogen was isolated. However, bioassays that attempt to replicate these most closely are often burdened with experimental difficulties such as: time taken to perform a bioassay; cost of growing crops under horticultural conditions; availability of host germplasm; difficulty in precisely replicating environmental conditions; and less than optimal controls. In order to combat such problems, many studies have resorted to small-scale bioassays which simulate in planta environments and are designed for the rapid, economical identification of numerous pathogenic bacteria. Examples include: direct application of bacteria in planta (e.g. Pseudomonas syringae (Klement, 1963; Jackson et al., 1999)) and the in vitro application of Pseudomonas marginalis to sections of carrot tissue (Godfrey and Marshall, 2002). The development of a bioassay(s) is also critical for the determination of loss of a given phenotype in any mutational study. Mutational studies for gene identification Mutagenesis studies have been invaluable for many years in the elucidation of the genetic basis of other bacterial pathogenic determinants. Many of these have used the classical ‘genes-to-phenotype’ approach to ‘knockout’ genes within bacterial chromosomes and then screen mutants for loss or change of a desired phenotype. Two different approaches can be used. 1. Targeted knockout. This requires a priori knowledge of genes ‘likely’ to be involved in the disease process. These may be determined by comparison of phenotypic and/or epidemiological data with previously identified pathogens; they can then

be directly targeted for knockout (using methods such as those described by Katzen et al., 1999) to determine the genes-tophenotype relationship after screening for changes in the disease symptom in bioassay (see, for example, Ried and Collmer, 1988). 2. Random mutagenesis. This is used when no a priori knowledge is available; genes are randomly mutated and assessed for their involvement in pathogenicity, with no assumptions made regarding the nature of genes involved. The basic premise of random transposon mutagenesis is that a target organism can be transformed with a transposon that has the ability to integrate randomly into the genome. If integration occurs within a functional gene, the gene will be inactivated and (depending on bioassay efficiency) can be identified, isolated and the insertion site determined by sequence analysis of regions flanking the integrated transposon. A fully annotated genome sequence facilitates a more efficient mutagenic screen for identification of target insertion sites (Fraser et al., 2002). The genetic complexity of pathogenicity means that transposon mutagenesis is likely to result in the generation of many mutants with altered disease capabilities and, as a consequence, it will identify individual candidate genes involved in virulence whose cumulative expression represents total pathogenicity. As virulence is largely dependent on a bacterium’s ability to compete successfully on the host for limited nutrients and dominance in a given niche, many mutations in general physiological processes will be detected in bioassay with apparently reduced virulence. Therefore, it is important to perform many comparative tests between mutants and the wild-type strains (such as growth rates, competition assays, as well as sequence identification) to distinguish mutants with physiological defects from those with reduced virulence. A cautionary consideration is that mutagenesis has limitations if there is functional redundancy of virulence factors (i.e. when two or more distinct virulence factors carry out similar roles, such that knockout of one gene does not reveal any obvious change in phenotype).




Studies using transposon mutagenesis have been carried out to examine the molecular regulation of extracellular pectate lyase enzyme production (Liao et al., 1994), joint regulation of lipases and proteases in Pseudomonas brassicacearum (Chabeaud et al., 2001) and E. carotovora subsp. carotovora (Marits et al., 1999), and type III secretion in R. solanacearum (Cunnac et al., 2004). Promoter trapping Promoter trapping has been implemented in many studies for various applications (Rainey and Preston, 2000), including identification of niche-specific genes (see Rediers and DeMot, Chapter 4 this volume). It aims to identify genes that are upregulated in a given environment. Many studies have used promotertrapping methodologies to identify genetic regions of bacteria involved in colonization of plants (e.g. Erwinia chrysanthemi 3937; Yang et al., 2004; Zhang et al., 2004; Marco et al., 2005), as well as for investigating general bacterial pathogenicity (Chiang et al., 1999). Therefore, this technique offers another way of identifying promoters (and subsequently genes) involved in virulence. Using specific DNA targets to develop characterization methodology Once a unique target has been identified, it may be utilized for downstream characterization of identity, geographical distribution and population diversity. This may be achieved by designing oligonucleotide primers for species-specific PCR amplification or by use of an appropriately labelled DNA probe in hybridization experiments. SPECIES-SPECIFIC PCR AMPLIFICATION. Numerous publications have reported species-specific primers that target unique sequences to allow detection of a given pathogen within a heterogeneous mixture, and these are provided within two extensive reviews (Louws et al., 1999; Alvarez, 2004). Furthermore, multiplex PCR can be used to identify several pathogens simultaneously (Bertolini et al., 2003).

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HYBRIDIZATION METHODS. Nucleic acid probes are capable of identifying bacteria at many levels (genus to pathovars) within various samples (pure culture, isolated colonies (dot-blots) or environmental heterogeneous samples) using Southern hybridization, effective labelling of target probes, and optimizing stringencies. Hybridization and subsequent analysis has provided powerful discrimination when applied to techniques such as ribotyping and fluorescence in situ hybridization (FISH). Ribotyping has been shown to have both taxonomic and epidemiological value (Alwegg and Mayer, 1989) and involves hybridizing probes specific for the 16S or 23S rRNA genes to Southerntransferred restriction-digested DNA. FISH has the added benefit of being able to identify and monitor organisms in the natural environment (Amann et al., 1995); a valuable aid to understanding the epidemiology and disease processes of a given bacterial pathogen. An example is the use of R. solanacearum probes targeted to 23S rRNA to enable direct analysis in potato samples (Wullings et al., 1998).

Discussion Plant pathogenic bacteria represent an enormously diverse and dynamic population in the environment and, as such, they often require multiple complementary tests for effective characterization. The molecular techniques reviewed in this chapter have provided greatly increased resolution for efficient characterization of both known and unknown bacterial plant pathogens. This has allowed continual progress in the taxonomic assignment of species, elucidation of their pathogenicity and virulence factors, and their characterization within environmental samples. Phenotypic characterization methodology Phenotypic non-molecular methods for the identification of bacterial plant pathogens have long been established and are still considered efficient methods of identification. For example, confirmation of known



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bacterial plant pathogens at the genus level may be achieved rapidly using a minimal number of key diagnostic features; subsequent growth on differential and semiselective media may advance presumptive identification (Schaad et al., 2002). Phenotypic analysis is essential for obtaining a putative identification, and both commercially available biochemical assimilation kits (such as API strip tests, BioMerieux Inc.; or GN Microplates, Biolog Inc.) and immunodiagnostic assays can be used for characterization. Although biochemical assays are based on carbon assimilation, the production of enzyme intermediates in these tests may not effectively discriminate between isolates of close relatedness (i.e. at the subspecies/pathovar level); however, they do result in a putative identification to aid further phenotypic and molecular analyses. Immunodiagnostic analysis detects highly specific antigenic molecules on bacterial cell surfaces (such as proteins, lipopolysaccharides and extracellular polysaccharides). These vary greatly and can provide for characterization of genera, species, subspecies and pathovars of bacterial pathogens. The impact of immunodiagnostics on bacterial plant pathogen analysis has been comprehensively reviewed (Alvarez, 2004), and immunological techniques are now used in agglutination assays, enzyme-linked immunosorbent assay (ELISA), western blot, immunofluorescence (IF) or immunofluorescence colony-staining (IFC) and lateral flow devices. Additionally, it is interesting to note that characterization methods vary between the research and commercial sectors. A 2004 survey of commercially available products for identification of human, animal and plant bacterial pathogens showed that of 205 items (diagnostic reagents and kits), 81% were immunodiagnostic tests, 8% were DNA-based tests, 10% bacterial metabolism and 2% ‘others’. Of these, products specific for plant pathogenic bacteria were 94% immunodiagnostic and 6% metabolic with none being DNA based (Alvarez, 2004). In contrast, research publications reviewed over the last 5 years indicate a rapid movement towards DNA-based protocols for

diagnostic purposes and aetiological and epidemiological studies. Of approximately 200 publications, 80% described the use of genomic methods, with < 20% using immunodiagnostic methods (Alvarez, 2004). These observations can be interpreted as a time lag between research and commercial development or that DNA methods may not be currently cost effective, given the necessity for specialized equipment and staff. Whatever the reason, commercial operators are often influenced by time and financial constraints, and their priority is to identify a disease quickly and efficiently in order to expedite implementation of control measures (Stowell and Gelernter, 2001). In addition to biochemical assimilation and immunodiagnostic analyses, two other powerful non-DNA-based molecular phenotypic methods that have provided effective bacterial characterization are sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS–PAGE) and fatty acid methyl ester (FAME) analysis. Electrophoretic separation of outer membrane proteins and proteins from cell lysates using SDS–PAGE has been used to generate characteristic banding patterns or size (mass) bands that allow bacterial strain comparisons to be made (Vauterin et al., 1992). Similarly, FAME utilizes variation in the lipid fractions within cytoplasmic membranes that, when broken down to their constituent parts, can be used as markers for bacterial characterization (see Lipski, Chapter 8 this volume).

Sampling of bacterial pathogens Sampling parameters are very important when developing characterization methodologies, as ultimately sample quality (and quantity) will determine the degree of data resolution. Many methods are only suitable for purified cultures where a high quality and/or concentration of material can be provided (e.g. PFGE, 16S rDNA, RFLP and rep-PCR), while other methods are used for variable environmental samples. The latter may under-represent data because of insufficient material (e.g. DGGE, species-specific




probes) or over-represent the real situation when using ultrasensitive DNA-based methods (e.g. PCR, nested-PCR, enrichment). In terms of sample content, purified bacterial cultures will not be contaminated with other biotic or abiotic inhibitors and there will be unlimited material, but in vitro expression of many traits requires specialized media and there is enhanced opportunity for genetic recombination (loss of plasmid, mutation, reversion, etc.). Moreover, purified cultures are sometimes not an option and environmental samples may provide certain advantages (e.g. no incubation time, in vivo expression of virulence). As with pure cultures, some potential disadvantages need to be considered: a limited number of organisms reduces sensitivity; detection is confined to assays aimed at a specific target; potentially poor condition (quality and/or yield) of target organisms; and the frequent requirement for additional processing and/or enrichment. In all cases, it is important that epidemiological data are collected in relation to initial inoculum density and the threshold cell density required for infection levels before applying characterization and detection methodologies. Many diseases occur only when a pathogen reaches a threshold density; at this point, signal molecules have accumulated sufficiently for virulence factors to be expressed and/or virulence factors are present in sufficient quantity for observation of a disease symptom. One prominent example of the former condition is quorum sensing in which the secretion of small diffusible signal molecules can interact with specific receptors to activate complex regulatory cascades of genes involved in colonization and pathogenicity on the plant surface (von Bodman et al., 2003).

Genetic plasticity of bacterial evolution Molecular techniques have revealed the plasticity of bacterial genomes (Dobrindt and Hacker, 2001) caused by recombinational events that contribute to the complexity of bacterial population genetics (Maynard Smith, 1995). Often, molecular methods (even

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polyphasic approaches) will not account for the acquisition of genes and accessory genetic elements (plasmids, transposons, integrons and phages) by lateral gene transfer (Ochman et al., 2000), classical ‘spontaneous’ mutation (Levin and Bergstrom, 2000) and recombination (Haubold and Rainey, 1996) – all of which are important sources of bacterial evolution and species diversity. Evolution may encompass previously non-pathogenic bacteria within an environmental population acquiring bacterial virulence genes, and this must be considered in analyses (Dobrindt and Hacker, 2001). Horizontal transfer and long-term persistence of acquired genes often suggest that they confer a selective advantage on the recipient (Koonin et al., 2001). This is in part due to evolutionary selective pressures arising when an environment eventually becomes depleted of nutritional resources. At this stage, competition becomes intense and selection will favour any mutant that can gain access to limiting resources (Spiers et al., 2000); for example, if a bacterium can colonize a plant, then it would be maximizing its chances of survival by exploiting an environmental niche that provides many nutrients. Furthermore, if a bacterium has the ability to produce virulence factors that enable greater nutrient acquisition, this will provide it with further evolutionary advantage and niche specialization. Often, such an adaptation equates to production of virulence factors that provide nutrients at the expense of host tissue (and thus a disease symptom is observed).

Significance of PCR for molecular characterization As noted earlier, of all the molecular characterization methods, those involving PCR have most greatly facilitated bacterial characterization. They enable analysis of variation amongst conserved DNA moieties in a population, generation of genomic fingerprinting profiles or amplification of species-specific target sequences. Target DNA can be from coding or non-coding



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regions, non-culturable or fastidious bacteria or environmental samples, and discriminatory power is always greater than that of phenotypic analyses (Young et al., 1990). Despite these recognized advantages, a lack of robustness and reproducibility has been a barrier to the commercial development of PCR-based techniques.

With the recognition of this type of plasticity and the availability of methods which resolve such details, it is suggested that the species concept is in need of revision (Noor, 2002). Until scientists can agree on universally accepted characterization parameters, the process of species identification will remain confined to performing and interpreting the results of current methodologies.

Phylogenetic assignment of bacterial isolates Data sets from individual characterization methods described previously in this chapter can be used to determine the phylogenetic relationships within collections of bacterial strains. Phylogenetic relationships between distantly (genus) or closely (species, subspecies and/or pathovars) related bacteria can be obtained by using computer algorithms for cluster analysis of data sets derived from 16S rDNA sequence, rep-PCR genomic fingerprints, RFLP profiles and PFGE. Polyphasic approaches to characterization have been stressed throughout and, by the same token, caution should be applied when attempting phylogenetic characterization using limited data. For example, 16S rDNA analysis has undoubtedly aided taxonomic resolution, but many studies show discrepancies between phenotypic and genotypic analyses based on single genes: even phenotypically indistinguishable genomovars of Pseudomonas stutzeri contain up to six nucleotide differences within the 16S rRNA gene (Moore et al., 1996), and many phenotypic traits of pseudomonad species do not reflect their phylogenetic relationships (Yamamoto et al., 2000). Such discrepancies will continue to arise until criteria are adequately defined for universal assignment to and definition of a given species (a species concept).

Species determination or detection of virulence factors – which is more important? Many bacterial isolates can be assigned to the same species while exhibiting differing pathogenicity traits (Godfrey et al., 2001a).

Molecular targeting of virulence factors A critical characterization requirement for pathogenic bacteria is the reproducibility of pathogenicity. Although bioassays are ultimately the most efficient means of determining bacterial pathogenicity, they are often time-consuming and expensive, and a move to methods that rapidly identify the genetic regions involved in disease is desirable. When identifying virulence genes, it is important to establish their direct involvement within a given disease before using them as a definitive target for future characterization purposes. The ‘knocking out’ of many genes results in reduced virulence; this may simply indicate a gene’s role in ecological performance (e.g. colonization and/or growth on the host). The understanding of pathogenicity gained from targeted knockouts is consequently limited. A number of methods have been used to analyse genes expressed in bacteria during interaction with a host organism. Signature tagged mutagenesis (STM) and PCR-STM are methods that simplify the identification of mutants that are unable to grow in a particular environment, such as a plant or animal host (Hunt et al., 2004; Lehoux et al., 2004). A modification of a tagged mutagenesis system is immunocapture differential display described by Timms-Wilson et al. (2000). Here, mutants containing a transposon encoding promoterless lacZY reporter genes were screened for environmentally induced genes – these were detected using an anti-LacY antibody to identify surface-expressed LacY, which is detectable only when the reporter genes are themselves expressed in the environment.




Differential fluorescence induction (DFI) has been used in combination with optical trapping to identify environmentally induced promoters in Rhizobium (Allaway et al., 2001). More focused methods have been developed for studying systems or specific genes within bacteria. For example, a computational approach combined with proofof-principle experiments has identified exported proteins (those with N-terminal signal peptides) in P. aeruginosa (Lewenza et al., 2005). Phage display has been used to identify bacterial proteins that interact specifically with host receptors (e.g. the Harpin effector protein (Li et al., 2005)), and quantitative immunofluorescence was used by Kang et al. (1999) to monitor the expression of extracellular polysaccharide by R. solanacearum in tomato plants. Advantageous to the molecular identification of virulence genes is the ability to identify previously described non-pathogenic bacterial species that may have acquired pathogenicity genes (due to recombination events within a population). If characterization methods are based purely on species identification, this class of strains will be overlooked. Previous studies have shown bacterial isolates to exhibit the same pathogenicity trait (and genes), even when they belong to quite distinct bacterial species (Godfrey et al., 2001b). Therefore, PCR methods have been developed to amplify a virulence factor (e.g. the hrp gene fragments from many xanthomonads) for disease diagnosis, but not for species identification (Leite et al., 1994). Conversely, a limitation of targeting virulence genes is that given the recognized rapid plasticity of a bacterial genome, the evolution of a target pathogen is likely to occur within an environmental population (Genin and Boucher, 2004) and virulence genes may undergo nucleotide substitutions, thus altering the specificity of DNA targeting approaches. Furthermore, some nonpathogens (e.g. Pseudomonas fluorescens) carry genes encoding type III protein secretion systems (Preston et al., 2001) and other ‘virulence’ factors, when there appears to be no association of the bacterium with plant disease.

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A way forward for characterization of bacterial plant pathogens The power to discriminate between strains is only as reliable as the precision of the methodology used and the number of representative strains (both closely and distantly related) available for comparison. The added benefit of the methodologies reviewed in this chapter is that they are currently in widespread use, and therefore comparative data are available for rapid characterization of an unknown bacterium. As they continue to increase in power and sophistication, molecular methods will undoubtedly be applied to bacterial characterization to provide an extensive framework for a new era of microbial genetics, classification and identification. One such development is the increasing availability of complete bacterial genome sequences; this has not only greatly complemented the current molecular methodologies by providing insights into the total distribution and order of coding and non-coding regions, but it has also enabled the generation of new molecular tools for rapid, high resolution pathogen detection. Bioinformatic approaches to comparative genome analyses have encouraged the proposal of many theoretical gene regulatory pathways (Bhattacharyya et al., 2002); however, there is an ever-widening gap between theoretical and biologically relevant investigations. With the arrival of comprehensive genome annotations, there is a demand for techniques that look beyond individual genes and seek to understand global regulation of biological processes such as pathogenicity. Methods that facilitate this endeavour are now becoming established. Promoter trapping, or in vivo expression technology (IVET) as it is often referred to, is one method that is currently being used and refined to gain biologically relevant insights into the complexity of bacterial gene regulation and expression in a range of habitats (Rediers et al., 2005; Rediers and De Mot, Chapter 4 this volume). Microarray analysis (see Loy et al., Chapter 2 this volume) is another technique that is starting to provide invaluable information about comprehensive genetic changes



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within defined environments (Hinds et al., 2002). Microarrays are now being employed for gene discovery as well as for the analysis of gene expression (e.g. Xylella fastidiosa was shown to acquire several horizontally acquired elements using microarrays (Nunes et al., 2003)). Proteomics is also gaining in popularity for examining changes in the total expressed protein complement of an organism (Graves and Haystead, 2002), and its suitability for application to the plant

pathogen X. axonopodis has been demonstrated (Tahara et al., 2003). The challenges we are now faced with are: exploiting genome-based approaches to provide superior characterization methods, obtaining an extensive global understanding of the multiple determinants of bacterial plant pathogenicity and establishing methodology that will ultimately fulfil criteria for a universally accepted bacterial species concept.

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Index

Allorhizobium see Legume root nodule bacteria Ammonia-oxidizing bacteria (AOB) 6, 9–10, 11, 29, 30, 106, 147 see also Nitrifying bacteria Amplified fragment length polymorphism (AFLP) 204, 215, 240, 251, 252, 253, 276 Amplified rDNA restriction analysis (ARDRA) 277, 278 ANNAMOX process 2, 29 Arabidopsis thaliana mutant gene library URL 125 Arbitrarily primed (AP)-PCR 55, 251, 276 Arbuscular mycorrhizal (AM) fungi 101, 107–108, 124, 198–209, 213–215, 221–223, 225–227 cDNA libraries 214–215, 223–227 ecology of 202–204 expressed sequence tags (ESTs) 215, 222, 226 host plant gene expression 223–225 host plant ESTs 225 host specificity in 203–204 model genome sequences 226 phylogenetics 200–201, 206–209 population studies 204 rRNA genes (rDNA) 199–202 PCR primers for 200, 201–202, 205 ribosomal repeat unit of 200 sequence analysis of 205–206 sequence heterogeneity in 199–200 symbiotic development 223 transcriptomics 214–215, 221–223, 225–227 methods 214–215

Automated ribosomal intergenic spacer analysis (ARISA) 278 Azorhizobium see Legume root nodule bacteria

Biogeochemical cycles 1–17, 132–133 functional gene targets in 5 non-genomic methodologies for study of 2–4 summary of genomic approaches for study of 6, 13 Bioinformatics 46–47 BIOLOG plate method 2, 99, 122, 126, 241, 282 Bioremediation 49, 79, 213 Bradyrhizobium see Legume root nodule bacteria Broad range PCR 275

Chemiluminescent in situ hybridization (CISH) 278 Competitive PCR (cPCR) 6, 8, 13, 156, 158, 191

Denaturing gradient gel electrophoresis (DGGE) 6, 7, 94, 95, 99–111, 117, 123, 126, 148, 153–154, 170, 173, 175, 176, 185, 187–188, 189, 190, 201, 275–276, 282 data analysis 103–104 limitations of 109–110 methods 100–103 PCR primers and target microorganisms 102 for phylloplane microflora 104

293



294

Index

DGGE continued for rhizosphere and root populations 104–109 bacteria 104–107 fungi and archaea 108–109 mycorrhizae 107–108 Denitrifying bacteria 31, 146–161 community analysis 148, 158–160 immunological approaches to 158–159, 161 ecology of 159–160 see also Denitrification genes Denitrification enzymes 147, 159 Denitrification genes 6–7, 9, 10, 31, 88, 147, 149–160 clone libraries of 153 microarrays for assessing diversity and density of 158 PCR primers for 149–152 polymorphism of 152–156 by DGGE 153–154 by restriction fragment length polymorphism (RFLP) 154–155 by terminal (T)-RFLP 155–156 real-time PCR primers for 157 2,4-Diacetylphloroglucinol 116, 117, 118, 119, 120, 121, 122 Differential display reverse transcription PCR (DDRT-PCR) 8, 215, 220, 223–224, 226 Differential fluorescence induction (DFI) 55, 59, 60, 267, 285 DNA amplification fingerprinting (DAF) 251

Ectomycorrhizal (ECM) fungi 124, 183–194, 214–221, 227 cDNA libraries 214–215, 216–220, 227 community profiling 185–189 clone libraries for 188–189 by DGGE/TGGE 185, 187–188, 189 by internal transcribed spacer (ITS)-PCR RFLP analysis 183–184, 185, 189, 192, 194 by T-RFLP 185, 186–187, 189 expressed sequence tags (ESTs) of 215, 217, 219, 220, 221 interactions with host plants 191–192 ITS-PCR primers for 185–186 macro/microarrays for 193 microsatellite markers for 192 morphotyping of 184–185 phylogenetic analysis 189–190 quantification of 190–191 symbiotic development 216 transcriptomics 214–221, 227 methods 214–215 Erwinia chrysanthemi 56, 58, 59, 61–72, 75, 281

Fatty acid methyl ester (FAME) analysis 3–4, 132–143, 239, 241, 282 methods 134–138 see also Phospholipid fatty acid (PLFA) analysis, Stable isotope probing/ labelling (SIP), Sulphur- and iron-oxidizing autotrophic bacteria Fluorescence-activated cell sorting (FACS) 45, 46 Fluorescence in situ hybridization (FISH) 6, 9–10, 44, 45, 88, 123, 281 FISH-microautoradiography (MAR) 10, 13, 123, 126 Function unknown (FUN) genes 72–74, 79

Glomeromycota see Arbuscular mycorrhizal (AM) fungi Green fluorescent protein (GFP) 58, 59, 122, 260, 263

Habitat-inducible rescue of survival (HIRS) 59

Immunocapture differential display 284 see also Differential display reverse transcription PCR (DDRT-PCR) In vivo expression technology (IVET) 55–83, 123–124, 126, 267, 285 bacterial genes isolated by 61–72 benefits and shortcomings of 60 methodologies 56–60 gene selection strategies 56–59 recombinase-based IVET (RIVET) 58–59 for scrutinizing plant disease 75–76 Iron oxidation mechanisms see Sulphur and iron oxidation mechanisms Iron-oxidizing autotrophs see Sulphur- and iron-oxidizing autotrophic bacteria Isotope array see Microarrays, rRNA-based oligonucleotide arrays (PhyloChips) Isotope-labelled substrate utilization 36

Leaf surface microorganisms see Denaturing gradient gel electrophoresis (DGGE), for phylloplane microflora Legume root nodule bacteria 124–125, 236–253, 259–267 characterizing strains of 240–243 by FAME 241 by intrinsic antibiotic resistance 243 by numerical taxonomy 241 by PCR-RFLP of 16S and 23S ribosomal genes 240–241, 242, 244, 253



Index

by phage typing 243 by SDS-PAGE 241, 243 genera and species of 236–238 genomic polymorphisms 251–253 using rep-PCR 251 using AFLP 251, 252 grouping strains of 243–253 by ITS PCR-RFLP 243–245, 246 by PCR-RFLP of housekeeping genes 245, 247 by PCR-RFLP of symbiotic genes 245, 248–250 nodulating competitiveness of 260, 266–267 persistence in soils of 266 plasmid profiling of 252–253 reporter gene constructs/systems for 264–265 reporter/marker genes for 260–267 celB 262–263, 265 gfp 261, 263, 265, 266 gusA 261–262, 263, 265, 267 lacZ 260–261, 262, 266 luc 261 luxAB 261, 265, 266 phoA 261 xylE 261 root colonization by 266 see also Sinorhizobium meliloti Large insert library (LIL)-FISH 44 Length heterogeneity-PCR (LH-PCR) 94

295

limitations of 124 minimum information about a microarray experiment (MIAME) 124 rRNA-based oligonucleotide arrays (PhyloChips) 19, 20, 23–31 applications in functional community analysis 29, 31 applications in soil ecology 27–29 methods 24–27 PCR primers for amplifying 16S rRNA genes 25 sensitivity and specificity of 21–22 Model plant genome sequences 226 Multilocus enzyme electrophoresis (MLEE) 253, 277 Multilocus sequence typing (MLST) 275 Multiplex PCR 274, 281 Mycorrhizal fungi see Arbuscular mycorrhizal (AM) fungi, Ectomycorrhizal fungi

Niche-specific genes see in vitro expression technology (IVET) nifH 5, 6, 12, 10, 31, 88, 106 Nitrification inhibitors 29 Nitrifying bacteria 2, 6, 8, 9, 10, 11, 24, 29, 30 Nitrite-oxidizing bacteria (NOB) see Nitrifying bacteria

Oligonucleotide microarrays see Microarrays Macroarrays 6, 10–11, 193 Mesorhizobium see Legume root nodule bacteria Metabolomics 124, 193 Metagenomics 12, 13, 42–54, 193 for assigning function to soil microorganisms 49–50 metagenome-derived biocatalysts 47, 48–49 metagenome-derived therapeutics 47 methodology 43–47 Metatranscriptome analysis 36 Methane-oxidizing bacteria (MOB) 32, 33–34, 35 Methylotrophs 6, 11 Microarrays 6, 13, 18–41, 124, 126, 158, 193, 220, 267, 285–286 combined plant-Sinorhizobium meliloti microarray 124 community genome arrays (CGAs) 19, 22–23 data analysis 22 functional gene arrays (FGAs) 19, 20, 31–35 applications in soil ecology 33–35 methods 32–33 general methodology 19–21

PCR-DGGE see Denaturing gradient gel electrophoresis (DGGE) Phospholipid fatty acid (PLFA) analysis 123, 126, 137–138, 139–143, 190 methods 137–138 see also Fatty acid methyl ester (FAME) analysis, Stable isotope probing/ labelling (SIP), Sulphur- and iron-oxidizing autotrophic bacteria PhyloChips see Microarrays, rRNA-based oligonucleotide microarrays Plant growth-promoting rhizobacteria (PGPR) 76–78, 79, 116–126, 167–168 antibiotic synthesis by 118–122 functional diversity of 122–123 generating mutant strains of 118 isolating strains of 117–118 Plant pathogenic bacteria 75–76, 272–286 characterization 273–278 by non-PCR based methods 276–278 by PCR based methods 274–276, 278 sampling parameters for 282–283 significance of PCR for 283–284 value of ITS-PCR for 278



296

Index

Plant pathogenic bacteria continued mutational studies 280–281, 284 by random mutagenesis for 280 by targetted knockout 280 virulence factors of 279–281, 284–285 see also Erwinia chrysanthemi, Pseudomonas putida, Pseudomonas stutzeri, Pseudomonas syringae, Ralstonia solanacearum, Xanthomonas Promoter trapping 281, 285 see also in vitro expression technology (IVET) Potato 104, 105, 109, 110 Proteomics 13, 14, 124–125, 193, 286 Pseudomonas aeruginosa complete genome sequence URL 124 mutant gene library URL 125 Pseudomonas fluorescens 56, 61–72, 73, 76, 77, 78, 106, 107, 119, 120, 122, 123, 124, 225, 285 complete genome sequence URL 124 Pseudomonas putida 56, 58, 61–72, 73, 76 complete genome sequence URL 124 Pseudomonas stutzeri 56, 58, 60, 61–69, 73, 76, 77, 78, 284 Pseudomonas syringae 56, 58, 59, 63, 65–72, 75, 76, 78, 280 complete genome sequence URL 124 Pulsed field gel electrophoresis (PFGE) 239, 251, 277, 282, 284

Quantitative PCR 8–9, 36, 274–275

Ralstonia solanacearum 56, 61–72, 73, 75, 77, 273, 274, 275, 279, 281, 285 Real-time PCR 6, 8–9, 13, 34, 36, 156–158, 191 Repetitive sequence-based PCR (rep-PCR) 173, 240, 251, 253, 274, 276, 282, 284 Reverse sample genome probing (RSGP) 22 Rhizobia see Legume root nodule bacteria Rhizobium see Legume root nodule bacteria Ribosomal intergenic spacer analysis (RISA) 94 RNA arbitrarily primed (RAP)-PCR 8, 55 RNA interference technology (RNAi) 214 RubisCO 7–8

Sargasso Sea metagenome 46, 49 Scanning electron microscopy (SEM) 176–178 Selective capture of transcribed sequences (SCOTS) 55 Signature-tagged mutagenesis (STM) 55, 78, 284 Single strand conformation polymorphism (SSCP) 110, 123, 187, 201, 204

Sinorhizobium meliloti 9, 56, 59, 64, 66, 67, 72, 74, 124, 225, 238, 252, 266 symbiotic plasmids of 238 see also Legume root nodule bacteria Sodium dodecyl sulphate-polyacrilamide gel electrophoresis (SDS-PAGE) 239, 241, 282 Stable isotope probing/labelling (SIP) 6, 11–12, 13, 50, 117, 123, 126, 132–143, 193–194 methods 138–139 see also Fatty acid methyl ester (FAME) analysis, Phospholipid fatty acid (PLFA) analysis, Sulphur- and iron-oxidizing autotrophic bacteria Streptomyces 46, 166–178 biocontrol properties 168–170 community diversity analysis 172–176 by DGGE 175, 176 by RFLP 173–175 culture media for 168–170 gene sequence analysis 171–172 PCR primers for housekeeping genes 174 PCR primers for 16S rRNA genes 173 plant growth promotion by 167–168 as plant pathogens 272, 278 root colonization by 175–178 scanning electron micrographs (SEMs) of 177 Substrate-induced gene expression screening (SIGEX) 46 Substrate-induced respiration (SIR) 2 Subtractive hybridization 55, 224, 226, 278 see also Suppressive/suppression subtraction hybridization (SSH) Sulphate-reducing prokaryotes (SRPs) 6, 24, 27–29 Sulphur and iron oxidation mechanisms 132–133 Sulphur- and iron-oxidizing autotrophic bacteria 2, 3, 133–143 in acid mining lake sediment 139–142 diversity and properties of 133–134 fatty acid profiles of 134, 135–137 in hydromorphic gleysol soil 142, 143 Suppressive/suppression subtraction hybridization (SSH) 215, 220, 225, 226, 278 Symbiosis see Arbuscular mycorrhizal (AM) fungi, Ectomycorrhizal fungi, Legume root nodule bacteria, Plant growth-promoting rhizobacteria (PGPR), Sinorhizobium meliloti

TaqMan-PCR 8, 13 Temperature gradient gel electrophoresis (TGGE) 100, 117, 123, 187–188, 190



Index

Terminal restriction fragment length polymorphism (T-RFLP) 5–7, 84–95, 123, 126, 148, 153, 155–156, 159, 185, 186–187, 189, 190, 201, 278 advantages, drawbacks and extensions of 94–95 for analysis of functional groups 88 for community analysis 86–88, 92–93 data processing and analysis 91–94 methods 89–91 Transcriptomics 34, 124, 126, 213–227

297

Two primers random amplified polymorphic DNA (TP-RAPD) 251 Type III secretion system (TTSS) 59, 75, 78, 123, 279, 281, 285

Unculturable microorganisms see Metagenomics

Xanthomonas 56, 58, 170, 272, 273, 276, 285, 286


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