Plant Nutritional Genomics (Biological Sciences Series)

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Plant Nutritional Genomics

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Biological Sciences Series A series which provides an accessible source of information at research and professional level in chosen sectors of the biological sciences. Series Editor: Professor Jeremy A. Roberts, Plant Science Division, School of Biosciences, University of Nottingham. UK. Titles in the series:

Biology of Farmed Fish Edited by K.D. Black and A.D. Pickering Stress Physiology in Animals Edited by P.H.M. Balm Seed Technology and its Biological Basis Edited by M. Black and J.D. Bewley Leaf Development and Canopy Growth Edited by B. Marshall and J.A. Roberts Environmental Impacts of Aquaculture Edited by K.D. Black Herbicides and their Mechanisms of Action Edited by A.H. Cobb and R.C. Kirkwood The Plant Cell Cycle and its Interfaces Edited by D. Francis Meristematic Tissues in Plant Growth and Development Edited by M.T. McManus and B.E. Veit Fruit Quality and its Biological Basis Edited by M. Knee Pectins and their Manipulation Edited by Graham B. Seymour and J. Paul Knox Wood Quality and its Biological Basis Edited by J.R. Barnett and G. Jeronimidis Plant Molecular Breeding Edited by H.J. Newbury Biogeochemistry of Marine Systems Edited by K.D. Black and G. Shimmield Programmed Cell Death in Plants Edited by J. Gray Water Use Efficiency in Plant Biology Edited by M.A. Bacon Plant Lipids – Biology, Utilisation and Manipulation Edited by D.J. Murphy Plant Nutritional Genomics Edited by M.R. Broadley and P.J. White

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Plant Nutritional Genomics Edited by MARTIN R. BROADLEY, Plant Sciences Division, School of Biosciences, University of Nottingham, UK and PHILIP J. WHITE. Warwick HRI, University of Warwick, Wellesbourne, Warwick, UK

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c 2005 by Blackwell Publishing Ltd Editorial offices: Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK Tel: +44 (0) 1865 776868 Blackwell Publishing Asia Pty Ltd, 550 Swanston Street, Carlton, Victoria 3053, Australia Tel: +61 (0)3 8359 1011 ISBN-10 1-4051-2114-9 ISBN-13 978-14051-2114-9 Published in the USA and Canada (only) by CRC Press LLC, 2000 Corporate Blvd., N.W., Boca Raton, FL 33431, USA Orders from the USA and Canada (only) to CRC Press LLC USA and Canada only ISBN 0-8493-2362-2 The right of the Authors to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. First published 2005 Library of Congress Cataloging-in-Publication Data: A catalogue record for this title is available from the Library of Congress British Library Cataloguing-in-Publication Data: A cataloge record for this title is available from the British Library Set in 10.5/12 pt Times by TechBooks Printed and bound in Great Britain by MPG Books Ltd, Bodmin, Cornwall The publisher’s policy is to use permanent paper from mills that operate a sustainable forestry policy, and which has been manufactured from pulp processed using acid-free and elementary chlorine-free practices. Furthermore, the publisher ensures that the text paper and cover board used have met acceptable environmental accreditation standards. For further information on Blackwell Publishing, visit our website: www.blackwellpublishing.com

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Contents

Contributors Preface 1

Nitrogen

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FRANÇOISE DANIEL-VEDELE and SYLVAIN CHAILLOU 1.1 Introduction 1.2 Ammonium and nitrate uptake and transport within the plant 1.2.1 Ammonium uptake and transport 1.2.2 Molecular analysis of ammonium uptake 1.2.3 Regulation of ammonium uptake: physiological evidence and molecular basis 1.2.4 Nitrate uptake and transport 1.2.5 Identification of genes coding for nitrate transporters 1.2.5.1 The NRT1 family of transporters 1.2.5.2 The NRT2 family of transporters 1.2.6 Regulation of nitrate influx and the role of NRT1 and NRT2 genes 1.3 Nitrogen assimilation 1.3.1 Nitrate reduction 1.3.2 Ammonium assimilation 1.3.2.1 The GS/GOGAT cycle 1.3.2.2 Glutamate dehydrogenase (GDH) 1.4 Concluding remarks: the search for new genes 1.4.1 Search for homologues of genes from different organisms 1.4.2 Searches for candidate genes using high throughput screening 1.4.3 Naturally occurring variation References 2

Potassium

1 3 4 5 5 6 7 7 8 10 12 12 13 13 15 16 16 17 17 19 26

´ SABINE ZIMMERMANN and ISABELLE CHEREL 2.1 Introduction 2.2 Physiology of K+ transport

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Functional identification of K+ currents Potassium uptake by roots Potassium distribution in the plant Control of gas exchange by potassium-driven stomatal movements 2.3 Molecular identification of K+ transporters 2.3.1 Shaker-like channels 2.3.2 KCO channel family 2.3.3 KUP/HAK/KT family 2.3.4 K+ /H+ antiporters 2.3.5 Trk/HKT 2.3.6 CNGC family 2.3.7 Redundancy and specificity 2.3.8 From Arabidopsis to grapevine: potassium transport and wine quality 2.4 Regulation of K+ transport 2.4.1 Transcriptional regulation 2.4.1.1 Effects of nutritional status 2.4.1.2 Effect of drought stress and abscisic acid (ABA) 2.4.2 Post-translational regulation 2.5 Conclusions and perspective Acknowledgements References 2.2.1 2.2.2 2.2.3 2.2.4

3

Calcium

27 28 30 30 31 34 36 36 37 37 38 38 39 40 40 43 50 50 53 54 54 66

PHILIP J. WHITE 3.1 3.2 3.3 3.4

4

Introduction Plant species have different calcium requirements Identifying genes involved in calcium accumulation Identifying genes involved in calcium tolerance (protecting the cytosol from an excessive calcium load) 3.5 The genetics of calcium accumulation by plants Acknowledgements References

66 67 73

Sulphur

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78 81 82 82

MALCOLM J. HAWKESFORD 4.1 4.2

Introduction Acquisition of sulphate

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4.3 4.4

The sulphate transporter family Regulation of sulphate transporter expression and sulphate assimilation 4.5 Sulphate assimilation 4.6 Sulphurtransferases and sulphotransferases 4.7 Methionine biosynthesis 4.8 Glutathione 4.9 Nitrogen/sulphur interactions 4.10 Pathogen defence 4.11 Genomic studies 4.12 Outlook Acknowledgements References 5 Phosphorus

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KASHCHANDRA G. RAGHOTHAMA 5.1 Introduction 5.2 Phosphate acquisition is an inducible response in plants 5.2.1 Inducible phosphate acquisition is associated with increased transcription of high-affinity phosphate transporters 5.2.2 How do plants regulate phosphate homeostasis? 5.2.3 Plant root modifications lead to increased phosphate acquisition 5.3 Phosphate transporters 5.3.1 Functional analysis of phosphate transporters 5.3.2 Molecular regulation of phosphate uptake in plants 5.3.3 Global regulation of gene expression during phosphate deficiency 5.4 Perspective: Future genetic approaches to isolate phosphate signaling components Acknowledgements References 6

Sodium

112 112

113 115 116 116 116 117 119 120 121 122 127

HUAZHONG SHI, RAY A. BRESSAN, PAUL M. HASEGAWA and JIAN-KANG ZHU 6.1 Introduction 6.2 Arabidopsis as a model for salt-tolerance research 6.3 sos mutants

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6.4 SOS genes 6.4.1 SOS3 6.4.2 SOS2 6.4.3 SOS1 6.4.4 SOS4 6.4.5 SOS5 6.5 Other genes important for Na+ homeostasis 6.5.1 HKT1 6.5.2 NHX1 6.5.3 H+ pumps 6.6 Cellular Na+ homeostasis and SOS pathway 6.7 Prospects References

129 129 131 134 136 137 138 138 140 142 143 144 145

Mapping links between the genome and ionome in plants

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BRETT LAHNER and DAVID E. SALT

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7.1 7.2 7.3 7.4

Introduction Concept of the ionome Characterization of the plant ionome—A single ion at a time Characterization of the plant ionome—multiple ions at a time 7.4.1 High-throughput ion profiling 7.4.2 Sample preparation 7.4.3 Sample analysis 7.4.4 Potential rate limiting factors 7.4.5 Data handling 7.4.6 Bioinformatics 7.5 Environmental, temporal and spatial ionomics 7.6 Linking the ionome and genome 7.6.1 Forward genetic approaches 7.6.2 Exploiting natural variation 7.6.3 Reverse genetic approaches Acknowledgements References

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Transcriptional profiling of membrane transporters

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FRANS J.M. MAATHUIS and ANNA AMTMANN 8.1 Introduction 8.2 An overview of microarray technology 8.2.1 What microarray studies can do 8.2.2 Gene expression studies 8.2.3 Genomic analyses

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8.3 General aspects of microarray technology 8.3.1 Microarray manufacturing 8.3.2 Experimental design 8.3.3 RNA isolation and labelling 8.4 Transcriptomics data analysis and interpretation 8.4.1 Image analysis 8.4.2 Normalisation 8.4.3 Identifying differentially expressed genes 8.4.4 Gene clustering 8.4.5 Biological interpretation of data 8.5 Transporter transcriptomics 8.5.1 The role of membrane transporters in plant nutrition and stress 8.5.2 Membrane transporter genes 8.5.3 Questions that need an answer 8.5.4 A gene family-based transcriptomics study 8.6 Treatment based studies 8.7 Using publicly available transcriptomics data 8.8 Outlook Acknowledgements References 9 Exploring natural genetic variation to improve plant nutrient content

ix 174 175 175 176 177 177 178 178 179 180 182 183 183 184 185 187 191 193 194 194

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DICK VREUGDENHIL, MARK G.M. AARTS and MAARTEN KOORNNEEF 9.1 Introduction 9.2 The genetic and molecular analysis of natural variation 9.3 Genetic variation for nutrient content and related traits in model species 9.3.1 Arabidopsis 9.3.2 Rice 9.3.3 Heavy metal hyperaccumulating species 9.4 Genetic variation for nutrient content and related traits in crop plants 9.4.1 Wheat 9.4.2 Maize 9.4.3 Bean 9.4.4 Brassica rapa 9.5 Physiological processes underlying micronutrient content 9.6 Transferring knowledge from model to crop species References

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10 Mapping nutritional traits in crop plants

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MATTHIAS WISSUWA 10.1 Introduction 10.2 Objectives in mapping nutritional traits and resulting technical considerations 10.3 Choice of mapping population 10.4 Choice of environment and phenotypic evaluation method 10.5 Design example – mapping of QTLs for tolerance to Zn deficiency in rice 10.5.1 Choice of mapping population 10.5.2 Considerations on screening methods 10.6 Mapping of nutritional traits – just a starting point 10.6.1 Selecting QTLs for further analysis 10.6.2 QTL confirmation and fine mapping 10.6.3 QTLs, related physiological mechanisms and underlying genes 10.7 Case study – mapping of the Pup1 locus in rice 10.7.1 QTL mapping and confirmation 10.7.2 Fine mapping 10.7.3 Toward cloning of Pup1 10.7.4 The use of Pup1 in marker assisted breeding 10.8 Conclusions References 11 Sustainable crop nutrition: constraints and opportunities

220 222 223 223 224 225 226 227 228 228 229 230 230 234 235 237 238 239 242

R. FORD DENISON and E. TOBY KIERS 11.1 Introduction 11.2 Constraint/opportunity 1: conservation of matter 11.3 Constraint/opportunity 2: our crops’ legacy of preagricultural evolution 11.4 Constraint/opportunity 3: conflicts of interest in nutritional symbioses 11.5 A fourth constraint/opportunity: complexity References 12 Methods to improve the crop-delivery of minerals to humans and livestock

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MICHAEL A. GRUSAK and ISMAIL CAKMAK 12.1 Introduction 12.2 Plants as sources of dietary minerals

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12.2.1 Mineral nutrition for humans 12.2.2 Recommended intake versus actual intake in humans 12.2.3 Bioavailability 12.2.4 Mineral nutrition for livestock 12.3 Conceptual strategies for mineral improvement 12.4 Exploiting existing genetic variation 12.4.1 Wheat 12.4.2 Rice 12.4.3 Maize 12.4.4 Bean 12.4.5 Other crops 12.5 Integrating genomic technologies for mineral improvement 12.5.1 The path to gene discovery 12.5.2 The path to improved cultivars 12.6 Future needs Disclaimer Acknowledgements References

13 Use of plants to manage sites contaminated with metals

xi 266 267 268 269 270 271 272 275 275 276 276 277 278 280 281 282 282 282

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STEVEN N. WHITING, ROGER D. REEVES, DAVID G. RICHARDS, MIKE S. JOHNSON, JOHN A. COOKE, FRANÇOIS MALAISSE, ALAN PATON, J. ANDREW C. SMITH, J. SCOTT ANGLE, RUFUS L. CHANEY, ´ BOB ROSANNA GINOCCHIO, TANGUY JAFFRE, JOHNS, TERRY MCINTYRE, O. WILLIAM PURVIS, DAVID E. SALT, HENK SCHAT, FANGJIE ZHAO and ALAN J.M. BAKER 13.1 Introduction 13.1.1 Defining plants that can be used to manage contaminated sites 13.1.2 Evolution of metallophytes on metal-contaminated soils 13.1.3 How are plants exploited in the management of contaminated land? 13.1.4 Stabilizing metal-contaminated soils with vegetation 13.1.5 Ex situ ‘biotech’ applications for metallophytes 13.2 Global status of metallophytes – promoting conservation of a genetic resource 13.2.1 The need for field explorations using an ecological approach 13.2.2 Metallophyte ‘hotspots’

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13.2.3 The need to develop the resource base: databases, germplasm and living collections 13.3 Using metallophytes for the restoration or rehabilitation of mined and disturbed land 13.4 Access to metallophyte genetic resources 13.4.1 Access and benefit sharing 13.4.2 Action required 13.5 Metallophytes as a resource base for phytotechnologies 13.5.1 Phytostabilization 13.5.2 Phytoremediation 13.5.3 Looking to the future 13.6 Genetic modification to ‘design’ metallophytes for use in the remediation of contaminated land 13.6.1 Unravelling metal tolerance 13.6.2 Unravelling metal hyperaccumulation 13.6.2.1 Metal acquisition 13.6.2.2 Physiological dissection of hyperaccumulators 13.6.3 Strategies to develop plants for phytoremediation and restoration 13.6.4 Looking to the future 13.7 Does the prospect of using metallophytes in site remediation and reclamation raise ethical issues? 13.8 Conclusions: the use of metal-tolerant plants to manage contaminated sites Endnotes Acknowledgments References Index

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Contributors

Mark G.M. Aarts

Laboratory of Genetics, Arboretumlaan 4, 6703 BD Wageningen, The Netherlands

Anna Amtmann

Laboratory of Plant Physiology and Biophysics, Bower Building, IBLS, University of Glasgow, Glasgow, G12 8QQ, UK

J. Scott Angle

College of Agriculture and Natural Resources, University of Maryland, MD 20742, USA

Alan J.M. Baker

School of Botany, The University of Melbourne, Victoria 3010, Australia

Ray A. Bressan

Department of Horticulture and Landscape Architecture, Horticulture Building, 625 Agriculture Mall Drive, Purdue University, West Lafayette, IN 47907, USA

Martin R. Broadley

Plant Science Division, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire LE12 5RD, UK

Ismail Cakmak

Faculty of Engineering and Natural Sciences, Sabanci University, 81474 Tuzla, Istanbul, Turkey

Sylvain Chaillou

Plant Nitrogen Nutrition Unit, INRA Versailles, route de St Cyr, 78026 Versailles Cedex, France

Rufus L. Chaney

Animal and Environmental Sciences Laboratory, USDA-ARS, Beltsville, MD 20705, USA

Isabelle Chérel

INRA – Biochimie et Physiologie Moléculaire des Plantes, 1 place Viala, 34060 Montpellier Cedex 1, France

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CONTRIBUTORS

John A. Cooke

School of Life and Environmental Sciences, University of Natal, Durban 4041, South Africa

Fran¸coise Daniel-Vedele

Plant Nitrogen Nutrition Unit, INRA Versailles, Route de St Cyr, 78026 Versailles Cedex, France

R. Ford Denison

Agronomy and Range Science Department, University of California, One Shields Avenue, Davis, CA 95616-8515, USA

Rosanna Ginocchio

CIMM, Av. Parque Antonio Rabat 6500, Vitacura, Santiago, Chile

Michael A. Grusak

Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA

Paul M. Hasegawa

Department of Horticulture and Landscape Architecture, Horticulture Building, 625 Agriculture Mall Drive, Purdue University, West Lafayette, IN 47907, USA

Malcolm J. Hawkesford

Agriculture and the Environment Division, Rothamsted Research, Harpenden, Herts AL5 2JQ, UK

Tanguy Jaffré

Institut de Récherche pour le Developpement (IRD), BP A5, 98848 Nouméa, New Caledonia, Canada

Bob Johns

Royal Botanic Gardens, Kew, Surrey TW9 3AB, UK

Mike S. Johnson

School of Biological Science, University of Liverpool, Liverpool L69 7ZB, UK.

E. Toby Kiers

Agronomy and Range Science Department, University of California, One Shields Avenue, Davis, CA 95616-8515, USA

Maarten Koornneef


Brett Lahner

Department of Horticulture and Landscape Architecture, Purdue University, 625 Agriculture Mall Drive, West Lafayette, IN 47907-2010, USA

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CONTRIBUTORS

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Frans J.M. Maathuis

Department of Biology (Area 9), University of York, York, YO10 5YW, UK

Fran¸cois Malaisse

Laboratoire d’Ecologie, Faculté Universitaire des Sciences Agronomiques de Gembloux, 5030 Gembloux, Belgium

Terry McIntyre

Environmental Technology Advancement Directorate, Environmental Protection Service, 351 St. Joseph Blvd., Hull, Quebec, K1A 0H3, Canada

Alan Paton

Royal Botanic Gardens, Kew, Surrey TW9 3AB, UK

O. William Purvis

Department of Botany, The Natural History Museum, Cromwell Rd, London SW7 5BD

Kashchandra G. Raghothama


Roger D. Reeves

Institute of Fundamental Sciences – Chemistry, Massey University, Palmerston North, New Zealand

David G. Richards

Rio Tinto Plc, 6 St James’s Square, London SW1Y 4LD, UK

David E. Salt


Henk Schat

Department of Ecology and Ecotoxicology, Vrije Universiteit, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands

Huazhong Shi

Department of Chemistry and Biochemistry, Texas Tech University, Box 41061, Lubbock, TX 794091061, USA

J. Andrew C. Smith

Department of Plant Sciences, University of Oxford, Oxford, OX1 3RB, UK

Dick Vreugdenhil


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CONTRIBUTORS

Philip J. White

Horticulture Research International, Wellesbourne, Warwick CV35 9EF, UK

Steven N. Whiting

Golder Associates (UK) Ltd, Attenborough House, Browns Lane Business Park, Stanton-on-the-Wolds, Notts, NG12 5BL, UK

Matthias Wissuwa

International Rice Research Institute, DAPO Box 7777, Metro Manila, The Philippines

Fangjie Zhao

Agriculture and Environment Division, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK.

Jian-Kang Zhu

Department of Botany and Plant Sciences, Institute for Integrative Genome Biology, 2150 Batchelor Hall, University of California, Riverside, CA 92521

Sabine Zimmerman

INRA - Biochimie et Physiologie Moléculaire des Plantes, 1 place Viala, 34060 Montpellier Cedex 1, France

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Preface

A ‘textbook’ plant typically comprises about 85% water and 13.5% carbohydrates. The remaining fraction contains at least 14 mineral elements, without which plants would be unable to complete their life cycles. These essential mineral elements include six macronutrients – N, K, P, S, Mg and Ca – which are present in relatively large amounts in plant tissues (mg g−1 of dry tissue), and several micronutrients, including Fe and Zn, which are present in smaller amounts (µg g−1 of dry tissue). Tissue concentrations of these essential mineral elements must be maintained within a certain range, since mineral deficiencies limit growth and crop production, and mineral excesses are toxic. In addition, plants accumulate non-essential and/or toxic mineral elements such as Sr, Na, Cd and Pb, when these are present in the soil. Understanding plant nutrition and applying this knowledge to practical use is important for several reasons. First, nutrient deficiencies in crop production can be remedied by the application of fertilisers. However, fertiliser use incurs direct financial costs to the farmer and indirect costs to society. Indirect costs include the consumption of energy during the production, transport and application of fertilisers, and the depletion of finite natural resources. Further, since many crops do not recover fertilisers efficiently, unrecovered nutrients can pollute adjacent natural habitats, leading to a decline in species biodiversity. An understanding of plant nutrition allows fertilisers to be used more wisely. Second, the nutritional composition of crops must be tailored to meet the health of humans and livestock. Over three billion people worldwide do not receive adequate amounts of mineral elements such as Ca, Zn, Fe and Se in their diets, due to the low mineral content of many staple food crops. An understanding of plant mineral nutrition allows this ‘hidden hunger’ to be sated. Third, many regions of the world are currently unsuitable for crop production due to soil salinity, acidity, or contamination with toxic elements such as heavy metals or radionuclides. An understanding of plant nutrition can be used to develop strategies either for the remediation/restoration of this land, or for the cultivation of novel crops. The application of knowledge of plant nutrition can be achieved through genotypic or agronomic approaches. Genotypic approaches, based on crop selection and/or breeding (conventional or GM), have recently begun to benefit from technological advances, including the completion of plant genome sequencing projects. This book is intended to provide an overview of how plant nutritional genomics, defined as the interaction between a plant’s genome and

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PREFACE

its nutritional characteristics, has developed in light of these technological advances, and how this new knowledge might be usefully applied. In the first section of the book, the molecular physiology of the uptake, transport, and assimilation of the major plant mineral nutrients are reviewed. Françoise Daniel-Vedele and Sylvain Chaillou (INRA-Versaille) have described how genomics can help researchers to understand the mechanisms of uptake and utilisation of N (Chapter 1). Similarly, Malcolm Hawkesford (Rothamsted Research) has reviewed the genes impacting on the uptake, transport and assimilation of S (Chapter 4). Molecular aspects of P transport have been described by Kashchandra Raghothama (Purdue) (Chapter 5) and Philip White (Warwick HRI) has provided a comprehensive overview of the genetics of Ca accumulation (Chapter 3). Sabine Zimmermann and Isabelle Chérel (INRA-Montpellier) have described the molecular biology and regulation of K+ uptake (Chapter 2) and the first section concludes with a review of sodium (Na+ ) tolerance and Na+ transport (Chapter 6) by Huazhong Shi (Texas) and colleagues. In the second section, techniques to enable the study of plant nutritional genomics are discussed, including the use of high throughput ionomic profiling, by Brett Lahner and David Salt (Purdue) (Chapter 7), and transcriptional profiling, by Frans Maathuis (York), and Anna Amtmann (Glasgow) (Chapter 8). The use of natural genetic variation to study plant nutrition in both model and crop species is reviewed by Dick Vreugdenhil and colleagues (Wageningen) (Chapter 9) and by Matthias Wissuwa (IRRI) (Chapter 10). The final section of the book provides insights into how plant nutritional genomics might be useful in an applied context. Depending upon your viewpoint, these chapters illustrate either (i) how far we have come in a short period of time or (ii) how far we have yet to travel. In Chapter 11, Toby Kiers and Ford Denison (Davis) have provided a thoughtprovoking insight into the long-term sustainability of crop nutrition. Michael Grusak (Baylor College of Medicine, Houston) and Ismail Cakmak (Sabanci University, Istanbul) have described international efforts to improve the mineral composition of crops in Chapter 12. The book concludes with an in-depth discussion by Steven Whiting, Alan Baker (Melbourne) and colleagues of the role of plants in the restoration or remediation of sites contaminated with heavy metals (Chapter 13). This book is aimed at researchers and professionals, together with postgraduate students. However, we hope that the material will also stimulate advanced undergraduate students and those interested in the application of this knowledge. We thank the authors for their contributions to this volume, and Graeme MacKintosh and David McDade (Blackwell Publishing) for helping to solicit and edit the material. We would also like to thank John Hammond (Warwick HRI) for his comments on certain chapters. Finally, we thank our families for their continued support. Martin R. Broadley Philip J. White

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Nitrogen Fran¸coise Daniel-Vedele and Sylvain Chaillou

1.1 Introduction Nitrogen is a major component of amino and nucleic acids. The main sources of nitrogen (N) for plants are nitrate (NO3 − ) and ammonium (NH4 + ), although plants are also able to exploit organic N sources including amino acids, amides and urea. Plant species from a small number of plant families (e.g. the Fabaceae) are able to use molecular dinitrogen (N2 ) as an N source through symbioses with N-fixing bacteria. Compared to C, H and O, which account for 90% of plant dry matter, the N content of plants is low, comprising 1–5% (Mengel & Kirkby, 1987; Marschner, 1995; Heller et al., 1998), although N levels of up to 7.5% have been observed in the shoots of Arabidopsis (Loudet et al., 2003). Proteins and NO3 − account for 50% and 40% of total shoot N, respectively (Loudet et al., 2003), and free amino acids account for 5–10% of total shoot N. Nitrate can be translocated in the xylem sap, although it is relatively phloem-immobile. In contrast, free amino acids circulate readily between roots and shoots through the xylem and phloem, and growing organs supply amino acids to this pool (Cooper & Clarkson, 1989). Ammonium occurs in the xylem sap, but only at low concentrations, for example 0.05 to 1 mM in pea or oilseed rape (Rochat & Boutin, 1991; Schjoerring et al., 2002). Nitrate accumulation in the vacuoles of leaf cells can reach high concentrations (40–70 mM), and thus vacuolar NO3 − can provide a reserve of N for the plant, and it may also contribute to the overall osmotic pressure of the leaf, and therefore to plant turgor (Chaillou & Lamaze, 2001). An osmotic role for NO3 − is supported by the observation that an Arabidopsis mutant, deficient in a NO3 − transporter (the chl1 mutant), has a reduced stomatal opening which correlates with reduced NO3 − accumulation in its guard cells (Guo et al., 2003). Nitrate has a further role in water relations since it can promote water transport from roots to shoot, possibly by regulating the expression of aquaporin genes (Limami & Ameziane, 2001; Wang et al., 2001). In addition to metabolic and turgor-related roles, NO3 − also has a signalling role, for example through the induction of genes involved in N and C metabolism (Crawford & Forde, 2002). Ammonium cannot replace NO3 − in its osmotic or signalling functions and it is toxic at the cellular level (von Wiren et al., 2001). However, NH4 + is a reduced form of N, which can be rapidly assimilated into amino acids without an energy-costly reducing step. It is therefore paradoxical that NO3 − is the preferential N source for most plant species, since a complex

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reduction pathway requiring two enzymes, (nitrate reductase, NR, and nitrite reductase, NiR) and energy equivalent to 15 moles of ATP per mole of NO3 − , is required for assimilation of NO3 − (Fig. 1.1). It is possible that this paradox reflects an adaptation of plants to the mineralisation of organic N, which is prevalent in the majority of aerobic soils of the world, particularly in temperate regions, which ultimately leads to the dominance of NO3 − as an N source in most soils. The amount of N necessary for a plant to complete its life cycle varies greatly between species. Some plants are less N demanding than others. For example, many non-agricultural plant species can thrive under conditions of low N whilst high-yielding agricultural species have a high N demand. The genetic basis of differing N requirements between species is still unknown, although quantitative genetics could offer promising insights into the phenomena (Glass & Siddiqi, 1995; Hirel et al., 2001; Loudet et al., 2003). Further, the N demand of a plant varies according to its developmental stage. For example, N demand is high during vegetative growth and decreases during the reproductive phase, which corresponds with the remobilisation of reserves accumulated as NO3 − , amino NO3–

Plasma membrane Cytosol NADH (2e−)

Export

NO3–

T

Vacuole

–

NO3

Amino acids

NR

NO2– ?

Fd (6e−)

Chloroplast

NO2– NiR

Protein Synthesis

ATP NH4+ Photorespiration

α-ketoacids

GS

Gln Glu 2 Glu

Transaminases

GOGAT

Export

Fd α KG

Amino acids

Figure 1.1 The N-assimilation pathway. Different cellular compartments are indicated in italic whilst the different steps of the pathway are underlined.

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acids or proteins in different organs during the vegetative growth. Knowledge of the chronological changes in N demand throughout the plant developmental cycle has led to improvements in N-fertilisation practices, allowing reductions in the use of N fertilisers, especially in cereal production. Further, a greater understanding of N-assimilation pathway has allowed crop physiologists to design methods to test the N status of a plant, for example by measuring the NO3 − content of xylem sap. This has allowed crop-based N demands to be determined and fertiliser applications adjusted accordingly. Reducing N-fertiliser inputs in crop production can reduce leaching losses of NO3 − , which therefore minimises the pollution of water courses, and can reduce unnecessary financial costs (Meynard et al., 2002). Knowledge of the N composition of plants is also important in food production. For example, wheat grain for use in bread production must have protein content in excess of 12%. Conversely, the protein content of barley grain for use in beer production must not exceed 10%. A further issue on the N composition of plants is the debate on the safe levels of NO3 − in fresh produce. This has led to intense debates between producers, researchers and the wider public. For example, it is possible that eating salad leaves such as lettuce (Lactuca sativa) or spinach (Spinacia oleracea) may be hazardous to human health if the NO3 − content exceeds 2500 mg NO3 − kg−1 f. wt, according to official European standards, whilst cattle may be poisoned by formation of methaemoglobin if the NO3 − content of fresh herbage exceeds 1500 mg NO3 − kg−1 f. wt (Van Diest, 1986). It is, therefore, clear that the study of N in plants is important in the context of sustainable agriculture, food quality and food safety. This chapter will show how genomics can help researchers understand the mechanisms of N uptake and transport. It will review the genomics approaches used to study the enzymes responsible for N assimilation, and describe the search for new genes and their target functions. The use of this information to create new cultivars with improved N-use efficiency will be discussed.

1.2 Ammonium and nitrate uptake and transport within the plant Both anionic and cationic forms (NO3 − and NH4 + , respectively) of inorganic N are usually available in natural soils but their relative concentrations can vary dramatically. In temperate climates with well-aerated soils, NH4 + concentrations are very low, due to rapid nitrification. Conversely, NH4 + is the main source of N in acidic or waterlogged soils, and under mixed NO3 − /NH4 + nutritional conditions NH4 + is often the preferential form of N taken up by the root system (Dubois & Grenson, 1979; Glass & Siddiqi, 1995; Gazzarrini et al., 1999). Nitrate and NH4 + concentrations can vary by three or four orders of magnitude in agricultural soils (Wolt, 1994). With certain exceptions, higher

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plants are able to cope with these variations and have developed uptake systems for each ion. These systems differ in their specificity and affinity, and their functioning is regulated at the level of gene expression (transcriptional) as well as post-transcriptionally. Inside root cells, NO3 − and NH4 + may be redirected towards different targets. Nitrate can be stored in the vacuole, where it may become the main source of N when the external supply becomes limiting (der Leij et al., 1998), or may contribute to the general osmoticum. It can also be reduced to nitrite (NO2 − ) in the cytosol by nitrate reductase (NR). Finally, it can be redirected out of the root cell either by export to the external medium or by unloading to xylem vessels, from where it can reach the aerial part of the plant (Forde & Clarkson, 1999). All of these NO3 − or NO2 − movements require transport across different membranes. Thermodynamic calculations show that NO3 − transport across the root plasma membrane is an active process (Glass & Siddiqi, 1995). The compartmentation of NH4 + is also highly complex, since ammonium is derived from NO3 − reduction, but most comes from photorespiration, degradation of proteins or transamination reactions. Intriguingly, evidence to challenge the assumption that NH4 + concentrations in normal plant tissues is low (Howitt & Udvardi, 2000) has recently been obtained (Britto et al., 2001). Further, although it is believed that NH4 + generated or absorbed in roots is assimilated immediately, translocation of NH4 + from the root to the shoot can occur (Schjoerring et al., 2002). Dissecting the molecular basis of soil-to-plant, or within-plant, fluxes of N has been the challenge for the past decade. The enormous and rapid progress in plant functional genomics has already revealed some of the molecular components of these complex pathways. In this section, we will describe the characteristics of these transport systems, their known molecular components and the regulation of their activities at the physiological and molecular levels. 1.2.1 Ammonium uptake and transport Net uptake of NH4 + by root cells is the difference between influx and efflux. Influx is usually measured using isotopes as 13 NH4 + or 15 NH4 + during shortterm experiments (Clarkson et al., 1996). A biphasic pattern of influx is observed for many species such as Lemna gibba, rice or Arabidopsis. Below external NH4 + concentrations ([NH4 + ]ext ) of 1 mM, influx operates via a saturable highaffinity transport system (HATS), whilst a non-saturable low-affinity transport system (LATS) is active at [NH4 + ]ext above 1 mM (Wang et al., 1993). The kinetic parameters calculated for the HATS may vary from one species to the other and within the same species depending on environmental conditions (von Wiren et al., 2001). This diversity may result from co-existing transporters, each of them being involved in a particular process and showing different kinetic properties. This hypothesis is strengthened by the discovery of a multigenic family potentially encoding several NH4 + transporters.

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1.2.2 Molecular analysis of ammonium uptake To identify genes involved in NH4 + transport, mutants resistant to methylammonium, a toxic homologue of NH4 + which shares the same transporters (Venegoni et al., 1997), have been isolated in many species, from yeast (Dubois & Grenson, 1979) and Chlamydomonas reinhardtii (Franco et al., 1987) to Nicotiana plumbaginifolia (Godon et al., 1996). Functional complementation of a yeast mutant defective for methylammonium uptake led to the identification of the first NH4 + transporter gene from yeast and simultaneously from Arabidopsis (Marini et al., 1994; Ninnemann et al., 1994). From southern blot analysis and, more recently, from the sequenced genome of Arabidopsis, the AtAMT1 gene family can be seen to comprise five homologous members and a more distantly related gene, AtAMT2. These encode hydrophobic proteins of 475–514 amino acids which belong to the ammonium transporter (AMT)/methylammonium permease (MEP) family, which are ubiquitous across bacteria, archae, fungi, plants and animals (Saier et al., 1999). Deduced amino acid sequences and prediction analyses indicate that an 11 trans-membrane domain is probably present in eukaryotic members of the family, with an outside localisation of the N terminus, which has been experimentally demonstrated for the yeast MEP2 protein (Marini & Andre, 2000). The yeast heterologous expression system has been successfully used to determine the kinetic properties of these proteins. Different substrate affinities (Km) for NH4 + were observed among the different AtAMT1 members. Whilst AtAMT1;2 and AtAMT1;3 showed Km values between 25 and 40 ␮M, AtAMT1;1 had a Km value lower than 0.5 ␮M (Gazzarrini et al., 1999). However, recent studies found no difference between AtAMT1;1 and AtAMT1;2 in their affinity for NH4 + (Shelden et al., 2001). AtAMT1;1, AtAMT1;2, AtAMT1;3 and AtAMT2 are expressed in roots. Other AMT homologues have been cloned from rice – OsAMT1;1 and OsAMT2 (Suenaga et al., 2003) – and tomato – LeAMT1;1, LeAMT1;2 and LeAMT1;3 (Lauter et al., 1996; von Wiren et al., 2000). In tomato, LeAMT1;1 and LeAMT1;2 are preferentially expressed in root hairs, thus raising the NH4 + uptake efficiency because NH4 + is strongly adsorbed to soil constituents. Interestingly, LeAMT1;3 is preferentially expressed in leaves and the protein exhibits unique features such as a short N terminus when compared to AMT proteins from Arabidopsis or rice (von Wiren et al., 2000). 1.2.3 Regulation of ammonium uptake: physiological evidence and molecular basis N uptake by roots is controlled by the N demand of the whole plant linked to the external N availability. For example, a decrease in the [NH4 + ]ext from 1 mM to 0.2 ␮M led to an adaptative response in rice that simultaneously decreased the Km (from 188 to 32 ␮M) and increased the maximum influx rate (Vmax) of the HATS (Wang et al., 1993). The regulation of gene expression in response to

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N starvation has been studied in Arabidopsis for the multigenic AtAMT family (Gazzarrini et al., 1999; Rawat et al., 1999; Shelden et al., 2001). AtAMT1;1 mRNA levels increased markedly over a 2-day period after N removal, whilst AtAMT1;2 and AtAMT1;3 were less affected. The high affinity of AtAMT1;1 for NH4 + , and its co-regulation with NH4 + influx, suggest that AtAMT1;1 is a good candidate for an important component of the HATS. When N-depleted plants were re-supplied with NH4 + or amino acid, feedback signals led to a rapid decrease of net NH4 + uptake in wheat (Glass and Siddiqi, 1995). The same was true for Arabidopsis (Rawat et al., 1999) and tomato (von Wiren et al., 2000) although gene expression studies provide evidence that the AtAMT1 and the LeAMT1 transporters are not regulated in the same way. Whilst LeAMT1;1 and AtAMT1;1 respond similarly by a decrease in mRNA levels, LeAMT1;2 is induced in roots by NH4 + , and even more strongly by NO3 − supply (von Wiren et al., 2000). When tomato plants are grown under NO3 − nutrition and low CO2 , the expression of LeAMT1;1 and LeAMT1;3 is slightly higher in leaves, suggesting that the corresponding protein could play a role in the retrieval of NH4 + derived from photorespiration. Gene expression was recently analysed in rice and revealed distinct N-dependent regulation for AMTs, differing from that in tomato or Arabidopsis (Sonoda et al., 2003). Light and/or photosynthesis also controls NH4 + uptake. During a day/night cycle, NH4 + uptake peaks at the end of the light period and is induced by sugar during the dark phase. Again, this corresponds to the regulation of AMT gene expression in Arabidopsis (Gazzarrini et al., 1999), tomato (von Wiren et al., 2000) and tobacco (Matt et al., 2001). Both diurnal variations and response to sucrose induce the expression of AtAMT1;2 and AtAMT1;3 which showed a more pronounced response to both signals than AtAMT1;1 (Lejay et al., 2003). In addition to transcriptional regulation of NH4 + uptake, several lines of evidence also point to the possibility of post-transcriptional control. Using l-methionine-dl-sulfoximine (MSX) to block NH4 + assimilation, Rawat and colleagues demonstrated a 30% decrease in NH4 + influx rates without any decline in AtAMT1;1 transcript levels (Rawat et al., 1999). The role of NH4 + ion itself in post-transcriptional regulation of the HATS is supposed to take place via a direct inhibition of AMT transport activity or by inhibiting the synthesis of AMT proteins (Crawford & Forde, 2002). 1.2.4 Nitrate uptake and transport Nitrate influx has been studied intensively at the physiological and molecular levels (Muller et al., 1995; Devienne et al., 1994). In contrast, NO3 − efflux, which redirects a significant proportion of the absorbed NO3 − , has been rarely studied. Nitrate influx is mediated by two distinct systems, the HATS and the LATS. When [NO3 − ]ext is low ( exchangeable > fixed (non-exchangeable) > structural or mineral (Sparks, 1987; Zeng & Brown, 2000). The transport processes across the root which constitute K+ nutrition can be described as, (i) movement across the root cortex to the endodermis mainly through the apoplast, (ii) uptake into the root symplast to pass through the casparian strip in the root endodermis, (iii) transport across the symplast and (iv) release into the xylem (De Boer, 1999). Physiological studies to characterise K+ uptake by plant roots have been performed over many years (Glass, 1976). Most studies referring to the unidirectional fluxes of K+ in roots have been done with 86 Rb+ as a tracer for K+ instead of 42 K+ ; however, extrapolating Rb+ measurements to the transport of K+ should be done with caution because of membrane permeability differences between K+ and Rb+ (Rodr´ıguez-Navarro, 2000; Santa-Maria et al., 2000). The soil is a relatively dilute source of K+ and, therefore, the uptake of this ion into root cells occurs against a steep concentration gradient mediated by specialised low- and high-affinity membrane transport systems. Potassium uptake is coupled to the activity of the plasma membrane H+ pump, which maintains the pH gradient and the negative membrane potential (Lohse & Hedrich, 1992). Hyperpolarisation of the plasma membrane represents the driving force for K+ uptake by ion channels, and the proton gradient delivers the counterion for the H+ -coupled co-transport. Sustained K+ uptake from the soil for nutrition and growth can only be achieved by the activity of non-inactivating transport molecules. This property has been found for inwardly rectifying K+ channels and K+ /H+ symporters of the root, both contributing to K+ uptake (Schroeder et al., 1994). External K+ status determines which mechanism is used by a plant root (Maathuis & Sanders, 1996). Cellular K+ concentrations and membrane potentials led to the initial prediction that channels would represent low affinity transport systems, operating at soil K+ concentrations greater than 0.2–1.4 mM (Kochian & Lucas, 1993; Maathuis & Sanders, 1993). High affinity transport systems, represented by K+ /H+ co-transporters, were thought to dominate K+ uptake at lower (micromolar) soil K+ concentrations (Epstein et al., 1963; Rodr´ıguez-Navarro et al., 1986). However, this strict discrimination between low- and high-affinity transport has since been challenged, through the observation that ion channels can be active even at very low concentrations (Hirsch et al., 1998) and, conversely, that transporters still work at higher concentrations (Fu & Luan, 1998). Even identical molecular structures have recently been found for proteins functioning as channels or exchange pumps (Accardi & Miller, 2004) and the functional distinction between a slowly conducting channel and a rapidly gating transporter has been discussed recently (Gadsby, 2004).

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2.2.3 Potassium distribution in the plant Following the uptake of K+ into the root symplast and its diffusion from cell to cell via plasmodesmata, K+ is distributed throughout the plant by loading of the root transpiration stream, i.e. the xylem. To be loaded into the xylem, K+ has to cross cellular plasma membranes within the stele (Tester & Leigh, 2001). The diffusion of K+ across the root is driven by a K+ gradient setting cytosolic K+ activities lower in stelar cells than in cortical cells, as well as by a difference of the membrane potential of xylem parenchyma cells and cortical cells (De Boer, 1999). The movement of K+ can be altered by shoot demand because the shoot acts as a sink for nutrients (Engels & Marschner, 1992). Indeed, outward K+ conductances have been found in xylem parenchyma cells characterised as KORC (K+ outward-rectifying conductance). Interestingly, KORC is regulated by external (apoplastic) K+ concentrations thus allowing K+ to be delivered to the xylem stream according to the needs of the plant shoot (Wegner & De Boer, 1997). In the leaves, K+ must be unloaded from the xylem. Potassium efflux by ion channels requires a membrane depolarisation caused by the concerted action of different membrane transport systems such as calcium-permeable and anion channels or by a down-regulation of the H+ pump. The translocation of photosynthates and nutrients within the plant from shoot to root, and to other sinks, is mediated by the phloem. In general, K+ transport in the phloem is directed from older to younger plant tissues, which ensures a redistribution of this ion towards growing tissues such as developing leaves and fruit (Mengel & Kirkby, 1987). The K+ uptake in leaves is stimulated by light as has been shown in intact plants (Lüttge & Higinbotham, 1979), isolated tissue (Blum et al., 1992) and single cell systems (Kelly et al., 1995). A strong connection between auxin-stimulated growth of coleoptiles and K+ uptake has been shown (Claussen et al., 1997; Philippar et al., 1999). Stiles and Volkenburgh (2004) recently concluded that K+ uptake in growing leaves is mainly required for the electrical counterbalance of the H+ pump activity. K+ re-translocation from the shoot back to the root via the phloem and subsequent re-loading back into the xylem might occur in the case of K+ delivery exceeding shoot requirements or under root K+ deficiency (Drew & Saker, 1984; Jeschke & Hartung, 2000). Also, K+ plays a role in phloem loading and unloading of other nutrients (Lang, 1983). Recent evidence, found in Vicia faba, maize and Arabidopsis, suggested that specific K+ channels might be linked to sugar loading and unloading (Bauer et al., 2000; Lacombe et al., 2000; Ache et al., 2001; Deeken et al., 2002; Philippar et al., 2003). 2.2.4 Control of gas exchange by potassium-driven stomatal movements Plant physiologists have long been interested in the role of K+ in stomatal guard cell function, in addition to its role in nutrition. Guard cells play a major role in CO2 and water exchange and are thus crucial for the transpiration stream and for long distance transport (Raschke, 1975). The control of the stomatal

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aperture for optimal CO2 assimilation and evaporation occurs via osmotic changes to guard cells, which respond to a variety of internal and environmental signals (MacRobbie, 1988; Assmann, 1993; Blatt & Thiel, 1993). Environmental parameters, including CO2 , light, temperature and water status, can trigger modulations of phytohormone or malate concentrations, in addition to changes in Ca2+ and cyclic nucleotide concentrations, pH and phosphorylation status, which will regulate ion channel activities in guard cells. The opening or closing of the stomatal pore is accomplished by the volume change of the two surrounding guard cells through a modulation of their H+ pump (Edwards et al., 1988) and ion channel activities (Blatt, 1991). Voltage-dependent K+ inward and outward rectifiers play a dominant role in stomatal opening or closing, respectively (Thiel et al., 1992; Brüggemann et al., 1999), acting together in a synchronised fashion with anion channels and H+ /Cl− symport across the plasma membrane as well as with transport across the tonoplast (MacRobbie, 1997). Monitoring ionic activities in the apoplast of the stomatal cavity by ion-selective microelectrodes demonstrates clearly an efflux of K+ during stomatal closure (Felle et al., 2000). The authors found an increase of the apoplastic K+ activity from around 2.5–16 mM. Besides voltage-dependent K+ channels, stretch-activated plasma membrane channels have also been found, suggesting a feedback regulation of the channel activity (Cosgrove & Hedrich, 1991). Further, epidermal and subsidiary cells participate in a well-coordinated transmembrane shuttle of K+ , thus controlling cell turgor as well as concentration gradients (Penny & Bowling, 1974). For this reason, membrane transport of subsidiary cells has been studied to gain more insight in the interplay between the guard cells and their surrounding cells (Majore et al., 2002). Studies on stomatal currents focused initially on guard cell protoplasts of Vicia faba (Schroeder et al., 1984, 1987) and later in Arabidopsis (Ichida et al., 1997; Roelfsema & Prins, 1997; Wang et al., 2001; Pandey et al., 2002). Since detailed analyses on both isolated and integrated systems must be combined to gain sufficient insight into physiological processes, recent effort has also been made to measure transport activities within intact cells and tissues (Blatt, 1990; Roelfsema & Hedrich, 2002; Webb & Baker, 2002). Such approaches should aid our understanding of the physiological context of stomatal functioning and will allow a more integrated understanding of transport protein action. 2.3 Molecular identification of K+ transporters Physiological measurements of K+ fluxes and currents and, in particular, the application of the patch-clamp method, have allowed insights into the functioning of transport proteins. Single channel analyses describe the action of a discrete channel protein and therefore come close to a functional molecular characterisation. During the last decade, all the physiological analyses of plant K+ fluxes have been largely substantiated by the molecular identification

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of the participating transporters (reviewed by Czempinski et al., 1999; Zimmermann & Sentenac, 1999; Rodr´ıguez-Navarro, 2000; Schachtmann, 2000; Véry & Sentenac, 2002). In addition to the identification of transporter molecules, the creation of transgenic plants has allowed further insights into the physiological role of the candidate genes in planta. Knocking out a specific gene can reveal gene function by the analysis of the phenotype of the corresponding knockout mutant. Alternatively, plants overexpressing the gene of interest or antisense and RNAi approaches might be helpful in elucidating the physiological role of the candidate. Fortunately, Arabidopsis can be transformed easily and large mutant collections of T-DNA insertion lines have been created. In forward genetic approaches, these mutant collections have been screened for certain phenotypes, and genes carrying the T-DNA insertion and thus a tag have been identified with polymerase chain reaction (PCR) based methods. In the reverse genetic approach, the mutant collections are screened for a T-DNA insertion in a given gene of interest by PCR and the physiological function of this gene might be uncovered by an in-depth analysis of the corresponding mutant. This approach has given a series of interesting results for genes encoding K+ transporting proteins. However, the creation of a transgenic plant is not neutral and, since it might affect the regulation of other genes, it should therefore be analysed carefully. Further, it may also be necessary to analyse double mutants due to functional redundancy. The first two ion channel genes from Arabidopsis, AKT1 (Arabidopsis K+ transporter; Sentenac et al., 1992) and KAT1 (K+ channel from Arabidopsis thaliana; Anderson et al., 1992), were isolated by functional complementation cloning in yeast mutants defective in K+ uptake (trk1 and trk2). AKT1 and KAT1 were found to be members of the ‘Shaker’ gene family. Since their molecular identification, a number of further K+ transport systems have been found in different species by means of molecular biological approaches. The production of expressed sequence tag (EST) libraries and the publication of the entire sequence of Arabidopsis (The Arabidopsis Genome Inititiative, 2000), and its subsequent in silico analysis, has greatly increased the pool of candidate genes encoding transmembrane proteins and among them putative transport proteins. Detailed information on predicted transmembrane proteins is available from the specialised databases, including ARAMEMNON (Schwacke et al., 2003) and PlantsT (Tchieu et al., 2003). In addition, the recent publication of the rice genome (Goff et al., 2002; Yu et al., 2002) will undoubtedly advance research on K+ transporters in other species. The Shaker ion channel family is the best-characterised family of plant channels and transporters to date. However, four other major families of K+ permeable transporters have been distinguished in Arabidopsis: the KCO channel family, the KUP/HAK/KT family, the K+ /H+ antiporters and the Trk/HKT transporters (Mäser et al., 2001). Members of these families represent 35 candidates who can transport K+ , each to a greater or lesser extent and specificity. Further, a recently discovered family of plant ion channels, the CNGCs, are

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assumed to conduct K+ in a rather unspecific manner (Talke et al., 2003). Finally, a family of 20 homologues of animal glutamate receptors has been identified in Arabidopsis (AtGLR), which may also contribute to non-selective cationic conductances in plants but probably mostly in signal transduction pathways (Lacombe et al., 2001; Davenport, 2002). Current knowledge about the different families contributing to K+ permeability of plant membranes will be presented in the following sections.

KAT1 KAT2 GORK ? KCO1 ABA KCO6 ? CNGC2 ABA ABA = CNGC6 ? ABA CNGC15 ABA AKT2? ABA ABA ABA =

ABA NaCl

AKT2 ? KAT1 ? KAT2 ABA KCO6 AtHKT1

ABA

ABA

?

AKT1 AKT2 KCO1 KCO6 AtKup12 KEA3 CNGC2 CNGC3

AKT1 AtKC1

? −K hydathodes

mc

trichomes

gc

e xyl

m

?

oem phl

AtKC1

shoot

ABA

SKOR

ABA ABA

AKT1 AtKC1 GORK

NaCl

ABA − Ca Drought, ABA −K

GORK

? ?

? ABA drought

Others in roots: KCO2 KCO6 AtKup6 AtKup1-5, 7-8, 10 AtHAK5

AtHKT1 KCO1

root

Figure 2.1 Schematic representation of main regulations affecting K+ transport systems in Arabidopsis. Only transporters and channels whose expression has been localised in the plant (e.g. by promoter-GUS fusion, tissue-specific RT-PCR, etc.) have been positioned on the figure. Corresponding references for localisation and regulation data are indicated in Tables 2.1 and 2.2, respectively. The sign ‘?’ on these symbols indicates that the regulation is either unclear (e.g. down-regulation of KAT2 in guard cells), not systematically observed depending on the source of experimental results (e.g. induction of AtKC1 by K+ deprivation, or of AKT2 by NaCl in shoots), or not localised in planta (e.g. up-regulation by ABA of KCO6 and AtKup6 in whole plants, and of GORK in leaves). Other uncertainties include the role of AKT2 in guard cells, where the transcript levels for this gene are low (Szyroki et al., 1998; Leonhardt et al., 2004), and the precise localisation of GORK expression in veins (Becker et al., 2003). mc: mesophyll cells, gc: guard cells.

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2.3.1 Shaker-like channels Shaker-type, voltage-dependent, K+ channels have been described extensively in animals (Armstrong & Hille, 1998) and their structural resolution has become a matter of detailed analyses (Yifrach & MacKinnon, 2002; Lainé et al., 2004). Their features include the presence of the conserved K+ selective pore region, a hydrophobic core with six transmembrane spanning domains and a voltage sensor in the fourth transmembrane domain. Functional channels are multimers formed by four subunits, either homomers or heteromers. In higher plants, a number of these Shaker-like channels have been cloned and characterised. Nine members of plant Shaker-like K+ channels have been identified in Arabidopsis, with divergent functional properties and expression patterns (Pilot et al., 2003a, b). The first two plant Shaker-like channels AKT1 and KAT1 were identified in Arabidopsis in 1992. Surprisingly, AKT1 and KAT1 act as inwardly rectifying channels (Véry et al., 1995) despite their homology to animal voltage-dependent, highly selective K+ channels, which mediate outward currents. This observation provoked a number of structure-function studies that have become key to our understanding of the molecular mechanisms of ion channel gating and regulation by voltage (Miller & Aldrich, 1996; Marten & Hoshi, 1998; Zei & Aldrich, 1998; Latorre et al., 2003). The non-inactivating inward gating of the root channel AKT1 and the guard cell channel KAT1 indicated that these channels played a role in sustained K+ uptake from the soil in root cells or from the apoplasm in stomatal guard cells, respectively. However, a study of the kat1 mutant has demonstrated that KAT1 is not essential for stomatal functioning, suggesting redundancy (Szyroki et al., 2001). AKT1 is expressed in the root epidermis and cortex (Lagarde et al., 1996), suggesting a role in K+ nutrition. This role has been confirmed by phenotypic characterisation of a knockout mutant, akt1-1. Under limiting [K+ ]ext , i.e. 90%), fl (1) cal, l, r, sil, fl (1) cal, r, shoot (2) not detected (2) r (1), not detected (2) not detected (2) cal, fl, l, r (1) not detected (2) cal, sil (1) not detected (2)

l, st, fl (27)

different shoot tissues (27), gc, mc (28) mc (28)

gc (28)

gc (28)

gc, mc (28)

MPSS = massively parallel signature sequencing, Brenner et al., 2000) also allow the quantification of transcript numbers. Results from these different methods indicate that most of the K+ transporters are widely distributed within the plant (Table 2.1) suggesting involvement in a wide variety of physiological roles. A second step in transcriptional analysis of K+ transporter genes concerns their response towards environmental conditions. Results from respective

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experiments are available from databases (e.g. at the Nottingham Arabidopsis Stock Centre [NASC], http://nasc.nott.ac.uk/home.html; at the Arabidopsis Information Resource [TAIR], http://www.arabidopsis.org/index.jsp and at the Stanford Microarray Database [SMD], http://genome-www5.stanford.edu, Sherlock et al., 2001). An in silico analysis of the expression of a given gene can yield important hints for its putative physiological function, however, caution should be taken since expression data might be contradictory between different experiments corresponding to various and complex growth and experimental conditions (Table 2.3). Data relating the variations of transcript accumulation with changes in plant nutritional status (macro-elements), drought stress and/or altered ABA levels have been analysed and are summarised in Table 2.2. When available, results concerning transporters from other species of agronomical or biological interest, obtained by classical methods or by microarray transcriptional analyses, are compared and discussed. 2.4.1.1 Effects of nutritional status The presence or absence of other nutritional elements may affect the transcript rates of proteins involved in K+ transport. However, N, P and S status have little effect on the transcriptional regulation of K+ transporters (e.g. see TAIR, NASC and SMD databases). However, cation deficiency (K+ , Ca2+ ) and salt stress (Na+ ) affect expression levels of some genes, especially in roots (Table 2.3). Potassium starvation was expected to lead to the induction of high-affinity root uptake systems, in line with physiological studies (Glass, 1976). Functionally, inwardly rectifying currents detected in root peripheral cells of 3-to 4-weekold plants are indeed induced by K+ starvation (Maathuis & Sanders, 1996). However, the expression of AKT1, which has been proposed as a dominant K+ uptake channel in roots (Hirsch et al., 1998), has repeatedly been found to remain stable under conditions of K+ deficiency in roots (Table 2.2) suggesting other regulation mechanisms. In contrast, the gene encoding the wheat channel TaAKT1 was up-regulated in response to withdrawal of K+ in roots of young seedlings (Buschmann et al., 2000). Another gene encoding a K+ transporter, AtHAK5, is strongly induced by K+ deprivation and down-regulated upon K+ re-supply in roots of mature plants (Ahn et al., 2004) indicating a significant role in K+ uptake at low [K+ ]ext . The closest homologue of the Arabidopsis AtHAK5, HvHAK1, expressed in barley roots and belonging to the AtKup family, was equally induced by the absence of K+ in the growth medium (SantaMaria et al., 1997). In tomato, expression of LeHAK5 and LeKC1, respectively homologous to AtHAK5 and AtKC1, is induced by P and K deficiencies (Wang et al., 2002). Also, AtKup3 transcript has been found to be induced in roots (Kim et al., 1998) during K+ starvation but also sometimes remains unchanged (Ahn et al., 2004), probably due to differences in the physiological stage of the plants (Table 2.3). The barley and wheat transporters in roots of young seedlings, HvHKT1 and TaHKT1, were up-regulated by long-term K+ starvation or rapidly

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44

SKOR

microarrays, MPSS

nsc (11-s)

nsc (11-r)

nsc (38-s), non rep. (30-r) (−) transient (2.5x at 24 h, 29-r)

(+) (6x, 38-s), nsc (29-r)

nsc (38-s; 29-r)

nsc (11-l, r ; (−) (2x, 38-s), 39-wp) nsc (29-r)

Northern, RT-PCR

KAT2 AtKC1 nsc (11-s, r)

SPIK AKT5 KAT1

AKT2

AKT1

Gene

Northern, RT-PCR

(−) (2x at 96 h, 29-r)

non rep. (29-r)

nsc (29-r)

strong (+) (11-s), nsc (11-r) nsc (11-r)

slight (−) from 7 days (11-s)

(+) transient (1.7x slight (−) (11-s), at 10 h, 29-r) nsc (11-r)

Ca starvation (29-r) microarray

(+) (up to 2.5x at 24 h) (29-r), nsc (32-s, r) nsc (29-r, 34-wp)

(+) at 24 h (3.7x, 31-wp), fluctuations (29-r), nsc (32-r) nsc (29-r), (+) transient (2.9x at 3 h, 32-s)

microarrays, MPSS

Salt stress

strong (−) and re-increase (11-l, r) (−) (6-r)

(+) (5, l)

slight (−) (11-s), nsc (11-r)

(−) (4x, 28-gc) (−)? (28-gc)

(−) (28-gc, low signal)

Abscisic acid (ABA) Drought stress microarray, Rehydratation Northern blot, microarray, MPSS microarray RT-PCR MPSS


K starvation

Table 2.2 Regulation of Arabidopsis genes encoding K+ transport systems under different nutritional conditions. Numbers 1 to 28 refer to citations in Table 2.1; 29: Maathuis et al., 2003; 30: TAIR, http://www.arabidopsis.org, Julian Schroeder; 31: Kim et al., 2003; 32: Kreps et al., 2002; 33: Seki et al., 2002a; 34: TAIR, http://www.arabidopsis.org, Todd Richmond; 35: Seki et al., 2002b; 36: Oono et al., 2003; 37: Hoth et al., 2002; 38: TAIR, http://www.arabidopsis.org, Philip White; 39: Desbrosses et al., 2003; 40: Birnbaum et al., 2003. References are linked by hyphen to the organ or tissue in which the transcripts have been detected (wp: whole plant, and cf table 2.1 for the other abbreviations). (+): up-regulation; (–): down-regulation; nsc: no significant change; non rep.: non reproducible results between two repeats.

P1: JYS CE initial: SM/TechBooks November 5, 2004 11:36

nsc (38-s, 29-r), non rep (30-r) nsc (29-r) nsc (29-r) nsc (29-r)

KCO1

KCO4

45 nsc (29-r) nsc (38-s, 29-r), (−) (2x, 30-r)

AtHKT1 AtKup1 nsc (22)

AtKup4

nsc (29-r, 32-s, r)

nsc (29-r)

nsc (29-r) (+) transient (2.5x at 24 h, 29-r) (−) transient (∼ 1.8x at 10 h and 24 h, 29-r) (−) transient (3.6x at 24 h, 29-r) (−) transient (1.7x at 24 h, 29-r) nsc (29-r, 32 r) nsc (29-r), (+) (1.9x at 12 h and 24 h, 31-wp)

nsc (29-r, 32-s, r)

nsc (29-r)

(+) 6-7x, 20-l)

(+) (5x, seedlings, cultured cells, root hairs (20), nsc (20-gc))

(Continued)

(+) (4x, 37-wp)

non rep (28-gc)

(+) (9x, 37-wp)


AtKup3

fluctuations (29-r) nsc (29-r)

nsc (29-r)

nsc (29-r)

nsc (22), nsc (38-s) slight (−) (24-r) nsc (22), nsc (38-s, 29-r), nsc (29-r) (+) (24) non rep. (30-r) nsc (34-wp) nsc (38-s, 30-r, nsc (29-r) 29-r)

nsc (30-r, 29-r)

KCO6

AtKup2

nsc (29-r)

KCO5

nsc (29-r)

non rep. (29-r)

non rep. (29-r)

non rep. (29-r)


KCO2 KCO3

nsc (29-r)

GORK

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nsc (29-r) nsc (29-r) (−) transient (4.4x at 10 h, 29-r) nsc (29-r)

nsc (29-r) nsc (29-r) nsc (29-r) nsc (29-r)

nsc (29-r) nsc (38-s, 29-r)

nsc (38-s, 29-r) nsc (38-s, 29-r) nsc (38-s, 29-r) (+) (2x, 38-s), nsc (29-r)

nsc (30-r, 29-r) nsc (38-s, 29-r) nsc (38-s, 29-r) nsc (38-s, 29-r), non rep (30-r)

nsc (22)

nsc (22) nsc (22) nsc (22)

AtKup7

AtKup8 AtKup9

AtKup10 nsc (22) AtKup11 nsc (22) AtKup12 nsc (22)

AtHAK5 strong (+) (1–6 days) (22-r) KEA1 KEA2 KEA3 KEA4

nsc (29-r)

(−) (2x at 24 h, 29-r) nsc (29-r) nsc (29-r)

(+) (2.4x at 10 h, 1.7x at 24 h, 29-r)

nsc (29-r)

AtKup6

(−) (38-s), nsc (30-r, 29-r) nsc (38-s, 29-r)

nsc (22)

Gene


AtKup5

microarrays, MPSS

K starvation

(Continued)

Northern, RT-PCR

Drought stress microarray, MPSS

46

Abscisic acid (ABA)

(35-wp)

(+) (max. 3x at 5 h)

Rehydratation Northern blot, microarray, microarray RT-PCR MPSS


nsc (29-r, 34-wp) nsc (29-r) nsc (29-r) nsc (29-r), (+) (2x at 24 h, 31-wp)

fluctuations (29-r)

fluctuations (29-r) non rep. (29-r), (+) (up to 1.7x, 32-s) nsc (29-r) nsc (29-r, 31-wp) nsc (29-r)

nsc (29-r), (+) (up (+) (max 6x at 24 h) to 2x at 5 h, 33-wp) (35-wp) fluctuations (29-r)

nsc (29-r)

microarrays, MPSS

Salt stress


Northern, RT-PCR

Table 2.2


nsc (30-r, 29-r) (−) (2x, 38-s), nsc (30-r, 29-r)

KEA6 CNGC1

47 nsc (38-s, 29-r) nsc (29-r) nsc (29-r)

nsc (29-r)

CNGC4 CNGC5 CNGC6 CNGC7 CNGC8

CNGC9 CNGC10

nsc (38-s, 29-r) nsc (38-s, 29-r)

CNGC12

CNGC13

CNGC11

nsc (38-s)

CNGC3

(+) (2x at 96 h, 29-r) fluctuations (29-r)

fluctuations

nsc (29-r) nsc (29-r) slight (−) (10 h96 h, 29-r)

nsc (29-r)

nsc (29-r) nsc (29-r)

nsc (29-r)

nsc (36-wp)

nsc (29-r, 32-s, r, 31-wp) (+) transient (2.3x at 24 h, 29-r)

fluctuations (29-r)

nsc (29-r) nsc (36-wp) fluctuations (29-r) (+) (up to 2x, 2 h96 h, 29-r)

(+) (2x at 96 h, 29-r), (−) (2x at 3 h, 32-s, r), nsc (31)

nsc (29-r) (+) transient (2.4x at 5 h, 29-r), nsc (32)

nsc (29-r)

nsc (36-wp)

nsc (36-wp)

nsc (36-wp)


(Continued)

(−) 4.2x, 37-wp) (−) (4.3x, 37-wp) (−) (28x, 37-wp)

nsc (28-gc), (+) (28-mc) non rep (28-gc)

(−) (4.2x, 37-wp)


CNGC2

nsc (30-r)

KEA5


48 nsc (38-s, 30-r, 29-r)

nsc (29-r)

CNGC19 CNGC20

KAB1

nsc (29-r)

CNGC18

CNGC17

nsc (29-r)

CNGC16

microarrays, MPSS nsc (38-s, 29-r) nsc (38-s, 29-r)

Northern, RT-PCR

CNGC14 CNGC15

Gene

K starvation

(Continued)

(−) (3.5x at 96 h, 29-r)

(−)? (low signal, 29-r)

(−) (2x at 24 h and 96 h, 29-r) nsc (29-r)

nsc (29-r) (−) transient (2x at 10 h)


Northern, RT-PCR

nsc (28-gc, mc)

Abscisic acid (ABA) Drought stress microarray, Rehydratation Northern blot, microarray, MPSS microarray RT-PCR MPSS

(+) transient (2.5x at 5 h, 29-r) fluctuations (29-r), nsc (36-wp) nsc (32-s, r), nsc (34-wp)

(−) transient (1.9x at 24 h, 29-r)

(+) transient (6x at 24 h, 29-r) fluctuations (29-r)

nsc (29-r, 31-wp) nsc (29-r, 31-wp)

microarrays, MPSS

Salt stress


Table 2.2


Substrate liquid

liquid

liquid agarose plates agarose plates liquid liquid soil liquid agar plates liquid

Authors

Pilot et al., 2003 (11)

Maathuis et al., 2003 (29)

White (TAIR) (38) Schroeder (TAIR) (30) Kim et al., 1998 (24) Kreps et al., 2002 (32) Hoth et al., 2002 (37) Leonhardt et al., 2004 (28)

Kim et al., 2003 (31) Oono et al., 2003 (36)

49

Ahn et al., 2004 (22)

0.45 mM

10 mM 20 mM

none none none 10 mM 10 mM unknown

none

1 mM

NH4 +

none

? 2%

none 3% 3% 0.01% 1% none

none

1%

Sucrose

16 h/8h day/night

constant light 16 h/8h day/night

constant light for 24 h 16 h/8 h day/night constant light 12 h/12 h day/night 16 h/8 h day/night 16 h/8h day/night

10 h/14 h day/night

16 h/8 h day/night

Light

6 weeks, roots and older leaves

3 weeks, rosette leaves 3 weeks, roots 2-3 weeks, roots 4 weeks, roots and shoots 4 weeks, whole plants 5 to 6 weeks, mesophyll and guard cell protoplasts 2 weeks, whole plants 3 weeks, whole plants

just before flowering, roots

3 weeks, roots and shoots

K+

1/0 mM Na+ 0/100 mM ABA 0/100 mM K+ 1.875/0 mM Na+ 0/80 mM Ca2+ 0.5/0 mM K+ 0.75/0 mM K+ 2 mM/120 mM K+ 2 mM/40 mM Na+ 0/100 mM ABA 0/100 mM spray with 100 mM ABA Na+ 0/150 mM dehydratation/ rehydratation K+ 1.75/0 mM

Age at harvest and sampling

Treatments

1 to 6 days

12 h and 24 h

3 and 27 h pool of 3 and 5 h 4h

1, 4, 7 days 1, 4, 7 days 1, 3, 6, 12 h 5, 10, 24, 96 h 2, 5, 10, 24, 96 h 5, 10, 24, 96 h 28 h

Time points


Table 2.3 Experimental conditions for large-scale studies (microarray experiments or analysis of the expression of a gene family) presented in Table 2.2. Numbers in brackets refer to citations in this table.



50


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after K+ deprivation dependent upon age (Wang et al., 1998; Horie et al., 2001). In rice plants, OsHKT1 was highly up-regulated in roots upon long-term K+ starvation, but not after a 40 h deprivation, which decreased the root K+ content only slightly (Garciadeblas et al., 2003). In contrast, expression of the Arabidopsis gene AtHKT1 remained stable throughout a 96 h period of K+ deprivation (Maathuis et al., 2003). However, the experiment was performed at a late physiological stage, just before flowering. Calcium deficiency in some cases leads to a moderate (up to 2.5-fold) and transient induction of K+ transport systems. However, for AtKup12, CNGC9, CNGC10 and KAB1, a significant suppression of transcription has been demonstrated (Table 2.2) underlining the role of Ca2+ as second messenger in complex regulatory networks. Despite the dramatic effects of salt stress (Na+ oversupply) on plant performance and the interference of Na+ with K+ homeostasis, Na+ levels have only a little and/or transient influence on transcript levels of Arabidopsis K+ transporters (Table 2.2). However, the AKT1 homologue of the halophyte Mesenbryanthemum crystallinum (MKT1) is strongly down-regulated by salinity (Su et al., 2001). Transcripts of the rice homologue of AKT1 disappear from the root exodermis in a salt-tolerant variety but not in a salt-sensitive one (Golldack et al., 2002). This is unexpected since AtAKT1 is highly selective for K+ and thus seems unable to contribute to Na+ uptake. The authors, however, suggest that AKT1-type channels might be permeable to Na+ when this ion is present at a high external concentration. In rice roots, OsHKT1 and OsHKT2 transcripts decreased in the presence of Na+ (Horie et al., 2001). This kind of regulation was not observed for AtHKT1 (Maathuis et al., 2003; Table 2.2). Microarray analysis has revealed that HvHAK1, unlike AtHAK5 (Table 2.2), is also down-regulated by salt stress (Ozturk et al., 2002). Thus far, results from the high-throughput methods for assaying the transcription levels of genes for K+ transporters must be regarded as preliminary, and the experimental conditions must be taken into careful consideration (Table 2.3). Verification by other experimental methods, or by a more detailed study, is normally needed. For example, AKT1 and AtKC1 transcript levels can vary significantly especially when the plants are treated with different NH4 + and salt (Na+ ) conditions (Pilot et al., 2001; Kreps et al., 2002; Kim et al., 2003; Maathuis et al., 2003; Pilot et al., 2003a). 2.4.1.2 Effect of drought stress and abscisic acid (ABA) Soil drying significantly enhances ABA biosynthesis (Wilkinson & Davies, 2002) and ABA, in turn, regulates K+ channel activity (Luan, 2002). In contrast to the relatively minor effects of cation concentrations on the expression of genes encoding K+ transporters, drought stress and exogenous ABA induce major changes in their transcription (up to more than threefold induction or fourfold suppression; Table 2.2). This impacts on, for example, root K+ uptake

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or stomatal aperture. Expression of AtKup6, being localised in roots (Table 2.1) and therefore possibly implicated in K+ uptake, increased sixfold upon drastic dehydration (Seki et al., 2002a). This induction demonstrates the strong relationship between water potential and K+ homeostasis within the plant. Also, the outwardly rectifying channel GORK, which is widely expressed in roots, leaf vasculature and guard cells, is strongly up-regulated by drought or ABA treatment in excised leaves, whole seedlings, cultured cells and root hairs (Hoth et al., 2002; Becker et al., 2003). However, GORK transcript levels are not influenced in guard cells (Becker et al., 2003) despite the role of GORK in ABA-mediated K+ efflux and stomatal closure (Hosy et al., 2003), suggesting regulation at another level. In accordance with the role of the inwardly rectifying channels KAT1 and KAT2 in guard cell physiology, the expression of KAT1 and KAT2 is down-regulated by ABA (Leonhardt et al., 2004), which might favour stomatal closing. One member of the KCO-family, AtKCO6, which is expressed throughout the plant including the guard cells, was found to be induced by ABA (Hoth et al., 2002) but its membrane localisation and role are not yet known. Finally, other K+ transporter genes of unknown physiological function (KEA5, CNGC10, CNGC11, CNGC12) are down-regulated by ABA. Another plant hormone, auxin, which regulates, for example, growth and development, has been shown to influence the expression of K+ channel genes in maize coleoptiles (Philippar et al., 1999) and in Arabidopsis seedlings (Philippar et al., 2004). Overall, the transcriptional regulation of K+ transport might offer the plant a basic control mechanism, and analyses performed on whole plants or organs might currently be masking transcriptional regulation at a fine-scale. However, a series of other regulatory steps allow even more precise and rapid regulation of ion homeostasis. 2.4.2 Post-translational regulation Regulation of K+ transport at the functional level has been studied extensively (reviewed by Zimmermann et al., 1999; Schachtman, 2000; Véry & Sentenac, 2003; Chérel, 2004). Targeting of transport proteins towards their respective membrane, their homo- or heteromerisation, interaction with other regulatory subunits and direct regulation by voltage, ligands and cytosolic factors represent different levels of post-translational regulatory mechanisms. Further, environmental stimuli such as light, temperature, salinity or drought influence the activity of K+ transporters, in part by signalling pathways involving changes of pH or Ca2+ (Shabala, 2003). For example, K+ channels have been described to function as osmosensors (Liu & Luan, 1998). All of these different regulation mechanisms highlight the complexity of the system, which is increased because ion transport is not only a target of signal transduction but also an integral part of it (Zimmermann et al., 1999).

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Molecular mechanisms of post-translational regulation have been studied in detail for members of the plant Shaker-like K+ channels that involve direct voltage gating (Krol & Trebacz, 2000). Structure–function relationships have been explored by introducing point mutations in functional domains of the ion channel (e.g. Dreyer et al., 1998; Zei & Aldrich, 1998; Ros et al., 1999; reviewed by Zimmermann & Sentenac, 1999). A milestone for the understanding of the K+ selective pore was the determination of the three-dimensional structure of a bacterial K+ channel (Doyle et al., 1998). Further, regulation by pH (Hoth & Hedrich, 1999), Ca2+ and the effects of different blockers (Ichida et al., 1997; Moroni et al., 1998) have been investigated in detail. All of these studies have been enabled by the use of heterologous expression systems for plant channels and transporters (Dreyer et al., 1999). In members of the KCO-family, the presence of Ca2+ -binding motifs (EF-hands) suggest regulation by Ca2+ . Plant CNGCs, harbouring overlapping binding domains for cyclic nucleotides and calmodulin, are thought to be regulated by calcium/calmodulin and by cAMP/cGMP (Talke et al., 2003). An important regulatory element for the activity of channels and transporters is the interaction of subunits within heteromers (Dreyer et al., 1997), with other proteins forming protein complexes or cytoskeletal connections, and with enzymes modulating transport by, for example, phosphorylation or dephosphorylation. Altogether, this increases the possibility for fine-tuning K transport activity tremendously. Phosphorylation has been described for members of the Shaker channel family (Li et al., 1998; Tang & Hoshi, 1999; Mori et al., 2000). The activity and voltage-dependence of the weakly inward rectifier AKT2 is affected by a phosphatase, AtPP2CA (Chérel et al., 2002). In animals, a family of regulatory subunits of the Shaker channels, modulating their kinetics or current magnitude, are called ␤-subunits (Gulbis et al., 1999). Homologues of these protein subunits have been discovered in plants and found to interact with some plant Shaker channels (e.g. KAB1 in Arabidopsis; Tang et al., 1995, 1996; Zhang et al., 1999). The KAB1 gene, which is highly and ubiquitously expressed (Tang et al., 1996, 1998), has been shown by microarray experiments to be slightly and transiently down-regulated in roots by NaCl treatment (Maathuis et al., 2003) and regulated neither by K+ deprivation nor ABA treatment (Table 2.2). In rice, a homologue of KAB1 (KOB1) was found to be down-regulated by K+ deficiency in old K+ starved leaves (Fang et al., 1998), whereas in young leaves, which had retained K+ , expression of KOB1 remained unaffected. The physiological consequences of regulation by such proteins, and also by 14-3-3 proteins, G-proteins and syntaxins, are still in the beginning of their elucidation. Hormones could regulate ion transport directly as ligands or via second messengers. For example, ABA controls K+ release mediated by KORC channels that correspond to the SKOR channel but K+ influx is controlled by G proteins at the symplast/xylem boundary (De Boer, 1999).

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The effect of syntaxins on ion channel activity (Leyman et al., 1999) might open a new exciting field of research on transporter regulation by coordinated targeting mechanisms.

2.5 Conclusions and perspective In describing current knowledge of K nutrition it might, at first, seem surprising that we have focused on the model plant Arabidopsis. Yet most molecular and genetic results to date have been obtained with this easily manageable laboratory plant. Research during the last decade has favoured Arabidopsis because of the complete genome sequence, as well as the availability of mutants. However, model plants have their intrinsic limits and researchers concerned with specific physiological questions like stress responses, resistance to heavy metals and most notably agricultural problems like K deficiency (Steingrobe & Claassen, 2000) or salt stress (e.g. in rice; Bohra & Dörffling, 1993) have switched to other species. The recent publication of the rice genome sequence (Goff et al., 2002; Yu et al., 2002) has accelerated progress in this field and K+ transporters are now characterised in this species (Bañuelos et al., 2002). Another limitation to the use of Arabidopsis relates to questions connected to plant:microbial symbioses, a phenomenon of broad importance for plant nutrition (e.g. see Chapter 11). Arabidopsis belongs to the family Brassicaceae; one of the few plant families which does not tend to form mutualistic interactions with symbiotic fungi or bacteria. Therefore, other plant models must be studied. Molecular mechanisms involved in the establishment of symbiosis between the host plant and fungi or bacteria as well as in symbiotic nutrient exchange have become a topic of current interest in plant physiology (Chalot et al., 2002). To study N2 -fixing bacteria in legumes, Medicago truncatula or Lotus japonicus are being used. Other important symbioses are plant-fungi interactions forming specialised structures for nutrient exchange called mycorrhiza. Mycorrhiza improve plant mineral nutrition and resistance to abiotic and biotic stresses. Roughly two main forms, the endo- and ectomycorrhiza, are distinguished. Here, a model to study ectomycorrhizal symbiosis affecting most of the woody plants from temperate and boreal regions is presented because of its direct impact to advance knowledge concerning plant K+ nutrition. The basidiomycete Hebeloma cylindrosporum associated to Pinus pinaster has been chosen as a model plant/mycorrhizal system because of the accessibility to molecular and genetic manipulations. A cDNA library was prepared in a yeast expression vector allowing cloning of H. cylindrosporum genes by functional complementation of yeast mutants. By means of a sequencing project, 4200 ESTs have been obtained and constitute one of the largest public EST resource for an ectomycorrhizal fungus. For the first time, analysis of the EST

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resource has allowed the identification of a large set of genes coding for fungal membrane transport proteins in an ectomycorrhizal fungus. These include putative phosphate, potassium, sulphate and micronutrient transporters (Wipf et al., 2002, 2003; Lambilliotte et al., in press). Amongst H. cylindrosporum ESTs, the identification of a K+ transporter of the TRK type as well as of K+ channels homologous to animal Shaker channels, is promising with regard to the functions of the two specialised fungal membranes, the hyphal membrane in contact with the soil mediating nutrient uptake and the mycorrhizal membrane in contact with root cortical cells allowing exchange of nutrients (Zimmermann, unpublished observations). The hypothetical model of H. cylindrosporum HcTRK in the external hyphae having an uptake function as well as the K+ channel HcSKC in the mycorrhiza having a function for K+ secretion remains to be proven. Functional data, localisation experiments as well as studies on transgenic lines of fungi will bring evidence for the function of these candidate genes in K nutrition. We have accumulated a huge quantity of functional and molecular data since the first electrophysiological analysis of plant membrane currents and since the very first cloning of plant ion channels in 1992. However, there are many experiments and analyses left to do in the coming years, until the complex network between signals, receptors, membrane transporters, expression levels, regulating factors and the physiological responses are thoroughly understood. Acknowledgments We are grateful for scientific discussions with Hervé Sentenac and for helpful comments on the manuscript from Ina Talke and Clare Vander Willigen. References Accardi, A. & Miller, C. (2004) Secondary active transport mediated by a prokaryotic homologue of ClC− channels. Nature, 427, 803–807. Ache, P., Becker, D., Deeken, R., Dreyer, I., Weber, H., Fromm, J. & Hedrich, R. (2001) VFK1, a Vicia faba K+ channel involved in phloem unloading. Plant J., 27, 571–580. Ache, P., Becker, D., Ivashikina, N., Dietrich, P., Roelfsema, M.R. & Hedrich, R. (2000) GORK, a delayed outward rectifier expressed in guard cells of Arabidopsis thaliana, is a K+ -selective, K+ -sensing ion channel. FEBS Lett., 486, 93–98. AGI (The Arabidopsis Genome Initiative) (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature, 408, 796–815. Ahn, S.J., Shin, R. & Schachtman, D.P. (2004) Expression of KT/KUP genes in Arabidopsis and the role of root hairs in K+ uptake. Plant Physiol., 134, 1–11. Assmann, S.M. (1993) Signal transduction in guard cells. Annu. Rev. Cell Biol., 9, 345–375. Anderson, J.A., Huprikar, S.S., Kochian, L.V., Lucas, W.J. & Gaber, R.F. (1992) Functional expression of a probable Arabidopsis thaliana potassium channel in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. USA, 89, 3736–3740.

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Calcium Philip J. White

3.1 Introduction A plant cannot complete its life cycle without calcium (Ca). A healthy plant generally has a shoot Ca concentration between 0.1 and 5% d. wt, which supports a variety of indispensable biophysical and biochemical processes (White & Broadley, 2003). First, Ca2+ serves as a structural component of cell membranes and cell walls. In cell membranes Ca2+ contributes to membrane integrity by binding to negatively charged proteins and lipids (Marschner, 1995). In the cell wall it cross-links pectins, which not only defines the pore size of the wall matrix but also provides strength and rigidity to the plant (Carpita & McCann, 2000). Structural weaknesses in cell walls lacking sufficient Ca result in physiological disorders such as fruit cracking following increased humidity or rainfall (White & Broadley, 2003) and susceptibility to bacterial, fungal or viral pathogens (Marschner, 1995). Second, Ca2+ provides a counter-cation for inorganic and organic anions in the vacuole. This not only allows plant cells to accumulate solutes to enable turgor-driven cell expansion, but also allows them to store, digest and detoxify metabolites. The ability to precipitate calcium salts, such as calcium oxalate, without osmotic consequence is an advantage in dry habitats (White & Broadley, 2003) and may also provide a defence against herbivores (Franceschi & Horner, 1980). Third, Ca2+ is required as an intracellular messenger in the cytosol of plant cells. Changes in cytosolic Ca2+ concentration ([Ca2+ ]cyt ) co-ordinate responses to numerous developmental cues and environmental challenges (White & Broadley, 2003). The low solubility product of Ca2+ and phosphate required the first living cells to evolve mechanisms to remove Ca2+ from the cytoplasm to maintain the submicromolar [Ca2+ ]cyt required for energy metabolism (Sanders et al., 1999). This low [Ca2+ ]cyt was then ideal for the subsequent evolution of a sensitive intracellular signalling system. It is thought that the chemistry of Ca2+ , which can co-ordinate six to eight uncharged oxygen atoms, enabled the evolution of proteins that could change conformation upon binding Ca2+ , allowing the cellular perception and transduction of [Ca2+ ]cyt signals (Sanders et al., 1999). It is possible that the need for submicromolar [Ca2+ ]cyt has driven many of the physiological responses of plants to contrasting Ca availability and has impacted on the mechanisms by which plants accumulate and sequester Ca. This chapter will provide an overview of the plant genes that are likely to impact on shoot Ca accumulation.

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Initially, it is observed that Ca deficiency is rare in nature and that the ability of plants to tolerate excessive Ca is often related to the rhizosphere Ca concentration ([Ca2+ ]ext ) in their native habitats. It is also observed that the ability of different plant species to accumulate Ca in their shoots is related to their phylogenetic position and, in particular, whether they are eudicot or monocot. This implies that shoot Ca concentration ([Ca]shoot ) is genetically determined and was influenced by ancient evolutionary events. Since Ca is acquired by the root system from the soil solution, and little Ca is translocated from the shoot to the root via the phloem, it is argued that [Ca]shoot will be determined principally by the rate of Ca delivery to the xylem relative to the absolute growth rate of the shoot. Thus, the influence of root morphology, anatomy and biochemistry on Ca delivery to the xylem is considered. In particular, the properties of the root apoplast and of the Ca channels present in the plasma membrane of root cells that allow Ca2+ into the symplast are reviewed. Evidence is presented that manipulation of these will influence [Ca]shoot . Finally, the influence of mechanisms, such as the Ca2+ -ATPases and Ca2+ /H+ -antiporters in cell membranes and Ca2+ buffering in the cell wall, vacuole and cytoplasm, that maintain low [Ca2+ ]cyt on the ability of a plant to tolerate high [Ca]shoot are considered, since these may also influence Ca accumulation.

3.2 Plant species have different calcium requirements When plants are grown in flowing nutrient solutions, the solution Ca concentration ([Ca2+ ]ext ) required to produce maximal growth varies between 2.5 and 1000 ␮M, depending upon plant species (Loneragan et al., 1968; Islam et al., 1987). It is commonly, but not exclusively, observed that grasses and cereals need a lower [Ca2+ ]ext to achieve maximal growth than other plants (Loneragan et al., 1968; Islam et al., 1987), and this has been attributed to a lower tissue Ca requirement (Loneragan & Snowball, 1969; Islam et al., 1987). Interestingly, the optimal [Ca2+ ]ext for the growth of a plant species in solution culture is often directly related to the [Ca2+ ]ext of the rhizosphere in its natural habitat (Jeffries & Willis, 1964). Calcifuges, which occur on acid soils with low Ca, grow well at low [Ca2+ ]ext and generally respond little to increased [Ca2+ ]ext , which may even inhibit growth. Calcicoles, on the other hand, which occur on calcareous soils, often require a higher [Ca2+ ]ext for optimal growth, but also tolerate high [Ca2+ ]ext . It is thought that the mechanisms that enable calcicole plants to maintain low [Ca2+ ]cyt in their natural habitat might restrict their growth at low [Ca2+ ]ext by inducing Ca deficiency (Lee, 1999; White & Broadley, 2003). This is consistent with the phenotype of plants overexpressing Ca2+ -transporters that remove Ca2+ from the cytoplasm to the vacuole, which show Ca-deficiency symptoms at low [Ca2+ ]ext , but tolerate high [Ca2+ ]ext (Hirschi, 2001).

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Calcium deficiency is rare in nature, but may occur in soils with low base saturation and/or high levels of acidic deposition (McLaughlin & Wimmer, 1999). However, Ca-deficiency disorders occur frequently in agriculture when insufficient Ca is available via the transpiration stream for the demand of rapidly growing tissues, such as young leaves or fruit (White & Broadley, 2003). They arise because Ca is immobile in the phloem and cannot be redistributed from older tissues. Since the susceptibility to Ca-deficiency disorders has a genetic component, plant varieties that are less susceptible to Ca-deficiency disorders have been developed through breeding programmes (Clark, 1983; Hochmuth, 1984; English & Barker, 1987; Caines & Shennan, 1999). It has been observed that varieties less susceptible to Ca-deficiency disorders often have greater Ca transport through their vasculature. There is considerable variation in the ability of different plant species to accumulate Ca (Fig. 3.1). This implies that [Ca]shoot is genetically determined. A large proportion of this variation can be attributed to the phylogenetic division between eudicots and monocots (Table 3.1; Thompson et al., 1997; Broadley et al., 2003). Eudicot orders generally have a greater [Ca]shoot than monocot orders. There appears to be little variation in the [Ca]shoot of eudicot orders, although derived orders within the rosid (Brassicales, Cucurbitales, Malvales and

25

Frequency

20

15

10

5

0 0.0

1.0

2.0

3.0

4.0

5.0

Leaf Ca concentration (% d. wt) Figure 3.1 Frequency distribution of the shoot Ca concentration of 117 plant species, representing 24 angiosperm orders and one unassigned family sampled in proportion to the number of species they contained, grown hydroponically in a nutrient solution containing 2 mM Ca2+ . Data taken from Broadley et al. (2003).

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Table 3.1 Variance in shoot Ca concentration and shoot Mg concentration at informal group, ordinal and specific levels estimated using data from a literature survey of plants grown under comparative conditions (n = 206 species), a phylogenetically-balanced experiment in hydroponics (n = 117 species) and an ecological survey of plants from their natural environments in central England (n = 81 species). Data were obtained from Broadley et al. (2003, 2004) and Thompson et al. (1997) Variation in [Ca]shoot partitioned (%)

Variation in [Mg]shoot partitioned (%)

Classification level

Literature survey

Hydroponic experiment

Ecological survey

Hydroponic experiment

Ecological survey

Informal group Order Species

34.6 19.9 45.5

36.6 27.2 36.2

52.5 21.0 26.5

33.4 31.6 35.1

55.8 23.5 20.7

Rosales) and asterid (Apiales, Asterales, Lamiales and Solanales) clades have the highest [Ca]shoot . By contrast, there is considerable variation in the [Ca]shoot of monocot orders. The [Ca]shoot is significantly lower in the commelinoid orders (e.g. Arecales and Poales) than in the non-commelinoid orders (e.g. Asparagales). Interestingly, a recent survey of [Ca]shoot in orders within the Magnoliid clade suggests that the Laurales, Magnoliales and Piperales also have lower [Ca]shoot than the eudicots (White & Broadley, unpublished observations). All these data imply that ancient evolutionary events have impacted significantly on the [Ca]shoot of angiosperms. Phylogenetic differences in [Ca]shoot have not yet been resolved at taxonomic levels lower than the order. However, it is noteworthy that the three distinct physiotypes for Ca nutrition, the ‘calciotrophes’, ‘oxalate plants’ and ‘potassium plants’ (Fig. 3.2) are characteristic of particular plant families (Kinzel, 1982; Kinzel & Lechner, 1992). The calciotrophes, such as calcicole plants in the Crassulaceae (Rosales), Brassicaceae (Brassicales) and Fabaceae (Fabales), contain high concentrations of water-soluble Ca complexes in their vacuoles and their accumulation of Ca is stimulated greatly by increasing [Ca2+ ]ext . The oxalate plants can be divided into species that deposit Ca-oxalate crystals in their vacuoles, as exemplified by certain families in the Caryophyllales and Malpighiales, and those that contain soluble oxalate, such as the Oxalidaceae. Increasing [Ca2+ ]ext stimulates Ca accumulation in plants that precipitate Ca-oxalate, but not in plants containing soluble oxalate. The potassium plants, which are characteristic of the calcifuge families Apiaceae (Apiales), Campanulaceae and Asteraceae (Asterales), contain little mineralised or watersoluble Ca and have a high [K]shoot :[Ca]shoot ratio. The ability to accumulate Ca appears to be unrelated to the modifications in organic acid metabolism associated with photosynthetic adaptations to low water availability, such as C4 or Crassulacean acid metabolism (CAM). The C4 trait occurs in about 7500 (3%) of angiosperm species, and appears to have evolved independently over

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Concentration in shoot dry matter (mmole kg−1)



2500

Sedum album

Oxalis acetosella

Silene inflata

Carex pendula K

2000 K

1500 Ca 1000 K

Ca

500 K

Ca

Ca

water soluble

0 −500

acid soluble

Figure 3.2 Examples of Ca nutritional physiotypes. Calciotrophes, such as Sedum album (Rosales), contain high concentrations of water-soluble Ca complexes in their vacuoles. Oxalate plants are divided into species that deposit Ca-oxalate crystals in their vacuoles, such as Silene inflata (Caryophyllales), and those that contain soluble oxalate, such as Oxalis acetosella (Oxalidales). Potassium plants, such as Carex pendula (Polaes) contain little mineralised or water-soluble Ca and have a high [K]shoot :[Ca]shoot ratio. Figures were adapted from the data of Horak and Kinzel (1971) and Longin and Neirinckx (1977) assuming a d. wt/f. wt quotient of 0.1.

45 times in 19 different families (Sage, 2004). It is present in both commelinoid (Poales) and non-commelinoid (Alismatales) monocot orders, as well as in eudicot orders (Boraginaceae, Brassicales, Caryophyllales, Lamiales, Malpighiales, Zygophyllales), in species with remarkably different abilities to accumulate Ca. Similarly, the ability to perform CAM appears to have evolved many times (Sayed et al., 2001), and this trait has also been reported in both commelinoid (Bromeliaceae, Commelinales) and non-commelinoid (Asparagales) monocot orders, as well as in other Magnoliid (Piperales) and eudicot orders (Asterales, Brassicales, Caryophyllales, Curcurbitales, Gentianales, Lamiales, Malpighiales, Oxalidales, Saxifragales, Vitaceae). There is a strong correlation between the ability of a plant to accumulate Ca and its ability to accumulate other divalent cations, such as strontium (Sr), barium (Ba) and magnesium (Mg). Andersen (1967) observed a positive correlation between the accumulation of Ca and radiostrontium in the shoots of 44 plant species grown in a loamy sand soil contaminated with 89 Sr (Fig. 3.3). White (2001) subsequently demonstrated that the ratio of Sr:Ca concentrations in leaves of Arabidopsis grown on agar was identical to the Sr:Ca concentration

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Shoot 89Sr concentration (µCi g−1 d. wt)

100

80

60

40

20

0 0

5

10

15

20

25 −1

Shoot Ca concentration (mg g

30

35

d. wt)

Figure 3.3 The relationship between the accumulation of radiostrontium and calcium in shoots of 44 different plant species grown on a loamy sand soil containing 2.2 g calcium (Ca), 7.2 mg strontium (Sr) and 2.5 ␮Ci radiostrontium (89 Sr) per kg of dry soil. Data taken from Andersen (1967).

ratio in the medium. An identical observation was made for the accumulation of Ba and Ca in Arabidopsis leaves (White, 2001). These observations suggest that the mechanisms whereby Sr, Ba and Ca are taken up and accumulated by plants lack the ability to discriminate between them, and have been cited as evidence for apoplastic movement of these cations to the xylem of the root (White, 2001). By contrast, although the Mg and Ca concentrations in shoots of different plant species are correlated (Fig. 3.4), a [Ca]shoot /[Mg]shoot quotient of 7.7 was observed, not only when plants were grown hydroponically in the same solution with a Ca:Mg concentration ratio of 2.7:1 (Broadley et al., 2004), but also when plants were collected from their natural habitats (Garten, 1976; Thompson et al., 1997). This suggests that a homeostatic mechanism might maintain the [Ca]shoot /[Mg]shoot quotient irrespective of the rhizosphere Ca and Mg concentrations. Since the phylogenetic variation in [Mg]shoot is similar to that observed for [Ca]shoot (Table 3.1), it is likely that traits having an impact on both [Ca]shoot and [Mg]shoot evolved simultaneously. The Caryophyllales, however, provide an exception to these observations. The [Mg]shoot of Caryophyllales species is often exceptionally high, whilst their [Ca]shoot is no greater than that of other eudicots (Fig. 3.4). This phenomenon warrants further investigation.

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Shoot Mg concentration (% d. wt)

1.0

0.8

0.6

0.4

0.2

0.0 0.0

1.0

2.0

3.0

4.0

5.0

Shoot Ca concentration (% d. wt) Figure 3.4 The relationship between Mg and Ca concentrations in shoots of 117 plant species, representing 24 angiosperm orders and one unassigned family, grown hydroponically in a nutrient solution containing 2 mM Ca2+ and 0.75 mM Mg2+ . Filled circles are species of Caryophyllales, grey triangles are species of Poales and open circles are all other species. Figure redrawn from Broadley et al. (2004).

Hypotheses can be formulated to account for the differences in [Ca]shoot between angiosperm orders (White & Broadley, 2003). Although a plant’s physiotype for Ca nutrition may determine its ability to tolerate Ca within the shoot, since Ca is acquired by the root system from the soil solution and little Ca is translocated in the phloem, [Ca]shoot will be determined principally by the rate of Ca delivery to the xylem relative to the absolute growth rate of the shoot. This will be influenced by root morphology, anatomy and biochemistry, as well as by the Ca transport processes in the plasma membrane and tonoplast of root cells. Differences in the activities of Ca2+ transporters in root cell membranes or in the relative contributions of symplastic and apoplastic pathways to the delivery of Ca to the xylem could impact significantly on [Ca]shoot . The abundance and/or activity of Ca2+ transport proteins might influence Ca2+ fluxes through the symplastic pathway, whereas the structural characteristics of the cell wall, such as its cation exchange capacity (CEC) or presence of Casparian bands, and transpiration rates might influence Ca2+ fluxes through the apoplast. The Ca physiotype of a plant could influence Ca fluxes to the shoot by influencing the sequestration of Ca in the vacuole of root cells. All these characteristics are genetically determined, and this provides the genetic rationale for strategies to improve the Ca content of crops.

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3.3 Identifying genes involved in calcium accumulation Calcium is acquired from the rhizosphere solution and is delivered to the xylem either at the extreme root tip or in places where lateral roots are being initiated (Clarkson, 1993; White, 2001). In these regions the contiguous Casparian band between endodermal cells is absent or disrupted and/or the endodermal cells surrounding the stele are unsuberised. It is speculated that Ca might reach the xylem via an extracellular route in places where the Casparian band is absent or disrupted, or might circumvent the Casparian band by entering the cytoplasm of unsuberised endodermal cells when the Casparian band is present. These are referred to as the apoplastic and symplastic pathways of Ca movement, respectively. Each pathway has distinct advantages and disadvantages (White & Broadley, 2003). The apoplastic pathway allows Ca to be delivered to the xylem without it affecting [Ca2+ ]cyt . This is important because the Ca2+ influx required to initiate important [Ca2+ ]cyt signals is minute compared to that required for adequate nutrition, and could be compromised by high nutritional Ca2+ fluxes through root cells (White, 1998, 2001). However, the apoplastic pathway cannot discriminate effectively between divalent cations, which could result in the accumulation of toxic cations in the shoot (White, 2001; White et al., 2002b), and is influenced markedly by transpiration, which could lead to vagaries in Ca delivery and the development of Ca disorders in developing tissues (Marschner, 1995; McLaughlin & Wimmer, 1999). Moving Ca through the symplastic pathway to the xylem allows the plant to control the rate and selectivity of Ca transport to the shoot (Clarkson, 1993; White, 2001). It is speculated that Ca2+ enters the cytoplasm of endodermal cells through Ca2+ -permeable channels on the cortical side of the Casparian band, and that Ca2+ is pumped from the symplast by the plasma membrane Ca2+ -ATPases or Ca2+ /H+ antiporters of cells within the stele. By regulating the expression and activity of these transporters, Ca could be delivered selectively to the xylem at a rate consistent with the requirements of the shoot. It has been noted that the [Ca]shoot of plant species is correlated with the CEC of their cell walls (White & Broadley, 2003). In the root, the CEC is located in the apoplast, and is attributed to the free carboxyl groups of galacturonic acids in the pectins of the middle lamella. Like [Ca]shoot , root CEC is highest in the eudicots, intermediate in the non-commelinoid monocots and lowest in the commelinoid monocots (White & Broadley, 2003). At low ionic activities in the rhizosphere, root CEC may affect Ca2+ movement to the xylem through both the apoplastic and symplastic pathways (White & Broadley, 2003). The fixed negative charges, and charge screening, associated with the CEC influences both the absolute and relative concentrations of cations in the apoplast. Thus, root CEC exerts a direct effect on the movement of Ca2+ through the apoplast and affects the symplastic movement of Ca2+ indirectly by influencing the rate and selectivity of cation influx across the plasma membrane of root cells.

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Calcium influx to root cells is mediated by Ca2+ -permeable ion channels in their plasma membranes (White, 1998; Miedema et al., 2001; White et al., 2002a; White & Broadley, 2003). These channels not only generate the [Ca2+ ]cyt signals that initiate cellular responses to specific environmental challenges and developmental cues, but may also contribute to nutritional Ca2+ fluxes in particular cell types. The Ca2+ -permeable channels in the plasma membrane of plant cells have been classified on the basis of their voltagedependence into depolarisation-activated (DACC), hyperpolarisation-activated (HACC) and voltage-independent (VICC) cation channels (White, 1998, 2000; Miedema et al., 2001; Demidchik et al., 2002b; Sanders et al., 2002; White et al., 2002a; White & Broadley, 2003). Several types of Ca2+ -permeable DACCs, with distinct pharmacological and electrophysiological characteristics, have been recorded in the plasma membranes of root cells (White, 1998, 2000; White et al., 2002a). Most of these activate at voltages more positive than about –150 to –100 mV under physiological conditions and are thought to transduce general stress-related signals that are initiated by membrane depolarisation (White, 1998, 2000; Miedema et al., 2001; White et al., 2002a). The remainder, which are classified as outward-rectifying K+ channels (KORCs), activate only at voltages more positive than about –50 mV under physiological conditions and catalyse a large K+ efflux simultaneously with a small Ca2+ influx (White, 1997; Gaymard et al., 1998; White et al., 2002a). It has been proposed that the Ca2+ influx through KORCs co-ordinates ion transport, metabolism and gene expression via changes in [Ca2+ ]cyt (De Boer, 1999). The HACCs in root cells activate at voltages more negative than about –100 to –150 mV at physiological [Ca2+ ]cyt , but increasing [Ca2+ ]cyt shifts their activation potential to more positive voltages (Véry & Davies, 2000; Demidchik et al., 2002a). It is thought that their activity is required to raise [Ca2+ ]cyt to initiate and maintain cell expansion, both in cells of the elongation zone and at the apex of root hairs (Kiegle et al., 2000; Véry & Davies, 2000; Miedema et al., 2001; Demidchik et al., 2002a, 2003; White et al., 2002a; Foreman et al., 2003). In addition, mechanosensitive HACCs might orchestrate the changes in morphology induced by gravity, touch or flexure, and elicitor-activated HACCs could raise [Ca2+ ]cyt in response to pathogens (White, 2000; White & Broadley, 2003). Several distinct Ca2+ -permeable VICCs are present in the plasma membrane of root cells. These differ in their cation selectivity, voltage-dependence and pharmacology (Demidchik et al., 2002b, 2003; White et al., 2002a). It has been suggested that Ca2+ influx to root cells through VICCs, which are generally insensitive to cytoplasmic modulators and appear to be the only Ca2+ -permeable channels open in the plasma membrane at physiological voltages, is required to balance the perpetual Ca2+ efflux from the cytosol catalysed by Ca2+ -ATPases and H+ /Ca2+ antiporters and, thereby, provide [Ca2+ ]cyt homeostasis in an unstimulated root cell (White & Davenport, 2002; Demidchik et al., 2002a).

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There has been some recent speculation on the identity of genes encoding Ca2+ -permeable channels in the plasma membranes of plant cells (Clark et al., 2001; Davenport, 2002; Demidchik et al., 2002b; Sanders et al., 2002; Véry & Sentenac, 2002; White et al., 2002a; Talke et al., 2003). It has been suggested that (1) homologues of the Arabidopsis AtTPC1 gene encode DACCs regulated by [Ca2+ ]cyt ; (2) homologues of the Arabidopsis AtSKOR and AtGORK genes encode KORCs; (3) the annexin genes encode HACCs and (4) the genes for cyclic-nucleotide gated channels (CNGCs) and glutamate receptors (GLRs) encode VICCs. In addition, homologues of the low-affinity cation transporter in the plasma membrane of wheat root cells (TaLCT1) could also catalyse Ca2+ influx to plant cells (Clemens et al., 1998). Significantly, many of these genes are expressed in root cells of Arabidopsis. These include AtTPC1 (Furuichi et al., 2001), AtSKOR, which is expressed in the pericycle and xylem parenchyma cells (Gaymard et al., 1998), AtGORK, which is expressed in several cell types (Véry & Sentenac, 2002; Becker et al., 2003), all seven Arabidopsis annexin genes, with the possible exception of AnnAt6 (Clark et al., 2001), at least 14 of the 20 AtCNGCs (Talke et al., 2003) and all 20 AtGLRs (Chiu et al., 2002; Davenport, 2002; White et al., 2002a). Only the expression of AtCNGC4, AtCNGC7, AtCNGC11, AtCNGC15, AtCNGC16 and AtCNGC20 has not been observed in roots. It is not yet known whether the activities of Ca2+ -permeable cation channels in the plasma membranes of root cells impact significantly on [Ca]shoot . However, examining the [Ca]shoot of mutants or transgenic plants, in which their activities are modified, might test this hypothesis. Remarkably, Arabidopsis mutants lacking AtSKOR had a greater [Ca]shoot than wild-type plants, which is consistent with AtSKOR removing Ca2+ from the xylem sap (Gaymard et al., 1998). However, there is little evidence that misexpressing CNGCs or GLRs affects [Ca]shoot . Tobacco mutants overexpressing NtCBP4 (an ortholog of AtCNGC1) or a truncated version of NtCBP4 lacking its C-terminal regulatory domains had the same [Ca]shoot as wild-type plants (Sunkar et al., 2000) and Arabidopsis mutants lacking AtCNGC2 had the same [Ca]shoot as their wild type (Chan et al., 2003). However, the Arabidopsis cngc2 mutant exhibited a reduced tolerance of high [Ca2+ ]ext , which suggested to Chan et al. (2003) that it might be perturbed in a signalling pathway that allows normal growth at high [Ca2+ ]ext . Similarly, Arabidopsis overexpressing AtGLR3.2 had the same [Ca]shoot as wild-type plants, but required a greater [Ca2+ ]ext than wild-type plants to achieve maximal growth (Kim et al., 2001), and when the expression of AtGLR1.1 (Kang & Turano, 2003) or AtGLR3.2 (Davenport et al., 2000) were reduced by antisense, plants became more sensitive to [Ca2+ ]ext toxicity. Again, these observations suggest that the Ca-related phenotypes of Arabidopsis mutants misexpressing AtGLR1.1 or AtGLR3.2 are a consequence of altered Ca homeostasis. The effects of misexpressing AtTPC1, AtGORK or any annexin genes on [Ca]shoot are unknown.

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In addition to testing the hypotheses that known Ca2+ transporters are involved in Ca accumulation, several strategies to identify other genes involved in Ca accumulation have been pursued through the application of functional genomics. Attempts have been made to identify genes that impact on shoot Ca accumulation through the resolution of quantitative trait loci (QTL) detected using mapping populations that show variation in their ability to accumulate Ca in the shoot (Fig. 3.5; see also Chapters 9 and 10). It has been observed that Arabidopsis accessions differ in their abilities to accumulate, and also to tolerate, Ca in their shoots (Bowen, Cotterill, Khoshkoo & White, unpublished data). Analysis of recombinant inbred lines (RILs) derived from a cross between the Arabidopsis accessions Landsberg erecta (Ler) and Cape Verde Island (Cvi) grown on agar containing a [Ca]agar of 3 mM consistently revealed putative QTL for [Ca]shoot at both the top and bottom of chromosome 1 (Fig. 3.5) and occasionally revealed putative QTL for [Ca]shoot on chromosome 4 (44 cM) and chromosome 5 (16 and 76 cM). For each of these putative QTL, with the exception of the second putative QTL on chromosome 5, the Ler allele made a positive contribution to [Ca]shoot . White and Broadley (2003) suggested that a unique insight into the physiology of Ca accumulation might be obtained through the transcriptional profiling of plants subjected to extreme [Ca2+ ]ext . Recently, Maathuis et al. (2003) used a customised AMT (Arabidopsis membrane transporter) oligonucleotide microarray to identify the transcriptional changes that occur when Arabidopsis are starved of Ca. They assayed the expression of 1096 genes encoding representatives of over 20 families of transport proteins. Several hundred AMT genes (48% of the total) responded specifically to Ca starvation, and many more responded to both Ca starvation and other cation stresses. In total, 443 of the 1096 AMT genes responded to Ca starvation by a greater than twofold change in expression. This presumably reflects the essential, and unique, functions of Ca in the plant. The expression of AMT genes encoding members of most families of transport proteins responded to Ca starvation, often by down-regulation. These ranged from genes encoding V-ATPases and P-type ATPases, to those encoding aquaporins, and anion and metal transporters. In particular, the expression of genes for several Ca2+ ATPases (AtECA1/AtECA4, AtACA1, AtACA2, AtACA4, AtACA8, AtACA10) and vacuolar Ca2+ /H+ -antiporters (AtCAX2, AtCAX8) that remove Ca2+ from the cytosol was decreased by Ca starvation, but the expression of others (AtACA7, AtACA11, AtACA12, AtCAX5, AtCAX6) was increased (Maathuis et al., 2003). The expression of genes for several (putative) Ca2+ -permeable cation channels also responded to Ca starvation. The expression of three AtCNGCs (AtCNGC8, AtCNGC9, AtCNGC17) and four AtGLRs (AtGLR1.4, AtGLR2.2, AtGLR2.3, AtGLR3.5) was decreased by Ca starvation, and the expression of three AtCNGCs (AtCNGC2, AtCNGC12, AtCNGC19) and three AtGLRs (AtGLR1.2, AtGLR2.4, AtGLR2.8) was increased by Ca starvation. Other genetic responses to Ca starvation, such as changes in the expression of genes for enzymes in the

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A CVI

Frequency

30

20

Ler

10

0 0

5

10

15

20

Shoot Ca concentration (µmol g

−1

25

30

f. wt)

3.0 Lod score (−) or Ler allele effect (- - -)

B 2.5 2.0 1.5 1.0 0.5 0.0

0

20

40

60

80

100

120

Chromosome 1 (cM)

Figure 3.5 (A) Frequency distribution of the shoot Ca concentration of 157 recombinant inbred lines (RILs) derived from a cross between the Arabidopsis accessions Landsberg erecta (Ler) and Cape Verde Island (Cvi). Plants were grown for 21 days on 0.8% (w/v) agar containing 1% (w/v) sucrose and basal salts according to Murashige and Skoog (1962). The agar Ca concentration was 3 mM. (B) Putative quantitative trait loci (QTL) on Arabidopsis chromosome 1 consistently found to impact on shoot Ca concentration. The QTL analysis was performed on a sub-population of 46 RILs derived from a cross between Ler and Cvi accessions in which 99 molecular markers have been mapped (Alonso-Blanco et al., 1998). Data were analysed using the interval mapping option of the mapQTL programme (van Ooijen & Maliepaard, 1996). Again, plants were grown for 21 days on 0.8% (w/v) agar containing 1% (w/v) sucrose and basal salts according to Murashige and Skoog (1962). Courtesy of Bowen, Cotterill, Khoshkoo & White, unpublished observations.

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Table 3.2 Mean percentage differences in the elemental content of leaves of soil-grown Arabidopsis mutants compared to leaves of wild-type plants (P ≤ 0.05, n = 8–11; Lahner et al., 2003). Eighteen elements were analysed. Mutant Li Na

Mg

P

K

Ca

71:13 –25 –16 76:24 –20 –16 –12 89:54 –28 –16 94:68 –10 110:35 20 –31 112:50 22 –33 120:01 29 13 121:33 –29 124:05 –22 –23 132:01 32 53 30 15 132:31 –10 145:01 30 –37 152:54 –9 10 –9

Cr Mn Fe

Co

Ni

Cu

Zn

As

Se Mo Cd Pb –40 36

–10 –35 –10 –23 29 –62 –10

30

13 90 22 19

–18 40

37

112 –10 –14

34 10 39 –40

–30 30 18

20

biochemical pathways producing Ca-chelators, or of transcriptional cascades that lead to the modification of plant anatomy and/or morphology, await fullgenome transcriptional profiling. The genetic responses to excessive [Ca2+ ]ext also remain to be identified. Information from both QTL and microarray analyses could be used to formulate hypotheses on the impact of specific genes on [Ca]shoot , which can be tested by investigating the phenotype of transgenic plants misexpressing candidate genes. To complement these ‘reverse-genetic’ approaches, Lahner et al. (2003) have proposed mineral element profiling of mutant plants to identify the genes involved in Ca accumulation. In a pioneering study, they identified 13 fast-neutron generated Arabidopsis mutants, derived from 2373 parental lines, whose leaf Ca concentrations differed from wild-type plants (Table 3.2). Most of these mutants had reduced leaf Ca concentrations. Assuming that each was mutated in a different gene, this implies that over 0.5% of the Arabidopsis genome (>140 genes) might impact on Ca accumulation. Remarkably, none of these mutants were perturbed only in leaf Ca concentration. This suggested to Lahner et al. (2003) that homeostasis in the concentrations of mineral elements were linked by complex biochemical interactions.

3.4

Identifying genes involved in calcium tolerance (protecting the cytosol from an excessive calcium load)

Submicromolar [Ca2+ ]cyt is essential for energy metabolism, because of the low solubility product of Ca2+ and phosphate (Sanders et al. 1999, 2002; White & Broadley, 2003). In a plant cell, therefore, it is likely that the mechanisms

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catalysing Ca2+ efflux from the cytosol operate continuously, and that [Ca2+ ]cyt is effectively buffered as Ca2+ -chelates. The Ca2+ -ATPases and Ca2+ /H+ -antiporters maintain a submicromolar [Ca2+ ]cyt by removing cytosolic Ca2+ to either the apoplast or the lumen of intracellular organelles, such as the vacuole, endoplasmic reticulum (ER) or golgi (White & Broadley, 2003). Plant Ca2+ ATPases belong to either the Ptype ATPase type-IIA or type-IIB families (Geisler et al., 2000; Sze et al., 2000; Axelsen & Palmgren, 2001). The absence (IIA) or presence (IIB) of an N-terminal autoregulatory domain, containing a binding site for Ca-calmodulin (CaM) plus a serine-residue phosphorylation site, distinguishes these families. The Arabidopsis genome contains four type-IIA (AtECAs 1 to 4) and ten typeIIB Ca2+ -ATPases (AtACAs 1, 2, 4 and 7 to 13; Axelsen & Palmgren, 2001). Different Ca2+ -ATPases may be present in the same cell and even on the same membrane. This suggests that each is functionally distinct and specialised to specific cellular processes requiring distinct spatial or temporal expression. The CaM binding-sites of type-IIB Ca2+ -ATPases are also quite diverse, and it has been speculated that each type-IIB Ca2+ -ATPase may have a unique affinity for CaM or may bind a different CaM isoform. In Arabidopsis, a small gene family encodes CaM isoforms (McCormack & Braam, 2003), which suggests a considerable flexibility in the regulation of plant Ca2+ -ATPase activities. Interestingly, the disruption of Ca2+ sequestration in the ER in Arabidopsis lacking AtECA1 results in reduced growth at low [Ca2+ ]ext (Wu et al., 2002), but the reason for this is unclear. Small gene families also encode the Ca2+ /H+ -antiporters of plants. Eleven genes encoding putative Ca2+ /H+ -antiporters have been identified in the Arabidopsis genome (AtCAX: Hirschi, 2001; Mäser et al., 2001). The transporters AtCAX1, AtCAX2 and AtCAX4 have been shown to reside in the tonoplast (Hirschi, 2001; Cheng et al., 2002a, 2003). Hirschi (2001) speculated that the role of CAXs was to maintain [Ca2+ ]cyt homeostasis by removing excess cytosolic Ca2+ to the vacuole. This is consistent with the phenotype of transgenic tobacco overexpressing AtCAX1, AtCAX2 or the yeast vacuolar Ca2+ /H+ -antiporter gene VCX1, which have higher [Ca2+ ]shoot than wild-type plants (Hirschi, 2001; Hirschi et al., 2001), Arabidopsis mutants lacking AtCAX1, which have lower [Ca2+ ]shoot than wild-type plants (Catalá et al., 2003) and plants overexpressing a vacuolar H+ -pyrophosphatase gene, which show increased vacuolar Ca accumulation (Gaxiola et al., 2001). It is also consistent with the increased expression of AtCAX1 and AtCAX3 (but not AtCAX2 or AtCAX4) when [Ca2+ ]ext is increased (Shigaki & Hirschi, 2000; Hirschi, 2001; Cheng et al., 2002a). Significantly, tobacco overexpressing AtCAX1 exhibit Cadeficiency disorders, but those overexpressing AtCAX2 do not (Hirschi, 2001), and Arabidopsis mutants lacking AtCAX1 grow better than wildtype at low [Ca2+ ]ext (Cheng et al., 2003) but worse at high [Ca2+ ]ext (Catalá et al., 2003). This suggests that misexpression of vacuolar Ca2+ /H+ -antiporter genes impacts on [Ca2+ ]cyt homeostasis. It is possible that the activity of Ca2+ -ATPases and

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Ca2+ /H+ -antiporters in root cells could influence Ca transport to the xylem and, thereby, Ca accumulation. However, the elevated [Ca2+ ]shoot in transgenic tobacco overexpressing AtCAX1 or AtCAX2 (Hirschi, 2001) and the reduced [Ca2+ ]shoot in Arabidopsis mutants lacking AtCAX1 (Catalá et al., 2003) appears to countermand this, since the accumulation of Ca2+ in the vacuoles of root cells might be expected to reduce the symplastic Ca2+ flux to the shoot. Calcium tolerance might also be affected by the mechanisms buffering Ca2+ within the apoplast, cytoplasm or vacuole. Within the shoot, Ca follows the apoplastic route of the transpiration stream (Marschner 1995; Karley et al., 2000). Already, it has been noted that cell walls are capable of binding Ca2+ and that the [Ca]shoot of different plant species is correlated with the CEC of their cell walls. It is likely that some aspects of Ca tolerance are associated with the ability of plants to sequester Ca2+ in their cell walls, which will protect the cytosol from excessive Ca2+ influx from the apoplast. Thus, genes influencing cell wall CEC will impact on the ability of a plant to accumulate Ca. Accumulation of Ca in specific cell types has also been suggested, and it is speculated that the ability of some calcicole species, such as Leontodon hispidus and Centaurea scabiosa, to tolerate high [Ca2+ ]ext may be related to their ability to accumulate Ca in their trichomes (De Silva et al., 1996). The cell’s ability to buffer cytosolic Ca2+ is also critical. It is important that [Ca2+ ]cyt signals are initiated in response to appropriate developmental cues and environmental challenges, but that they are not triggered serendipitously. The cell’s buffering capacity for cytosolic Ca2+ is relatively high (0.1 to 1 mM; Malhó et al., 1998). This is affected by inorganic and organic anions and by Ca2+ -binding proteins. However, the inorganic and organic anions also participate in energy metabolism and the Ca2+ -binding proteins are obligate components of signal-transduction cascades (Reddy, 2001; Snedden & Fromm, 2001; Cheng et al. 2002b; Luan et al., 2002; Sanders et al., 2002; White & Broadley, 2003). The Ca2+ -binding proteins include CaM, with an estimated cytosolic concentration of 5 to 40 ␮M and four Ca2+ -binding sites with K d s between 10−7 and 10−6 M per molecule (Zielinski, 1998; Snedden & Fromm, 2001; Luan et al., 2002; McCormack & Braam, 2003), CaM-like proteins with one to six putative Ca2+ -binding sites per molecule (Reddy, 2001), calcineurin B-like proteins (CBLs) with at most three Ca2+ -binding sites (but four EF hand motifs) per molecule (Kolukisaoglu et al., 2004) and calcium-dependent protein kinases (CDPKs) with one to four functional EF hand motifs per molecule (Reddy, 2001; Cheng et al., 2002b), all having K d s between 10−9 and 10−5 M, and annexins, which can constitute up to 0.1% of the cellular protein and bind Ca2+ within their ‘endonexin fold’ (White & Broadley, 2003). Interestingly, the expression of many genes encoding proteins involved in signal transduction is responsive to changes in [Ca2+ ]ext and/or Ca accumulation (Luan et al., 2002; White & Broadley, 2003), and it is likely that a plant’s ability to alter the

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abundance of these proteins in response to changes in apoplastic and/or vacuolar Ca2+ concentrations will influence its aptitude to signal using [Ca2+ ]cyt and, thereby, its tolerance of high [Ca]shoot . In the ER, calreticulin, calsequestrin, calnexin and molecular chaperone binding proteins (BiPs) bind Ca2+ , and their abundance impacts on cellular Ca accumulation and [Ca2+ ]cyt homeostasis. Plants that overexpress calreticulin, the main Ca2+ -binding protein in the ER, have a greater [Ca]shoot than wild-type plants (Wyatt et al., 2002) and plants with reduced calreticulin concentrations grow worse than wild-type plants at low [Ca2+ ]ext (Persson et al., 2001). Similarly, Ca sequestration within the vacuole, which is the main cellular Ca store, contributes significantly to Ca accumulation, [Ca2+ ]cyt homeostasis and Ca tolerance. The accumulation of Ca in the vacuole has been discussed earlier in this chapter in terms of the three physiotypes for Ca nutrition: Calciotrophes, which contain high concentrations of water-soluble Ca complexes in their vacuoles; oxalate plants, which contain soluble oxalate or deposit Ca-oxalate crystals in their vacuoles; and potassium-plants, which contain little mineralised or watersoluble Ca in their vacuoles (Fig. 3.2). It is noteworthy that both calciotrophes and oxalate plants are capable of buffering large Ca2+ concentrations in their vacuoles, allowing them to tolerate a high [Ca]shoot , whereas potassium plants are predominantly calcifuge. Thus, genes involved in the synthesis and transport of organic acids appear to influence a plant’s ability to tolerate high [Ca]shoot . In addition, the vacuole also contains Ca2+ -binding proteins, such as the radish RVCaB protein (Yuasa & Maeshima, 2000). The abundance of these proteins may impact significantly on [Ca]shoot . It is expected that transgenic plants overexpressing these proteins will have a greater [Ca]shoot than wild-type plants, and that plants lacking these proteins will have a lower [Ca]shoot than wild-type plants.

3.5

The genetics of calcium accumulation by plants

In conclusion, there are differences in [Ca]shoot between plant species (Fig. 3.1) and between individuals of a particular species (Fig. 3.5) grown in the same environment. This attests to the genetic basis of Ca accumulation by plants. Various strategies to identify the genes impacting on [Ca]shoot have been pursued. These include (1) the analysis of transgenic plants misexpressing candidate genes (described in Sections 3.3 and 3.4), (2) the identification of genes whose expression changes when plants are exposed to unnaturally high or low [Ca2+ ]ext (e.g. Maathuis et al., 2003) and (3) the identification of genes from QTL analyses of RILs (Fig. 3.5) or the examination of mutants with altered [Ca]shoot (Table 3.2). Using these strategies, differences in the [Ca]shoot of plants have been attributed to differences in the regulation of genes impacting on Ca uptake by roots or Ca

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transport to the shoot, such as AtSKOR, or to the expression of genes impacting the apoplastic or vacuolar chelation of Ca2+ , such as those influencing cell wall CEC, organic acid metabolism and the expression of vacuolar Ca2+ /H+ antiporters or Ca2+ -chelating proteins. In addition, although the misexpression of genes encoding CNGCs and GLRs does not appear to affect [Ca]shoot , it does alter growth responses to [Ca2+ ]ext , presumably by affecting [Ca2+ ]cyt homeostasis (Section 3.3), and analogous effects on plant growth responses to [Ca2+ ]ext are also observed in plants misexpressing Ca2+ -ATPases or Ca2+ /H+ antiporters (Section 3.4). It is thought that the practical benefits of identifying genes impacting [Ca]shoot will be twofold. First, the prevention of Ca-deficiency disorders in agriculture and second, an increase in the Ca content of crops. Regarding the latter, it has been observed that changing the dietary habits of a population from a bean-rich to a rice-rich source of food increases the incidence of Ca-deficiency disorders in humans (Graham et al., 2001). Knowledge of the genetic potential for increasing the Ca content of edible portions of commelinoid monocots could inform plant-breeding strategies to alleviate Ca-deficiency disorders in populations reliant on these crops. Alternatively, phylogenetic information could be used to identify crops with higher Ca content. Thus, Ca malnutrition in humans might be addressed.

Acknowledgements I thank all my colleagues and collaborators, especially Martin Broadley and John Hammond, for their contributions to the ideas and figures presented here, the Biotechnology and Biological Sciences Research Council (UK) for financial support, and the Victoria and Albert Museum (London) for the inspirational label on exhibit A.7-1917 ‘The way that can be told is not the constant way. The name that can be named is not the constant name’.

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Sze, H., Liang, F., Hwang, I., Curran, A.C. & Harper, J.F. (2000) Diversity and regulation of plant Ca2+ pumps: insights from expression in yeast. Annu. Rev. Plant Physiol. Plant Mol. Biol., 51, 433–462. Talke, I.N., Blaudez, D., Maathuis, F.J.M. & Sanders, D. (2003) CNGCs: prime targets of plant cyclic nucleotide signalling? Trends Plant Sci., 8, 286–293. Thompson, K., Parkinson, J.A., Band, S.R. & Spencer, R.E. (1997) A comparative study of leaf nutrient concentrations in a regional herbaceous flora. New Phytol., 136, 679–689. van Ooijen, J. & Maliepaard, C. (1996) mapQTLTM , Version 3.0: Software for the Calculation of QTL Positions on Genetic Maps, CPRO-DLO, Wageningen. Véry, A.-A. & Davies, J.M. (2000) Hyperpolarisation-activated calcium channels at the tip of Arabidopsis root hairs. Proc. Natl. Acad. Sci. USA, 97, 9801–9806. Véry, A.-A. & Sentenac, H. (2002) Cation channels in the Arabidopsis plasma membrane. Trends Plant Sci., 7, 168–175. White, P.J. (1997) Cation channels in the plasma membrane of rye roots. J. Exp. Bot., 48, 499–514. White, P.J. (1998) Calcium channels in the plasma membrane of root cells. Ann. Bot., 81, 173–183. White, P.J. (2000) Calcium channels in higher plants. Biochim. Biophys. Acta, 1465, 171–189. White, P.J. (2001) The pathways of calcium movement to the xylem. J. Exp. Bot., 52, 891–899. White, P.J., Bowen, H.C., Demidchik, V., Nichols, C. & Davies, J.M. (2002a) Genes for calciumpermeable channels in the plasma membrane of plant root cells. Biochim. Biophys. Acta, 1564, 299–309. White, P.J. & Broadley, M.R. (2003) Calcium in plants. Ann. Bot., 92, 487–511. White, P.J. & Davenport, R.J. (2002) The voltage-independent cation channel in the plasma membrane of wheat roots is permeable to divalent cations and may be involved in cytosolic Ca2+ homeostasis. Plant Physiol., 130, 1386–1395. White, P.J., Whiting, S.N., Baker, A.J.M. & Broadley, M.R. (2002b) Does zinc move apoplastically to the xylem in roots of Thlaspi caerulescens? New Phytol., 153, 199–211. Wyatt, S.E., Tsou, P.-L. & Robertson, D. (2002) Expression of the high capacity calcium-binding domain of calreticulin increases bioavailable calcium stores in plants. Transgenic Res., 11, 1–10. Wu, Z., Liang, F., Hong, B., Young, J.C., Sussman, M.R., Harper, J.F. & Sze, H. (2002) An ER-bound Ca2+ /Mn2+ pump, ECA1, supports plant growth and confers tolerance to Mn2+ stress. Plant Physiol., 130, 128–137. Yuasa, K. & Maeshima, M. (2000) Purification, properties, and molecular cloning of a novel Ca2+ binding protein in radish vacuoles. Plant Physiol., 124, 1069–1978. Zielinski, R.E. (1998) Calmodulin and calmodulin-binding proteins in plants. Annu. Rev. Plant Physiol. Plant Mol. Biol., 49, 697–725.

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Sulphur Malcolm J. Hawkesford

4.1 Introduction Sulphur (S) is required for plant growth. It is found in amino acids, cysteine and methionine, and therefore most proteins. In addition, cysteine is the essential functional component of the ubiquitous tripeptide glutathione which is involved in many cellular redox processes as well as being a major form of transported and stored reduced S. Sulphur occurs in a variety of other organic compounds within the cell, including Fe-S proteins, co-enzymes, thioredoxins, sulpholipids and glucosinolates. Uptake and assimilation of sulphate (SO4 2− ), as well as the various biosynthetic pathways, are coordinated with nutrient supply and plant demands, resulting in a complex and regulated interacting network of plant metabolism. Many of these interactions have only become apparent with the advent of recent genomic studies. In addition, genome projects have revealed unexpectedly large gene families for many of the components of the SO4 2− uptake and assimilatory pathway. This chapter will highlight the roles of the gene family members, and survey the extensive network of metabolism that is interconnected with primary S nutrition (Fig. 4.1). Recent impetus to the study of S in an agricultural context has arisen as a consequence of the recognition of S deficiency as a limiting factor in crop yield and quality. This has become an increasing problem due to decreased aerial inputs of S to agricultural land originating from emissions of fossil fuel burning activities (McGrath et al., 2002). Wheat (Triticum aestivum) requires 15–20 kg ha−1 and oilseed rape (Brassica napus) in the region of 50 kg ha−1 for optimum growth and quality, quantities that were previously provided by atmospheric deposition. Since the early 1990s S emissions in the United Kingdom have reduced several-fold and deposition in most areas is insufficient to meet fertiliser requirements. Remedial action involves appropriate fertiliser applications and correct early diagnosis is a challenge. Deficiency symptoms may easily and catastrophically be confused with N deficiency as N-use efficiency is absolutely dependent upon balanced S availability (see Byers & Bolton, 1979; Randall et al., 1981; McGrath & Zhao, 1996; Fismes et al., 2000). In addition to yield benefits, there are important quality and health aspects to optimise plant S nutrition. Grain protein composition may be substantially modified by S availability. Apart from decreased nutritional quality (lower amounts of the amino acids, cysteine and methionine, which are essential for human and

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Pathogen defences S-rich proteins e.g. thionins

allins

glucosinolates

Elemental S

Secondary S-compounds S-assimilation

GSH

sulpholipids

PCs

Aspartatederived amino acid SMM pathway

SAM

Indole metabolism Flavonoids Auxins Jasmonates

ethylene spermidine DMSP

Methyl donor

Stress responses

Metabolite biosynthesis

Figure 4.1 Sulphur assimilation is linked to multiple metabolic pathways, responsible for a diverse range of physiological functions. Three major areas include primary metabolite biosynthesis, stress responses and pathogen defences. GSH: glutathione; PCs: phytochelatins; SAM: S-adenosylmethionine (S-AdoMet); SMM: S-methylmethionine; DMSP: dimethylsulfoniopropionate.

animal nutrition), decreased content of glutenins affects dough extensibility and resistance, which directly influence bread texture (Zhao et al., 1999). Limiting S availability has been shown to favour the synthesis and accumulation of S-poor or low-S storage proteins such as ␻-gliadin and high molecular weight subunits of glutenin at the expense of S-rich proteins. Sulphur deficiency also decreases the proportion of polymeric proteins in total proteins, but shifts the distribution of polymeric proteins towards lower molecular weight. These changes in protein composition are associated with alterations of dough rheology. Appropriate fertiliser treatments are able to remediate deficiencies, and significant responses of bread-making quality of wheat grain to the addition of S fertilisers have been established under field conditions. Efforts to improve the S content of agricultural crops are driven by the need to supplement the rather low levels of the essential S-containing amino acids in animal and human diets, particularly when there is a dependency on legumes. Engineering seed-specific expression of sunflower seed albumin (S rich) in a lupin substantially increased the methionine content of the seed (Molvig

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et al., 1997). A cautionary aspect was that there was little difference in seed total S content, rather a shift of the S pools, even in the presence of adequate S supply. It is apparent that internal controls exist which limit the biosynthetic pathway. This may be as simple as S delivery to the seed via the SO4 2− transporters, or it may be a more complex control of the pathway, and underlines the need for a broad approach to pathway engineering.

4.2 Acquisition of sulphate Sulphate uptake through the roots has been described as a combination of a high affinity and saturable process along with non-saturable components at higher concentrations (Leggett & Epstein, 1956). The recent identification of a gene family for SO4 2− transporters with differing affinities for SO4 2− , is a vindication for this pioneering work. Accumulation in the cell is driven against a concentration gradient by coupling transport with the proton gradient, with a ratio of 3H+ :SO4 2− ion (Lass & Ullrich-Eberius, 1984). Sulphur must then be distributed around the plant to meet biosynthetic requirements, and this is achieved primarily with movement of SO4 2− . In some circumstances, transport of organic S forms may be important, for example in the form of the tri-peptide, glutathione, or as the methionine derivative, s-methyl methionine (SMM) (Rennenberg et al., 1979; Herschbach & Rennenberg, 2001; Bourgis et al., 1999). Initial transport processes facilitate inwardly directed radial transfer within the root and unloading (efflux) into the xylem. Subsequently, in the shoot, further influx/efflux steps are required for xylem unloading, cell-to-cell transfer, phloem loading/unloading and finally transport into the chloroplast, as the site of reductive assimilation. In addition, an important contribution to cytoplasmic homeostasis is achieved by storage in the vacuole, requiring influx/efflux systems across the tonoplast. Despite the contrasting energetic circumstances of these different membranes, it is proposed that members of a single gene family are responsible for many of these transport steps. This specialisation of function is outlined below (see also Hawkesford, 2003). Differential activity of these various transport systems will determine the fate of S and ultimately S use efficiency with regard to final sink destination within the plant (Hawkesford, 2000). Whilst the S demand of the plant is primarily met by the uptake of SO4 2− from the soil via the root system, some plants are also able to utilise, to a limited extent, H2 S and SO2 and both of these gases can supply S to the aerial parts of the plant for plant growth (Westerman et al., 2000; Tausz et al., 2003). H2 S is accumulated as a saturable process, reflecting enzyme-catalysed incorporation by OAS(thiol)lyase (OAS-TL), whilst SO2 appears not to saturate as its conversion to SO4 2− is rate limiting prior to its utilisation in the assimilatory pathway.

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4.3 The sulphate transporter family The cloning of the first SO4 2− transporters was achieved by screening plant cDNA libraries in a SO4 2− transport deficient mutant of yeast (Smith et al., 1995a,b, 1997). Complementation of the mutant led to the isolation of three SO4 2− transporter genes from the tropical legume, Stylosanthes hamata (Smith et al., 1995a) and a single gene from barley (Smith et al., 1997; Vidmar et al., 1999). Subsequently, SO4 2− transporter genes were identified from a wide range of organisms including wheat (Buchner et al., 2004), Sporobolus stapfianus (Ng et al., 1996) and tomato (Howarth et al., 2003b). The most comprehensive analysis has been for Arabidopsis (see Takahashi et al., 1996, 1997, 2000; Vidmar et al., 2000; Shibagaki et al., 2002; Yoshimoto et al., 2002, 2003). In addition, there are numerous accessions for rice and Brassica species (see legend of Fig. 4.2). Although the first cDNAs for SO4 2− transporters were cloned directly and were limited to those that complemented the yeast mutant or were highly expressed, the availability of full genome sequences from Arabidopsis and rice has allowed the entire gene family to be defined. In Arabidopsis, there are 14 members of this family with a similar number of related genes in rice and Brassica species (mostly Brassica napus, Buchner & Hawkesford, unpublished observations 2004). At present, only some of the respective gene products have had their function verified as SO4 2− transporters, however, no alternative substrates other than selenate (as used in mutant selection screens, Smith et al., 1995b) have been described to date. The genes encode strongly hydrophobic membrane proteins with 12 predicted possible trans-membrane helices. In all sequences except two, there are long N and C terminal regions and no other large extra membrane loops. In the C-terminal region, a STAS (SO4 2− transporters and antisigma factor antagonist) domain, potentially involved in post-translational regulation or binding to cytoskeletal elements, has been identified (Aravind & Koonin, 2000). A phylogenetic tree of the Arabidopsis, Brassica species and rice SO4 2− transporter amino acid sequences, based on sequence similarity, is presented in Fig. 4.2. On the basis of sequence alone, the SO4 2− transporters fall into five definable clusters, referred to as Groups 1–5. The SO4 2− transporters within the clusters have distinct functional characteristics which supports the idea of the sub-types of SO4 2− transporter. All of the putative SO4 2− transporter genes sequenced to date, irrespective of species, fall into these five groups and may be usually assigned as homologues of one of the specific Arabidopsis types which may be considered as reference isoforms (see Hawkesford, 2003). An exception is some partitioning within the groups between dicotyledonous and monocotyledonous plants; however, the group divisions still occur and a similar number of genes exist for all species examined to date. The implication is that gene duplication has occurred in an ancestral plant species and that each isoform survives because of a required specialised function rather than because of functional redundancy.

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OsST4;1 AtSultr4;1 BST4;1 AtSultr4;2 BST4;2

Group 4

OsST5;1 AtSultr5;1 BST5;1 AtSultr5;2 OsST5;2 AtSultr2;1 BST2;1 AtSultr2.;2 BST2;2 OsST2;1 OsST2;2 OsST1;3 AtSultr1;1 BST1;1 AtSultr1;3 BST1;3 AtSultr1;2 BST1;2 OsST1;1 OsST1;2 AtSultr3;5 BST3;5 OsST3;5 OsST3;6 AtSultr3;1 BST3;1 AtSultr3;2 BST3;2 OsST3;1 OsST3;2 OsST3;4 AtSultr3;3 BST3;3 OsST3;3 AtSultr3;4 BST3;4

Group 5

Group 2

Group 1

Group 3

0.1

Figure 4.2 Phylogenetic representation of the plant sulphate transporter amino acid sequences showing subdivision into 5 groups. Accession numbers: Arabidopsis: AtSultr1;1, AB018695; AtSultr1;2, AB042322; AtSultr1;3, AB049624; AtSultr2;1, AB003591; AtSultr2;2, D85416; AtSultr3;1, D89631; AtSultr3;2, AB004060; AtSultr3;3, AB023423; AtSultr3;4, B054645; AtSultr3;5, AB061739; AtSultr4;1, AB008782; AtSultr4;2, AB052775; AtSultr5;1, NP 178147; AtSultr5;2, NP 180139; rice: OsSultr1;1, AF493790; OsSultr1;2, AAN59764.1; OsSultr1;3, BAC98594; OsSultr2;1, AAN59769; OsSultr2;2, AAN59770; OsSultr3;1, NP 921514; OsSultr3;2, AAN06871; OsSultr3;3, AK104831; OsSultr3;4, AK067270; OsSultr3;5, NM 192602; OsSultr3;6, NM 191791; OsSultr4;1, AF493791; OsSultr5;1, BAC05530; OsSultr5;2, BAB03554 and for Brassica sp.: BST1;1, AJ416460; BST1;2, AJ311388; BST2;2, AJ311388; BST3;1, AJ581745; BST3;2, AJ601439; BST4;1, AJ416461; BST4;2, AJ555124; BST5;1, AJ581745; BST1;3, 2;1, 3;3, 3;4 and 3;5 are all unpublished. Alignments were performed using CLUSTAL W program (Thompson et al., 1994) version 1.7 and the tree was drawn using the Treeview32 program (Page, 1996).

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The SO4 2− transporters in Group 1 are characterised by a high affinity for SO4 2− (Km typically 1–10 ␮M). For all species, there seems to be three Group 1 SO4 2− transporters (see Fig. 4.2; Takahashi et al., 2000; Vidmar et al., 2000; Shibagaki et al., 2002; Yoshimoto et al., 2002). Arabidopsis and Brassica each have identifiable homologues; however, it is not possible on a sequence basis alone to assign the rice genes as direct homologues. Based on expression and localisation studies, AtSultr1;1 and AtSultr1;2 appear to be responsible for initial uptake in the root. However, expression also occurs in other tissues, reflecting needs for high affinity SO4 2− transport in other cells and organs. AtSultr1;1 showed the greatest inducibility by S starvation indicating a specific role under nutrient stressed conditions (Yoshimoto et al., 2002). AtSultr1;3 appears to be localised in the sieve element-companion cells complexes of phloem of both roots and cotyledons (Yoshimoto et al., 2003). All SO4 2− transporters in this group show a classical de-repression of expression under S-limiting conditions (Clarkson et al., 1983). In contrast to the Group 1 SO4 2− transporters, all Group 2 transporters examined to date, show a lower (Km > 0.1 mM) affinity for SO4 2− . Group 2 includes two genes for each of Arabidopsis, Brassica and rice, again with the rice sequences being somewhat distinct. The Arabidopsis isoforms have been localised in the vascular tissues (Takahashi et al., 1997, 2000): AtSultr2;1 was localised in the xylem parenchyma cells of roots and leaves, the root pericycle and leaf phloem, and AtSultr2;2 was localised in root phloem and leaf vascular bundle sheath cells. One of the first cloned plant SO4 2− transporters, SHST3, which was shoot expressed and complemented yeast giving a Km for SO4 2− of 100 ␮M (Smith et al., 1995b) belongs to this group. Group 3 sequences have been previously referred to as the ‘leaf group’, based on the localisation of AtSultr3;1, AtSultr3;2 and AtSultr3;3 (Takahashi et al., 1999b, 2000). In addition, a SO4 2− transporter aligning in this group was isolated from shoot tissues of Sporobolus stapfianus (Ng et al., 1996). The clustering that is apparent with types 3;1/3;2, 3;3/3;4 and 3;5/3;6 for Arabidopsis, rice and Brassica is suggestive that these sequence groups diverged in the distant evolutionary past. It is possible that these sub-groups will have distinct functions. At present there is very little information on the function and specific expression patterns of Group 3, and yeast expression studies have failed to confirm their roles as SO4 2− transporters. A Group 4 SO4 2− transporter was reported to be plastid localised (Takahashi et al., 1999a), and by implication it was suggested that this transporter was responsible for the essential step of import of SO4 2− into the plastid prior to reduction. A potential chloroplast targeting sequence at the N-terminus is predicted and would support this localisation (Takahashi et al., 1999a; Godwin et al., 2003). However, recent data utilising a range of reporter constructs suggests that this transporter may be localised in the tonoplast (Takahashi et al., 2003) and may, therefore, be a SO4 2− transporter responsible for either transport in or out of the

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vacuole. Such transporters would be essential to facilitate the observed storage function of the vacuole. The sequences assigned to Group 5 are the least homologous to the rest of the gene family and fall into two groups, which are rather dissimilar to each other. They are truncated proteins and lack the usual long N- and C-terminal domains that are thought to extend into the cytoplasm. These transporters may also be tonoplast located (Buchner & Hawkesford, unpublished observations 2004). If Groups 4 and 5 transporters are confirmed to be tonoplast located, they would be candidates to enable efflux and influx of SO4 2− . 4.4 Regulation of sulphate transporter expression and sulphate assimilation A basic characteristic of SO4 2− uptake is its regulation by S supply. Sulphate uptake capacity has been documented to be ‘de-repressed’ during S starvation in algae (Passera & Ferrari, 1975), in intact plants (e.g. Lee, 1982; Clarkson et al., 1983), in cell cultures (Smith, 1975) and in isolated vesicles (Hawkesford et al., 1993). Under S-limiting conditions, either as a result of interrupted supply or as a result of increased demand (see Lappartient & Touraine, 1996), SO4 2− -uptake is increased, and subsequently reduced following SO4 2− re-supply (Smith et al., 1997; Bolchi et al., 1999; Lappartient et al., 1999). This corresponded to observed changes in abundance of SO4 2− -transporter mRNA, with a de-repression of SO4 2− -transporter expression under conditions of S limitation (Smith et al., 1995a, 1997; Takahashi et al., 2000). In parallel, the internal content of SO4 2− and of reduced-S compounds such as cysteine and glutathione decreased (Smith et al., 1997). In tobacco de-repression of SO4 2− -uptake and transport to the shoots was repressed by glutathione and L-cysteine (Herschbach & Rennenberg, 1994), whereas in S-deficient maize seedlings SO4 2− transporter expression and ATP-sulphurylase were both down regulated only by L-cysteine and not glutathione (Bolchi et al., 1999, and see Section 4.8). In young barley seedlings grown hydroponically, SO4 2− transporter expression increased substantially after only two days of S starvation, in parallel with an observed depletion of intracellular S pools. Following re-supply of external SO4 2− , a decrease in mRNA pools, transporter protein and transporter activity occurred within just a few hours, with a concomitant increase in tissue concentrations of SO4 2− , cysteine and glutathione. There was a rapid turnover of both mRNA and protein. In both cases, activity of SO4 2− transport paralleled mRNA abundance (Smith et al., 1997) and SO4 2− transporter protein occurrence in a plasma membrane fraction (Hawkesford & Wray, 2000). There was no indication of post-transcriptional or post-translation regulation under these conditions. In addition to the transporters involved in uptake, many other members of the SO4 2−

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transporter family expressed in other tissues, as well as some enzymes of the assimilatory pathway, are under similar regulation (see, for example, Takahashi et al., 1997). These results support the idea of a simple negative feedback or de-repression model of regulation, in which under S-sufficient conditions, the accumulation of SO4 2− -assimilation end products (such as glutathione [GSH] and cysteine) act as repressors of SO4 2− uptake at the level of gene expression. Under S limitation, a decrease in concentration of these compounds removes the repression, increasing transporter activity and maximising SO4 2− uptake. This model is further elaborated with the inclusion of an inducer molecule, O-acetylserine (OAS). The addition of exogenous OAS has been shown to increase ATP-sulphurylase and APS-reductase activity in Lemna minor (Neuenschwander et al., 1991). In a similar experiment with barley seedling roots, increased SO4 2− transporter mRNA pools and transporter activity, together with increased cysteine and glutathione content was observed (Smith et al., 1997). Under these circumstances, OAS acts as an overriding inducer of gene expression, even in the presence of putative repressor molecules. OAS accumulates when insufficient sulphide is available to utilise the OAS for cysteine synthesis. From such observations a model of a ‘regulatory circuit’ has been proposed (Hawkesford & Smith, 1997). In this model, expression of genes involved in uptake and assimilation are under both negative and positive control. The feedback repression and the OAS inducer control loops are able to act antagonistically to modulate SO4 2− uptake and maximise SO4 2− uptake with fluctuating supply and cellular demand (see Hawkesford et al., 2003). This regulatory model is based on that described for prokaryotes (reviewed in Kredich, 1992, 1993). In bacteria, sulphide acts as a repressor and N-acetylserine (N AS; formed non-enzymatically from OAS) acts as an inducer. Both of these molecules interact with the promoter region of the regulated genes via the cysB protein. Although observations suggest that such a mode of regulation, as described above, may occur in plants, no plant homologue of cysB has been identified. Sulphide is responsible for the negative feedback in prokaryotes, however, the S pool responsible for the negative feedback in plants remains to be verified. Mutants of Chlamydomonas reinhardtii altered in their response to S limitation have been identified (Davies et al., 1994, 1996, 1999; Yildiz et al., 1996; Ravina et al., 2002). Three classes of sac (S acclimation) mutant were identified: the sac1 mutants (mutation in a gene with homology to the sodium carboxylate transporter) were unable to de-repress gene expression upon SO4 2− deprivation (Davies et al., 1996); mutants of sac2 (which has not been cloned) show low APS reductase activity but does not block transcription, indicating some post-transcriptional control (Ravina et al., 2002); sac3 represents mutation of a serine/threonine kinase in the Snf1 family (Davies et al., 1999), and shows

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both aberrant high expression of aryl sulphatases and an inability to de-repress SO4 2− transporter expression. In C. reinhardtii at least, there is clear evidence for a sensing and transduction pathway. No homologous genes, or such a regulatory pathway, have been detected or shown to be involved in S signalling in higher plants. The promoter region of the ␤-conglycinin gene, a major S-poor storage protein of soybean, is S-status responsive (see Awazuhara et al., 2002, and references therein). Activity of this promoter and its S regulation (increased expression under S-limiting conditions) was preserved upon transfer into Arabidopsis. The promoter region examined was 1046 bp in length and contained two S-responsive elements as well as an N-responsive element. An element in this promoter region, the so-called SEF4 motif (soybean embryo factor), was also found in the promoter region of the watermelon serine acetyl transferase gene, which is slightly increased in expression during S starvation (Lessard et al., 1991; Saito et al., 1997). The delineation of S nutritional status-responsive promoter elements is high priority and should be facilitated by the large number of S status-responsive genes in higher plants.

4.5 Sulphate assimilation Reductive assimilation of SO4 2− into cysteine occurs in the plastid. This is a multi-step pathway (Fig. 4.3) involving an initial activation of SO4 2− to adenylphosphosulphate (APS), followed by reduction to sulphite (SO3 2− ) catalysed by APS reductase, further reduction to sulphide catalysed by sulphite reductase and finally incorporation into the precursor molecule OAS by the bi-enzyme complex, OAS-TL/serine acetyl transferase. In many cases, the enzymes involved are encoded by multi-gene families (see Table 4.1). Experimental evidence accumulated over many years has demonstrated activities and/or isoforms of many of the enzymes in different sub-cellular compartments. This surprising observation is an indication that these gene families have wider roles than simply reductive SO4 2− assimilation in the plastid. ATP sulphurylase activity has been detected in the cytosol and in chloroplasts of spinach (Lunn et al., 1990; Renosto et al., 1993) and cDNAs encoding cytosolic and plastid isoforms have been cloned from potato (Klonus et al., 1994). Four genes encode isoforms of ATP sulphurylase in Arabidopsis, however, they all have putative transit peptides and would be predicted to be plastid localised (Hatzfeld et al., 2000a). However, depending upon the translation start site, APS2 may be a candidate for a non-plastid localised isoform in Arabidopsis. A suggested function for the cytosolic isoform is in generating APS for sulphation reactions rather than in SO4 2− assimilation, for example, in the synthesis of glucosinolates (Rotte & Leustek, 2000).

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sulphate (out) sulphate transporter

sulphate (in) ATP sulphurylase APS kinase

APS

PAPS

O -sulphated metabolites

APS reductase

sulpholipids

sulphite sulphite reductase

Serine + CoA

Serine acetyl transferase

sulphide OAS thiol lyase

OAS

cystathionine synthase

OPHS

cysteine

EC synthetase

EC

cystathionine

glutathione synthetase

glutathione

cystathionine lyase

homocysteine protein

methionine synthase

methionine homocysteine S-methyltransferase

SAM synthetase

SAM

methionine S-methyltransferase

SMM

Figure 4.3 Biosynthetic pathways for S-containing amino acids and their derivatives. APS: adenosine-5 phosphosulphate; PAPS: phosphoadenosine-5 -phosphosulphate; ␥ -EC: ␥ -glutamyl-cysteine; OAS: O-acetylserine; CoA: acetyl coenzyme A; SAM: S-adenosylmethionine (S-AdoMet); SMM: S-methylmethionine.

Several reports have identified APS reductase as having the highest control of flux through the assimilatory pathway. Increased ATP-sulphurylase and APSreductase mRNA abundance in response to S deprivation has been observed (Gutierrez-Marcos et al., 1996; Yamaguchi et al., 1999). Regulation of protein and enzyme activity in response to S nutrition has been demonstrated for ATP-sulphurylase (Lappartient & Touraine, 1996; Lappartient et al., 1999) and

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Table 4.1 Gene families involved in sulphate uptake, assimilation and related amino acid metabolism Arabidopsis gene family size

Enzyme Sulphate transporter

14

ATP sulphurylase

4

APS kinase

4

APS reductase

3

Sulphite reductase

1

OAS(thiol)lyase

4–9

Serine acetyl transferase

5

Cystathionine ␥ -synthase Cystathionine ␤-lyase Methionine synthase

2

SAM synthetase

1 3

5

AtSultr1;1 AtSultr1;2 AtSultr1;3 AtSultr2;1 AtSultr2;2 AtSultr3;1 AtSultr3;2 AtSultr3;3 AtSultr3;4 AtSultr3;5 AtSultr4;1 AtSultr4;2 AtSultr5;1 AtSultr5;2 APS1 APS2 APS3 APS4 APK1 AKN2

APR1 APR2 APR3

A B C A2 1 (SAT5) 2 (SAT106) 3 (SAT-1) 4 (Atsat-4) 5 (SAT52)

Loci At4g08620 At1g78000 At1g22150 At5g10180 At1g77990 At3g51900 At4g02700 At1g23090 At3g15990 At5g19600 At5g13550 At3g12520 At1g80310 At2g25680 At3g22890 At1g19920 At4g14680 At5g43780 At2g14750 At4g39940 At3g03900 At5g67520 At4g04610 At1g62180 At4g21990 At5g04590

Sub-cellular locations

Selected references

See text

Takahashi et al., 1997, 1999a,b, 2000; Hawkesford, 2003

Cytosol? Plastid Hatzfeld et al., 2000a

Cytosol? Plastid Leustek & Saito, 1999; Lillig et al., 2001

Plastid

Plastid

Gutierrez-Marcos et al., 1996; Suter et al., 2000 Brühl et al., 1996; Nakayama et al., 2000 Hell et al., 1994; Hesse & Höfgen, 1998; Hesse et al., 1999 Howarth et al., 2003a,b; Noji et al., 1998

At4g14880 At2g43750 At3g59760 At3g22460 At1g55920 At2g17640 At3g13110 At4g35640 At5g56760 At1g33320 At3g01120 At3g57050

Cytosol Plastid Mitochondria

Plastid

Ravanel et al., 1998

At5g17920; At3g03780; At5g20980 At3g17390; At4g01850; At1g02500; At2g36880; At5g16450

Cytosol

Eichel et al., 1995; Zeh et al., 2001

Cytosol

Schroder et al., 1997 Shen et al., 2002

Plastid Unknown Mitochondia Unknown Cytosol Plastid

Ravanel et al., 1998

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for APS-reductase (Vauclare et al., 2002). Flux control analysis for Arabidopsis SO4 2− assimilation has indicated that SO4 2− transport and APS-reductase contribute most to pathway control (Vauclare et al., 2002). APS-reductase mRNA abundance was shown to be influenced (decreased) by thiols more than ATPsulphurylase mRNA. The abundance of mRNAs for SO4 2− transporters and APS-reductase, as indicated by RT-PCR, were increased by S limitation in both roots and shoots of Arabidopsis (Takahashi et al., 1997). In contrast, whilst ATP-sulphurylase mRNA abundance was increased in root tissues, a decreased abundance was observed in the shoots. Over-expression of APS-reductase increases flux to cysteine, further supporting the idea that this enzyme limits pathway flux (Tsakraklides et al., 2002). Uniquely, sulphite reductase is not encoded by a gene family. A single gene encodes this enzyme, which is solely located in the plastid. It is this step which determines the subcellular site for the reductive SO4 2− -assimilatory pathway in plants. The Arabidopsis genome contains 9 OAS-TL genes including plastid, cytosolic and mitochondrially-located isoforms (Hell et al., 1994; Hesse et al., 1999; Jost et al., 2000). Many family members (see Yamaguchi et al., 2000) are not well characterised but may have specific functions such as acting as a ␤-cyanoalanine synthase catalysing the detoxification of cyanide (Warrilow & Hawkesford, 1998, 2000, 2002; Hatzfeld et al., 2000b). The position of OAS-TL at the branch point linking SO4 2− assimilation with C/N metabolism provides this enzyme with a critical role in controlling pathway flux. The provision of the substrate, O-acetylserine (OAS) is dependent upon the enzyme serine acetyl transferase (SATase). This also occurs in a small gene family of five genes in Arabidopsis, with isoforms expressed in specific compartments and with tissue specificity. There is also evidence for differential regulation of isoforms in response to stresses such as metals (Howarth et al., 2003a). OAS-TL is present in excess over SATase and the two enzymes occur as a complex, which is dissociated by free OAS. OAS-TL is most active in incorporation of sulphide into cysteine when in the dissociated state. As a consequence of the molar excess of OAS-TL, there will always be un-complexed enzyme and the incorporation of free sulphide into cysteine will always be favoured. However, the state of complex formation between OAS-TL and SATase regulates OAS formation. SATase is most active when in the complexed state. If sulphide is limiting (S-stressed conditions), OAS accumulates and disrupts the complex, hence placing a brake on further OAS synthesis (see Hell & Hillebrand, 2001; Hell et al., 2002; Droux, 2003). Uniquely, in C4 monocotyledonous species such as Zea mays, cysteine synthesis seems to be located in the bundle sheath cells and is spatially separated from glutathione synthesis in the mesophyll cells (Burgener et al., 1998). A consequence of this is that cysteine must function as a transport metabolite between the two cell types. The transporter that would be involved has not been determined. In contrast, a plant glutathione transporter has been recently described (Zhang et al., 2004).

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The role of plant APS kinase, particularly in the plastid, is not well understood (Lillig et al., 2001). Whilst in bacteria and yeast, SO4 2− assimilation proceeds via the production of PAPS catalysed by APS kinase and the subsequent reduction by PAPS reductase, this does not appear to be the case in higher plants, where no gene corresponding to PAPS reductase has been identified. Uniquely, however, the moss, Physcomitrella patens appears to have both APS and PAPS routes to cysteine synthesis (Koprivova et al., 2002). The role of APS kinase in plants may be to provide substrate for the formation of S esters such as sulphated flavonols, glucosinolates and sulpholipids. The gene family size and probably (but not as yet confirmed) multi-compartment localisation must reflect the locations of the corresponding biosynthetic pathways and the cellular demands for these compounds. APS kinase in the plastid would be an important competitor for APS destined for the cysteine biosynthetic route.

4.6 Sulphurtransferases and sulphotransferases There are 18 proteins containing a sulphurtransferases/rhodanese domain in Arabidopsis, which may be subdivided into six groups according to their sequence similarity (Hatzfeld & Saito, 2000; Bauer & Papenbrock, 2002). At least some of these proteins transfer reduced S. Two closely related sulphurtransferases have been characterised in higher plants (Papenbrock & Schmidt, 2000a,b). At least one is mitochondrially located and both appear to be induced during aging and under stressed conditions. It is suggested that they have a role in scavenging and mobilising sulphane-S, rather than, as previously suggested having roles in SO4 2− assimilation or cyanide metabolism. The two isoforms have very similar enzyme properties and expression patterns. It is not known whether their expression is influenced by S nutrition. In addition, there is a separate group of sulphotransferases (also 18 genes in Arabidopsis), which use PAPS as substrate and transfer a SO4 2− group (Gidda et al., 2003; Klein & Papenbrock, 2004). A suggested function of one sulphotransferase is in the inactivation of excess jasmonic acid (Gidda et al., 2003).

4.7 Methionine biosynthesis The synthesis of methionine represents a link of cysteine biosynthesis to the aspartate-derived amino acid biosynthetic pathway (for review, see Hesse & Hoefgen, 2003). Biosynthesis of methionine from cysteine involves three enzymatic steps. OPHS (O-phosphohomoserine) derived from the aspartate pathway is a common substrate for both threonine and methionine synthesis, catalysed by threonine synthase (TS) and methionine synthase (MS), respectively. Cystathionine ␥ -synthase (CgS) catalyses the synthesis of cystathionine from cysteine and OPHS by a trans-sulphuration. This is then converted to

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homocysteine (a ␤-cleavage reaction) by cystathionine ␤-lyase (CbL). Homocysteine is exported from the chloroplast and converted to methionine (by a methylation) by MS. As such the relative activities of CgS and TS will influence biosynthesis of methionine and threonine, respectively. CgS activity almost certainly exerts a major flux control and is probably feedback regulated by methionine or a derivative. Similarly, TS activity is regulated by S-adenosylmethionine (SAM, also known as S-AdoMet), which is a derivative of methionine. These controls effectively maintain the methionine pool within close constraints. Rather small gene families encode the proteins of this pathway (CgS: 2 genes, CbL: 1 gene, MS: 3 genes). Furthermore, methionine is a gateway to many other important S-containing metabolites including SMM, SAM and DMSP. S-methylmethionine (SMM) is a transportable derivative of methionine (Bourgis et al., 1999) which can be converted back to methionine by donating a methyl group to homocysteine in a reaction catalysed by homocysteine S-methyltransferase. Under some circumstances, SMM may be the major S constituent of the phloem sap and functions in S delivery to sink tissues such as seeds. SAM is an important methyl donor and a precursor of the polyamine synthesis pathway (spermidine/spermine biosynthesis pathway), and up to 80% of the methionine pool may be converted to SAM, at the expense of ATP utilisation (Ravanel et al., 1998) by SAM synthetase (SAMS: five genes in the family). Spermidine and spermine have multiple proposed roles including stress responses, pH regulation, DNA replication and senescence processes. Consumption of SAM may increase S demands to meet these needs, although ultimately methionine is recycled. SAM is also the precursor for ethylene (catalysed by ACC (1-aminocyclopropane-1-carboxylic acid) synthase and ACC oxidase), which is a potent modulator of plant growth and development and involved in stress signalling (Wang et al., 2002). Methionine is also not consumed in this reaction but recycled resulting in no net S demand. A side product of the final biosynthetic step for ethylene is cyanide, which is detoxified to ␤-cyanoalanine by ␤-cyanoalanine synthase, an isoform of OASTL (Hatzfeld et al., 2000b; Warrilow & Hawkesford, 1998, 2000, 2002). Dimethylsulphoniopropionate (DMSP) is produced in high concentrations in many marine algae and some higher plants such as salt marsh grasses of the genus Spartina, in sugar cane and in Wollastonia biflora. Its biosynthesis in higher plants is via SMM. It is generally present in low concentrations in other plant species. Several roles have been proposed including salt tolerance and herbivore deterrent.

4.8 Glutathione Glutathione is a tri-peptide containing glutamate, cysteine and usually glycine. Homologues contain serine (hydroxymethylglutathione) or alanine (homoglutathione). The estimated cellular content is 3–10 mM in all cellular

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compartments and undoubtedly its major role is in buffering the redox state of the cell and acting as an anti-oxidant. Glutathione also serves as an important storage compound of reduced S and tissue glutathione content has been shown to be dependent upon S nutrition (Blake-Kalff et al., 1998). Along with SMM, glutathione is a major transportable form of reduced S and may contribute significantly to the delivery of S to storage or reproductive organs such as the seed (Rennenberg et al., 1979; Bourgis et al., 1999). The presence of glutathione in the phloem and the correlation of its abundance with S-nutritional status are suggestive that it is an important long-distance signalling molecule. As such, the leaf tissue S status may be transduced to the root where SO4 2− transporter expression must be modulated (Herschbach & Rennenberg, 1994, 2001; Lappartient et al., 1999). The role of glutathione in signalling S deficiency has been questioned, as both SO4 2− transporter and APS reductase were not induced in the presence of buthionine sulphoximine, a specific inhibitor of ␥ -glutamyl-csyteine synthetase which drastically reduced accumulation of glutathione (Bolchi et al., 1999). In this study, the addition of cysteine under conditions of blocked glutathione synthesis repressed gene expression, giving rise to the suggestion that cysteine rather than glutathione is the regulatory molecule. Glutathione is synthesised in a two-step process involving ␥ -glutamyl synthetase and glutathione synthetase. Evidence from transgenic manipulation of the pathway clearly indicates that the former is rate limiting. The balance between the reduced (GSH) and oxidised forms (GSSG) is maintained in favour of the reduced form by glutathione reductase using NADPH as a source of reductant. Reducing conditions in the cell are required for structural integrity of proteins and for many enzyme activities. The nucleophillic nature of the molecule facilitates its role in reacting with reactive oxygen species (ROS), herbicides, xenobiotics and metals. Synthesis is often induced under stressed conditions. Thorough reviews on the plant biology of glutathione may be found in Grill et al. (2001). A related molecule, phytochelatin (␥ GC)n is involved in metal chelation (reviewed in Cobbett & Goldsbrough, 2002). Phytochelatins (PCs) are synthesised from glutathione by PC synthase. At least two genes occur in Arabidopsis (AtPCS1 and AtPCS2), however, a mutation in AtPCS1 is sufficient to effectively prevent PC synthesis. AtPCS2 is able to function in PC synthesis but it is generally expressed in low levels in all tissues examined to date. AtPCS1 is expressed constitutively and is generally not considered to be induced by metal exposure.

4.9 Nitrogen/sulphur interactions Nitrogen and sulphur metabolism are linked and the ratio of these elements in plants occurs within narrow limits, reflecting the abundance of S-containing

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amino acids in proteins. As described above, reduced S in the form of sulphide (S2− ) is combined with OAS, which represents the C/N skeleton input to the SO4 2− assimilatory pathway. As such, OAS represents an obvious molecule to enable co-ordination between N and S availability and assimilation. As already described, the SO4 2− uptake and reduction pathway is controlled at the transcriptional level by both S and by OAS availability, as determined by S depletion and OAS feeding experiments (for example Smith et al., 1997). Corroborative evidence of OAS accumulation under S-limiting conditions has been demonstrated (Kim et al., 1999). The N pathways are also controlled both transcriptionally and post-translationally. OAS availability will be influenced by N nutrition as well as consumption by the S pathway and could contribute to transcriptional regulation of the N-assimilation pathways. These mechanisms would coordinate SO4 2− uptake depending on demand and both N and S availability. When N is limiting, OAS synthesis is low and SO4 2− uptake/assimilation is not induced, irrespective of S supply. Conversely, high N supply induces SO4 2− uptake and assimilation (Hawkesford & Wray, 2000). When S supply is limiting and specific amino acids accumulate (species-specific), for example asparagine and glutamine in barley (Karmoker et al., 1991), levels of nitrate uptake/assimilation decrease. These molecules are further candidates to act as metabolite signals, which stimulate changes in expression of the various pathway components. Confirmation of these signalling roles and the extent of their effect will be revealed by the combination metabolomic and transcriptomic approaches. Nitrogen and S interactions may be observed at the crop scale in terms of yield and quality. From a practical point of view, diagnosis can be difficult, as both can result in visible symptoms of chlorosis. Whilst the correction for either S or N deficiency is readily achieved by application of appropriate fertilisers, misdiagnosis of either will exacerbate the deficiency problem (see Zhao et al., 1996; Blake-Kalff et al., 2000). At high rates of N input, demands for S will be greatest and S deficiency will be most apparent. Similarly, increasing N application can have an inhibitory effect on yield if S availably is limiting. Clearly, an appropriate combination of high N and S application is required for optimum plant growth (Byers & Bolton, 1979; Randall et al., 1981; McGrath & Zhao, 1996).

4.10 Pathogen defence The defence systems of plants include a variety of relatively small, basic, cysteine-rich proteins with antimicrobial activities (reviewed in Broekaert et al., 1997). These include thionins, defensins, and lipid transfer proteins, which usually contain an even number (4–8) of cysteine residues which seem to be involved in structural stability. Many are found extra-cellularly and their antimicrobial action is thought to be by acting on membrane permeability. They are synthesised

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as pre-proteins, may be found in specific sub-cellular compartments and their synthesis is induced by infection. Recently, elemental S has been detected in xylem parenchyma cells of Theobroma cacao (cocoa) and Lycopersicom esculentum (tomato) infected with Verticillium dahliae (Cooper et al., 1996; Williams et al., 2002). The elemental S appeared to be involved in the pathogen defence reaction. The biosynthetic route of the elemental S production is not clear, but the appearance is paralleled with increased expression of SO4 2− transporters and APS reductase (Howarth et al., 2003b) and with transient rises of the glutathione pools (Williams et al., 2002), indicating a route involving a reduced S pool. Induction of glutathione production during the pathogen response also has a role in the protection against associated oxidative stress and in detoxification processes (Gullner & Kömives, 2001).

4.11 Genomic studies Transcriptome profiling has been used for the analysis of global responses to nutrient availability, including S (see Chapter 8). Such analyses will reveal the extent of coordination of components of the pathway and identify other pathways that are co-expressed. To an extent, this indicates co-regulation, and in some cases reveals unexpected links of metabolism. Another outcome is the elucidation of regulatory elements, including global regulators, although this is limited to those subject to regulation themselves. Transcriptome profiling using microarrays containing Arabidopsis genes allows the simultaneous evaluation of the expression of thousands of genes. Using an array with 7200 non-redundant genes, a time course of S deprivation revealed 1507 S-responsive genes, of which 632 were increased by S starvation. This study revealed a co-responsiveness of the flavonoid, auxin and jasmonate biosynthetic pathways as part of the plant response to S limitation (Nikiforova et al., 2003). A similar study compared S-responsive genes under both conditions of S deprivation and in the presence of the putative regulator, OAS, to mimic S deprivation (Hirai et al., 2003). There was a substantial overlap in induced genes in both the S starvation and OAS treatments and substantially different sets of regulated genes identified in roots and shoots. Using arrays of 13 000 non-redundant ESTs corresponding to approximately 9000 genes, 216 and 282 were induced by S starvation in the leaf and root, respectively. Fewer were induced by OAS and larger numbers of genes were repressed in both treatments. Amongst the regulated genes, components of pathways involved in jasmonic acid metabolism and many transcription factors were identified. In this study, genes for SO4 2− transporters and components of the assimilatory pathway were specifically identified and smaller than expected effects upon their expression in response to the treatments were noted. A limitation of this type of array is

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that there may be poor discrimination between members of large gene families and effects on expression could be easily masked. A SO4 2− transporter-deficient mutant (sel-10, an AtSultr1;2 knock-out) was used as alternate approach to mimic S deficiency. Again, transcriptome profiling revealed multiple genes to be regulated by S supply, including genes involved in SO4 2− uptake, assimilation and in the turnover of secondary metabolites. In addition, there was an induction of genes that may alleviate oxidative damage and the generation of reactive oxygen species caused by the shortage of glutathione. (Maruyama-Nakashita et al., 2003). Array analysis of the influence of N supply on the transcriptome of Arabidopsis clearly demonstrated that in addition to expected N-responsive genes and a plethora of other genes (over 100 responsive genes from the 22 500 represented on the array), key S genes were also influenced by the N level, namely two SO4 2− transporters and an APS-reductase (Wang et al., 2003). In view of the known coordination of these pathways (see above), this must be expected.

4.12 Outlook Sulphur is involved in many processes within the cell. As a macro-nutrient it is an important primary resource and it is inevitable that SO4 2− uptake systems and the assimilatory pathway are linked to a network of biosynthetic processes. Recent intense research activity has defined the components of the uptake and assimilatory pathways. In many instances, these are multi-gene families. The degrees of redundancy or the specialised functions in many cases remain to be determined. In some cases, the gene families point to a diversity of function and indicate branch-points or interactions with specific areas of metabolism. Details of the links with the network of interacting pathways are still to be resolved, and undoubtedly further links will become apparent. Much more knowledge is required on the mechanisms and interacting factors which link and co-ordinate these pathways. The determination of cis elements in the respective promoters and of the various interaction factors remains a large gap in our knowledge. Elucidation of these underlying mechanisms remains a challenge for the post-genomic era.

Acknowledgements The work is sponsored by grants from the BBSRC, DEFRA (AR0911), and by Framework V of the EU (QLRT-2000-00103 and QLRT-2001-02928). Rothamsted Research receives grant-aided support from the Biotechnology and Biological Sciences Research Council of the United Kingdom.

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Yoshimoto, N., Inoue, E., Saito, K., Yamaya, T. & Takahashi, H. (2003) Phloem-localizing sulfate transporter, Sultr1; 3, mediates re-distribution of sulfur from source to sink organs in Arabidopsis. Plant Physiol., 131, 1511–1517. Yoshimoto, N., Takahashi, H., Smith, F.W., Yamaya, T. & Saito, K. (2002) Two distinct high-affinity sulfate transporters with different inducibilities mediate uptake of sulfate in Arabidopsis roots. Plant J., 29, 465–473. Zeh, M., Casazza, A.P., Kreft, O., Roessner, U., Bieberich, K., Wilmitzer, L., Hoefgen, R & Hesse, H. (2001) Antisense inhibition of threonine synthase leads to high methionine content in transgenic potato plants. Plant Physiol., 127, 1–11. Zhang, M.Y., Bourbouloux, A., Cagnac, O., Srikanth, C.V., Rentsch, D., Bachhawat, A.K. & Delrot, S. (2004) A novel family of transporters mediating the transport of glutathione derivatives in plants. Plant Physiol., 134, 482–491. Zhao, F.J., Hawkesford, M.J. & McGrath, S.P. (1999) Sulphur assimilation and effects on yield and quality of wheat. J. Cereal Sci., 30, 1–17. Zhao, F.J., Hawkesford, M.J., Warrilow, A.G.S., McGrath, S.P. & Clarkson, D.T. (1996) Responses of two wheat varieties to sulphur addition and diagnosis of sulphur deficiency. Plant Soil, 181, 317–327.

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Phosphorus Kashchandra G. Raghothama

5.1 Introduction Phosphorus (P) acquisition by plants is a major physiological process affecting plant growth and development. In many ecosystems around the world, P is one of the least available essential plant nutrients and it is the second-most limiting nutrient for crop production after N. An estimated 5.7 × 109 ha of land worldwide is deficient in inorganic forms of P (phosphate; Pi), leading to reduced crop production (Batjes, 1997). Tropical acid soils, rich in soluble Fe and Al, are notorious for fixing Pi into unavailable inorganic complexes. Further, in calcareous soils, active Ca reacts with Pi to form calcium phosphates. In other soils, significant amounts of P may be bound as organic forms, for example, in temperate regions where animal manure is spread on the fields. As a consequence of inorganic fixation and organic complexation, the concentration of plant-available Pi seldom exceeds 10 ␮M in soils (Bieleski, 1973; Marschner, 1995) and up to 80% of Pi applied as fertilizers may be unavailable to plants (Holford, 1997). Since plants are generally unable to obtain complex forms of either inorganic or organic P, plants have evolved biochemical and physiological adaptations to survive under P deficiency. Phosphate deficiency in plants affects all energy-requiring processes, including photosynthesis (Plaxton & Carswell, 1999), and it also impacts the composition of membranes, for example, by replacing phospholipids with sulpholipids (Essigmann et al., 1998). These processes are associated with changes in gene expression and altered biochemical pathways (Plaxton & Carswell, 1999; Raghothama 1999, 2000b; Smith et al., 2000; Vance et al., 2003; Franco-Zorrilla et al., 2004). This chapter provides an overview of recent studies of the physiological and genetic factors controlling the activity of Pi transporters and their role in Pi acquisition and distribution in plants. Particular emphasis is placed on the response of plants to Pi deprivation. 5.2 Phosphate acquisition is an inducible response in plants The low soil availability of Pi and its slow rate of diffusion in soils requires plants to obtain this nutrient against a Pi concentration gradient of three orders of magnitude or greater across the plasma membrane. Indeed, using treatments

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to disrupt membrane potential and/or alter cytosolic pH, it has been shown that Pi must be acquired against both its electrical and its chemical gradient (Bowling & Dunlop, 1978; Ullrich-Eberius et al., 1984). Although both highand low-affinity Pi uptake mechanisms have been described under experimental conditions, it is likely that high-affinity Pi uptake will dominate under natural, Pi limiting, conditions (Ullrich-Eberius et al., 1984; Sentenac & Grignon, 1985; Shimogawara & Usuda, 1995; Dunlop et al., 1997). Transport of orthophosphate (H2 PO4 − ), the preferred form of Pi for uptake across the plasma membrane, is mediated by plasma-membrane localized transporters. This process is energized by the co-transport of protons released by plasma-membrane associated H+ -ATPases (Ullrich-Eberius et al., 1984; Sakano et al., 1992). The number of protons (two to four) transported along with the nutrient depends on the availability and tissue Pi concentration (Sakano, 1990). In general, plants respond to Pi deficiency by enhancing their ability to acquire this nutrient. In many instances, an episode of Pi deficiency followed by the resupply of Pi increases the uptake efficiency of plant roots and cell cultures. This response has been observed in both dicotyledon and monocotyledon species. Kinetic analyses have shown that there are no differences in the affinity (Km) of the transport system for Pi uptake whereas the maximum rate of Pi uptake (Vmax) increased during Pi deficiency compared to Pi-replete plants (Drew & Saker, 1984; Shimogawara & Usuda, 1995). This observation suggests that the number of high-affinity Pi transporters with similar kinetic properties increases during Pi deficiency. In contrast, the low-affinity transport system is expressed in a constitutive manner (Furihata et al., 1992). There is one reported gene (Pht2;1), representing a low-affinity Pi transporter that is constitutively expressed in the leaves of Arabidopsis (Daram et al., 1999). Pht2;1 has been shown to be associated with chloroplast membranes suggesting a novel function for this transporter (Versaw & Harrison, 2002). 5.2.1 Inducible phosphate acquisition is associated with increased transcription of high-affinity phosphate transporters The high-affinity transporters, which respond to Pi deficiency belong to the major facilitator superfamily (MFS) family of transporters, which contain 12 membrane spanning regions (Pao et al., 1998). All of the plant high-affinity Pi transporters characterized to date are of a similar size and require a H+ gradient to drive the transport process. These transporters are represented by closely related gene families consisting of multiple members (Raghothama, 1999). In Arabidopsis, nine high-affinity Pi transporters have been described. There are at least 11 members in the rice genome (Uta et al., 2002) and similar number of genes can also be found in the maize genome. Functional analysis of the tomato high-affinity Pi transporter, LePT1, was achieved by complementing a

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yeast mutant (PM971) lacking the high-affinity Pi transport mechanism (Daram et al., 1998). Uptake studies with 32 P-orthophosphate showed that Pi uptake in yeast cells complemented with LePT1 was nearly seven times higher than in the uncomplemented mutant control. The Pi uptake showed a strong dependence on pH, whereas uncouplers carbonyl cyanide m-chlorophenylhydrazone (CCCP) and 2,4-dinitrophenol(DNP) that dissipate pH-gradients across membranes, strongly depress Pi uptake. Plant Pi transporters have a pH optimum of 4.5 to 5.0 in the yeast expression system (Leggewie et al., 1997; Daram et al., 1998), which is comparable to the pH range for optimal Pi uptake in plants (Ullrich-Eberius et al., 1984; Furihata et al., 1992). Many of the high-affinity Pi transporters are preferentially expressed in the roots in response to Pi starvation (Liu et al., 1995, 1998a, 2001; Muchhal et al., 1996; Leggewie et al., 1997; Smith et al., 1997; Daram et al., 1998; Raghothama, 1999, 2000a), which accords with their presumed function in nutrient acquisition. Transcript levels of these transporters increase both with duration and severity of Pi deficiency. This induction is very rapid and transcript accumulation has been observed within 3 to 6 h of exposing cell cultures to Pi deficiency (Liu et al., 1998a). In both Arabidopsis and tomato, accumulation of transcripts can be observed within 12 to 24 h of transferring Pi-fed plants to a Pi-deficient hydroponic medium. The rapid accumulation of transcripts under these conditions suggests that plants activate Pi-starvation response mechanisms well ahead of depletion of cellular Pi. This mechanism could be a part of survival strategy of plants to grow under Pi deficiency. In addition, Pi replenishment studies with Pi deficient plants showed that the transporter transcripts disappeared rapidly after transferring plants to Pi-rich medium. Western blot analysis with antibodies raised against high-affinity Pi transporters has confirmed that transcription is increased during Pi deficiency and that the transcribed message is promptly translated into proteins (Muchhal & Raghothama, 1999). Accumulation of high-affinity transporter proteins in Pi starved roots occurred within 24 h of transfer of plants from Pi sufficient to Pi deficient medium. Analysis of proteins isolated from different membrane fractions revealed that high-affinity Pi transporters were enriched in the plasma membrane. These studies clearly demonstrate that the increased transcription of high-affinity Pi transporters during Pi deficiency is, at least in part, responsible for an increased capacity to acquire the nutrient (Muchhal & Raghothama, 1999). Thus, increases in Vmax in Pi deprived plants, observed in many physiological experiments, can be explained by the increased number of high-affinity Pi transporters in the plasma membrane. Phosphate replenishment studies have further confirmed that both transcript and protein levels of high-affinity Pi transporters are rapidly turned over in roots in response to changes in Pi supply (Shimogawara & Usuda, 1995; Liu et al., 1998a; Muchhal & Raghothama, 1999). This pattern of regulation allows the plants to modulate nutrient uptake without leading to accumulation of Pi to toxic levels. Such a process is essential

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for cells to maintain Pi homeostasis under constantly changing Pi levels in the rhizosphere. 5.2.2 How do plants regulate phosphate homeostasis? At the whole plant level, the combined action of both high- and low-affinity Pi transporters help plants to distribute Pi across plant tissues and organs. Phosphate is relatively mobile in plants and it can move efficiently both in the xylem and in the phloem. For example, during grain filling or reproductive organ development, there is a preferential loading of nutrients, including Pi, to these organs. In addition, Pi is constantly recycled from old and senescing leaves under Pi deficiency. This pattern of movement indicates the active participation of multiple different Pi transporters for the loading and unloading of the nutrient from the xylem and phloem. Analysis of reporter genes, fused to different Pi transporters, has indeed revealed that different high-affinity Pi transporters are involved in recycling the nutrient from senescing leaves and loading into developing siliques of Arabidopsis (Karthikeyan et al., 2002; Mudge et al., 2002). Phosphate homeostasis is vital for proper functioning at the cellular level, and millimolar concentrations of Pi must be maintained in the cytoplasm to support biological activities. Since it is common to find most of the cellular Pi (85% to 95%) in vacuoles under conditions of Pi sufficiency (Anghinoni & Barber, 1980; Nátr, 1992), and since vacuoles play an important role in storing excess Pi and moderating the fluctuations in cytosolic Pi levels, it follows that Pi homeostasis at the cellular level is controlled by Pi-fluxes across the tonoplast membranes. During short-term Pi deficiency, cytosolic Pi levels seem to be maintained at the expense of vacuolar Pi (Bieleski, 1973; Tu et al., 1990; Sakano et al., 1992; Lee & Ratcliffe, 1993; Mimura et al., 1996). 31 P-NMR studies have shown that under Pi deficiency, vacuolar Pi decreases whereas the cytoplasmic Pi levels remain relatively unchanged. These studies have also provided evidence for bidirectional movement of Pi across the tonoplast. The involvement of both tonoplast pyrophosphatase and H+ -ATPase have been proposed for this transport process (Mimura et al., 1990; Sakano et al., 1995). Indeed, kinetic analyses of Pi uptake in intact vacuoles have confirmed the stimulation of transport process by both ATP and pyrophosphate (Massonnearu et al., 2001; Sakano et al., 1992). The high affinity (Km = 5 mM) of the vacuolar Pi-uptake system implies the involvement of low-affinity transport mechanisms. Under certain conditions, the availability of excess Pi may create imbalances in cellular ion homeostasis leading to toxicity. Plants have developed additional Pi efflux mechanisms to maintain ion homeostasis (Elliott et al., 1984; Cogliatti & Santa Maria, 1990). Increased Pi efflux by roots could very well compensate for higher Pi influx under excess nutrient availability (Cogliatti & Santa Maria, 1990). A combination of regulated uptake, transport across organelles, recycling and efflux mechanisms help plants to maintain Pi homeostasis.

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5.2.3 Plant root modifications lead to increased phosphate acquisition Under conditions of persistent Pi deficiency, plants modify their root system to enhance Pi acquisition capacity. In Arabidopsis, Pi deficiency reduces primary root elongation and promotes secondary root branching. Further, root-hair formation and elongation is also enhanced under Pi deficiency (Ma et al., 2001). As a consequence of these modifications, total root surface area increases in relation to above ground parts. Recent molecular evidence suggests that newly formed roots and root hairs become targets for expression of high-affinity Pi transporters induced under Pi deficiency (Daram et al., 1998; Jungk, 2001; Karthikeyan et al., 2002; Mudge et al., 2002). This is an excellent example of a fine coordination between morphological modifications and molecular targeting of transporters to optimize Pi uptake. In addition to modifications to their root systems, the majority of flowering plants (angiosperms) form mycorrhizal symbiosis (Smith & Reid, 1997; Harrison, 1999; Smith et al., 2001). This plant–fungal interaction has been shown to increase the surface area of the root cylinder by >60-fold, and it can thus lead to significant increases in Pi uptake (Foshe & Jungk, 1983). Efficient transfer of Pi between fungi and plant require coordination amongst different Pi transporters. For example, Pi release by vesicular-arbuscular fungi must be acquired by plant Pi transporters located on membranes in the proximity of arbuscules. Both monocots and dicots have Pi transporters that are specifically expressed in arbuscule containing cortical cells, a logical target for acquiring Pi released by fungi (Harrison, 1999; Rausch et al., 2001). Expression of highaffinity Pi transporters of potato (StPT3) and Medicago truncatula (MtPT4) is associated with arbuscule forming root cells (Rausch et al., 2001; Harrison et al., 2002; Uta et al., 2002). One of the rice high-affinity Pi transporters, OsPT11, is specifically induced in response to mycorrhizal symbiosis (Uta et al., 2002). The level of induction is directly correlated with an increasing degree of colonization of roots by mycorrhizal fungi. The coordination between plant and fungi in Pi acquisition is further highlighted by suppression of several plant high-affinity Pi transporters and other Pi-starvation-induced genes upon symbiosis (Burleigh & Harrison, 1997; Liu et al., 1998b; Rausch et al., 2001; Uta et al., 2002); a phenomenon which highlights the ability of plants to switch between mycorrhizal and nonmycorrhizal modes of Pi uptake depending upon the levels of available Pi in the rhizosphere.

5.3 Phosphate transporters 5.3.1 Functional analysis of phosphate transporters There is growing evidence that, in general, plants have a small family of highaffinity Pi transporters. Some of these transporters are specifically induced under Pi deficiency, whilst others are induced in response to mycorrhizal fungi

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association. The variation in the spatial, temporal and Pi-induced patterns of expression of these transporters suggests diverse biological functions. Analysis of the role of high-affinity Pi transporters in plant growth and development can utilize both forward and reverse genetic approaches. The availability of an increasing number of gene-tag or gene knockout mutants of Arabidopsis (e.g. Alonso et al., 2003) is paving new avenues to study the function of individual members of this gene family. An example of the use of a genetic approach to identify and characterize mutants associated with Pi acquisition has been the extensive analysis of mutants such as pho1 and pho2. Analysis of these mutants suggests that mechanisms underpinning Pi distribution in plants may involve regulatory factors in addition to transporters. For example, the pho1 mutant of Arabidopsis exhibits strong symptoms of Pi deficiency due to lack of Pi loading into xylem vessels (Poirier et al., 1991). The mutated gene resembles the mammalian receptor for murine leukemia retrovirus (Rcm1) (Hamburger et al., 2002). The Pho1 gene is predicted to have a regulatory role in Pi transport within the plants. In contrast pho2 mutant has higher levels of Pi in leaves as a consequence of defective regulation of Pi loading into shoots (Delhaize & Randall, 1995). In addition to pho1 and pho2, the Arabidopsis mutant, psr1, was isolated based on its inability to grow in the presence of nucleic acids as a sole source of P (Chen et al., 2000). A reduction in Pi-starvation-inducible-ribonuclease and phosphatase activity in the psr1 mutant suggest that a mutation may have occurred in a regulatory gene. The availability of large collections of mutants, where practically all of the genes in the Arabidopsis genome have been disrupted using T-DNA (e.g. Alonso et al., 2003), has increased the opportunities to isolate and characterize those that are involved in Pi acquisition and distribution in plants and it is likely that information will accrue rapidly in the future. However, one of the difficulties faced in understanding the physiological role of these transporters is the overlapping expression patterns or potential functional redundancy of other members of the family leading to a lack of discernable phenotype in a single gene mutant. This may require crossing individual mutants to develop double and triple mutants to analyze the function of different transporters. 5.3.2 Molecular regulation of phosphate uptake in plants Changes in Pi homeostasis in cells may trigger a response by either activating or inactivating Pi transporter expression. This phenomenon has been studied much more intensively in model organisms like yeast and bacteria. In yeast, the ‘Pho regulon’ integrated signaling and response pathway involves both positive and negative regulatory factors (Oshima, 1997). Many of the Pho regulon genes are activated by the interaction of the pho4 trans factor under Pi deficiency. The conserved cis element CACGTG is located on many of the Pi responsive genes and is the target for pho4 binding. Some of the Pi-starvation-induced genes in plants also contain this conserved element (Mukatira et al., 2001) and

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there is growing evidence for the presence of an integrated Pi signaling and response mechanism in plants. Although little is known about regulatory factors mediating the Pi starvation response – including the activation of Pi transporter genes – split-root studies show that changes in Pi homeostasis are likely to be responsible for transcriptional regulation of genes. These studies have shown that even if Pi is supplied to a portion of the root system, increased expression of Pi transporters in other portions of roots exposed to Pi deficiency did not occur as long as the internal requirement of Pi was satisfied (Liu et al., 1998a; Baldwin et al., 2001). Split-root-system studies have also revealed that changes in shoot Pi levels play a role in the regulation of P-deficiency-induced responses, including its uptake by roots (Anghinoni & Barber, 1980; Drew & Saker, 1984; Liu et al., 1998a; Baldwin et al., 2001). In addition, foliar sprays of Pi suppressed the production of proteoid roots, a typical response of white lupin to Pi deficiency (Gilbert et al., 1997). The expression of Pi-starvation-induced genes, including a high-affinity Pi transporter in Arabidopsis, is also regulated by hormones. Hormones such as cytokinin and auxin suppress the expression of the Pi transporter (Pht1;1) in Arabidopsis (Martin et al., 2000; Karthikeyan et al., 2002). It is likely that both hormone-dependent and independent regulatory pathways are functioning under Pi deficiency, leading to increased Pi uptake. Recent studies in our laboratory shows that C also plays an important role in Pi deficiency mediated responses. For example, the presence of sugar in the medium appears to be essential for the expression of Pi-starvation-induced genes including Pi transporters in Arabidopsis (Karthikeyan et al., 2004). These studies suggest that multiple growth and hormonal factors affect the expression of Pi-starvation-induced genes. This type of activation or inactivation of gene expression may involve specific interaction between cis elements located on promoters and trans factors that are either induced or activated during Pi stress. DNA-protein interaction studies have also revealed the existence of trans factors interacting with specific cis elements of the Pi transporters (Mukatira et al., 2001). Interestingly, the protein factors either disappear or are unable to bind to the cis elements during Pi deficiency. The regions of promoter interacting with nuclear factors contain conserved sequences similar to that of Nit2 binding cis element. Nit2 is a positive trans factor associated with activation of several genes in the N metabolism in Neurospora. Similar DNA:protein interactions have also been reported for the promoter of other Pi-starvation-induced genes (Malboobi et al., 1998). It is becoming apparent that Pi signaling networks in plants are hugely complex in nature, comprising many branches with multiple outputs. At present, information is rather fragmentary, although it has been observed that a regulatory gene Psr1 activates multiple responses to Pi deficiency in Chlamydomonas (Wykoff et al., 1999). A homolog of Psr1 in Arabidopsis, called Phr1, has been shown to bring about a similar response (Rubio et al., 2001). The Phr1 protein belongs to the family of MYB transcription factors and interacts with a specific cis element found in some of the Pi-starvation-induced genes including a Pi

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transporter. Mutations in Phr1 alter plant responses to Pi starvation. Further, a bZIP-like transcription factor and a MAP kinase are induced when the Pi content in a tobacco cell culture is altered (Wilson et al., 1998; Sano & Nagata, 2002). And the Pi-response domain of vegetative storage protein in soybean has been shown to bind the homeodomain leucine zipper proteins (Tang et al., 2001). Further research into Pi signaling networks will be needed to understand these complex processes. 5.3.3 Global regulation of gene expression during phosphate deficiency The availability of plant genomic sequence information and the accessibility of microarrays for Arabidopsis and rice to the scientific community has created opportunities to evaluate global changes in gene expression (the transcriptome) in response to Pi deficiency. Indeed, several studies have obtained ‘snap shots’ of changes in gene expression in response to alterations in Pi nutrition in both Arabidopsis and rice (Wang et al., 2002; Hammond et al., 2003; Wu et al., 2003; Wasaki et al., 2003). For example, a cDNA microarray of rice was probed for analyzing the expression of genes under Pi deficiency (Wasaki et al., 2003). This analysis revealed distinct changes in the expression of genes involved in C and lipid metabolism, and in cell wall synthesis. In addition, many genes with unknown function and those associated with Fe and Zn nutrition, and Al toxicity were altered under Pi deficiency. Analysis of a microarray consisting of 6172 genes from Arabidopsis with mRNA isolated from plants exposed to different durations of Pi deficiency revealed extensive changes in gene expression (Wu et al., 2003). Expression of nearly 29% of the genes in the microarray was affected within 72 h of Pi deficiency. These genes included hundreds of genes representing transcription factors, cell-signaling components and proteins involved in numerous metabolic processes. Although large number of genes altered during Pi deficiency may not be directly involved in Pi acquisition or utilization, many of them may play a role in the adaptation of plants to Pi deficiency. In another study, Hammond et al. (2003) analyzed changes in the expression of 8100 Arabidopsis genes in response to Pi deficiency, using microarrays based on high density, oligonucleotide-based GeneChip (Affymetrix, Santa Clara, USA) technology (Lipshutz et al., 1999). Interestingly, there were changes in the expression of only a small number (61) of genes expressed in shoots of Arabidopsis after 100 h of Pi deficiency (Hammond et al., 2003). The majority of the Pi-starvation-induced genes in leaves were not affected by either N or K deficiency. Several genes responding to Pi deficiency were also found to be induced by wounding and pathogen attack, heavy metals and oxidative stresses. This suggests that Pi deficiency responses may overlap with several other biotic and abiotic stress responses of plants. Cross talk between different signals has been illustrated in tomato plants subjected to different nutrient deficiencies (Wang et al., 2002). In their study, high-density tomato cDNA arrays consisting of 1280 genes obtained from RNA

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subtraction libraries were searched for genes induced by P, K, or Fe deficiency (Wang et al., 2002). Expression of Pi transporters was up-regulated in response to P, K, or Fe deficiency suggesting a crosstalk between different nutrient deficiencies and gene expression. Thus, some of the signal transduction pathway components of nutrient deficiency responses may be shared in plants, which is not surprising since some of the biochemical and morphological changes during nutrient stress such as P and Fe are similar. Interestingly, both P and Fe deficiency enhances the secretion of organic acids and protons into the rhizosphere. Furthermore, root hair elongation in response to nutrient deficiencies is a general response to nutrient limitation. It is therefore likely that some of the features which allow roots to scavenge for nutrients and to modify their rhizosphere are altered by multiple nutrient deficiencies leading to overlapping responses. However, a distinct response of roots to Pi deficiency is the formation of proteoid or cluster roots of white lupin. By probing of nylon filter arrays containing 2102 expressed sequence tags (ESTs) representing proteoid root cDNA libraries, differential expression of genes during Pi deficiency have been revealed (Uhde-Stone et al., 2003). Thirty-five genes were strongly induced in proteoid roots under Pi deficiency. These differentially expressed genes represented those involved in C and secondary compound metabolism, Pi scavenging and remobilization, and signal transduction and hormone metabolism. These studies further emphasize the interaction between Pi nutrition and C metabolism. During Pi deficiency there is greater demand for ATP and phosphorylated sugars to maintain normal function. In addition, much of the C is diverted toward roots to support enhanced growth and exudation of organic acids to increase the availability of Pi in the rhizosphere. Changes in expression of genes associated with carbon metabolism aptly represent changes in biochemical pathways during Pi deficiency. Most of the studies thus far into the global regulation of gene expression during Pi deficiency have been performed on partial genome chips or cDNA arrays. Thus, further studies using entire genome chips of Arabidopsis and other species will be required to obtain a comprehensive picture of changes in gene expression during Pi deficiency. It must also be emphasized that biological and technical controls must be used in microarray experiments to reduce nonspecific effects on gene expression and thus misrepresentation of the treatment effect. Moreover, microarray data needs further confirmation by Northern or RT-PCR analysis to authenticate changes in gene expression. 5.4 Perspective: future genetic approaches to isolate phosphate signaling components Molecular genetic approaches based on mutants have become powerful and indispensable research tools to dissect signal transduction pathways in plants (Ishitani et al., 1998). One of the efficient ways to obtain genes involved in Pi signaling and response pathways is the genetic analysis of mutants carrying a reporter gene under the regulation of a Pi-starvation-induced gene

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promoter. This approach involves the generation of transgenic plants expressing reporter genes such as luciferase or ␤-glucaronidase (GUS), under the control of a Pi-starvation-induced gene promoter (Rubio et al., 2001; Hammond et al., 2003). These transgenic plants could subsequently be treated with mutagens such as EMS or bombarded with fast neutrons to generate mutants. The mutated gene can be obtained by map based cloning of candidate genes. For example, the MYB transcription factor Phr1 was identified from a mutagenized population of Arabidopsis carrying a GUS reporter gene under the regulation of the Pi-starvation-induced gene (AtIPS1) promoter (Rubio et al., 2001) An effective method to isolate and analyze Arabidopsis mutants in Pi signaling components could be to use a T-DNA transformation strategy. The Agrobacterium tumefaciens mediated transformation generally results in low copy number insertion in the genome thus avoiding many backcrosses. The availability of modified T-DNA vectors facilitates the identification of the site of insertion in the genome (Weigel et al., 2000). The location of T-DNA insertions can be easily identified and junction sequences can be amplified by the technique of Thermal Asymmetric InterLaced Polymerase Chain Reaction also known as TAIL-PCR (Liu et al., 1995). The alternate procedure of plasmid rescue could also be used in identifying the location of DNA insertion (Weigel et al., 2000). The power of insertional mutagenesis can be combined with reporter gene expression to enhance the efficiency of screening for specific mutants (Rus et al., 2001). This has been accomplished by transforming Arabidopsis plants expressing the reporter gene luciferase under the regulation of an inducible promoter. Luciferase is an efficient reporter gene used in visualizing real-time changes in gene expression in plants (Ow et al., 1986). The luciferase produced by the transgenic plant reacts with the substrate luciferin to produce yellow-green light with the peak emission at 560 nm. The bioluminescence can be recorded using a slow scan closed circuit digital (CCD) camera. The noninvasive nature of the assay allows continuous monitoring of gene expression during plant development, or in response to multiple inducers (Ishitani et al., 1997, 1998; Rus et al., 2001; Koiwa et al., 2002). This property of luciferase expression can be used to set up multiple screens on the same plant population. Since luciferase activity can be measured by a nondestructive assay, the identification and rescue of the mutants is entirely feasible. This approach has been used very efficiently in studying plant responses to cold, salt and osmotic stresses (Ishitani et al., 1997, 1998; Rus et al., 2001; Koiwa et al., 2002). Therefore, a similar strategy could be adapted to screen for mutants representing genes involved in Pi starvation signaling and rescue processes.

Acknowledgments Research in the K.G. Raghothama laboratory is supported by grants from United States Department of Agriculture (2003-35100-13402) and Binational Agricultural Research and Development (US-3231-01R).

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Liu, Y.G., Mitsukawa, N., Oosumi, T. & Whittier, R.F. (1995) Efficient isolation and mapping of Arabidopsis thaliana T-DNA insert junctions by thermal asymmetric interlaced PCR. Plant J., 8, 457-463. Ma, Z., Walk, T.C., Marcus, A. & Lynch, J.P. (2001) Morphological synergism in root hair length, density, initiation and geometry for phosphorus acquisition in Arabidopsis thaliana: a modeling approach. Plant Soil, 236, 221-235. Malboobi, M.A., Hannoufa, A., Tremblay, L. & Lefebvre, D.D. (1998) Towards an understanding of gene regulation during the phosphate starvation response. In Phosphorous in Plant Biology: Regulatory Roles in Molecular, Cellular, Organismic and Ecosystem Processes (eds J.P. Lynch & J Deikman), American Society of Plant Physiologists, Rockville, MD, pp. 215–226. Marschner, H. (1995). Mineral Nutrition of Higher Plants. 2nd edn, Academic Press, San Diego, CA. Martin, A.C., del Pozo, J.C., Iglesias, J., Rubio, V., Solano, R., de la Pena, A., Leyva, A. & Paz-Ares, J. (2000) Influence of cytokinins on the expression of phosphate starvation responsive genes in Arabidopsis. Plant J., 24, 559–567. Massonnearu, A., Langlade, N., Leon, S., Smutny, J., Vogt, E., Neumann, G. & Martinoia, E. (2001) Metabolic changes associated with cluster root development in white lupin (Lupinus albus L.): relationship between organic acid excretion, sucrose metabolism and energy status. Planta, 213, 534–542. Mimura, T., Dietz, K.J., Kaiser, W., Schramm, M.J., Kaiser, G. & Heber, U. (1990) Phosphate transport across biomembranes and cytosolic phosphate homeostasis in barley leaves. Planta, 180, 139–146. Mimura, T., Sakano, K. & Shimmen, T. (1996) Studies on the distribution, re-translocation and homeostasis of inorganic phosphate in barley leaves. Plant Cell Environ., 19, 311–320. Muchhal, U.S. & Raghothama, K.G. (1999) Transcriptional regulation of plant phosphate transporters. Proc. Natl. Acad. Sci. USA, 96, 5868–5872. Muchhal, U.S., Pardo, J.M. & Raghothama, K.G. (1996) Phosphate transporters from the higher plant Arabidopsis thaliana. Proc. Natl. Acad. Sci. USA, 93, 10519–10523. Mudge, S.R., Rae, A.L., Diatloff, E. & Smith, F.W. (2002) Expression analysis suggests novel roles for members of the Pht1 family of phosphate transporters in Arabidopsis. Plant J., 31, 341–353. Mukatira, U., Liu, C., Varadarajan, D.K. & Raghothama, K.G. (2001) Negative regulation of phosphate starvation induced genes. Plant Physiol., 127, 1854–1862. Nátr, L. (1992) Mineral nutrients – a ubiquitous stress factor for photosynthesis. Photosynthetica, 27, 271–294. Oshima, Y. (1997) The phosphatase system in Saccharomyces cerevisiae. Genes Genet. Syst., 72, 323– 334. Ow, D.W., Wood, K.V., DeLuca, M., de Wet, J.R., Helinski, D.R. & Howell, S.H. (1986) Transient and stable expression of the firefly luciferase gene in plant cells and transgenic plants. Science, 234, 856–859. Pao, S.S., Paulsen, I.T. & Saier, M.H. (1998) Major facilitator superfamily. Microbiol. Mol. Biol. Rev., 62, 1–34. Plaxton, W.C. & Carswell, M.C. (1999). Metabolic aspects of the phosphate starvation response in plants. In Plant Responses to Environmental Stresses: From Phytohormones to Genome Reorganization (ed. H.R. Lerner), Dekker, New York, pp. 349-372. . Poirier, Y., Thoma, S., Somerville, C. & Schiefelbein, J. (1991) A mutant of Arabidopsis deficient in xylem loading of phosphate. Plant Physiol., 97, 1087–1093. Raghothama, K.G. (1999) Phosphate acquisition. Annu. Rev. Plant Physiol. Plant Mol. Biol., 50, 665– 693. Raghothama, K.G. (2000a) Phosphate transport and signaling. Curr. Opin. Plant Biol., 3, 182–187. Raghothama, K.G. (2000b) Phosphorus acquisition; plants in the driver’s seat! Trends Plant Sci., 5, 411–413. Rausch, C., Daram, P., Brunner, S., Jansa, J., Lalal, M., Leggewie, G., Amrhein, N. & Bucher, M. (2001) A phosphate transporter expressed in arbuscule-containing cells in potato. Nature, 414, 462–466.

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Rubio, V., Linhares, F., Solano, R., Martin, A.C., Iglesias, J., Leyva, A. & Paz-Ares, J. (2001) A conserved MYB transcription factor involved in phosphate starvation signaling both in vascular plants and in unicellular algae. Genes Dev., 15, 2122–2133. Rus, A., Yokoi, S., Sharhuu, A., Reddy, M., Lee, B., Matsumoto, T.K., Koiwa, H., Zhu, J.K., Bressan, R.A. & Hasegawa, P.M. (2001) AtHKT1 is a salt tolerance determinant that controls Na+ entry into plant roots. Proc. Natl. Acad. Sci. USA, 98, 14150–14155. Sakano, K. (1990) Proton/phosphate stoichiometry in uptake of inorganic phosphate by cultured cells of Catharanthus roseus (L.) G. Don. Plant Physiol., 93, 479–483. Sakano, K., Yazaki, Y. & Mimura, T. (1992) Cytoplasmic acidification induced by inorganic phosphate uptake in suspension cultured Catharanthus roseus cells. Plant Physiol., 99, 672–680. Sakano, K., Yazaki, Y., Okihara, K., Mimura, T. & Kiyota, S. (1995) Lack of control in inorganic phosphate uptake by Catharanthus roseus (L.) G. Don cells. Plant Physiol., 108, 295–302. Sano, T. & Nagata, T. (2002) The possible involvement of a phosphate-induced transcription factor encoded by Phi-2 gene from tobacco in ABA-signaling pathways. Plant Cell Physiol., 116, 447– 453. Sentenac, H. & Grignon, C. (1985) Effect of pH on orthophosphate uptake by corn roots. Plant Physiol., 77, 136–141. Shimogawara, K. & Usuda, H. (1995) Uptake of inorganic phosphate by suspension-cultured tobacco cells: Kinetics and regulation by Pi starvation. Plant Cell Physiol., 36, 341–351. Smith, S.E. & Reid, D.J. (1997) Mycorrhizal Symbiosis. Academic Press, San Diego, CA. Smith, F.W., Ealing, P.M., Dong, B. & Delhaize, E. (1997) The cloning of two Arabidopsis genes belonging to a phosphate transporter family. Plant J., 11, 83–92. Smith, F.W., Rae, A.L. & Hawkesford, M.J. (2000) Molecular mechanisms of phosphate and sulphate transport in plants. Biochim. Biophys. Acta Biomembr., 1465, 236–245. Smith, S.E., Dickson, S. & Smith, F.A. (2001) Nutrient transfer in arbuscular mycorrhizas: How are fungal and plant processes integrated? Aust. J. Plant Physiol., 28, 683–694. Tang, Z., Sadka, A., Morishige, D.T. & Mullet, J.E. (2001) Homeodomain leucine zipper proteins bind to the phosphate response domain of the soybean VspB tripartite promoter. Plant Physiol., 125, 797–809. Tu, S.I., Cananaugh, J.R. & Boswell, R.T. (1990) Phosphate uptake by excised maize root tips studied by in vivo 31 P nuclear magnetic resonance spectroscopy. Plant Physiol., 93, 778–784. Uhde-Stone, C., Zinn, K.E., Ramirez-Yanez, M., Li, A., Vance, C.P. & Allan, D.L. (2003) Nylon filter arrays reveal differential gene expression in proteoid roots of white lupin in response to phosphorus deficiency. Plant Physiol., 131, 1064–1079. Ullrich-Eberius, C.I., Novacky, A. & Van Bel, A.J.E. (1984) Phosphate uptake in Lemna gibba G1: energetics and kinetics. Planta, 161, 46–52. Uta, P., Kroken, S., Roux, C. & Briggs, S.P. (2002) Rice phosphate transporters include an evolutionarily divergent gene specifically activated in arbuscular mycorrhizal symbiosis. Proc. Natl. Acad. Sci. USA, 99, 13324–13329. Vance, C.P., Uhde-Stone, C. & Allan, D.L. (2003) Phosphorus acquisition and use: critical adaptations by plants securing a nonrenewable resource. New Phytol., 157, 423-447. Versaw, W.K. & Harrison, M.J. (2002) A chloroplast phosphate transporter, PHT2;1, influences allocation of phosphate within the plant and phosphate-starvation responses. Plant Cell, 14, 1751– 1766. Wang, Y.H., Garvin, D.F. & Kochian, L.V. (2002) Rapid induction of regulatory and transporter genes in response to phosphorus, potassium, and iron deficiencies in tomato roots. Evidence for cross talk and root/rhizosphere-mediated signals. Plant Physiol., 130, 1361–1370. Wasaki, J., Yonetani, R., Kuroda, S., Shinano, T., Yazaki, J., Fujii, F., Shimbo, K., Yamamoto, K., Sakata, K., Sasaki, T., Kishimoto, N., Kikuchi, S., Yamagishi, M. & Osaki, M. (2003) Transcriptomic analysis of metabolic changes by phosphorus stress in rice plant roots. Plant Cell Environ., 26, 1515–1523.

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Weigel, D., Ann, J.H., Blaquez, M.A., Borevitz, J.O., Christensen, S.K., Fankhauser, C., Ferrandiz, C., Kardailsky, I., Malancharuvil, E.J., Neff, M.M., Nguyen, J.T., Sato, S., Wang, Z.Y., Xia, Y.J., Dixon, R.A., Harrison, M.J., Lamb, C.J., Yanofsky & M.F., Chory, J. (2000). Activation tagging in Arabidopsis. Plant Physiol., 122, 1003–1013. Wilson, C., Pfosser, M., Jonak, C., Hirt, H., Heberle-Bors E. & Vincent, O. (1998) Evidence for the activation of a MAP kinase upon phosphate-induced cell cycle re-entry in tobacco cells. Physiol. Plantarum, 102, 532–538. Wu, P., Ma, L., Hou, X., Wang, M., Wu, Y., Liu, F. & Deng, X.W. (2003) Phosphate starvation triggers distinct alterations of genome expression in Arabidopsis roots and leaves. Plant Physiol., 132, 1260–1271. Wykoff, D.D., Grossman, A.R., Weeks, D.P., Usuda, H. & Shimogawara, K. (1999) Psr1, a nuclear localized protein that regulates phosphorus metabolism in Chlamydomonas. Proc. Natl. Acad. Sci. USA, 96, 15336–15341.

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Sodium Huazhong Shi, Ray A. Bressan, Paul M. Hasegawa and Jian-Kang Zhu

6.1 Introduction Salinity is a worldwide problem that limits land usage and reduces crop production. About 7% of all land area is affected by salinity. Over 20% of irrigated land has been significantly affected by salinity, and this proportion is increasing because of bad agricultural practices such as irrigation without adequate drainage. Salinity causes enormous economic losses all over the world. Amongst the ions that adversely affect crop production, sodium (Na+ ) is the predominant and deleterious ion in saline soil. Therefore, understanding the mechanisms by which plants tolerate Na+ is of great significance for agriculture. Plants require the essential potassium ion (K+ ) for growth and development. High concentrations of Na+ interfere with K+ uptake and decrease the cytosolic K+ :Na+ ratio, thus affecting K+ -stimulated enzyme activities, metabolism, and photosynthesis. Under high salinity, plants experience two types of stress: osmotic stress, caused by high solute concentrations in soil; and Na+ toxicity, resulting from the altered K+ :Na+ ratio. Although plant species have different levels of tolerance to salt stress, they share common cellular and biochemical mechanisms to combat NaCl stress, namely, accumulating compatible solutes, such as glycine betaine, proline, ectoine or polyols to combat osmotic stress, and reducing Na+ accumulation in the cytosol to combat ion-specific toxicity. This chapter focuses on our discovery of genes in Arabidopsis and their functions in ion homeostasis under NaCl stress. The word ‘salt’ will be used interchangeably with Na+ or NaCl. For information on water-deficit response and osmotic adjustment in plants under salt stress, readers can refer to many reviews that have been published.

6.2 Arabidopsis as a model for salt-tolerance research Plants have evolved different abilities to tolerate NaCl because of their growth niche. Some plants are extremely tolerant of NaCl (halophytes), but others are sensitive (glycophytes). Much attention has been paid to the salt-tolerant halophytes in an attempt to elucidate salt-tolerance mechanisms in plants, simply because these plants obviously possess the salt-tolerance machinery.

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Indeed, we have obtained much knowledge on plant salt tolerance, especially the physiological mechanisms by which halophytes tolerate Na+ (Bohnert et al., 1999). Initial doubts about Arabidopsis as a model organism for salt-tolerance studies might have existed because Arabidopsis is not particularly a salt-tolerant plant. However, physiological studies of salt adaptation in glycophytic plants and cell cultures have revealed that salt-sensitive plants contain salt-tolerance genes (Hasegawa et al., 1994). Tobacco suspension cells grown in gradually increasing concentrations of NaCl can adapt to up to approximately 500 mM NaCl in the growth medium (Hasegawa et al., 1994), which indicates that tobacco cells possess genes conferring salt-tolerance, but these genes in glycophytic cells may not be as active as in halophytic cells. Furthermore, the genes critical for salt tolerance in Arabidopsis cloned in our laboratory show high sequence homology with those important for salt tolerance in salt tolerant yeast cells, which suggests that plants and yeast have a common machinery for Na+ detoxification. Our research over the last decade validates the notion that Arabidopsis is a good model system for the study of salt-tolerance in plants (Zhu, 2000). Besides its completed genome sequence, Arabidopsis is also an ideal plant for genetic analysis. Its short life cycle, numerous genetic markers, ease of transformation, and publicly available resources of T-DNA insertional lines have made Arabidopsis the plant of choice for both forward and reverse genetic studies on gene function.

6.3 sos mutants Taking advantage of Arabidopsis, we employed a forward genetic approach to studying plant salt tolerance. The simple logic was that if mutation in a gene causes increased salt sensitivity in the plant, the gene must be required for salt tolerance. To search for salt-sensitive mutants, we established a simple and efficient mutant-screening system, the ‘root bending’ assay (Wu et al., 1996). After screening for mutants in a large mutagenized Arabidopsis population, we identified more than 40 salt-hypersensitive mutants, named salt overly sensitive (sos) mutants. Allelism tests by pair-wise crosses between the mutants revealed that they fell into five complementation groups, defining five salt-tolerance genes, namely SOS1 (Wu et al., 1996), SOS2 (Zhu et al., 1998), SOS3 (Liu & Zhu, 1997), SOS4 (Shi et al., 2002b), and SOS5 (Shi et al., 2003a). sos1, sos2, and sos3 mutants display normal growth and development under normal growth conditions. However, under salt stress these mutants are more inhibited in growth and show more damage, compared to wild-type plants. The sos1, sos2 and sos3 mutants are hypersensitive to Na+ and Li+ but not K+ , Cs+ , Mg2+ , Ca2+ , Cl− , NO3 − or SO4 2− , which indicates that SOS1, SOS2, and SOS3 are specific to Na+ and Li+ tolerance. That the mutants are specifically hypersensitive to both Na+ and Li+ , a more toxic analog of Na+ , suggests that

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Arabidopsis cells may share a common transport system for detoxification of both Na+ and Li+ . sos2 and sos3 mutants exhibit the same sensitivity to high concentrations of mannitol as the wild type does. Intriguingly, sos1 seedlings appear to be more inhibited by low to medium levels of mannitol stress but are no different from the wild type under high concentrations of mannitol. These findings suggest that SOS2 and SOS3 are specific to salt but not general osmotic stress tolerance, whereas SOS1 may also play a role in osmotic stress. sos1, sos2, and sos3 mutants accumulate more proline under salt stress, the extent of which is correlated with the level of salt sensitivity and stress damage. Interestingly, sos1, sos2, and sos3 display greatly attenuated growth on agar medium depleted of K+ , which indicates that SOS1, SOS2, and SOS3 are not only important for salt tolerance but also critical for K+ acquisition. 86 Rb+ uptake experiments revealed that sos1 seedlings have reduced capacity for highaffinity K+ uptake (Ding & Zhu, 1997), whereas sos2 and sos3 show no difference from the wild type in high-affinity K+ transport. Indeed, sos1 mutant plants are most sensitive to Na+ and require the highest level of K+ for normal growth. It has long been implicated that under salt stress, plants predominantly use a high-affinity K+ uptake system with high K+ /Na+ selectivity to avoid excessive toxic Na+ uptake (Kochian & Lucas, 1988). These three SOS genes are likely involved in the molecular machinery that controls K+ and Na+ homeostasis. In fact, genetic evidence from double mutant analysis suggested that SOS1, SOS2, and SOS3 function in a linear pathway to control salt tolerance. Distinct from sos1, sos2, and sos3, both sos4 and sos5 mutant plants are hypersensitive not only to Na+ and Li+ but also high concentrations of K+ and are not more sensitive to K+ deficiency. In addition, sos4 seedlings are defective in root hair formation. sos5 mutant plants display root swelling under salt stress because of abnormal cell expansion.

6.4 SOS genes All of the five SOS genes have been isolated using map-based cloning strategies. The following will describe each gene in the order they were cloned. 6.4.1 SOS3 SOS3 was the first cloned SOS gene and it encodes a protein with three predicted EF-hands for Ca2+ -binding (Liu & Zhu, 1998). Sequence comparisons revealed that SOS3 protein has the highest similarity with the ␤-subunit of calcineurin (CnB) from yeast and the neuronal Ca2+ sensor (NCS) from animals. Calcineurin is a conserved Ca2+ /calmodulin-dependent protein phosphatase and functions as a heterodimer of the catalytic A-subunit (CnA) containing a C-terminal autoinhibitory domain and the regulatory ␤-subunit (CnB) containing four

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high-affinity EF-hand Ca2+ -binding sites. Ca2+ -calmodulin binding together with CnB activates CnA by relieving its self-inhibition. In animals, calcineurin activity is critical for many Ca2+ -regulated processes, including T-cell activation (Clipstone & Crabtree, 1992; O’Keefe et al., 1992) and neutrophil chemotaxis (Hendey et al., 1992; Lawson & Maxfield, 1995). Inhibition of calcineurin prevents activation of NFAT, a transcription factor necessary for the proliferation of T cells. In other cell types, calcineurin has been implicated in the control of ion homeostasis. For example, calcineurin modulates Na+ /K+ ATPase in renal tube cells (Aperia et al., 1992). The role of yeast calcineurin has been examined by characterizing cells that lack functional calcineurin or cells incubated with calcineurin-specific inhibitors. Calcineurin-deficient cells grow poorly under high concentrations of certain ions, including Na+ /Li+ (Nakamura et al., 1993; Mendoza et al., 1994), which suggests that calcineurin regulates ion homeostasis in yeast. The ion sensitivity is, at least in part, due to the altered levels of ion transporters. Calcineurin is required for transcriptional induction of PMR2, which encodes a Na+ -ATPase (Haro et al., 1991), and PMC1 and PMR1, which encode Ca2+ -ATPases (Cunningham & Fink, 1996). In yeast, calcineurin is also required to switch K+ transport from low- to high-affinity mode for improved K+ /Na+ selectivity and reducing Na+ influx under salt stress (Mendoza et al., 1994). Use of purified His-tagged SOS3 protein detected the capability of Ca2+ binding to the SOS3 EF-hand, which supports the notion that SOS3 is indeed a Ca2+ -binding protein. Deletion in one of the EF-hands in the SOS3 protein remarkably reduced the Ca2+ binding of SOS3 (Ishitani et al., 2000). SOS3 physically interacts with and activates SOS2, a serine/threonine protein kinase required for salt tolerance in Arabidopsis, in a Ca2+ -dependent manner (Halfter et al., 2000). SOS3 interacting with a protein kinase rather than a CnA suggests that the mechanism of SOS3 function differs from that of CnB in animal and yeast cells. In fact, SOS3 does not contain a conserved CnA-binding region of CnB and could not complement the yeast CnB-defective mutant phenotype. Like CnB and NCS, SOS3 is predicted to contain a myristoylation motif at its N-terminus. In vitro assays have revealed that the SOS3 protein can be myristoylated and mutation of G2A in the myristoylation motif prevents the myristoylation of SOS3. Expression of wild-type myristoylated SOS3, but not mutated (G2A) nonmyristoylated SOS3, can complement the salt-hypersensitive phenotype of sos3, which demonstrates that myristoylation of SOS3 is essential for SOS3 function in salt tolerance in Arabidopsis (Ishitani et al., 2000). Although myristoylated and nonmyristoylated SOS3 did not differ in membrane association in planta (Ishitani et al., 2000), myristoylation of SOS3 seems to enhance membrane binding of the SOS3/SOS2 complex in yeast (Quintero et al., 2002). SOS3 belongs to a gene family containing nine SOS3-like Ca2+ binding proteins in Arabidopsis. It is of interest to address the question with regard to the function of each family member. Use of RNA interference (RNAi) has revealed

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one member of this family, SCaBP5, to be a global regulator of abscisic acid (ABA) induced responses (Guo et al., 2002). Arabidopsis mutants with silenced SCaBP5 are hypersensitive to ABA in seed germination, seedling growth, stomatal closing, and display altered ABA-responsive gene expression (Guo et al., 2002). Moreover, a mutant with a silenced SCaBP5-interacting protein PKS3, a member of the SOS2 gene family, also shows hypersensitivity to ABA. PKS3 can physically interact with the 2C-type protein phosphatases ABI2 and ABI1, two important ABA signaling components (Guo et al., 2002). These results indicate that SCaBP5 and PKS3 are part of a Ca2+ -responsive negative regulatory loop controlling ABA sensitivity. Studies of the T-DNA knockout mutant and overexpression of SCaBP5 (also known as CBL1; Cheong et al., 2003; Albrecht et al., 2003) have implicated this protein in drought and cold response in Arabidopsis. 6.4.2 SOS2 SOS2 encodes a serine/threonine protein kinase that contains two functional domains: an N-terminal catalytic domain similar to that of the yeast SNF1 kinase and a novel C-terminal regulatory domain (Liu et al., 2000a). Both domains are essential for SOS2 function in plants, as evidenced by mutations in each domain causing hypersensitivity to NaCl stress (Liu et al., 2000a,b). Results of autophosphorylation assays confirmed that SOS2 is an active kinase. Use of synthetic peptides based on the recognition sequences of protein kinase C or SNF1/AMPK as substrates confirmed SOS2’s ability to phosphorylate either a serine or threonine residue in the peptides, which supports SOS2 as a serine/threonine protein kinase. SOS2 phosphorylation of the peptides depends on the presence of the SOS3 protein and Ca2+ . The C-terminal regulatory domain can interact with the N-terminal catalytic domain within the SOS2 protein, forming a self-inhibition structure to keep the kinase inactive, presumably by blocking substrate access to the catalytic site (Guo et al., 2001). Interestingly, SOS3 can physically interact with the SOS2 C-terminal regulatory domain, which results in an active SOS2 kinase presumably by relieving the self-inhibition (Halfter et al., 2000; Guo et al., 2001). A 21-amino acid sequence, designated as the FISL motif, in the regulatory domain of SOS2, which is necessary and sufficient for interaction with SOS3, has been identified by yeast two-hybrid assays. Deletion of SOS2’s regulatory domain, including the SOS3-binding FISL motif, resulted in constitutive activation of the SOS2 protein kinase that is SOS3 independent, which supports the notion that the SOS3-binding motif serves as a kinase autoinhibitory domain (Guo et al., 2001). Many protein kinases contain an activation loop between the conserved DFG and APE motifs. Phosphorylation of this segment is often required for kinase activation by upstream protein kinases (Johnson et al., 1996). The SOS2 protein also contains a putative activation segment in its N-terminal catalytic domain,

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with a threonine as a putative phosphorylated residue. A single amino acid substitution of Thr168 by Asp (to mimic phosphorylation by an upstream kinase) in this activation loop substantially increases SOS2 kinase activity and renders SOS2 independent of SOS3 (Guo et al., 2001). Combining the Thr168-to-Asp (T168D) mutation with the C-terminal truncation (SOS2308) results in a kinase that is more active than that of any previous single mutant. The wild-type form of SOS2 can complement the salt-hypersensitive phenotype of sos2 but not sos3, thus confirming that the SOS2 protein must be activated by SOS3 in vivo for function. However, the expression of the mutated form of SOS2 (T168D) can partially rescue the shoot but not the root salt hypersensitivity of both sos2 and sos3 mutant plants (Guo et al., 2004). Partial complementation of the sos3 salt-sensitive phenotype by SOS2T168D suggests that this active protein kinase can bypass the requirement of SOS3 for its function. Furthermore, overexpression of the wild-type form of SOS2 in Arabidopsis displays no significant elevation in salt tolerance, whereas that of SOS2T168D confers substantial salt tolerance in transgenic plants (Guo et al., 2004). This finding indicates that the level of activated but not inactivated SOS2 protein is probably limiting, and increasing the active level can lead to improved salt tolerance in Arabidopsis. The expression of superactive SOS2 (T/DSOS2/308, with the Thr168-to-Asp change, and the FISL motif and C-terminal 117 amino acids removed) or T/DSOS2/329 (with the Thr168-to-Asp change and the C-terminal 117 amino acids removed) containing a FISL motif could not complement either the sos2 or sos3 salt-hypersensitive phenotype, which indicates a critical role for the C-terminal region of SOS2 in plant salt tolerance. The expression of T/DSOS2DF (with the Thr168-to-Asp change and the FISL motif removed) can enhance salt tolerance in both wild-type and sos2 mutant plants but cannot restore the salt-hypersensitive phenotype of the sos3 mutant (Guo et al., 2004). These results further demonstrate that the C-terminal 117 residues are necessary for the in planta function of the active SOS2 kinase protein in the wild type and sos2 mutant, but this active kinase requires the FISL motif for its function in the sos3 mutant. Besides interacting with SOS3, SOS2 was also found to interact with the protein phosphatase 2C ABI2. Deletion analysis revealed a novel protein domain of 37 amino acid residues, designated as the protein phosphatase interaction (PPI) motif, of SOS2 that is necessary and sufficient for interaction with ABI2 (Ohta et al., 2003). The PPI motif is several amino acids downstream of the FISL motif. The FISL motif is not important for the interaction of SOS2 with ABI2, because deletion of the motif in SOS2 does not affect SOS2-ABI2 interaction. The N-terminal half of ABI2 is sufficient for interaction with SOS2. Deletion analysis revealed a 46-amino acid region residing in the N-terminal portion of ABI2 that is sufficient for interaction with SOS2. In the Arabidopsis abi2-1 mutant, a G168D substitution in the N-terminal half of ABI2 causes an ABA-insensitive phenotype in mutant plants. This single amino acid substitution

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(G168D) abolishes the interaction between ABI2 and SOS2 in a yeast two-hybrid system. Interestingly, although the minimal SOS2-interacting domain of ABI2 is highly conserved in ABI1, the interaction between SOS2 and ABI1 is weak. The only difference between ABI2 and ABI1 in the minimal SOS2-interacting domain is that the Thr-197 and Val-201 in ABI2 are replaced by Ala in ABI1. A double amino acid substitution in ABI1 (A197TA201V) confers stronger interaction with SOS2, which suggests that the two divergent amino acid residues are important for SOS2 to discriminate between ABI2 and ABI1 (Ohta et al., 2003). Although the physiological role of SOS2-ABI2 interaction needs further investigation, one possible mode of this protein interaction is that ABI2 may dephosphorylate and thus deactivate SOS2, serving as a negative regulator of salt tolerance. In fact, the abi2-1 mutant shows more tolerance to salt stress compared with wild-type plants (Ohta et al., 2003). Since the sos2 mutant has no ABA response phenotypes, it is unlikely that SOS2 kinase phosphorylates ABI2 to mediate ABA signal transduction. SOS2 belongs to the SOS2-like protein kinase (PKS) gene family containing 24 members. All PKS proteins contain a putative FISL motif that is necessary and sufficient for interaction with SOS3-like Ca2+ -binding proteins (SCaBPs). It seems that the FISL motif is a unique motif in proteins of the PKS family. Systematic yeast two-hybrid experiments revealed different specificity in interaction among the members of each protein family (Kim et al., 2000; Guo et al., 2001), which suggests that a specific interaction between a PKS and a SCaBP member may contribute to the response of plants to distinct external or internal stimuli. Gene silencing of one of the PKS members, PKS3, causes mutant plant hypersensitivity to ABA in seed germination, seedling growth, stomatal closing, and gene expression (Guo et al., 2002). PKS3 can physically interact with SCaBP5 and ABI2. However, results of in vitro assays indicated that PKS3 could not phosphorylate ABI2, nor could ABI2 dephosphorylate PKS3. It is likely that PKS3 and ABI2 function in an antagonizing way to control the phosphorylation status of target proteins and mediate ABA signaling. The Ca2+ -sensing protein SCaBP5 or a closely related SCaBP serves as a bridge between Ca2+ and downstream ABA signaling to regulate ABA response negatively by interacting with PKS3. This hypothesis is consistent with the notion of SCaBP5 and PKS3 being negative regulators of ABA signaling. Silencing the other member of the PKS family, PKS18, confers ABA insensitivity in mutant plants, whereas overexpression of an active form of PKS18 (PKS18T/D) causes ABA hypersensitivity, which indicates that PKS18 is a positive regulator of ABA signaling (Gong et al., 2002b). PKS11 is preferentially expressed in roots of Arabidopsis plants. Overexpression of an active form of PKS11 (PKS11T/D) results in transgenic plants being more resistant to high concentrations of glucose, which suggests the involvement of this protein kinase in sugar signaling in plants (Gong et al., 2002a). The functions of other members in this gene family remain to be elucidated.

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6.4.3 SOS1 SOS1 encodes a protein that has sequence similarity with mammalian and yeast Na+ /H+ antiporters (Shi et al., 2000). The predicted secondary structure of SOS1 contains 12 putative transmembrane domains and a long C-terminal region that is supposed to reside in cytosol. The entire C-terminal portion of SOS1 is essential for its function in plants, because a truncated form of SOS1 with only about 40 amino acid deletions at the C-terminal end in a sos1 mutant allele (sos1-11) is dysfunctional. The SOS1 C-terminal region is predicted to have several conserved motifs. One of them is a putative cAMP/cGMP binding motif, which suggests that the transport activity of SOS1 is possibly modulated by a cyclic nucleotide. It is well established that plant cells possess cyclicnucleotide-gated channels (CNGC) that transport ions such as Na+ , K+ or Ca2+ (Trewavas et al., 2002). In fact, single amino acid substitutions in the putative cyclic nucleotide binding motif of SOS1 completely disrupts SOS1 function in plants, as evidenced by the sos1-8 (Gly777 to Glu) and sos1-9 (Gly784 to Asp) alleles being salt hypersensitive. Consistent with its specific role in Na+ tolerance, SOS1 expression in both roots and shoots is up-regulated by NaCl but not by cold stress or ABA. Importantly, the up-regulation of SOS1 by NaCl is controlled partly by SOS3 and SOS2. In the sos2 mutant, SOS1 is up-regulated by NaCl stress in roots but not shoots. In the sos3 mutant, SOS1 up-regulation in both roots and shoots is abolished (Shi et al., 2000). Expression of SOS1 in the yeast nha1nhx1 mutant can partially restore cell growth on medium containing NaCl, which indicates that SOS1 functions as a Na+ /H+ antiporter. Localization of SOS1 on the plasma membrane of yeast mutant cells and reduced Na+ accumulation in the yeast cells expressing SOS1 revealed that SOS1 can functionally complement the yeast plasma-membrane Na+ /H+ antiporter NHA1 (Shi et al., 2002a). Subcellular localization of SOS1 by SOS1-GFP fusion as well as an antibody against the SOS1 C-terminal region confirmed that SOS1 is localized on the plasma membrane of Arabidopsis cells. Furthermore, Na+ /H+ antiporter activity on the plasma-membrane vesicles of the sos1 mutant is remarkably reduced, which supports SOS1 being a plasmamembrane Na+ /H+ antiporter (Shi et al., 2002a; Qiu et al., 2002, 2003). On the cellular level, SOS1 mediates Na+ efflux under NaCl stress. sos1 mutant callus cells are hypersensitive to NaCl and accumulate more Na+ and less K+ under NaCl stress (Shi et al., 2002a). Overexpression of SOS1 confers salt tolerance in both transgenic plants and callus cells because of reduced Na+ accumulation, which further supports the role of SOS1 in Na+ efflux (Shi et al., 2003b). SOS1 is preferentially expressed in the epidermal cells of root tips and the parenchymal cells surrounding vascular tissue (Shi et al., 2002a). The expression of SOS1 in the epidermal cells of root tips may play an important role in protecting meristematic cells against salt stress. Meristematic cells do not contain a central vacuole and cannot effectively compartmentalize Na+ into vacuoles to reduce Na+ accumulation in cytosol; thus, these cells are more

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sensitive to NaCl. Active SOS1 in the epidermal cells in root tips would enhance Na+ efflux and reduce Na+ accumulation and consequently Na+ entry from epidermal cells to meristematic cells through a symplastic pathway. Strong expression of SOS1 in the parenchymal cells surrounding vascular tissue suggests a possible role of SOS1 in controlling the long-distance transport of Na+ in plants. Under salt stress, leaves of the sos1 mutant accumulate more Na+ and SOS1-overexpressing transgenic plants accumulate less Na+ compared to control plants. Moreover, the xylem sap of the sos1 mutant contains a higher concentration of Na+ and that of SOS1-overexpressing transgenic plants a lower concentration than that of control plants (Shi et al., 2002a, 2003b). These results suggest that SOS1 functions in the parenchymal cells in roots to limit Na+ loading into a transpirational stream so as to reduce Na+ accumulation in leaves. SOS1 in the parenchymal cells in leaves may function to attenuate Na+ transport from the transpirational stream into leaf photosynthetic cells by transferring Na+ from these cells into phloem sap and recirculating Na+ back to the root. Na+ recirculation from the leaf to the root by the phloem to reduce leaf Na+ accumulation has been observed in several plant species (Winter, 1982; Munns et al., 1988; Blom-Zandstra et al., 1998; Lohaus et al., 2000). Studies also indicate that the extent of this recirculation is related to the plant’s tolerance to salinity (Matsushita & Matoh, 1991; Perez-Alfocea et al., 2000). The precise mechanism of SOS1 function in these parenchymal cells needs further investigation. Being in the same pathway with SOS2 and SOS3 for plant salt tolerance, SOS1 is a potential target of the SOS2/SOS3 protein complex. In yeast nha1nhx1 mutant cells, expression of SOS1 slightly increases salt tolerance. Interestingly, co-expression of SOS1, SOS2, and SOS3 together confers much more salt tolerance than expression of SOS1 alone, which suggests that SOS2/SOS3 can activate SOS1 activity (Quintero et al., 2002). Since SOS2 encodes a protein kinase, phosphorylation of SOS1 by the SOS2/SOS3 complex is the likely mechanism involved in the activation of SOS1. In yeast cells, SOS1 is clearly phosphorylated by SOS2/SOS3 or superactive SOS2 that is SOS3 independent, which suggests that activation of SOS1 by SOS2/SOS3 is probably through SOS1 phosphorylation (Quintero et al., 2002). In plants, measurement of Na+ /H+ antiporter activity indicated that the plasma-membrane vesicles of sos2 and sos3 mutants have substantially reduced Na+ /H+ exchange activity, which further supports SOS2/SOS3 regulating plasma-membrane Na+ /H+ antiporter activity (Qiu et al., 2002). Moreover, by adding purified superactive SOS2 proteins to plasma-membrane vesicles, Na+ /H+ antiporter activity is greatly increased in the wild type or the sos2 and sos3 but not sos1 mutants (Qiu et al., 2002). These results show that sos2/sos3 enhances plasma-membrane Na+ /H+ antiporter activity by activating SOS1 in plants. Arabidopsis contains a large family of putative Na+ /H+ antiporters. SOS1 belongs to a subfamily of eight members. At least five are NHX-like, with possible vacuolar-membrane localization, and two, including SOS1, are NHAlike, with plasma-membrane localization. One member (SOS1-like) has very

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high similarity with SOS1 and includes both the N-terminal transmembrane portion and the C-terminal sequence but is shorter than SOS1. The SOS1-like gene is also up-regulated by salt stress (Shi, unpublished observations, 2003). A T-DNA knockout mutant of the SOS1-like gene showed no hypersensitive phenotypic response to Na+ , Li+ , Zn2+ , Ni2+ , or K+ . Similarly, there were no conditional responses to deprivation of K+ , low concentrations of Ca2+ or high concentrations of mannitol. Under all of the stress conditions tested, no significant mutant phenotypes were found in the SOS1-like mutant. Interesting questions are why these two proteins are so similar but apparently have no functional overlap, what is the function of the SOS1-like gene, and what is the evolutionary significance for these two genes in Arabidopsis. The NHX-like genes, in particular AtNHX1 in this subfamily, also play an important role in Na+ homeostasis under salt stress. 6.4.4 SOS4 SOS4 encodes a pyridoxal kinase involved in the biosynthesis of pyridoxal-5phosphate (PLP), an active form of vitamin B6 (Shi et al., 2002b). Three natural, free forms of vitamin B6 – pyridoxine (PN), pyridoxal (PL), and pyridoxamine (PM) – could be converted to the biologically active PLP. PL can be converted to PLP by PL kinase. PN/PM can be converted to PNP/PMP by a presumably nonspecific PN/PM kinase, which then are turned into PLP by a PNP/PMP oxidase (McCormick & Chen, 1999). The expression of SOS4 cDNA complements an E. coli mutant defective in pyridoxal kinase. Mutations in the SOS4 gene result in hypersensitivity of the mutant plants to Na+ , Li+ and K+ . Supplementation of pyridoxine but not pyridoxal in the growth medium can partially rescue the sos4 defect in salt tolerance, which supports SOS4 being a pyridoxal kinase. The mutant plants accumulate more Na+ and less K+ compared to wild-type plants under salt stress, which indicates that SOS4 is involved in Na+ and K+ homeostasis in Arabidopsis. In animal cells, PLP and its derivatives are known to be antagonists of ATP-gated P2X-receptor ion channels (Ralevic & Burnstock, 1998). However, neither PLP binding nor the effect of PLP on plant ion channels has been investigated. It is also possible that PLP can regulate plant ion channels or ion transporters. In fact, when searching for motifs within the SOS1 C-terminal region, a putative PLP binding motif can be predicted. Therefore, the role of SOS4 in Na+ and K+ homeostasis is presumably by regulating the activities of SOS1 or other transporters. SOS4 is transcribed to two transcripts because of alternative splicing in its first intron. The short transcript is more abundant than the long one. The alternative splicing is spatially regulated, which results in more alternative splicing in roots, flowers and siliques but less splicing in leaves and stems. Interestingly, the alternative splicing appears to be regulated by stress, in particular, cold stress. Alternative splicing has recently emerged as one of the most significant generators

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of functional complexity in several relatively well-studied animal genomes. In humans, approximately 40% of the genes are alternatively spliced, which suggests that alternative splicing contributes significantly to human protein diversity and serves as an important control point in gene regulation (Modrek & Lee, 2002). However, the role of alternative splicing in higher plants has been little studied. SOS4 could be a good candidate for the study of alternative splicing in response to abiotic stress, in particular, for elucidating the signaling pathway that controls alternative splicing. Besides having a salt-hypersensitive phenotype, the sos4 mutant also displays defective root hair formation (Shi & Zhu, 2002b). Mutations in the SOS4 gene block the initiation of most root hairs and impair the tip growth of those that are initiated. The root hair defect can be partially rescued by in vitro application of PN and PM but not PL, which is consistent with SOS4 being a pyridoxal kinase. 1-Aminocyclopropane-1-carboxylic acid (ACC) and 2,4-dichlorophenoxyacetic acid (2,4-D) promote root hair formation in sos4 mutants, which indicates that, genetically, SOS4 functions upstream of ethylene and auxin in root hair development. Previous studies have indicated that both ethylene and auxin play critical roles in root hair development (Pitts et al., 1998). It is likely that SOS4 controls root hair formation through, at least in part, the control of ethylene and auxin biosynthesis in Arabidopsis. In fact, PLP is one of the most versatile enzyme cofactors in nature. Among the superfamily of PLP-dependent enzymes, ACC synthase belongs to the ␣-family, shares a modest level of sequence similarity with other members of this family, and contains a PLP binding site (Capitani et al., 1999). In plants, ACC synthase catalyzes the committed step in ethylene biosynthesis, the conversion of S-adenosyl-Met to ACC, which in turn is converted to ethylene. Several enzymes involved in auxin biosynthesis, for instance, Trp synthase and Trp aminotransferase, may also depend on PLP. The involvement of root hairs in the uptake of most major and micronutrients has been documented (Gilroy & Jones, 2000). Therefore, the root hair deficiency might also contribute to the salt-hypersensitive phenotype of the sos4 mutant by limiting the uptake of essential ions such as K+ in the root. 6.4.5 SOS5 SOS5 encodes a fasciclin-like arabinogalactan protein that is important for cell wall formation in plants (Shi et al., 2003a). Mutation in the SOS5 gene results in root tip swelling and arrested root growth under salt stress. The rootswelling phenotype is caused by abnormal expansion of epidermal, cortical, and endodermal cells. The predicted SOS5 protein contains an N-terminal signal sequence for plasma-membrane localization, two arabinoglactan protein (AGP)-like domains, two fasciclin-like domains, and a C-terminal glycosylphosphatidylinositol (GPI) lipid-anchor signal sequence. The AGP-like domains are rich in Hyp residues for the addition of O-linked arabinogalactan chains and also

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contain a number of putative O-linked Ser and Thr glycosylation sites, which suggests that SOS5 is a highly glycosylated protein. Indeed, the electrophoretic feature on SDS-polyacrylamide gels supports SOS5 being a proteoglycan. Fasciclin domains are present in proteins that are known to function as adhesion molecules in animals, insects, algae, and microbes, which suggests that SOS5 may be important for cell–cell adhesion. Mature SOS5 protein is predicted to contain a GPI anchor that is important for membrane association. Immunofluorescence detection indicates that SOS5 is localized on the outer surface of the plasma membrane of protoplasts, which supports SOS5 being a cell wall protein and its association with plasma membrane. The cell walls are thinner and less organized in the sos5 mutant compared to the wild type. Since the fine structure of the cell wall is disrupted in the sos5 mutant, it is likely that Na+ penetrates easier into the cell wall compartment of sos5, which disrupts the homeostasis of some essential ions such as Ca2+ in the cell wall that mediate cross-linking of pectin chains for appropriate rigidity and extensibility, thus causing abnormal cell expansion under salt stress.

6.5 Other genes important for Na+ homeostasis 6.5.1 HKT1 Plant HKT1 cDNA was first isolated from wheat root by its ability to complement the K+ uptake-deficient phenotype of a yeast mutant (Schachtman & Schroeder, 1994). A detailed study of the molecular mechanism revealed that the wheat HKT1 functions as a Na+ /K+ symporter when expressed in oocytes, that is, a Na+ -coupled K+ transporter (Rubio et al., 1995, 1999). A highly charged loop region in this protein is thought to be involved in Na+ uptake, and mutation or deletion of this loop provides the transporter with greater selectivity for K+ over Na+ and confers salt tolerance of the yeast cells expressing the modified HKT1 (Diatloff et al., 1998; Rubio et al., 1999; Liu et al., 2000b). In both wheat and barley, the HKT1 transcript was induced by K+ starvation, which supports a role of HKT1 in K+ uptake (Wang et al., 1998). However, a study of barley, wheat, and Arabidopsis showed no significant contribution of Na+ -coupled K+ transport to K+ uptake in terrestrial plants (Maathuis et al., 1996). Moreover, antisense wheat plants with decreased expression of HKT1 displayed reduced Na+ uptake and enhanced salt tolerance, which indicates that, despite wheat HKT1’s greater K+ selectivity, it mediates Na+ uptake (Laurie et al., 2002). The rice genome contains seven HKT genes. Expression of OsHKT1 in the yeast mutant defective in K+ uptake restored growth at micromolar concentrations of K+ and mediated hypersensitivity to Na+ , which suggests that OsHKT1 can transport both Na+ and K+ (Golldack et al., 2002). Further support for this notion was that, when expressed in Xenopus oocytes, rice OsHKT1 showed uptake

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characteristics of a Na+ transporter but also mediated transport of other alkali cations, including K+ , Li+ , and Cs+ (Golldack et al., 2002). However, in another two studies, OsHKT1 was reported to transport only Na+ (Horie et al., 2001; Garciadeblás et al., 2003). Parallel experiments of K+ and Na+ uptake in yeast expressing the wheat or rice HKT1 transporters showed that wheat HKT1 transported K+ and Na+ , and rice HKT1 transported only Na+ (Garciadeblás et al., 2003). In addition, Horie et al. (2001) reported that low K+ conditions (less than 3 mM) induced the expression of OsHKT1 in roots, but mRNA accumulation was inhibited by the presence of 30 mM Na+ . The ion transport properties of the Arabidopsis HKT1 homolog AtHKT1 differed significantly from that of its wheat counterpart (Uozumi et al., 2000). Electrophysiological measurements revealed that AtHKT1 functions as a selective Na+ -uptake transporter in Xenopus oocytes, and the presence of external K+ has no effect on the AtHKT1-mediated ion conductance. When expressed in yeast cells, AtHKT1 confers hypersensitivity to a high level of Na+ , in agreement with AtHKT1 mediating Na+ influx. Unlike the wheat HKT1, AtHKT1 could not complement the K+ uptake-deficient phenotype of the yeast mutant but could rescue E. coli mutants carrying deletions in K+ transporters, which indicates that AtHKT1 has a limited capacity to transport K+ (Uozumi et al., 2000). HKT proteins contain P-loop-like domains that are proposed to be K+ selectivity filters. A glycine at the predicted filter position in P-loop A is necessary and sufficient for K+ permeation in HKT1 proteins. A single point mutation of this glycine into serine in wheat HKT1 abrogated K+ permeability, whereas a change of serine in AtHKT1, corresponding to the glycine of wheat HKT1, into glycine was sufficient to restore K+ permeability to AtHKT1 (Mäser et al., 2002b). Interestingly, all HKT1 homologues known from dicots have a serine at the filter position in P-loop A, which suggests that these proteins function as Na+ transporters. The important role of AtHKT1 in salt stress tolerance in plants was revealed from forward genetic screening of sos3 suppressors (Rus et al., 2001). In a screen of 65 000 individual T-DNA insertion lines generated in the sos3 mutant background, eight phenotypically identical mutants were found to belong to a single complementary group. All these mutants completely suppressed the sos3 salt-hypersensitive phenotype at 75 mM NaCl. Gene cloning revealed that all the mutants contain a T-DNA insertion or deletion in the AtHKT1 gene. Mutation in the AtHKT1 gene suppresses both the Na+ -hypersensitive and K+ deficient phenotype of the sos3 mutant. However, the suppression of the Li+ hypersensitivity of the sos3 mutant by athkt1 mutation was much lower than that of Na+ sensitivity, which suggests that AtHKT1 has higher selectivity to Na+ than Li+ . The suppression of sos3 hypersensitivity to NaCl by an athkt1 mutation is Ca2+ dependent. The suppression is substantially reduced when sos3hkt1 seedlings are grown in medium with a low level of Ca2+ (0.15 mM), which reveals a Ca2+ -dependent Na+ influx system in Arabidopsis. Under NaCl

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stress, the suppressors (sos3hkt1) accumulated less Na+ but more K+ in leaves compared to the sos3 mutant, which indicates that AtHKT1 controls Na+ entry into plant roots. Not surprisingly, mutation in AtHKT1 also suppresses the sos1 and sos2 Na+ -hypersensitivity and K+ -deficient phenotype, which further supports that SOS1, SOS2, and SOS3 function in the same pathway to control ion homeostasis under salt stress. These results indicate that AtHKT1 is an important salt-tolerance determinant that coordinately works with SOS genes to control Na+ and K+ homeostasis in Arabidopsis. Studies of the single mutant athkt1 suggest a more complicated role of AtHKT1 in salt tolerance (Mäser et al., 2002a; Berthomieu et al., 2003). athkt1null mutant plants exhibit lower root Na+ levels and are more salt tolerant than the wild type in short-term root growth assays. However, shoot tissues of the athkt1 mutant accumulate high levels of Na+ and display Na+ hypersensitivity in long-term growth assays. Therefore, AtHKT1 seems to control root/shoot Na+ distribution and counteract salt stress by reducing leaf Na+ accumulation (Mäser et al., 2002a). Screening for mutants with sodium over-accumulation in shoot (sas) identified two allelic recessive mutants of Arabidopsis, sas2-1 and sas2-2 (Berthomieu et al., 2003). Map-based gene cloning revealed that the sas2 locus corresponds to the AtHKT1 gene. When grown in a medium supplemented with either K+ , Na+ , Li+ , Mg2+ , or Ca2+ , the sas2 mutant plants overaccumulated Na+ but not other ions, which indicates that AtHKT1 has high ionic selectivity for Na+ over other cations such as K+ and even the toxic Na+ analog Li+ , this is consistent with the electrophysiological properties of AtHKT1 and sos3hkt1 being weaker suppressors for Li+ but stronger suppressors for Na+ hypersensitivity of sos3. The sas2 mutant plants displayed increased sensitivity to NaCl and strongly decreased Na+ concentration in the phloem sap. Together with findings of the restricted expression of AtHKT1 in the phloem tissues in all organs, it was concluded that AtHKT1 is involved in Na+ recirculation from shoots to roots, probably by mediating Na+ loading into the phloem sap in shoots and unloading in roots, the recirculation removing large amounts of Na+ from the shoots and playing an important role in plant salt tolerance (Berthomieu et al., 2003). However, the Na+ recirculation theory of AtHKT1 does not explain the sos3hkt1 suppressor phenotype, in particular, reduced Na+ accumulation in the suppressors under salt stress. Therefore, the function of AtHKT1 in the whole plant is probably more complicated than simple Na+ recirculation or Na+ entry. The precise function of AtHKT1 in the specific cells in a whole plant remains elusive. 6.5.2 NHX1 Yeast complementation assays identified a yeast NHX1 homologue, AtNHX1, as complementing the yeast nhx1 mutant (Gaxiola et al., 1999). AtNHX1 confers salt tolerance to yeast cells by sequestering Na+ into the vacuole driven by the H+ gradient across the vacuolar membrane. The Na+ /H+ exchange mediated

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by AtNHX1 is electroneutral and sensitive to amiloride (Quintero et al., 2000; Darley et al., 2000). The expression of AtNHX1 is up-regulated by salt stress and ABA, which suggests the importance of AtNHX1 in salt tolerance in Arabidopsis (Gaxiola et al., 1999; Quintero et al., 2000; Shi & Zhu, 2002a). The important role of AtNHX1 in salt tolerance in plants was suggested by overexpression of AtNHX1 in different plant species conferring salt tolerance to transgenic plants (Apse et al., 1999; Zhang & Blumwald, 2001; Zhang et al., 2001). The transgenic plants overexpressing AtNHX1 accumulated more Na+ than wildtype controls, which supports the role of AtNHX1 in Na+ compartmentation into vacuoles. AtNHX1 was shown to mediate K+ /H+ exchange, albeit with a lower specificity for K+ than for Na+ (Zhang & Blumwald, 2001). When the AtNHX1 protein was reconstituted into lipid vesicles, the measurement of cation-dependent H+ exchange revealed that AtNHX1 mediates Na+ and K+ transport with similar affinity but less so for Li+ and Cs+ transport (Venema et al., 2001). The capacity of AtNHX1 for K+ transport suggests that AtNHX1 may also function in pH regulation and osmotic adjustments in plant. In animals, the NHE-like Na+ /H+ antiporters are important for pH regulation and cell volume control (Counillon & Pouyssegur, 2000). The yeast ScNHX1 is required for endosomal protein trafficking (Bowers et al., 2000), adaptation to acute hypo-osmotic shock (Nass & Rao, 1999), and resistance to toxic cations (Gaxiola et al., 1999). In fact, a mutation in an NHX1 homologous gene of Ipomoea nil (InNHX1) encoding a vacuolar Na+ /H+ antiporter abrogated the capacity to increase vacuolar pH, a requirement for flower color shift from reddish-purple to blue, which clearly indicates that plant NHX1-like proteins are crucial in pH regulation (Fukada-Tanaka et al., 2000). The AtNHX1 expression is up-regulated by general osmotic stress and ABA, and this up-regulation is, at least in part, controlled by the ABA signaling pathway and independent of SOS genes (Shi & Zhu, 2002a; Shi, 2003, unpublished observations), which suggests that AtNHX1 may also function in osmotic adjustment in Araidopsis under salt and osmotic stress. Indeed, the T-DNA knockout mutant atnhx1 had smaller epidermal cells than the wild type (Apse et al., 2003), possibly because of an attenuated capacity of osmotic adjustment in the mutant cells. AtNHX1 belongs to a protein subfamily of eight members, six of which are NHX-like and two SOS1-like. Six NHX-like genes show distinct expression patterns and abundance (Yokoi et al., 2002). AtNHX1 and AtNHX2 are the most prevalent transcripts, whereas AtNHX4 and AtNHX6 have low abundance in seedling shoots and roots. AtNHX3 is expressed predominantly in roots. Both AtNHX1 and AtNHX2 are up-regulated by NaCl, osmotic stress, and ABA, which indicates that these two genes are regulated by osmotic stress. AtNHX3 transcripts are constant, with or without stress treatment. Interestingly, AtNHX5 is up-regulated only by NaCl but not by an equi-osmolar concentration of sorbitol or ABA, which suggests that AtNHX5 might have a specific role for NaCl detoxification in Arabidopsis. The expression mode of AtNHX5 could also provide a platform for study of the Na+ -specific signaling pathway. Similar to

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AtNHX1, AtNHX2 is localized on the vacuolar membrane of plant cells, which suggests that the functions of these two genes are probably redundant. Among AtNHX1, AtNHX2, and AtNHX5, AtNHX2 exhibits the strongest suppression of Li+ and Na+ hypersensitivity in yeast mutant cells, which indicates that AtNHX2 has a major function in vacuolar compartmentation of Na+ . The upregulation of NHX-like gene expression by stress treatment is independent of SOS genes. However, the expression levels of AtNHX1, AtNHX2, and AtNHX5 are higher in sos1, sos2, and sos3 mutants, respectively, than in the wild type in the absence of stress. Besides controlling plasma-membrane Na+ /H+ antiporter activity, the SOS2 gene has also been implicated in regulating vacuolar Na+ /H+ exchange (Qiu et al., 2004). When compared with tonoplast Na+ /H+ exchange activity in the wild type, that in sos1, sos2, and sos3 mutants is significantly higher, greatly reduced, and unchanged, respectively. In vitro application of the activated SOS2 protein increased tonoplast Na+ /H+ exchange activity in vesicles isolated from the sos2 mutant, which indicates that SOS2 is an important regulator of vacuolar Na+ /H+ antiporters. Since SOS3 is not required for vacuolar Na+ /H+ antiporter activity, the effect of SOS2 on tonoplast Na+ /H+ exchange may be conferred through other components such as the SCaBP proteins that may activate SOS2 in plants. The sos1 mutant has substantially increased tonoplast Na+ /H+ exchange activity, which indicates a coordination between the Na+ transporters on tonoplast and the plasma membrane. The plasma-membrane Na+ /H+ antiporter activity is remarkably reduced in the sos1 mutant (Qiu et al., 2002). Under salt stress, this reduced activity could be a signal sensed by cells to enhance vacuolar Na+ /H+ antiporter activity and compensate for the reduced plasma membrane activity. The mechanism underlying the coordinated regulation of these two types of antiporters remains unanswered but could simply consist of elevated Na+ concentration in the cytosol as a signal. Vacuolar Na+ /H+ antiporter genes have been identified in several plant species besides Arabidopsis, including both glycophytes and halophytes. Characterization of these genes, together with results from previous physiological studies, revealed that the vacuolar Na+ /H+ antiporter is an important salttolerance determinant in plants. 6.5.3 H+ pumps Plasma-membrane and vacuolar H+ -ATPase as well as vacuolar H+ -inorganic pyrophosphatase are essential for generating and maintaining membrane H+ gradients crucial for nutrient uptake and pH regulation. H+ -ATPases have been implicated in many aspects of plant growth, development, and response to environmental stress. Both plasma-membrane (P-type) and vacuolar (V-type) H+ ATPases are up-regulated by salt stress (Barkla & Pantoja, 1996; Portillo, 2000). Nicotiana plumbaginifolia contains at least nine plasma-membrane H+ -ATPase

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genes in its genome. Most were expressed in all organs tested but some in a cell-type specific manner. Expression of some of them in root hairs, companion cells, and guard cells suggests the involvement of P-type H+ -ATPases in mineral nutrition, phloem loading, and control of stomatal aperture (Moriau et al., 1999). Salt stress induces V-type H+ -ATPase expression in different plant species, including the halotolerant ice plant, sugar beet, and barley (Lehr et al., 1999; Golldack & Dietz, 2001; Fukuda et al., 2004). In Arabidopsis, at least 12 genes encode P-type H+ -ATPases. One, AHA4, is expressed most strongly in the root endodermis and flowers. A disruption of this gene by T-DNA insertion (aha4) results in a salt-hypersensitive phenotype (Vitart et al., 2001). aha4 mutant plants had a four- to fivefold increase in the Na+ :K+ ratio than the wild type when subjected to Na+ stress, which indicates that AHA4 is important for the control of ion homeostasis. The important role of vacuolar H+ -pyrophosphatase in salt tolerance was evidenced by the overexpression of the Arabidopsis vacuolar H+ -pyrophosphatase, AVP1, conferring salt tolerance in transgenic plants (Gaxiola et al., 2001). Transgenic plants overexpressing AVP1 accumulate more Na+ and K+ in their leaf tissue than the wild type, which results from enhanced cation uptake on the vacuolar membrane of the transgenic plants. Interestingly, transgenic plants overexpressing AVP1 also display enhanced drought tolerance, which suggests that increasing the vacuolar proton gradient results in increased solute accumulation and water retention (Gaxiola et al., 2001). 6.6 Cellular Na+ homeostasis and SOS pathway High Na+ accumulation in the cytosol is toxic to plant cells. Under salt stress, plant cells employ at least three strategies to reduce the accumulation: limiting Na+ entry, Na+ exclusion, and Na+ compartmentation (Zhu, 2003). Results of physiological studies have suggested that Na+ enters the plant cell through nonselective cation channels (Amtmann & Sanders, 1999). However, the molecular basis of such channels remains to be elucidated. Characterization of AtHKT1 in heterologous systems indicated that AtHKT1 is a Na+ influx transporter (Uozumi et al., 2000). Suppressor screening and gene identification revealed that a mutation in AtHKT1 suppresses the sos3 salt-hypersensitive phenotype and reduces Na+ accumulation in shoots of the sos3 mutant, which leads to the conclusion that AtHKT1 controls Na+ entry into cells (Rus et al., 2001). Na+ efflux is accomplished by the plasma-membrane Na+ /H+ antiporter SOS1. Mutation in the SOS1 gene results in substantial reduction of plasma-membrane Na+ /H+ exchange activity and hyperaccumulation of Na+ in cells under salt stress (Qiu et al., 2002; Shi et al., 2002a). Na+ compartmentation is executed by vacuolar membrane Na+ /H+ antiporters. In Arabidopsis, the AtNHX family of Na+ /H+ antiporters functions in the vacuolar sequestration of Na+ (Blumwald, 2000). Under salt stress, all three processes must be regulated to maintain Na+

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homeostasis in plant cells. Na+ entry must be suppressed and Na+ exclusion and compartmentation enhanced. Mutant isolation and gene cloning led to the identification of SOS genes crucial to plant salt tolerance. Follow up studies revealed that three SOS genes, SOS1, SOS2, and SOS3, genetically and biochemically function in the same pathway. Under salt stress, cellular Ca2+ concentration increases, which leads to Ca2+ binding onto the SOS3 protein. Upon Ca2+ binding, SOS3 interacts with the SOS2 protein and activates SOS2 kinase activity. Because SOS3 contains a myristoylation site at its N-terminus, the myristoylated SOS3 brings the complex onto the plasma membrane, providing the opportunity for SOS3/SOS2 to interact with SOS1. SOS2 phosphorylates SOS1, which enhances SOS1 Na+ /H+ exchange activity and promotes Na+ efflux. SOS4 catalyzes the formation of PLP that might bind to the SOS1 C-terminus and regulate SOS1 activity. SOS2 activates vacuolar Na+ /H+ exchange in a SOS3-independent manner to control Na+ compartmentation. Thus, SCaBPs other than SOS3 might serve as a SOS2 partner and activate it to regulate AtNHX-type Na+ /H+ exchange. Whether the SOS pathway regulates Na+ entry through AtHKT1 is still unknown. However, evidence shows that AtHKT1 works in coordination with SOS genes. 6.7 Prospects In the last decade, much progress has been made toward understanding the molecular mechanisms of plant salt tolerance, particularly the ion homeostasis aspect. Forward genetic screening, combined with gene cloning and functional analysis, is largely responsible for this progress. The identification of the SOS signaling pathway and other key components important in ion homeostasis in Arabidospis give rise to the question of whether different plant species use the SOS pathway and employ common machineries to cope with the saline environment. The answer will be important for both basic knowledge and the application of knowledge to improve salt tolerance in crops. Several homologous genes of SOS1 have been isolated from different plant species, including rice, wheat and tomato, salt-sensitive crops, and salt cress, a salt-tolerance plant. Use of RNAi technology could reveal the in planta role of these SOS1 homologues. Although it is more difficult to identify the SOS2 and SOS3 counterparts in different plant species because they belong to large gene families and share high sequence similarity, efforts should be made to clone and functionally analyze these homologous genes in crops. However, the precise physiological function of the SOS genes and AtHKT1 in specific cells where they are preferentially expressed is still elusive. Physiological tools such as X-ray microanalysis and magnetic resonance imaging might be helpful to measure subcellular Na+ distribution in different cell layers in roots and leaves of both the wild type and mutants, providing evidence of how these genes control Na+ movement in the whole plant.

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Moreover, the identification of additional signaling components that mediate the salt stress regulation of gene expression and activity of ion transporters will enrich our knowledge of plant salt tolerance. References Albrecht, V., Weinl, S., Blazevic, D., D’Angelo, C., Batistic, O., Kolukisaoglu, U., Bock, R., Schulz, B., Harter, K. & Kudla, J. (2003) The calcium sensor CBL1 integrates plant responses to abiotic stresses. Plant J., 36, 457–470. Amtmann, A. & Sanders, D. (1999) Mechanisms of Na+ uptake by plant cells. Adv. Bot. Res., 29, 75–112. Aperia, A., Ibarra, F., Svensson, L.B., Klee, C. & Greengard, P. (1992) Calcineurin mediates alphaadrenergic stimulation of Na+ , K+ -ATPase activity in renal tubule cells. Proc. Natl. Acad. Sci. USA, 89, 7394–7397. Apse, M.P., Aharon, G.S., Snedden, W.A. & Blumwald, E. (1999) Salt tolerance conferred by overexpression of a vacuolar Na+ /H+ antiport in Arabidopsis. Science, 285, 1256–1258. Apse, M.P., Sottosanto, J.B. & Blumwald, E. (2003) Vacuolar cation/H+ exchange, ion homeostasis, and leaf development are altered in a T-DNA insertional mutant of AtNHX1, the Arabidopsis vacuolar Na+ /H+ antiporter. Plant J., 36, 229–239. Barkla, B.J. & Pantoja, O. (1996) Physiology of ion transport across the tonoplast of higher plants. Annu. Rev. Plant Physiol. Plant Mol. Biol., 47, 159–184. Berthomieu, P., Conejero, G., Nublat, A., Brackenbury, W.J., Lambert, C., Savio, C., Uozumi, N., Oiki, S., Yamada, K., Cellier, F., Gosti, F., Simonneau, T., Essah, P.A., Tester, M., Very, A.A., Sentenac, H. & Casse, F. (2003) Functional analysis of AtHKT1 in Arabidopsis shows that Na+ recirculation by the phloem is crucial for salt tolerance. EMBO J., 22, 2004–2014. Blom-Zandstra, M., Vogelzang, S.A. & Veen, B.W. (1998) Sodium fluxes in sweet pepper exposed to varying sodium concentrations. J. Exp. Bot., 49, 1863–1868. Blumwald, E. (2000) Sodium transport and salt tolerance in plants. Curr. Opin. Cell Biol., 12, 431–434. Bohnert, H.J., Su, H., & Shen, B. (1999) Molecular mechanisms of salinity tolerance. In Molecular Responses to Cold, Drought, Heat and Salt Stress in Higher Plants (eds K. Shinozaki & K.Y. Shinozaki), R.G.Landes Company, Austin, TX, pp. 29–62. Bowers, K., Levi, B.P., Patel, F.I. & Stevens, T.H. (2000) The sodium/proton exchanger Nhx1p is required for endosomal protein trafficking in the yeast Saccharomyces cerevisiae. Mol. Biol. Cell, 11, 4277–4294. Capitani, G., Hohenester, E., Feng, L., Storici, P., Kirsch, J.F. & Jansonius, J.N. (1999) Structure of 1-aminocyclopropane-1-carboxylate synthase, a key enzyme in the biosynthesis of the plant hormone ethylene. J. Mol. Biol., 294, 745–756. Cheong, Y.H., Kim, K.N., Pandey, G.K., Gupta, R., Grant, J.J. & Luan, S. (2003) CBL1, a calcium sensor that differentially regulates salt, drought, and cold responses in Arabidopsis. Plant Cell, 15, 1833–1845. Clipstone, N.A. & Crabtree, G.R. (1992) Identification of calcineurin as a key signalling enzyme in T-lymphocyte activation. Nature, 357, 695–697. Counillon, L. & Pouyssegur, J. (2000) The expanding family of eucaryotic Na+ /H+ exchangers. J. Biol. Chem., 275, 1–4. Cunningham, K.W. & Fink, G.R. (1996) Calcineurin inhibits VCX1-dependent H+ /Ca2+ exchange and induces Ca2+ ATPases in Saccharomyces cerevisiae. Mol. Cell. Biol., 16, 2226–2237. Darley, C.P., van Wuytswinkel, O.C., van der Woude, K., Mager, W.H. & de Boer, A.H. (2000) Arabidopsis thaliana and Saccharomyces cerevisiae NHX1 genes encode amiloride sensitive electroneutral Na+ /H+ exchangers. Biochem. J., 351, 241–249.

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Mapping links between the genome and ionome in plants Brett Lahner and David E. Salt

7.1 Introduction The genome is the foundation on which all life is built. Living systems are supported and sustained by the genome through the action of the transcriptome, proteome, metabolome, and ionome – the four basic biochemical pillars of functional genomics. These pillars represent the sum of all the expressed genes, proteins, metabolites, and elements (Lahner et al., 2003) within an organism. The dynamic response and interaction of these biochemical ‘omes’ defines how a living system functions; and its study, ‘systems biology’, is now one of the biggest challenges in the life sciences. Studies on the functional connections between the genome and the transcriptome (Martzivanou & Hampp, 2003; Becher et al., 2004; Leonhardt et al., 2004), proteome (Koller et al., 2002) and metabolome (Fiehn et al., 2000) are well underway. However, the study of the ionome, in contrast, is still in its infancy (Lahner et al., 2003; reviewed by Hirschi, 2003; Rea, 2003), with the majority of genes and gene networks involved in its regulation still unknown. Moreover, because the ionome is involved in such a broad range of important biological phenomena including electrophysiology, signaling, enzymology, osmoregulation, and transport, its study promises to yield new and significant biological insight. Uptake and translocation of mineral ions is essential for plant growth, human health and nutrition, and the development of plant based bioremediation (Guerinot & Salt, 2001). Significantly, bioremediation and enhanced nutritional value of crops were recently ranked in the top 10 biotechnologies for improving human health in developing countries (Daar et al., 2002). In spite of recent advances (Mäser et al., 2001), gene networks that control acquisition of individual mineral ions remain largely unknown. An understanding of the ‘ionome’ and how it interacts with other cellular systems such as the genome, the proteome, and the environment are integral to our full understanding of how plants integrate their organic and inorganic metabolisms. Nearing the completion of a four-year project measuring the Arabidopsis ionome, we feel that the time is ripe for sharing what we learned along the way, both how we succeeded and what we could have improved.

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7.2 Concept of the ionome Lahner and colleagues first described the ionome to include all the metals, metalloids, and nonmetals present in an organism (Lahner et al., 2003); extending the term metallome (Williams, 2001; Outten & O’Halloran, 2001; Szpunar, 2004) to include biologically significant nonmetals such as N, P, S, Se, Cl, and I. Based on this definition, the ionome of plants, for example is composed of the macronutrients Ca, K, P, N, Mg, S, and Na, the micronutrients Mn, Cu, Co, Cl, Ni, Si, Mo, Fe, and Zn, and non-nutritional but environmentally significant elements such as Al, As, Se, Cd, and Pb. Because all of these elements occur mainly as ions within cells we felt the term ion-ome was a fair description of this important class of biochemicals (Lahner et al., 2003), though we are aware that certain elements such as Fe and S do occur in their elemental form in organisms. We have excluded C and O from the ionome because they mainly fall within the metabolome, though clearly the carbonate ions would be an exception. It is important to note here that the boundaries between the ionome, metabolome, and proteome are blurred. Compounds containing the nonmetals P, S, or N, for example, would fall within both the ionome and metabolome, and metals such as Zn, Cu, Mn, and Fe in metalloproteins would fall within the proteome, or metalloproteome as it has been described (Szpunar, 2004). By considering the ionome as a whole, the concept of ion homeostasis networks arises, in which various ions within an organism are coordinately regulated. The observation that only 11% of the 50 Arabidopsis ion-profile mutants recently identified (Lahner et al., 2003) showed changes in only one element strongly supports the existence of such regulatory networks in the ionome of plants. Characterization and mapping of these ion homeostasis networks should help uncover not only their genetic basis but also how they interact with both the proteome and the metabolome. The elements to be measured in the ionome will be determined by their biological importance or environmental relevance, in conjunction with their amenability to quantitation. However, each element measured must be present in sufficient concentrations in the plant tissue so as to be well above the limit of quantitation (LOQ), defined as the concentration equal to 10 standard deviations of the blank signal.

7.3 Characterization of the plant ionome – a single ion at a time Over the last 50 years remarkable progress has been made in describing and understanding the basic biology of nutrient ion homeostasis in plants (Marschner, 1995). The development and application of modern molecular genetic

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techniques, and completion of the Arabidopsis and rice genomes has accelerated progress. However, much remains to be discovered. During evolution, the first proto cells faced a major obstacle. The outer membrane, whilst needed to keep the cellular contents organized as a functional unit, created a barrier that prevented the uptake of nutrient ions. Thus, one of the key advances enabling individual cells to survive was the evolution of ion transport systems. As multicellular and terrestrial organisms evolved, the challenges of moving solute ions from the environment to the appropriate tissues increased. Because of their central importance, ion transporters have been the primary focus of most work involved in characterizing the ionome in plants. In the past few years transporters for many different ions have been characterized (Mäser et al., 2001). As previously shown, multiple genes and even multiple gene families appear to be responsible for transport. This is not surprising considering that different plant tissues have different nutritional and energy requirements and because transport across different membranes is required. In addition, multiple membrane proteins may be needed for ion uptake from the soil to adapt to varying extracellular conditions and nutrient availability. Such paradigms are exemplified in our current understanding of the regulation of numerous mineral ions in plants, including Fe, Zn, Na, P, K, and Ca (Sanders et al., 2002; Rausch & Bucher, 2002; Curie & Briat, 2003; Véry & Sentenac, 2003; Zhu, 2003). Though extensive progress has clearly been made, a careful analysis of the Arabidopsis genome reveals the existence of approximately 1000 ion transporters, most of which have not yet been characterized. Further, it is estimated that 5% of the approximately 25 000 genes in the Arabidopsis genome are involved in regulating the ionome (Lahner et al., 2003). Clearly, a major challenge to understanding the genes and gene networks involved in ion homeostasis in plants is to design ways to probe gene function on a genomic scale.

7.4 Characterization of the plant ionome – multiple ions at a time The advent of DNA microarray technology has certainly accelerated the pace at which genes regulated by ionic changes can be identified (see Chapter 8). Not surprisingly, many genes are transcriptionally responsive to changes in nutrient availability, including transporters, transcription factors, and signaling factors (Thimm et al., 2001; Negishi et al., 2002; Maathuis et al., 2003; Wang et al., 2003; Wintz et al., 2003). It is clear from these and other studies that plants can respond specifically to the availability of individual nutrients and nutrient deficiencies, suggesting that many regulatory pathways exist. The challenge is how to integrate alterations in transcription with a functional understanding of how ion homeostasis networks operate. As part of this challenge we have developed a strategy for genomic scale profiling of nutrient and trace elements,

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which we feel will help map the ionome onto the genes and gene networks that regulate it (Lahner et al., 2003). 7.4.1 High-throughput ion profiling For comparative ionomics on a genome-wide scale wild-type and mutant plants are ideally grown side-by-side under identical conditions, in a perfectly uniform growth media. They all grow to the same size and are harvested at the same time, sampling exactly equivalent amounts and parts of tissue in every case. They are grown and harvested under clean room conditions, using tools that will not impart any measured elements to the samples, washed of any surface contaminants, dried, and weighed accurately and precisely. Their analysis is performed flawlessly, with all reagents added in the correct amounts to every sample, with no instrumental drift or error, and with no mislabeling or mix up of samples or data. The data is processed without human intervention, summarized in an easily understood format, and made accessible to all interested parties preferably via Internet access. Unfortunately, we do not live in a perfect world and this scenario never happens. In the imperfect world, a screen of a significant portion of the genome takes months or years. Conditions change, personnel change. Samples get contaminated with growth media, growth media varies from batch to batch. Plants grown on soil are under-or over-watered. Sample sizes vary, and include different tissues from different plants. Instruments vary with the maintenance cycle and operating conditions. Data are mixed up, lost, or misinterpreted, and programs have bugs. People who want the data cannot get it easily. The success or failure of such an ionomic project is determined by which of these two scenarios we stand closer to. Within these boundaries lies another dimension to consider: sample throughput versus the quality and breadth of the data collected. The best approach to this key tradeoff is by no means universally agreed upon. Nearer to one end of this dimension lie the scientists I shall refer to as the CAPs. Quoting the preeminent biologist Sydney Brenner, ‘. . . data that goes into a database. . . should be complete. . . accurate. . . and permanent, so you never have to do it again.’ (Duncan, 2004). At the other end lie scientists who emphasize speed to maximize the number of mutants found. These Speeders hope to find the lower-hanging fruit by screening larger portions of genomes, and tend to rely on various statistical tools to extract good information from noisy and incomplete data. It may be interesting to note that even the term ‘saturation’, as applied to an ionomics screen, has different meanings for CAPs and Speeders. Depending on how much is drawn from each of these two philosophies in planning an ionomics screen, various parts of what follows may apply. The saturation of a screen might be defined by the number of interesting mutants found. The most efficient strategy involves a higher speed first screen

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followed by a more careful analysis of the likely interesting mutants uncovered in that initial screen. For Speeders, it is desirable to minimize oversampling. In order to do so, it is necessary to sample evenly from all of the available pools of seeds. The first screen should be sampled at close to n = 1 until the approach of saturation forces oversampling. The saturation of an ionomics screen might instead be defined by the net amount of genetics data obtained overall. In this case the consistency of a screen over its entire duration can greatly affect the net result. Apart from comparing candidate mutants with their (temporally) local peers, it is desirable to compare each mutant with every other mutant from the entire screen. Doing so will allow the screeners to answer questions like, ‘How much of the genome affects the ionome?’ and ‘Which elements tend to be affected in concert?’ as well as ‘Is this a likely repeat of an earlier mutant?’ Statistical analysis of the data will yield much clearer answers when the data are all collected under exactly (so much as possible) the same conditions, and more subtle genetic effects may become discernable. However, keeping the procedures consistent is much more easily stated than accomplished. Difficulties that may be encountered in trying to maintain consistency include suppliers changing their formulations (buy enough soil or media at the outset for the entire screen), the departure of key personnel (cross train multiple people) and enthusiastic technicians tweaking procedures to improve growth, throughput, or to fit their individual schedules (good supervision and clear areas of responsibility). Projects relying heavily on students may be particularly affected by semester breaks, exam periods, and personnel change (save the students for shorter term projects). Even the project manager may be tempted to make major changes in the middle of the game. The key here is not to make these changes lightly. One aide in maintaining consistency throughout a long experiment is a pilot experiment through which bugs can be worked out of the system before final long-term conditions are set. A handbook detailing all of the critical procedures should be produced from the pilot project and every new worker should be trained with this in hand. Laboratory notebooks left by an ‘escaping’ post doc are often of surprisingly little use to a new post doc. The procedural handbook must be simple and clear enough for any technician to follow and should include photographs whenever verbal descriptions are difficult or ambiguous. If changes to the experimental procedure must be made, they should be documented in a table of changes and this data must be incorporated into the database to improve statistical analyses. The prudent programmer would start off with a number of extra fields in the database to accommodate any departures from the pilot procedure. 7.4.2 Sample preparation This includes plant growth, harvesting, washing, and digestion. Each step invites both pitfalls and opportunities for improvement. Plants may be grown on soil

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or soil-less mixtures, on MS media in plates, or in hydroponic nutrient solution. This is not to say that each method will yield the same result. Since plants evolved in soil, it seems reasonable to assume that a number of genes are present in order for the plant to deal with the soil environment. Those genes affecting root exudates, for example, may well not be found in a hydroponic screen. We chose to use a soil-like mixture in our screen for just this reason. Achieving uniform growth conditions is imperative in a typical plant genome screen. A relatively small difference in light intensity or soil moisture can lead to a 10% to 20% change in an ion of interest, for example, and may often be enough to hide minor perturbations in the plants’ ionome. Harvesting the plants may seem like such a trivial operation that it is not worth mentioning. But two major sources of error may creep in at this point, and an assiduous technician will be able to attain much better results. These two sources are obtained from sampling different parts of the plant and taking different amounts of sample from each plant. The issue with the former practice arises from the localization of compounds in different parts of the plants (e.g. old vs new leaves, petioles vs leaves, leaf edges vs center portions). The difference between old and new leaves may well be several times larger than the total difference between a wild-type and interesting mutant. Furthermore, surface contamination tends to build up unevenly, especially that from the soil. The importance of the latter issue, not taking samples of the same size, is due to more technical reasons involving the effect of the sample matrix (with ‘sample’ now referring to the digested and diluted solution which is introduced to the analytical instrument) on the analytes, and the errors introduced due to the nonlinearity of calibration curves. One further noise source that may arise in harvesting is by contamination of the plant samples with particles of the harvesting implements. Tools that scrape two surfaces together, as do scissors and hole punches, abrade minute pieces of metal and force them into the plant tissue. These pieces appear as Cr, Fe, Ni, Co, Mn, Mo, and Cu spikes in the data. A scalpel works well enough for nondestructive sampling without contamination. Samples must be washed to decrease the amount of surface contamination. For this step pure water may be used or for example a 0.1% Triton solution followed by rinsing. After drying and weighing, samples are typically digested in concentrated acid and diluted before analyzing. Nitric acid digests most plant material easily and, of the common inorganic acids, interferes with ICP-MS analysis the least. Open-air digestion below the boiling point works well, while microwave digestion is becoming more common, especially where loss of an analyte of interest is a concern. While suppliers assert (correctly) that digestion time is much shorter under the higher pressure and temperatures of the microwave digestion apparatus, capacity issues shift the overall speed equation toward open-air digestion, where several hundred samples can be run in the same hood that would vent the microwave, and with far less sample handling.

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7.4.3 Sample analysis Which is the best analytical tool for ionomic analysis? The three most common methods for elemental analysis are Atomic Absorption (AA), InductivelyCoupled Plasma Optical Emission Spectroscopy (ICP-OES), and InductivelyCoupled Plasma Mass Spectrometry (ICP-MS). Each is discussed below. AA uses an acetylene flame from about 2300 to 2700 ◦ C to create ground-state neutral atom vapor (GSNAV) from the analyte solution. Light of one specific wavelength is passed through the flame and the absorbance measured to quantitate one element at a time (traditionally) or a few elements in rapid succession (in newer instruments). This method is very well established and quite precise, but not nearly as sensitive as ICP-MS and with a dynamic range of only three or four orders of magnitude. The initial cost of an AA is only about a quarter of that of ICP, but operational costs are about the same. While an ionomics screen is feasible using AA, perhaps with several machines running in parallel, it seems likely that the ICP technologies will push it into extinction in the foreseeable future. ICP-OES, often referred to as simply ICP, uses an argon plasma at about 8000 K to induce sample atoms to emit characteristic photons, an optical filter to separate the photons by wavelength, and a charge injection device (CID) detector to measure the intensities. Although ICP is less sensitive than ICPMS, some of this sensitivity is won back by the robustness of ICP in more concentrated matrices. While ICP-MS struggles with matrices with greater than about 0.1% solids, ICP can handle up to about 3%. ICP is a reasonable choice for an ionomics screen, at the possible expense of some of the trace elements due to its lower sensitivity. ICP-MS uses an argon plasma similar to ICP-OES but measures the concentration of atomic ions (and small molecular ions) in the plasma. One critical advantage of ICP-MS is that it allows for a smaller sample size due to its greater sensitivity. Smaller sample size equates to less-growth room space and more uniformity of samples, since plants have less time to diverge due to uneven growth conditions. Further, less material may be required for digestion, which in turn may be faster and easier, and more capacity may be found on the autosamplers (for overnight runs). The small sample size required for ICP-MS makes nondestructive sampling of small plants possible, a prerequisite in a random forward genetic screen – interesting mutants need to be saved not destroyed by the ICP. In the deficit column, smaller samples require cleaner conditions, are harder to weigh accurately, and may be more difficult to handle. One additional advantage of ICP-MS over the two other methods mentioned here is that individual isotopes may be measured, which introduces the possibility of isotopic spiking. Not too long ago ICP-MS was considered difficult to use, requiring a chemist for operation, but with recent advances in the hardware and software now it is not much more complex to use than AA. Either ICP or ICP-MS can be effectively used in an ionomics screen, with ICP having the advantages of lower cost and simplicity, and ICP-MS having the edge in sensitivity.

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7.4.4 Potential rate limiting factors As mentioned previously, the time to grow enough plant tissue for one sample is dependent on the amount of sample required for the analysis, which in turn is limited by the sensitivity of the instrument and the elements chosen for determination. This requirement equates to growth space and technician time, as the plants can be grown in parallel. So growth space and grower time taken together is the first potential limiting factor. Every sample must be prepared in some way for analysis with the chosen instrument, with 10 000–20 000 plant samples needing to be harvested, dried, weighed, and digested. Whatever process is settled upon, this is the second potential limiter. The third potential limiting factor, and the one most often focused on when planning a screen, is the throughput of the analytical instrument. The final group of tasks that can limit the screen is the data handling, including everything from getting the data off the analytical instrument up to publication of the data. It is fine to speed up any one segment of the screen (assuming no increase in noise), but if it is not the limiting segment, the ionome will not be screened any faster. 7.4.5 Data handling Data handling must be considered from the outset of the project if a reasonable degree of efficiency is to be achieved. Areas to be considered include sample labeling, group size, exporting data, attached data, data analysis, and presentation of results to the end users. Sample labeling is time-consuming and is best avoided to improve throughput. A superior technique is to identify samples by their position in the screen; that is, which group they are in and where within that group. When plants must be retained (identified as mutants) then they must be labeled, often by hand. A label printer can prevent mistakes due to ambiguous handwriting. Group size is the number of samples to be taken as a unit. This number is most conveniently set at the normal size of one run on the chosen analytical instrument, but can easily be set at one plant tray, one test tube rack, one petri dish, or one microtiter plate. The group must somehow map onto both the growth format and the analysis format if labeling is to be avoided. The analytical batch size is most easily varied, since growth format is constrained by physical considerations. Data may be directly exported to a database, or may first be subjected to analysis. The advantage of exporting immediately is that the data can be made instantly available to anyone with Internet access, so the workgroup does not need to be co-located. There may be a risk, however, in separating the technician too far from the product of his or her labors. Attached data should be included by the technician at the time of upload. Planning to transcribe hand-written notes into the database at a later date is plainly a bad decision. The analysis of the raw data generated in an ionomics screen can be carried out in a number of valid ways. The key objective is identification of plants with

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disturbed ionomes. In other words, we want to find mutants that differ significantly in their chemical makeup from the wild type. This goal does not require that actual concentrations of any of the elements be determined. Instead, interesting mutants can be identified by looking at their overall elemental profiles using discriminant analysis, neural net trolling, average signal normalization, or some other scheme. Notice that these methods do not necessarily require weighing (hence nor drying) of the samples. In our screen we weighed a handful of the wild-type samples and used these weights to calculate the weights of the rests of the samples from their raw signals. To our great surprise, we found that the calculated weights were more precise than the ones obtained by weighing on a balance. We then determined the mutants through a straightforward t-test for each element. Although perhaps inelegant compared to the methods listed above, this technique provided elemental concentrations and was easy to grasp. 7.4.6 Bioinformatics To maximize the value of any large-scale genomics effort it is critical that the data be made available to as wide a selection of people as possible. Such a statement is not simply based on philosophical musings of ‘fairness and openness’ but rather on the very practical consideration that ‘two heads are always better than one.’ Genomic data is simply a new type of raw scientific information, that like all scientific information requires careful consideration and analyses before any meaningful conclusions can be drawn. To facilitate such a process we have developed a searchable online database containing ionomic information on many thousands of plants. The database can be searched for mutants altered in a specific or set of elements, as well as on gene name and gene number. The ionomics database can be found at http://hort.agriculture.purdue.edu/Ionomics/database.asp. The ionomics database will be periodically updated and expanded, and our ultimate goal is to provide a biologist-friendly ‘synthetic laboratory’ that integrates genomic, transcriptomic, proteomic, metabolomic, and ionomic data. Such an environment will allow in silico experiments to be performed that will facilitate the development of sophisticated hypotheses that can be tested with ‘wet’ experiments. It is hoped that such an environment will accelerate the speed at which researchers design and perform experiments that deliver new and significant biological insight. In order to develop such an environment it is not only critical to collect the appropriate genomic data but also to bring together the appropriate expertise in information storage and processing. Graphic designers and educators are also needed for the development of intuitive interfaces for the efficient exchange of sophisticated biological information. If such expertise can be brought together for the development of realistic computer gaming environments and computer aided design (CAD) applications one would hope we could do something similar for ‘computer aided biology.’

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7.5 Environmental, temporal, and spatial ionomics Broad differences exist in the complement of expressed genes and metabolic capacity of plant cells under different environmental conditions and in different organs, tissues, and at different developmental stages. Given such differences we would also expect the ionome of plants to vary. During our high-throughput ionomic work we have endeavored to maintain relatively constant environmental conditions and sampling procedures to avoid such changes. However, over time we have observed significant changes in the shoot ionome of Arabidopsis in response to changes in the environment, including the soil matrix. Switching between two different commercial blends of artificial soil caused significant adjustments in the shoot ionome of Arabidopsis (Fig. 7.1). Alteration in the 180

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Figure 7.1 Impact of different artificial soil mixes on the shoot ionome of Arabidopsis. Wild-type Arabidopsis (Col-0) seeds were planted in 72-place grow packs (26 × 52 cm) and allowed to grow in a climate-controlled room at 19–24 ◦ C with 8 h light at 90–150 ␮E of photosynthetic photo flux (PPF). Shoot tissue (0.03–0.14 g f. wt) was harvested after 44 days, washed with 0.1% Triton X-100 followed by 18 M water, placed in preweighed Pyrex digestion tubes, dried overnight at 92 ◦ C, weighed and digested for 4 h at 118 ◦ C in concentrated NHO3 . Samples were then diluted with 18 M water and analyzed for Li, B, Na, Mg, P, K, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, and Pb by ICP-MS (Thermo Elemental PQ ExCell). Plants in trays 1–8 were grown in Metro Mix 360 (Scotts), and in Sunshine Mix no. 2 (Sun Gro Horticulture) in trays 9–19. All soil mixes were spiked with As (V) (7.5 ppm), Cd (0.09 ppm), Co (0.59 ppm), Cr (VI) (0.26 ppm), Li (0.7 ppm), Na (4.7 ppm), Ni (0.59 ppm), Pb (20 ppm), and Se (VI) (7.9 ppm). The relative concentrations of Mg (circles), K (squares), Mn (triangles), and Zn (diamonds) are displayed as a percentage of the concentration in plants harvested from tray 1. Data represent an average (n = 10) of samples from independent plants within a tray. Arrow represents the change from Metro Mix 360 to Sunshine Mix no. 2.

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fertilization regime also has a selective but significant impact on the Arabidopsis shoot ionome. As fertilization is increased, most elements in the ionome stay remarkably constant with the exception of K, Mn, and Zn which increase and Ca which decreases (Fig. 7.2). More subtle changes in soil composition such as aging time after addition of various elements also have significant effects on the ionome, with certain elements such as Li, Na, K, Mn, Co, As, and Se increasing in concentration in the shoot tissue as the soil ages, whereas B, Zn, and Mo decrease (Fig. 7.3). Slight changes in the soil composition can also have unexpected effects on the ionome. For example, reducing arsenate and selenate concentrations in the soil caused a significant lowering of shoot Mn concentrations (Fig. 7.4). However, other than As and Se no other changes were observed in the ionome. The systematic mapping of such ionomic responses will be critical if we are to fully undertand how plants adjust ion homeostasis networks in response to the environment. Such ionomic differences are also seen when the ionome of different organs, including shoot and seed are compared in Arabidopsis (Fig. 7.5). In general, the concentration of all the elements measured is reduced in seed compared to the leaf tissue, with the exception of P and As which appear to be increased. 350

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Figure 7.2 Impact of fertilization regime on the shoot ionome of Arabidopsis. Plants were grown in Metro Mix 360 and analyzed as described in Fig. 7.1, and fertilized with water or Hoaglands solution at 0.1, 0.25, 0.5 and 1 X concentrations. The relative concentrations of K (circles), Ca (diamonds), Mn (triangles), and Zn (squares) are displayed as a percentage of the concentrations in plants fertilized with just water. Data represent the average (± 1 S.D.) of plant samples from three independent plants.

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Figure 7.3 Impact of soil aging on the shoot ionome of Arabidopsis. Plants were grown in Metro Mix 360 aged for either 9 or 29 days after amending with As, Cd, Co, Cr, Li, Na, Ni, Pb, and Se and analyzed as described in Fig. 7.1. Data are presented as the percentage change between the shoot ionome from plants grown in soil aged for 9 and 29 days, and data are the average (n = 20) of shoot samples from replicate plants.

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Plant tray Figure 7.4 Impact of addition of As (V) and Se (VI) to soil on the shoot ionome of Arabidopsis. Plants were grown in Metro Mix 360 and analyzed as described in Fig. 7.1. Soil in tray 1–10 amended with As(V) and Se(VI) at 7.5 and 7.9 ppm, and trays 11–20 at 1.9 and 2.0 ppm, respectively. Data represent average (n = 10) of shoot samples from replicate plants. Arrow represents change in As and Se concentration in soil.

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−500 Figure 7.5 Difference in the shoot and seed ionome of Arabidopsis. Plants were grown in Sunshine Mix no. 2 and analyzed as described in Fig. 7.1. After sampling shoots plants were transferred to continuous light to induce flowering, plants were allowed to self-and-seeds collected once mature. Seed from two individual plants was subsampled thrice each and analyzed by ICP-MS as described for shoot material. Data are presented as the percentage difference of the ionome of the shoot versus seed, and represents the average of six samples.

The ability to profile the elemental content of different plant tissues such as meristematic and vascular tissue requires a 10–50 ␮m spatial sampling resolution. Such imaging has been achieved for individual elements such as Se in plants using X-ray spectroscopy (Pickering et al., 2000, 2003), though not for multielement analysis using ICP-MS. However, use of laser ablation sampling coupled with ICP-MS (LA-ICP-MS) holds promise for development of high-resolution ionomic imaging (Narewski et al., 2000; Kang et al., 2004). If developed in living plant tissue such technology would open up a completely new window onto the ionome, allowing changes in the total shoot or root ionomes, for example to be mapped to specific tissues and cell types. Such ionomic imaging would also allow colocalization of in vivo gene expression and protein localization patterns with ionomic changes, providing spatial linkage between gene, protein, and ionomic function. 7.6 Linking the ionome and genome Uncovering the genes that underpin mineral ion homeostasis in plants is a critical first step toward understanding the biochemical networks that regulate the ionome. Identification of genes underlying any biological phenomena can take

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the two different but complementary approaches of forward and reverse genetics. Each approach has advantages and disadvantages, which will be discussed. 7.6.1 Forward genetic approaches Forward genetics is the more traditional approach of mapping genotypic variation to a specific phenotype. Genotypic variation can be either naturally occurring, such as between different ecotypes of Arabidopsis (Alonso-Blanco & Koornneef, 2000), or induced using various mutagens including ethyl methanesulphonate (EMS), X-rays, and fast neutrons (FN) (Koornneef et al., 1982). More recently, insertional mutagenesis using transfer DNA (T-DNA) (AzpirozLeehan & Feldmann, 1997; Krysan et al., 1999) and transposable elements such as Dissociation (Ds) (Parinov et al., 1999) have also been successfully used in Arabidopsis. The heterologous expression of plant cDNA libraries in model systems such as yeast can also be considered to induce genetic variation and will be considered here as a forward genetic approach, as has been argued previously (Stark & Gudkov, 1999). Mutagenesis with EMS and FN are random processes, and integration of T-DNA also appears to be essentially random, at least in Arabidopsis (Barakat et al., 2000). However, transposon mutagenesis shows a preference for inserting at sites closely linked to the initial insertion (Sundaresan, 1996). Such linkage is problematic if saturation mutagenesis is required. However, it can be advantageous if targeted mutagenesis of gene clusters is required. Due to the single nucleotide polymorphisms (SNP) produced using EMS it is possible to obtain both loss- and gain-of-function mutants. However, because FN mutagenesis produces deletions they rarely cause gain-of-function mutations. Mutagenesis using T-DNA is via insertion and also rarely produces gain-offunction mutants, though to some extent this can be circumvented by the use of activation tagging where a multimerized transcriptional enhancer is incorporated into the mutagenic T-DNA (Weigel et al., 2000). Once a plant population has been established with significant genotypic variation a suitable screen needs to be developed to identify plants with the phenotype of interest. The probability of identifying a plant harboring a mutation in a gene that affects the trait of interest, in this case the ionome, is dependent on both the mutation frequency and size of the gene(s). Mutation frequency varies between mutagens with FN and EMS producing on average 30–60 mutations per diploid genome (Koornneef et al., 1982), compared to 1.4 mutations for T-DNA (Feldmann, 1991). To perform a saturation screen using an EMS or FN mutagenized population would therefore require phenotyping of approximately 10 000–20 000 M2 plants, whereas the same screen with T-DNA would require 200 000–400 000 M2 plants. Clearly, even when using an EMS or FN mutagenized population the screening system used to identify plants with an altered ionome needs to be relatively high throughput in order to achieve saturation. As we have discussed above, achieving high throughput ICP-MS analysis at the high

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precision needed to produce a viable screen of the ionome is challenging and requires both good analytical techniques and data handling. We have performed such a screen on an Arabidopsis FN mutagenized population of approximately 6,000 M2 plants, and identified 51 mutants with altered shoot ionomes (Lahner et al., 2003). Of these mutants, only one mutant, frd3-5, appeared to show dominance after analysis of the high Mn phenotype in the M3 generation. However, to confirm the recessive nature of the majority of these mutations backcrossing to wild type (Col-0), selfing of the hybrid F1 , and scoring of the ionomic phenotype in the F2 is required. Such analyses have been performed on three of these ionomics mutants that show alterations in Ca, K, and Mo, and all were found to be recessive. In order to determine the genetic change responsible for a given ionomic phenotype identified in such a forward genetic screen it is necessary to map the mutation in the genome. To achieve such mapping, different approaches are needed depending on the mutagen used. Mapping mutations derived from an EMS or FN population requires positional cloning using marker assisted mapping. Such an approach has been facilitated by the completion of the Arabidopsis genome, the availability of over 50 000 genetic markers in the Cereon Arabidopsis Polymorphism Collection and development of rapid PCR-based methods for identification of such polymorphisms (Jander et al., 2002). Such an approach requires making an outcross of the ionomic mutant to another Arabidopsis ecotype with a large collection of known polymorphisms. Landsberg erecta (Ler) is the ecotype of choice if Col-0 is used in the primary screen, due to the large collection of SNP and indels (insertions/deletions) between these ecotypes. It is important to note here that for mapping of genes involved in the ionome, potential ecotypic variations in the ionome must be considered before an ecotype is chosen for mapping (Lahner et al., 2003), and this is elaborated upon later in this chapter. Hybrid F1 plants from the ecotypic cross are allowed to self and approximately 4000 F2 plants are screened for both the ionomic phenotype and cosegregating genetic polymorphisms. Once the mutation has been mapped to a region of approximately 40 kb the entire region can be sequenced to find the mutation assuming the original mutant was in Col-0. Use of FN mutagenesis facilitates this processes because the deletions produced are easily identified, compared to the SNP produced by EMS. Once the mutation has been mapped to a region containing 10–20 genes it is also possible to identify T-DNA insertional mutants in all these genes and test them for the ionomic phenotype. The availability of Arabidopsis high-density gene arrays now makes it possible to simultaneously genotype plants for several hundred thousand loci. By using total genomic DNA instead of mRNA for hybridization, and pooling DNA from only 15 homozygous recombinants displaying the mutant phenotype it is possible to map a locus to approximately 12 cM (Borevitz et al., 2003), and simulation suggest that using DNA from a pool of >200 plants would allow

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mapping down to 20% of total variation. 2

Number of major QTL difficult to estimate because a multiple QTL analysis was not shown.

Paran and Zamir (2003) reviewed the progress toward gene identification at QTLs in plants. Without exception, all cloned QTLs were associated with either flowering time or morphological attributes such as plant height or fruit characteristics. This raises the question why comparable success has not been achieved for nutritional traits. One possible reason could lie with the relatively short time that has elapsed since the majority of nutritional traits have been mapped. Success may therefore come with time, particularly as additional powerful tools such as complete sequence data and gene microarrays become available for an increasing number of crops. The lack of apparent achievements may, however, be partly due to the peculiarities of traits such as tolerance to nutrient deficiencies or toxicities. These traits are arguably more complex than flowering time or

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plant morphology because they encompass the quantitative genetic response to an environmental factor that also varies quantitatively. Whilst factors such as day length that influence flowering time are easily controlled and show little spatial variability even in large fields, such variability is inherently large for nutrient availability/toxicity and can only partly be controlled experimentally. Accurate phenotyping therefore represents a challenge in mapping nutritional traits that should not be underestimated. Examples listed in Table 10.1 indicate that progress in mapping nutritional traits is not hampered by a scarcity of identified QTLs but possibly by a lack of relevant QTLs identified by suitable screening procedures. Hence, a considerable portion of this chapter is devoted to discussing phenotyping strategies.

10.2 Objectives in mapping nutritional traits and resulting technical considerations Results obtained in mapping experiments can be used for a multitude of purposes. They provide plant breeders with additional tools to modify those traits in their breeding populations that have been difficult to improve via conventional breeding methods. Nutritional traits such as tolerance to nutrient deficiencies or toxicities are potentially good examples because progress in developing highly tolerant genotypes by traditional breeding methods has been slow (Mackill, in press). Mapping of tolerance loci can supply tightly linked markers to be used in transferring the beneficial locus from a donor to the breeding material for which improvement is sought. This process may take the form of marker assisted backcrossing (MAB), in which the transfer of a desired locus from the donor to the recipient line is monitored, or marker assisted selection (MAS), in which marker analysis is used to maintain and ultimately fix the positive locus in a segregating population. Mapping also provides an entry point for genetic analysis of traits. The detection of interesting loci in initial mapping experiments represents the first step toward identifying the gene(s) involved in the trait of interest. Finally, mapping has been a powerful tool for physiologists as it allows for the dissection of complex traits into distinct factors that are associated with QTLs. Subsequently, the effect of each factor/QTL can be studied in isolation by developing near isogenic lines (NILs) for the locus under investigation. The design of mapping experiments will depend on whether marker assisted breeding, gene identification, or physiological dissection of complex traits is the main objective. It can be argued, however, that all three objectives are achievable with a suitable experimental design. In the discussion of design issues, it is important to bear in mind that mapping itself represents only the first step toward achieving any of the long-term objectives and that subsequent steps require the commitment of resources that may considerably exceed those committed in the initial mapping step. Breeders are reluctant to disrupt their existing breeding schemes and to dedicate scarce resources to marker assisted breeding programs

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unless convinced that the benefits outweigh costs. It is therefore necessary to balance the desire to minimize the complexity of the phenotypic evaluation process with the necessity to identify loci that justify further commitments. The following section examines technical issues that are crucial if mapping of nutritional traits is going to have an impact on applied breeding as well as on more basic research on genetic and physiological aspects of plant nutrition.

10.3 Choice of mapping population Genotypic variation exists within crops for nutritional traits typically investigated in mapping studies, such as tolerance to P deficiency (Wissuwa & Ae, 2001b). This sets studies in crops apart from studies in model organisms like Arabidopsis, for which researchers frequently depend on variation generated through mutagenesis. As mutagenesis most often produces inferior phenotypes as a result of a loss of function mutation, the typical study in Arabidopsis compares normal and intolerant plants. This has been a powerful tool for detecting genes involved in pathways related to the trait of interest without necessarily identifying ways to improve tolerance. Here, I argue that maximum benefits from mapping nutritional traits in crops will not be achieved by following the Arabidopsis model but that we should instead focus on identifying loci linked to alleles with higher than average tolerance. This will largely depend on the choice of a suitable mapping population. To increase the probability of identifying alleles with a high degree of tolerance, one parent should possess a desirable phenotype for the trait of interest. This is not necessarily a prerequisite to QTL identification per se because tolerance QTLs can also be detected in populations derived from two average parents. However, not all detected QTLs are equally suitable for achieving the objectives discussed previously. Breeders are more inclined to include novel alleles with large effects in their programs and these have in the past been detected in mapping populations that have included one of the parents as a donor of high tolerance (Mackill, in press).

10.4 Choice of environment and phenotypic evaluation method Table 10.1 shows that approaches differ considerably on how phenotypic evaluations have been conducted. Mapping populations have been screened more often in solution culture than in the field. This is particularly evident for tolerance to Al or Fe toxicity, for which all experiments have been conducted in nutrient solution. This choice may be driven by a lack of suitable field sites, by the need to avoid confounding effects caused by the presence of multiple stresses in the field, by the desire to increase the heritability of traits through maximum control of treatment factors, or by the convenience a simplified screening method offers. However, such simplifications pose the danger that identified QTLs are specific

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to the artificial evaluation environment and that QTLs with greater relevance for tolerance under natural conditions will remain undetected. This will be of particular relevance for traits that involve complex soil-plant interactions such as deficiencies for P, Fe, and Zn but may also be important for tolerance to toxicities if a potential tolerance mechanism involves a rhizosphere process. Therefore, I believe that screens under natural conditions should be an integral component in mapping nutritional traits. Solution culture experiments would then ideally complement field trials. Although additional resources are required to conduct field trials, these costs will be relatively small compared to the initial cost of developing a mapping population and the subsequent cost of follow-up studies. In the long run, an increased focus on the initial QTL mapping experiments may actually reduce overall costs by providing information from multiple environments that can be used to reduce the number of QTLs suitable for follow-up studies. Reducing the number of QTLs may appear counterproductive, however, past experience has shown that the number of QTLs identified has not been the limiting factor in advancing our understanding of nutritional traits (see Table 10.1). Much can be gained by identifying loci of relevance for the direct trait (e.g. performance under stress in the field) followed by a comparison of results from alternative environments that provide a first confirmation and some information regarding possible mechanisms. The additional information obtained in this manner then makes it possible to identify those loci that merit further in-depth analysis. Some design issues shall now be discussed using an example from my own work.

10.5 Design example – mapping of QTLs for tolerance to Zn deficiency in rice Mapping of loci conferring tolerance to Zn deficiency will be used to illustrate experimental design issues in mapping nutritional traits, since there are complex interactions between multiple stress factors and tolerance mechanisms for this trait. Zinc deficiency causes multiple symptoms that usually appear two to three weeks after transplanting rice seedlings; leaves develop brown blotches and streaks that may fuse to cover older leaves entirely, plants remain stunted and in severe cases may die, while those that recover will show a substantial delay in maturity and a reduction in yield (Neue & Lantin, 1994). Low Zn availability in deficient soils is only one of several factors responsible for these symptoms. Other factors are high levels of soil bicarbonate (HCO3 − ), low soil redox potential, high concentrations of other nutrients (Fe, Mg, Mn), and high solar radiation (van Breemen & Castro, 1980). Zinc tolerance in rice has also been associated with multiple mechanisms: solubilization of soil-bound Zn, translocation of Zn from root to shoot, avoidance of nutrient imbalances in shoots, tolerance to

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high soil HCO3 − levels associated with Zn deficiency, and protection against radical damage of cell membranes (Neue et al., 1998). The interaction of several stress factors with multiple tolerance mechanisms already suggests that a single screening method would be insufficient to detect QTLs for all relevant factors. It also raises the question whether loci associated with multiple tolerance mechanisms could be detected in a single mapping population. 10.5.1 Choice of mapping population Genotypes have routinely been evaluated at a Zn-deficient field site at the International Rice Research Institute (IRRI); one landrace from India, Jalmagna, repeatedly showed high tolerance. A survey of available mapping populations revealed that Jalmagna had been used as a parent in a population developed to map shoot elongation as a tolerance mechanism under submergence. The second parent used to develop this population was IR74 and field tests showed that it was highly susceptible to Zn deficiency. To confirm the apparent suitability of this population, 15 randomly chosen lines were grown in an observation field trial under Zn deficiency. Measurements for total dry matter, plant mortality, and leaf symptoms indicated that these traits were only weakly associated (Fig. 10.1). Some lines rated as tolerant in terms of low plant mortality exhibited a high degree of leaf browning (L379) whilst other lines had low dry matter, despite showing few leaf symptoms (L512), or high dry matter despite having high mortality (L614). Additional tests of the set of random sample lines, based on growth in a lowZn nutrient solution, showed that the tolerance ranking differed dramatically from results obtained in the field. The most intolerant line in soil (L597) was

80

4

60

3

40

2

20

5 Field Solution

4

3

2

1

Plant dry matter (g)

5

Leaf browning score

Mortality (%)

100

1

Mortality Browning 0

0 74 na 79 74 07 12 97 14 39 IR lmag L3 L4 L5 L5 L5 L6 L6 Ja

0 74 na 79 74 07 12 97 14 39 IR lmag L3 L4 L5 L5 L5 L6 L6 Ja

Figure 10.1 Genotypic differences in tolerance to Zn deficiency, expressed in plant mortality and degree of leaf browning or as dry matter accumulated under Zn-deficient conditions in the field or in nutrient solution.

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found to be the second best in solution, with almost twice as much dry matter produced as the most tolerant line (L507) (Fig. 10.1). Leaf symptoms typically observed in the field disappeared almost entirely in low-Zn nutrient solution. These differences between field and solution could partly be reversed by adding HCO3 − , which had been implemented as an additional stress factor in submerged soils. The combination of low Zn and HCO3 − in solution caused leaf symptoms to reappear and severely reduced root growth, particularly in lines that had been rated as intolerant in the field (data not shown). Conducting a preliminary trial with a random subset of lines, therefore, established several facts of importance for a successful mapping study. The high degree of variability among lines in combination with the observation that different tolerance mechanisms segregated independently confirmed that the IR74 x Jalmagna population would be appropriate for mapping Zn deficiency-related traits. The discrepancy between results from the field and nutrient solution experiments furthermore indicated that rhizosphere processes were of fundamental importance for the tolerance response and that genotypic differences in internal Zn efficiency, which are typically assessed in nutrient solution, were of little importance. 10.5.2 Considerations on screening methods Based on these preliminary observations, it was decided that the main QTL mapping experiment would be conducted in the field, using plant mortality, leaf symptoms, and dry weight as Zn-tolerance indicators. Subsequent mapping experiments in low-Zn nutrient solutions, with or without HCO3 − , would serve to confirm QTLs identified in the field and to establish a link between QTLs and a specific stress factor or potential tolerance mechanism. The results shown in Table 10.2 are entirely hypothetical and are intended to serve only as an Table 10.2 Hypothetical results of a component QTL mapping analysis of Zn-deficiency tolerance in rice QTL1

QTL2

QTL3

QTL4

QTL5

QTL6

*

*

Zn-deficient field Mortality Leaf symptoms Dry weight Leaf symptoms Dry weight Root growth

* * **

** *

*

*

Solution with HCO3 − ** * **

** **

* Solution without HCO3 − *

Leaf symptoms Dry weight Root growth Promising QTL

* **

** yes

yes

yes

no

no

yes

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illustration of the potential benefits of employing a more holistic approach in addressing a complex trait such as tolerance to Zn deficiency. In this hypothetical example, QTLs 1, 2, and 3 would represent main QTLs for each of the tolerance indicators in the field. Additional information from mapping in solution culture would suggest that QTL 1 was specifically related to the negative effect of high HCO3 − levels on root growth. QTL 2 was detectable only in the field and may be related to a rhizosphere process. QTL 3 was associated with leaf symptoms in the field and in solution, possibly indicating the involvement of a plant internal stress factor. In addition, three minor QTLs were detected in the field (QTLs 4, 5, and 6). A comparison to mapping results in solution can provide important clues as to which of these minor QTLs would deserve further attention. QTL 4 appears to be a general (nonstress) root growth locus with little benefit under Zn deficiency whereas no additional information was available for QTL 5. QTL 6, however, could clearly be linked to HCO3 − tolerance and would seem promising. This hypothetical example serves to show that a component QTL approach would provide the additional information needed (i) to select the most promising QTL for further studies, and (ii) to identify a screening environment that allows rapid and reliable detection of genotypic differences at the locus of interest. For QTL 2, this would be possible only in the field, whereas QTL 6 is more reliably identified in solutions containing HCO3 − . A potential additional benefit of a component QTL approach using multiple environments is related to the power to detect QTLs. Fields are highly variable environments, particularly for stress factors such as nutrient deficiencies or toxicities. This variability is typically reflected in the phenotypic data used to map QTLs. Environmental variation reduces the R2 value, which indicates how much of the overall variance was explained by an individual QTL. Selection of promising QTL based purely on their R2 value therefore poses the risk of important loci being disregarded. This problem can be overcome partly by comparing field results with those obtained in more efficiently controlled environments such as solution culture experiments. QTL 6 in Table 10.2 exemplifies this point. It was of minor importance for tolerance in the field but may have been a major QTL for one of the tolerance mechanisms, root development despite the inhibitory effect of HCO3 − . Whether the data from ongoing experiments will yield anticipated results remains to be seen. The point to be made by this hypothetical example is that the complexity of traits should be taken into account in mapping experiments. 10.6 Mapping of nutritional traits – just a starting point Having successfully identified QTL associated with the trait of interest represents only the first step toward achieving any of the longer term objectives. Subsequent steps typically involve further confirmation of effects associated with QTLs and, depending on the objectives, will require fine-mapping, development of NILs, or molecular genetic approaches to identify underlying genes. As these

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steps require the commitment of substantial resources, it will be important to first decide which QTLs justify further attention. 10.6.1 Selecting QTLs for further analysis The selection will be straightforward in the case of major QTLs, but more difficult for less influential loci. In the absence of clear guidelines, the decision on what constitutes a relevant locus depends on the objectives pursued, on the genetic material used, and on the experimental environment. If the objective is to provide markers for marker assisted breeding schemes, the most important criterion should be whether the transfer of a locus is likely to improve the performance of breeding stock in the field. Frequently, one parent used in mapping populations was chosen because it showed an unfavorable phenotype that contrasted well with the more tolerant parent. A locus capable of improving the performance of a highly intolerant parent is not necessarily suited to improving breeding stock that already shows the favorable phenotype to some degree. In that case, only QTLs with large effects will be of relevance. If, on the other hand, both parents were considered to be of a favorable phenotype, even QTLs with small effects may be of interest. Less influential loci may also be relevant if the primary interest is to gain a better understanding of the physiological mechanisms involved in the trait studied. Another crucial aspect for the identification of suitable QTLs is whether the effect of that QTL can be detected reliably in subsequent experiments. Chaney et al. (1989) pointed out that measurements on a single genotype may typically vary by as much as 20% even under highly controlled conditions of solution culture experiments. A QTL with an effect in the range of 20% may therefore not be detectable in subsequent experiments. Nutritional traits typically show higher variability in the field. My experience with tolerance to P deficiency has shown that even a relatively large QTL expected to increase dry weight by 50% may not be detectable in the large-scale field experiments needed to fine-map QTLs. Most computer programs used in QTL mapping provide an estimate of the additive effect of individual QTLs. A comparison of this additive effect with the standard error anticipated in the experimental environment could therefore serve as a criterion in selecting promising loci. Alternatively, this approach could be used to adjust experimental procedures with the aim of reducing experimental error to a level that permits QTL detection. 10.6.2 QTL confirmation and fine mapping Initial mapping experiments provide estimates regarding chromosomal location and phenotypic effect of QTLs. These estimates contain a degree of uncertainty because populations used in primary QTL mapping typically segregate for multiple genetic factors on the whole genome simultaneously. Genetic parameters of each QTL are thus affected by the segregation at other loci. For the practical

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application of a QTL in marker assisted breeding, or for map based cloning, the position of a QTL needs to be known with higher precision than typically achieved in initial experiments (Paterson et al., 1990; Yano et al., 2000). The development of secondary mapping populations in combination with a high degree of marker saturation in the putative QTL region has therefore been a crucial component in subsequent experiments. Before embarking on fine mapping, it is desirable to confirm the effect of a QTL without confounding effects caused by segregation at other loci. NILs that resemble the inferior parent at all loci except for the chromosomal region containing a putative QTL are the type of plant material ideally suited to confirm QTLs. Developing NILs, however, is a process that can take several years to accomplish if it has to be started anew. NILs can be selected as a by-product in the development of secondary mapping populations but that also takes considerable time. During the initial phases following QTL detection, one may therefore have to resort to less ideal plant material. The use of well-established permanent mapping populations offers the advantage that substitution lines (SLs) may already be available. SLs suitable for confirming a putative QTL would carry the positive allele from the donor parent at the putative QTL interval but would contain few other donor segments. Ideally, the confirmation would include a second SL carrying the same introgressed segments without the one containing the QTL to be confirmed. A comparison of these contrasting SLs with the inferior parent will yield a more precise estimate of the phenotypic effect at the putative QTL than obtained during the initial mapping experiment. Based on this confirmation, a secondary mapping population can be developed by crossing the superior SL to the inferior parent. A detailed discussion of aspects involved in fine mapping will follow in Section 10.7. 10.6.3 QTLs, related physiological mechanisms and underlying genes Traditionally, physiological studies on mechanisms related to nutritional traits have relied on comparisons between different species or between a few genotypes of the same species. Several prominent hypothesis on the role of root exudates and other rhizosphere processes in nutrient uptake mechanisms have been derived in this manner (Romheld & Marschner, 1986; Ae et al., 1990). However, the comparison of diverse species or cultivars within species has not provided good opportunities to test these hypotheses because contrasting genotypes differed for several other traits with potentially confounding effects on the mechanism under investigation. The use of NILs that differ for individual QTLs offers an opportunity to unravel the complexity of nutritional traits by reducing the number of genetic factors that differ in a set of contrasting genotypes. This unique power should justify the additional efforts required in developing NILs, at least for those QTLs that match the relevant criteria discussed previously. Physiological analysis of NILs can furthermore provide valuable clues regarding gene

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function that can facilitate gene identification. Ishimaru et al. (2004) have successfully used physiological evidence in combination with mapping data to identify a plant height gene. This candidate-gene strategy also appears highly suitable for the detection of genes underlying nutritional traits. Gene microarray analysis offers an alternative and possibly more direct approach in detecting associations among QTL, genes, and hypothetical mechanisms. The principles of microarray analysis are outlined in Chapter 8. Using microarrays, it is possible to identify transcripts that are up- or down-regulated during stress or differentially regulated in tolerant versus intolerant genotypes. Typically, dozens or even hundreds of transcripts can be distinguished in this manner. Subsequently, the challenge is to identify those transcripts whose differential expression is related causally to the observed phenotype. Although this will not be possible by array analysis alone, it should be possible to combine QTL mapping data, physiological evidence, and arraying, to identify positional candidates for the phenotype of interest (Wayne & McIntyre, 2002).

10.7 Case study – mapping of the Pup1 locus in rice This section describes the development of a QTL mapping project on tolerance to P deficiency in rice, from the initial QTL mapping experiment to advanced stages in gene identification and applied aspects in marker assisted breeding. In addition to presenting results obtained at various stages of the project, the section will focus on the various experimental design issues encountered during the project, in order to add a practical perspective to the theoretical considerations presented in preceding sections. 10.7.1 QTL mapping and confirmation The evaluation of 30 rice genotypes of different origin and plant type in a highly P-deficient field revealed the presence of considerable genotypic variation for tolerance to P deficiency in rice (Wissuwa & Ae, 2001b). Parents of several QTL mapping populations had been included for the purpose of identifying a suitable population for the soil conditions at the experimental site. Parents of one particular population showed the desired contrast: ‘Kasalath’, a traditional indica variety from Assam, India, was one of the most tolerant lines, whereas the modern japonica variety ‘Nipponbare’ was intolerant (Fig. 10.2). The population of 98 backcross inbred lines (BILs) derived from the [Nipponbare x Kasalath] x Nipponbare cross was therefore chosen for QTL mapping. The screen of 30 genotypes revealed a high degree of soil heterogeneity at the field site. Reliable phenotypic evaluation of 100 lines under highly variable soil conditions appeared questionable; hence, alternative screening methods were considered. The use of low-P nutrient solutions was dismissed because

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231

14 12

–1 P uptake (mg plant )

Kasalath 10 8 6 4 Nipponbare 2 0 Figure 10.2 Genotypic variation among rice cultivars for P uptake from a highly P-deficient field. Nipponbare and Kasalath are parents of a QTL mapping population that appears ideally suited to map QTLs for P uptake because of the large differential between parents.

rhizosphere processes, which could not be simulated using nutrient solutions, were expected to be of high importance for tolerance to P deficiency. The use of pots filled with soil from a more homogeneous part of the field was considered, but this would have necessitated the use of pots of a relatively small volume to accommodate 100 lines at several replicates. The use of small pots would have restricted root growth, thus, reducing the chances of identifying potentially important root growth-related QTLs. A compromise between field and pot was found by using a fiberglass container of dimensions 11.60 × 0.85 × 0.22 m (length × width × depth), filled with topsoil from one of the less variable parts of the P-deficient field (Wissuwa et al., 1998). Phenotypic data were collected for dry weight, shoot P concentration, and P uptake of individual plants (five replicates) after a 125-day growth period. Four putative QTLs were detected for P uptake, which together explained 54.5% of the variation observed among BILs (Table 10.3). A QTL linked to marker C443 on chromosome 12 had a major effect. It accounted for half of the explained variation and the estimate of additive effects suggested that lines containing the positive Kasalath allele at this locus would have twice the P uptake compared with Nipponbare. Two of these P-uptake QTLs were also associated with shoot dry weight. Three putative QTLs were detected for internal P-use efficiency (g dry matter produced per mg P). The major QTL on chromosome 12 and a minor one on chromosome 2 coincided with the QTLs for P uptake; however, Nipponbare alleles increased P-use efficiency, whereas Kasalath

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232 Table 10.3

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PLANT NUTRITIONAL GENOMICS Putative QTLs for plant dry weight, P uptake, and P-use efficiency under low-P stress Marker interval1

Chromosome

LODscore2

Variation explained3

Substitution effect4

Dry weight (g plant−1 )

C1488 - C63 C191 - C498 G2140 - C443

3 6 12

3.1 4.7 10.5

6.4 9.7 26.5

−0.99 +1.56 +3.19

P uptake (mg plant−1 )

G227- C365 C498 - R1954 R1629 - R2447 G2140 - C443

2 6 10 12

2.8 3.5 4.7 10.7

5.8 9.8 7.7 27.9

+0.97 +0.71 −0.62 +1.94

P-use efficiency (g d. wt mg−1 P)

G227 - C365 C946 - R1854 G2140 - C443

2 4 12

5.2 4.3 6.6

9.8 9.4 19.1

−0.35 +0.30 −0.47

1

Marker nearest to QTL is underlined. The LOD-score is the log of the ratio of the likelihoods of there being one vs no QTL. 3 Percent of phenotypic variation explained by QTL using a single-QTL model. 4 Phenotypic effect of replacing both Nipponbare alleles by Kasalath alleles. 2

alleles increased P uptake. Such unfavorable linkage would pose problems in breeding but further data analysis revealed that dry weight depended entirely on P uptake (r = 0.96), whereas P-use efficiency was negatively correlated to dry weight (r = −0.60), not positively as expected. The high apparent internal use efficiency of lines with Nipponbare alleles at QTLs on chromosomes 2 and 12 was the consequence of insufficient P uptake, which then led to severe P deficiency and to dry weight production at highly suboptimal tissue-P concentrations below 0.05%. These low-P concentrations may represent the absolute minimum for survival rather than efficient P use that would be worth exploiting in crop improvement. QTLs for P-use efficiency on chromosomes 6 and 12 were therefore considered to be ‘Pseudo-QTLs’. Based on these results, it was decided to focus primarily on the major QTL linked to marker C443 and on the less influential yet consistently detected one on chromosome 6. A set of substitution lines had been developed by M Yano and colleagues at the National Institute of Agrobiological Sciences (NIAS)/Institute of Society for Techno-Innovation of Agriculture, Forestry, and Fisheries (STAFF) from selected BC1 F2 lines of the mapping population that were then backcrossed to Nipponbare thrice. This set was searched for lines suitable to serve as SLs for putative QTLs. The candidate line for QTL C443 (SL-C443; Table 10.4) was genetically 91.1% identical to Nipponbare (based on 118 RFLP markers). In addition to a 50 cM Kasalath insert at the putative QTL location, SL-C443 also carried small Kasalath inserts at unrelated loci on chromosomes 2, 6, and 10 (Fig. 10.3). To account for potential effects of these unrelated loci, the set of SLs was screened once more to identify a line (SL-82) that contained Kasalath inserts at the same loci on chromosomes 2, 6,

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SL-C443 Chr1

Chr8

Chr10 R2174

Chr12

G124A

(30.0 cM)

C732 7.7

S1778 S10520

R665 S2572 G124A

7.7 R1869

S10520 C443 R2447

QTL C443 G2140

R2635

C449

3.5 W161 S10704 C443 S14025 S13126 S13752 S1436 C61722

1.7 1.5 0.2 3.0

Pup1

2.0 0.8 0.5 4.3

R1877

S826 C2808 C502

G2140 C2

W326 R404

C901

4.2 0.6 4.8

V124 C449

2.0

Figure 10.3 Graphical genotype of rice line SL-C443 showing Kasalath segments on chromosomes 1, 8, 10, and 12; and linkage map of the Kasalath segment on chromosome 12 based on marker data of 150 F2 plants of the secondary QTL mapping population.

and 10 but lacked the insert at QTL C443. In a similar fashion, two contrasting SLs for QTL C498 on chromosome 6 were identified. Contrasting pairs (SL-C443 vs SL-82 and SL-C498 vs SL-4) were grown together with the recurrent parent, Nipponbare, for 100 days in 60-L containers, filled with the same P-deficient soil used in mapping QTLs. SLs carrying Kasalath alleles at C443 and C498 had twice the P uptake of their complementary lines with Nipponbare alleles (Table 10.4). These results represented the first confirmation of the presence of P uptake QTLs on chromosomes 6 and 12. It furthermore indicated that SL-C443 and SL-C498 were suitable lines for further investigations on both QTLs because the Kasalath chromosomal segments unrelated to the QTL had

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PLANT NUTRITIONAL GENOMICS P uptake and root size of contrasting pairs of SLs for a QTL at markers C443 and C498

Genotype

QTL/ chromosome

Nipponbare SL-C443 SL-82 SL-C498 SL-4

+C4433 /12 −C443/12 +C498/6 −C498/6

Genotypic similarity1 %

92.0 96.2

Nipponbare2 %

91.1 88.8 93.7 96.9

P uptake (mg)

Root surface area (cm2 )

2.9 c 9.0 a 4.4 bc 5.6 b 3.2 c

430 c 1094 a 737 ab 1106 a 547 bc

1

Between SL-C443/SL-82 and SL-C498/SL-4, based on 118 marker loci. Portion of the genome carrying Nipponbare alleles. 3 +C443/+C498 signifies that SL is a carrier of an allele increasing P uptake (from Kasalath). 2

little or no effect on P uptake of SLs. The comparison of pairs of SLs also provided first clues as to the potential mechanisms involved in tolerance. QTL C498 was probably related to root growth whereas QTL C443 more likely improved uptake efficiency (P uptake per root size). 10.7.2 Fine mapping QTL C443 had been mapped to a 13 cM marker interval (C443-G2140) and the confirmation of QTL C443 was based on a SL containing a 50 cM Kasalath segment (Fig. 10.3). Such relatively low resolution is insufficient for marker assisted breeding purposes because a large interval of 13 cM may contain a number of undesirable genes that would be transferred together with the desirable allele at the QTL. This danger is particularly high when landraces of low overall agronomic value such as Kasalath are donors of QTLs. Furthermore, it is possible that a QTL mapped to a large interval corresponds to a cluster of genes, each with relatively small effects, rather than to a single locus. The identification and subsequent confirmation of a major QTL for P uptake therefore represented only two important first steps that needed to be augmented by more precise mapping before QTL C443 could be used in plant breeding. Secondary mapping populations developed by backcrossing a SL or NIL to the recurrent parent are perfectly suited for fine-mapping QTLs because most genetic factors not related to the QTL no longer segregate. This concept has been used successfully to fine map QTLs in maize (Dorweiler et al., 1993), tomato (Alpert & Tanksley, 1996), and rice (Yamamoto et al., 1998) and was also followed here. A secondary mapping population was developed by backcrossing SL-C443 to the recurrent parent Nipponbare. All markers of the most recent rice linkage map published by the rice genome project of Japan (http://rgp.dna.affrc.go.jp/publicdata/geneticmap2000/index.html) were used to genotype F2 lines at the 50-cM Kasalath introgression. Selected F2 families were evaluated in a highly P-deficient field plot with 60 individual lines per family. Two different mapping strategies were employed (Wissuwa et al., 2002). A conventional QTL mapping approach was based on individual F2 RFLP data and

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phenotypic evaluation of family means in the F3 . The second strategy employed a substitution mapping approach (Paterson et al., 1990). Phenotypic and marker data were obtained for 160 F3 individuals of six highly informative families that differed in the size of donor chromosomal segments in the region of the putative QTL. QTL mapping showed that close to 80% of the variation between families was due to a single locus, hereafter referred to as Pup1 (phosphorus uptake 1). Pup1 was placed in a 3-cM interval flanked by markers S14025 and S13126 (Fig. 10.3). Other chromosomal regions and epistatic effects were not significant. Substitution mapping revealed that Pup1 co-segregated with marker S13126 and that the flanking markers, S14025 and S13752, were outside the interval containing Pup1. Both mapping strategies therefore yielded almost identical results. The advantage of a conventional QTL mapping approach was to clearly attribute phenotypic variation between families to a single locus without additional epistatic interactions, whereas substitution mapping placed clearly defined borders around the QTL. A secondary mapping population for QTL C498 was also phenotyped but high soil variability at the field site prevented successful fine mapping of this minor QTL. To further increase the precision in mapping Pup1, flanking markers S14025 and S13752 were used to identify additional recombinant lines. By using this set of lines in combination with a sufficiently high number of new markers to saturate the interval, it was possible to map Pup1 to a 242-kb region spanning 3 BAC clones (Fig. 10.4). This step marked the transition from a linkage mapbased approach to one relying on genome sequence data for further analysis. 10.7.3 Toward cloning of Pup1 With the exception of the tb1 gene in maize, which was cloned by transposon tagging (Doebley et al., 1997), all QTLs, which have been subsequently cloned in crop plants have employed techniques based on positional cloning (Frary et al., 2000; Fridman et al., 2000; Yano et al., 2000; Takahashi et al., 2001). This method should also be successful in the case of Pup1, since fine mapping of recombinant lines has repeatedly proved to yield reliable results. At this stage, a lack of additional recombinants is the barrier that needs to be overcome in order to delimit the exact location of Pup1. Efforts are therefore being directed toward identifying new recombinants in the interval defined by markers M31 and M69 (Fig. 10.4). In the meantime, alternative methods for gene detection are considered. The availability of complete sequence data for rice has made it feasible to detect genes at QTLs by the candidate-gene strategy (Ishimaru et al., 2004). Gene annotation in the Pup1 region identified 34 putative genes, but only four of these showed sequence similarity to genes of known function. None of these genes seem to relate to processes involved in P uptake or metabolism. This would suggest that Pup1 is most likely a novel gene and that the candidategene approach would not facilitate gene identification at the Pup1 locus at this

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Figure 10.4 Fine mapping of Pup1 to a 240-kb interval on chromosome 12 of rice, using selected lines with overlapping recombinant chromosomal segments. The phenotype of lines B and C places Pup1 upward of marker M69 while line E places Pup1 downward of M31.

point. At present, gene specific primers are being developed for the 34 putative genes located between markers M31 and M69. Physiological analyses suggest that Pup1 is expressed in root tissue where it helps maintain high root growth rates under P deficiency. RNA has been isolated from roots of a Pup1 NIL and Nipponbare that were both cultured under P-deficient and P-sufficient conditions. RT-PCR performed on transcribed RNA samples may identify expression patterns that would suggest the involvement of specific genes in the P-deficiency response. Ecotilling might also be a suitable alternative approach for gene identification, since it does not rely on differences in gene expression as the principal cause for allelic differences (Comai et al., 2004). That none of the annotated genes in the Pup1 region can be associated with genes known to be involved in P metabolism or uptake is unfortunate in terms of facilitating gene identification. However, this is not necessarily surprising considering that most genes identified to date, such as P transporters, phytases, or phosphatases, have been identified using low-P nutrient solutions as the screening medium. These genes do possess important functions in P metabolism but may not contribute to improved tolerance to P deficiency in the field. In contrast, Pup1 was mapped and its effect was confirmed in P-deficient soil. This

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may be an indication that genes most useful for plant improvement will have to be identified in natural environments. 10.7.4 The use of Pup1 in marker assisted breeding The positive effect of Pup1 on tolerance to P deficiency has repeatedly been confirmed in the field using a NIL that was indistinguishable from the recurrent parent Nipponbare when grown with an adequate supply of P (Wissuwa & Ae, 2001a). Recently, it has also been validated that this effect was not limited to a Nipponbare genetic background or to the specific soil conditions at the field site used to map and confirm the QTL. Following a cross to the donor parent Kasalath, Pup1 was transferred into the background of two tropical rice cultivars, IR36 and IAC47. The introgression of Pup1 was monitored using flanking markers S14025 and S13752. Several introgression lines were evaluated together with a well-adapted local check variety at a P-deficient field site in the Philippines that had very different soil properties compared to the original field. The variability amongst introgression lines was high, but a majority outperformed both recurrent parents (Fig. 10.5). The best line exceeded the local check in grain yield by 26%. This represented a quite remarkable achievement considering that none of the lines had been selected at the test site. Given that variability among a small set of test lines was high, it appeared feasible to make additional gains through selection. This success has convinced breeders at IRRI to use the most promising

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0 Figure 10.5 The effect of the Pup1 locus can also be confirmed in different genetic backgrounds, which indicate that Pup1 would be useful in marker assisted breeding. Rice cultivars IR36 and IAC47 were crossed with the donor of Pup1 (Kasalath) and resulting lines advanced to the F4 while the presence of Pup1 was monitored by flanking marker analysis. Lines carrying the Pup1 allele from Kasalath had a higher yield under P deficiency than parents IR36 and IAC47. The best line even outyielded a tolerant local check.

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introgression line as a donor to transfer the Pup1 locus to elite breeding material lacking tolerance to P deficiency.

10.8 Conclusions The rate of gene discovery has accelerated over the past few years with Arabidopsis leading the way for plants. Of the genes involved in P nutrition, transporter genes represent the biggest group identified so far. A recent search of annotation databases has identified as many as 13 phosphate transporters in rice alone. Other examples include genes involved in metabolism and excretion of phosphatases and phytases (Mudge et al., 2003). The study of these genes has no doubt increased our understanding of processes involved in the response of plants to P deficiency but, as of now, conclusive evidence regarding their usefulness in improving the adaptation to P deficiency in the field is lacking (Delhaize et al., 2001). This may not be surprising considering that gene detection in most cases has been based on mutant screens, or on the analysis of gene expression patterns during low-P stress. To identify genes capable of improving tolerance to nutrient deficiencies or toxicities, one may instead have to rely on a different approach that makes better use of the genetic diversity already present in crops. The example of the Pup1 locus in rice has shown that QTL mapping is a powerful tool in this regard. That none of the genes in the Pup1 region were associated with genes known to be involved in P metabolism or uptake, furthermore illustrates well that novel and highly effective genes can be identified using a QTL mapping approach. The mapping of nutritional traits in crops represents the link between genotypic diversity and subsequent gene discovery. The challenge is to ensure that mapping does not become a weak link as increasingly powerful molecular tools such as genome sequence data, gene microarrays, and ecotilling become available in crops. Nutritional traits are typically highly complex because they involve the interaction of genetic factors with an environment that may show considerable variability. Meaningful and accurate phenotyping in such nonhomogeneous environments does represent an obstacle that may be partly responsible for a lack of success in cloning nutritionally relevant genes. Yet it is this complex interaction of genes with environment, and the fact that crops have already developed adaptations to most stressful environments, that precisely constitutes the advantage of mapping over other approaches that rely on simplified screens and artificially generated genotypic variation. To realize the full potential of QTL mapping for detecting novel and highly useful alleles, one may have to adjust phenotyping procedures to take the complexity of traits into account. Employing a component QTL approach, possibly involving multiple environments that should include one resembling the target environment, cannot provide results as quickly as simplified screens in nutrient solution. However, in the longer term,

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a more elaborate approach in mapping may be the most efficient way to make use of the allelic diversity present in crop plants.

References Ae, N., Arihara, J., Okada, K., Yoshihara, T. & Johansen, C. (1990) Phosphorus uptake by pigeon pea and its role in cropping systems of the Indian subcontinent. Science, 248, 477–480. Agrama, H.A.S., Zakaria, A.G., Said, F.B. & Tuinstra, M. (1999) Identification of quantitative trait loci for nitrogen use efficiency in maize. Mol. Breed. New Strateg. Plant Improv., 5, 187–195. Alpert, K.B. & Tanksley, S.D. (1996) High-resolution mapping and isolation of a yeast artificial chromosome contig containing fw2.2: a major fruit weight quantitative trait locus in tomato. Proc. Natl. Acad. Sci. USA, 93, 15503–15507. Bianchi-Hall, C.M., Carter, T.E. Jr., Bailey, M.A., Mian, M.A.R., Rufty, T.W., Ashley, D.A., Boerma, H.R., Arellano, C., Hussey, R.S. & Parrott, W.A. (2000) Aluminum tolerance associated with quantitative trait loci derived from soybean PI 416937 in hydroponics. Crop Sci., 40, 538–545. Chaney, R.L., Bell, P.F. & Coulombe, B.A. (1989) Screening strategies for improved nutrient uptake and use by plants. HortScience, 24, 565–572. Comai, L., Young, K., Till, B.J., Reynolds, S.H., Greene, E.A., Codomo, C.A., Enns, L.C., Johnson, J.E., Burtner, C., Odden, A.R. & Henikoff, S. (2004). Efficient discovery of DNA polymorphisms in natural populations by Ecotilling. Plant J., 37, 778–786. Delhaize, E., Hebb, D.M. & Ryan, P.R. (2001) Expression of a Pseudomonas aeruginosa citrate synthase gene in tobacco is not associated with either enhanced citrate accumulation of efflux. Plant Physiol., 125, 2059–2067. Diers, B.W., Cianzio, S.R. & Shoemaker, R.C. (1992) Possible identification of quantitative trait loci affecting iron efficiency in soybean. J. Plant Nutr., 15, 2127–2136. Doebley, J., Stec, A. & Hubbard, L. (1997) The evolution of apical dominance in maize. Nature, 386, 485–488. Dorweiler, J., Stec, A., Kermicle, J. & Doebley, J. (1993) Teosinte glume architecture 1: a genetic locus controlling a key step in maize evolution. Science, 262, 233–235. Fang, P., Tao, Q.N. & Wu, P. (2001) Quantitative trait loci (QTL) underlying N-absorbing capacity and N-utilization efficiency in paddy rice and their genetic effects, Plant Nutr. Fertil. Sci., 7, 159–165. Fawole, I., Gabelman, W.H., Gerloff, G.C. & Nordheim, E.V. (1982) Heritability of efficiency in phosphorus utilization in beans (Phaseolus vulgaris L.) grown under phosphorus stress. J. Am. Soc. Horticult. Sci., 107, 94–97. Frary, A., Nesbitt, T.C., Grandillo, S., van der Knaap, E., Bin, C., JiPing, L., Meller, J., Elber, R., Alpert, K.B. & Tanksley, S.D. (2000) fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. Science, 289, 85–88. Fridman, E., Pleban, T. & Zamir, D. (2000) A recombination hot spot delimits a wild-species quantitative trait locus for tomato sugar content to 484 bp within an invertase gene. Proc. Natl. Acad. Sci. USA, 97, 4718–4723. Ishimaru, K., Ono, K. & Kashiwagi, K. (2004) Identification of a new gene controlling plant height in rice using the candidate-gene strategy. Planta, 218, 388–395. Kaeppler, S.M., Parke, J.L., Mueller, S.M., Senior, L., Stuber, C. & Tracy, W.F. (2000) Variation among maize inbred lines and detection of quantitative trait loci for growth at low phosphorus and responsiveness to arbuscular mycorrhizal fungi Crop Sci., 40, 358–364. Lin, S., Cianzio, S. & Shoemaker, R. (1997) Mapping genetic loci for iron deficiency chlorosis in soybean, Mol. Breed. New Strateg. Plant Improv., 3, 219–229. Mackill, D. (in press) Breeding for resistance to abiotic stresses in rice: the value of QTLs. Proceedings of the International Symposium on Plant Breeding, CIMMYT, Mexico, August 17–22, 2003.

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Mickelson, S., See, D., Meyer, F.D., Garner, J.P., Foster, C.R., Blake, T.K. & Fischer, A.M. (2003) Mapping of QTL associated with nitrogen storage and remobilization in barley (Hordeum vulgare L.) leaves. J. Exp. Bot., 54, 801–812. Ming, F., Zheng, X., Mi, G., Zhu, L. & Zhang, F. (2001) Detection and verification of quantitative trait loci affecting tolerance to low phosphorus in rice. J. Plant Nutr., 24, 1399–1408. Mudge, S.R., Smith, F.W. & Richardson, A.E. (2003) Root-specific and phosphate-regulated expression of phytase under the control of a phosphate transporter promoter enables Arabidopsis to grow on phytate as a sole P source. Plant Sci., 165, 871–878. Neue, H.U. & Lantin, R.S. (1994) In Soil Mineral Stresses: Approaches to Crop Improvement (ed. T.J. Flowers), Springer-Verlag, Berlin, pp. 175–200. Neue, H.U., Quijano, C., Senadhira, D. & Setter, T. (1998) Strategies for dealing with micronutrient disorders and salinity in lowland rice systems. Field Crops Res., 56, 139–155. Nguyen, B.D., Brar, D.S., Bui, B.C., Nguyen, T.V., Pham, L.N. & Nguyen, H.T. (2003) Identification and mapping of the QTL for aluminum tolerance introgressed from the new source, Oryza rufipogon Griff., into indica rice (Oryza sativa L.). Theor. Appl. Genet., 106, 583–593. Ni, J.J., Wu, P., Senadhira, D. & Huang, N. (1998) Mapping QTLs for phosphorus deficiency tolerance in rice (Oryza sativa L.). Theor. Appl. Genet., 97, 1361–1369. Ninamango-Cárdenas, F.E., Guimarães, C.T., Martins, P.R., Parentoni, S.N., Carneiro, N.P., Lopes, M.A., Moro, J.R. & Paiva, E. (2003) Mapping QTLs for aluminum tolerance in maize. Euphytica, 130, 223–232. Obara, M., Kajiura, M., Fukuta, Y., Yano, M., Hayashi, M., Yamaya, T. & Sato, T. (2001) Mapping of QTLs associated with cytosolic glutamine synthetase and NADH-glutamate synthase in rice (Oryza sativa L.). J. Exp. Bot., 52, 1209–1217. Paran, I. & Zamir, D. (2003) Quantitative traits in plants: beyond the QTL. Trends Genet., 19, 303–306. Paterson, A.H., de Verna, J.W., Lanini, B. & Tanksley, S.D. (1990) Fine mapping of quantitative trait loci using selected overlapping recombinant chromosomes, in an interspecies cross of tomato. Genetics, 124, 735–742. Paterson, A.H., Lander, E.S., Hewitt, J.D., Peterson, S., Lincoln, S.E. & Tanksley, S.D. (1988) Resolution of quantitative traits into Mendelian factors by using a complete linkage map of restriction fragment length polymorphisms. Nature, 335, 721–726. Reiter, R.S., Coors, J.G., Sussman, M.R. & Gabelman, W.H. (1991) Genetic analysis of tolerance to low-phosphorus stress in maize using restriction fragment length polymorphisms. Theor. Appl. Genet., 82, 561–568. Romheld, V. & Marschner, H. (1986) Mobilization of iron in the rhizosphere of different plant species. Adv. Plant Nutr., 2, 155–204. Takahashi, Y., Shomura, A., Sasaki, T. & Yano, M. (2001) Hd6, a rice quantitative trait locus involved in photoperiod sensitivity, encodes the alpha subunit of protein kinase CK2. Proc. Natl. Acad. Sci. USA, 98, 7922–7927. van Breemen, N. & Castro, R.U. (1980) Zinc deficiency in wetland rice along a toposequence of hydromorphic soils in the Philippines. II. Cropping experiment. Plant Soil, 57, 215–221. Wan, J.L., Zhai, H.Q., Wan, J.M. & Ikehashi, H. (2003) Detection and analysis of QTLs for ferrous iron toxicity tolerance in rice, Oryza sativa L., Euphytica, 131, 201–206 Wayne, M.L. & McIntyre, L.M. (2002) Combining mapping and arraying: an approach to candidate gene identification. Proc. Natl. Acad. Sci. USA, 99, 14903–14906. Wissuwa, M. & Ae, N. (2001a) Further characterization of two QTLs that increase phosphorus uptake of rice (Oryza sativa L.) under phosphorus deficiency. Plant Soil, 237, 275–286. Wissuwa, M. & Ae, N. (2001b) Genotypic variation for tolerance to phosphorus deficiency in rice and the potential for its exploitation in rice improvement. Plant Breed., 120, 43–48. Wissuwa, M., Wegner, J., Ae, N. & Yano, M. (2002) Substitution mapping of Pup1: a major QTL increasing phosphorus uptake of rice from a phosphorus-deficient soil. Theor. Appl. Genet., 105, 890–897.

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Wissuwa, M., Yano, M. & Ae, N. (1998) Mapping of QTLs for phosphorus-deficiency tolerance in rice (Oryza sativa L.). Theor. Appl. Genet., 97, 777–783. Wu, P., Liao, C.Y., Hu, B., Yi, K.K., Jin, W.Z., Ni, J.J. & He, C. (2000) QTLs and epistasis for aluminum tolerance in rice (Oryza sativa L.) at different seedling stages. Theor. Appl. Genet., 100, 1295– 1303. Wu, P., Ni, J.J. & Luo, A.C. (1998) QTLs underlying rice tolerance to low-potassium stress in rice seedlings. Crop Sci., 38, 1458–1462. Yamamoto, T., Kuboki, Y., Lin, S.Y., Sasaki, T. & Yano, M. (1998) Fine mapping of quantitative trait loci Hd-1, Hd-2 and Hd-3, controlling heading date of rice, as single Mendelian factors. Theor. Appl. Genet., 97, 37–44. Yano, M., Katayose, Y., Ashikari, M., Yamanouchi, U., Monna, L., Fuse, T., Baba, T., Yamamoto, K., Umehara, Y., Nagamura, Y. & Sasaki, T. (2000) Hd1, a major photoperiod sensitivity quantitative trait locus in rice, is closely related to the Arabidopsis flowering time gene CONSTANS. Plant Cell, 12, 2473–2483.

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11 Sustainable crop nutrition: constraints and opportunities R. Ford Denison and E. Toby Kiers

11.1 Introduction Genomics, together with other important advances (e.g. in physiological instrumentation), should provide increasingly detailed information about how crop plants, their wild relatives and the microbial symbionts associated with crops acquire and use nutrients. This information should in principle lead to improvements in genetics of crops (and perhaps their symbionts) and, if used in concert with strategic management practices, will increase crop yields, while conserving scarce resources or reducing pollution. This chapter discusses how the recognition of three important constraints will speed progress in improving crop nutrition. The constraints we shall discuss are conservation of matter (for each element), the implications of past natural selection for genetic improvement of crops, and the implications of ongoing natural selection for effective use of symbiosis. Those who recognize these constraints will waste less time on approaches that are destined to fail, while focusing their efforts on the most promising opportunities. We focus on the role of crop nutrition in increasing long-term sustainability, i.e. enhancing crop production indefinitely, not just for a few years. Long-term sustainability requires replacing nutrients removed in harvested crops, maintaining soil physical properties, preventing accumulation of pathogens or weed seeds, maintaining favorable chemical properties (e.g. pH) and preventing erosion (Greenland, 1975). By these criteria, an adaptation that lets crops access some nutrient source that will quickly be exhausted is of limited value. Suppose, for example, that crop roots could excrete enzymes that break down soil organic matter, allowing them to recover the N contained in humus. In many soils, the humus fraction contains sufficient N to support good yields for at least several years. However, this N source would eventually run out. Furthermore, the resulting decrease in soil organic matter would have adverse effects on soil physical properties, increasing susceptibility to drought. We also recognize the importance of some problems not directly linked to long-term sustainability. For example, a decrease in loss of nutrients from agricultural land to rivers would be worthwhile, even if this nutrient conservation did not increase crop yields. Conservation of matter applies to each element essential to crop growth or human nutrition. Crop plants, their wild relatives and their associated

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symbiotic microorganisms (e.g. N2 -fixing Rhizobium bacteria, or mycorrhizal fungi) vary in their genetic capacity to acquire needed nutrients. Some have sophisticated adaptations that allow them to acquire nutrients not readily available to other organisms. None has the ability to transmute elements, however. Therefore, sustained crop production will always require some ongoing source of each essential element. Carbon, H, O and sometimes N may be obtained from rainwater or the atmosphere, but other nutrients must be supplied either from weathering of soil minerals or from some external source. Evolutionary constraints may also imply significant opportunities. Crop plants and associated microorganisms have been shaped by natural selection, operating over millions of years in pre-agricultural environments. This evolutionary legacy sometimes conflicts with our current agricultural goals. But the existence of such conflicts also implies that humans may be able to improve on natural selection in some cases (Denison et al., 2003b). These opportunities for improvement contrast with the constraints on improvement imposed by conservation of matter, and also with those cases where natural selection has already found efficient, possibly optimal, solutions. Ongoing evolution of the soil microorganisms involved in nutritional symbioses with crops may provide either a constraint or an opportunity (Kiers et al., 2002). Finally, complexity itself may also be an important constraint, despite improvements in computers and software. Crop genes and their environment (including other organisms) interact in complex ways, and the effects of even ‘simple’ genetic changes may be hard to predict. The situation is in some ways analogous to the challenges inherent in developing complex computer software. These challenges are not necessarily insurmountable, but they are more than a trivial problem.

11.2 Constraint/opportunity 1: conservation of matter High-yielding crops remove large amounts of N, P and other nutrients from soil. For example, 10 000 kg of maize grain (a reasonable yield from 1 ha) contains 150 kg N, 29 kg P and 37 kg K, all of which would be removed from the field with the harvested grain. A good wheat yield of 6000 kg grain would remove 138 kg N, 26 kg P and 29 kg K (elemental composition from Table 1 of Loomis & Connor, 1992). Of course, the nutrient removal per hectare is less with lower yields, but nutrient removal per kg grain depends only on the composition of the grain. Low-yield agriculture requires just as much N and P per ton of food produced as does high-yield agriculture. However, low-yielding systems export less nutrients per hectare. This quantitative difference may sometimes result in a qualitative difference as well, in that low-yield systems may be able to meet their needs for some elements from sources (e.g. rainfall or even windblown dust) that would not be adequate for high-yield systems.

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Nutrient removal in harvested crops cannot be changed without affecting human nutrition. Nitrogen, for example, is an essential component of all proteins. In fact, the relationship is so close that protein content is often estimated as 6.25 times the N content (Loomis & Connor, 1992). Any significant reduction in the N content of the seeds will inevitably reduce protein content. Other major elements like P and K are also essential to human nutrition, as are Mg and Ca (e.g. see also Chapter 3). Genetic decreases in the nutrient content of seeds would also presumably reduce seed germination or seedling vigor. For sustainability, all nutrients removed in harvested crops (or otherwise lost) must be replaced somehow. A long-term balance between nutrient input and removal in the harvested crop is essential. This principle follows directly from conservation of matter. If 150 kg N ha−1 is removed each year in harvest grain, soil N levels will decrease until they severely limit crop growth, unless this N is replaced. The same is true of other nutrients. Nutrient ‘cycling’ (reuse of nutrients from decaying vegetation) may be an adequate source of nutrients for natural ecosystems that do not export significant amounts of nutrients. But nutrients in the harvested portion of crops that are removed from the field are not available for recycling by subsequent crops. Nutrients lost from the system by less desirable pathways, such as leaching of nitrate (NO3 − ), must also be replaced. If improvements in crop genetics or crop management resulting from genomic information leads to reductions in such losses, that will reduce, but not eliminate, the need for inputs. Short-term experiments may underestimate the need for external inputs. Various alternative sources of nutrients may appear to support good yields (similar to fertilized control) for one or more years. However, some fraction of the crop’s nutrient needs may be supplied by ‘carryover’ of nutrients from a previous crop, or from mineralization of soil organic matter. The latter source is finite, but may be large enough to make a significant contribution for years or even decades. Therefore, a comparison with an unfertilized control is essential. Yields in the unfertilized wheat-fallow control in the University of California Davis’s Long-Term Research on Agricultural Systems (LTRAS) were initially similar to fertilized controls, showing that internal resources were adequate to meet crop needs. Yields of the unfertilized control tended to decrease over years, as soil N supply decreased, but it took 9 years to show that this trend was statistically significant (Denison et al., 2003a). The soil at LTRAS initially contained only about 1% organic matter, typical of warm climates, but that still represented about 1000 kg N ha−1 (2 × 106 kg soil in the plow layer × 0.01 kg organic matter (OM) kg−1 soil × 0.05 kg N kg−1 OM). Therefore, it is not surprising that the plots could export N in grain for some years without external inputs. The Haughley experimental farm in the United Kingdom included an organic treatment run without significant nutrient imports (aside from N2 fixation) from 1941 until 1970 (Stanhill, 1990). In 1952–1953, wheat yields in the organic system were 87% of average yields for the county, but by 1964–1965 they had

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fallen to 71% of county average. It was then decided to start importing nutrients in the form of manure from other farms. Note, however, that absolute yields in the organic system were slightly higher in 1964–1965 than in 1952–1953, presumably owing to genetic improvements in wheat over that period. It would have been interesting to continue this experiment a few more decades. Longer term experiments suggest that high yields may require external sources of nutrients. The world’s longest running agricultural field experiments were started at Rothamsted, England, in the mid-1800s. Several are still running today, with some changes in cultivars and weed management but with consistent fertility treatments. Yields of turnips in an unfertilized 4-year rotation of turnips, barley, clover or beans, and wheat were initially less than half the yield of fertilized plots (Powlson & Johnston, 1994). Turnip yields in the unfertilized treatment fell still further to one tenth of their initial value, over about 40 years, while yields of fertilized turnips increased. Yields of fertilized and unfertilized wheat in this experiment showed similar, but less dramatic changes over this period (Note: turnip yields of fertilized plots eventually fell also, but this resulted from disease linked to soil acidification rather than from nutrient deficiency). Wheat in another experiment at Rothamsted that has received annual inputs of N, P and K now yields >6000 kg ha−1 , whereas unfertilized plots yield 2

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Plate 6 Differential effect of ABA on two different members of the multidrug and toxin efflux (MATE) carrier family. Whereas one gene (MATE1) is strongly up-regulated by ABA in all tissues and under both K+ regimes, the other gene (MATE2) responds to ABA with a specific decrease in transcript level, which is stronger in roots than in shoots and occurs only in K+ deficient plants (‘+ABA in −K’).

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Plate 7 Typical expression profile of several aquaporin genes in response to ABA treatment. Transcripts generally shifted their response to ABA from a root specific increase in K+ sufficient plants (‘+ABA in +K’) to a shoot specific decrease in K+ starved plants (‘+ABA in −K’). For exact conditions see Figs. 8.1 and 8.2.

Plate 8 Field-testing for high Ni concentrations in plant leaves using a colorimetric reagent, dimethylglyoxime. The plant Phyllanthus orbicularis is a Ni-hyperaccumulator endemic to ultramafic soils in Cuba. Photo: Micheal Davis.

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Plant Nutritional Genomics (Biological Sciences Series)

Nutritional genomics

Plant Abiotic Stress (Biological Sciences Series)

Plant Growth and Climate Change (Biological Sciences Series)

Plant genomics and proteomics

Plant Genomics and Proteomics

Plant Genomics and Proteomics

Plant Functional Genomics

Nutritional Genomics: Impact on Health and Disease

Weedy and Invasive Plant Genomics

Plant Genomics. Methods and Protocols

Nutritional Sciences: From Fundamentals to Food

Quantitative Genetics, Genomics and Plant Breeding (Cabi)

Quantitative Genetics, Genomics, and Plant Breeding

Quantitative Genetics, Genomics and Plant Breeding

Plant Functional Genomics. Methods and Protocols

Quantitative Genetics, Genomics and Plant Breeding (Cabi)

Image Analysis for Biological Sciences

Emerging Trends in Biological Sciences

Spectroscopy for the Biological Sciences

Semiotics and the Biological Sciences

Plant Sciences: Macmillan Science Library

Plant Defence: Biological Control (Progress in Biological Control)

Nutritional Genomics: Discovering the Path to Personalized Nutrition

Biological Control of Microbial Plant Pathogens

Biological Control of Microbial Plant Pathogens

Genomics

GENOMICS

Mathematical Problems in the Biological Sciences

Nutritional anemia

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