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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
xiii xvii 1
FRANC¸OISE 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
26 27
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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
87
78 81 82 82
MALCOLM J. HAWKESFORD 4.1 4.2
Introduction Acquisition of sulphate
87 89
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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
vii 90 93 95 99 99 100 101 102 103 104 104 105 112
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
150
BRETT LAHNER and DAVID E. SALT
8
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
150 151 151 152 153 154 156 157 157 158 159 162 163 165 166 167 167
Transcriptional profiling of membrane transporters
170
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
170 171 172 173 174
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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
201
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
201 202 205 205 207 209 211 211 211 212 212 213 214 215
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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
242 243 249 251 259 260
265
MICHAEL A. GRUSAK and ISMAIL CAKMAK 12.1 Introduction 12.2 Plants as sources of dietary minerals
265 266
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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
287
STEVEN N. WHITING, ROGER D. REEVES, DAVID G. RICHARDS, MIKE S. JOHNSON, JOHN A. COOKE, FRANC¸OIS 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
293 294 296 297 298 299 299 300 301 302 302 303 303 304 304 306 307 308 308 310 310 317
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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 Ch´erel
INRA – Biochimie et Physiologie Mol´eculaire 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 Jaffr´e
Institut de R´echerche pour le Developpement (IRD), BP A5, 98848 Noum´ea, 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
Laboratory of Genetics, Arboretumlaan 4, 6703 BD Wageningen, The Netherlands
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
xv
Frans J.M. Maathuis
Department of Biology (Area 9), University of York, York, YO10 5YW, UK
Fran¸cois Malaisse
Laboratoire d’Ecologie, Facult´e 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
Department of Horticulture and Landscape Architecture, Purdue University, 625 Agriculture Mall Drive, West Lafayette, IN 47907-2010, USA
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
Department of Horticulture and Landscape Architecture, Purdue University, 625 Agriculture Mall Drive, West Lafayette, IN 47907-2010, USA
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
Laboratory of Genetics, Arboretumlaan 4, 6703 BD Wageningen, The Netherlands
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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 Mol´eculaire 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−)
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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 (<1 mM), the HATS mediates NO3 − influx,
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first, at a low rate, assuming that the plants have not been previously exposed to NO3 − , and then at a higher rate, as evidenced by changes in Km and Vmax (Hole et al., 1990; Aslam et al., 1992; Kronzucker et al., 1995). These characteristics indicate that there are two components in the HATS, one which is constitutive (cHATS) and the other inducible (iHATS). When [NO3 − ]ext exceeds 500 M, the non-saturable LATS system becomes evident. Electrophysiological studies have demonstrated that both the HATS and LATS are mediated by electrogenic 1 NO3 − /2H+ symporters (Glass et al., 1992). 1.2.5 Identification of genes coding for nitrate transporters Two gene families encode proteins that are involved in either the low (NRT1) or the high (NRT2) affinity NO3 − systems. These families share structural features but no homology at the amino acid level. 1.2.5.1 The NRT1 family of transporters The first gene encoding a low-affinity NO3 − transporter was cloned in Arabidopsis by isolating and characterising a chlorate resistant T-DNA insertion mutant chl1 (Tsay et al., 1993). Chlorate is an analogue of NO3 − which is reduced to toxic chlorite by NR (see Section 1.3.1). chl1 showed reduced NO3 − uptake, particularly when plants were grown in the presence of NH4 + (Huang et al., 1996; Touraine & Glass, 1997). The corresponding AtNRT1.1 cDNA encodes a 590-amino acid protein, containing 12 putative membrane-spanning domains. When expressed in Xenopus oocytes, this cDNA allowed NO3 − uptake (Tsay et al., 1993) with biphasic kinetics (Liu et al., 1999). The dual affinity of the AtNRT1.1 transporter has since been shown to be regulated by a phosphorylation/de-phosphorylation mechanism (Liu & Tsay, 2003). Further, three other AtNRT1 genes have since been identified in Arabidopsis, AtNRT1.2, AtNRT1.3 and AtNRT1.4, which show 36%, 51% and 42% identity, respectively at the amino acid level with AtNRT1.1. Functional analysis of AtNRT1.2 in Xenopus oocytes showed that it is also a low-affinity (Km = 6 mM) NO3 − transporter (Liu et al., 1999). The functions of the two other genes are still not known. Another member of this family, AtPTR2B, encodes a peptide transporter (Rentsch et al., 1995; Song et al., 1996). Oligopeptide transport seems to be a feature of the NRT1 family as BnNRT1.2, which was one of the two cDNAs identified in Brassica napus, is also able to transport NO3 − and l-histidine when expressed in oocytes (Zhou et al., 1998). Using AtNRT1.1 as a heterologous probe, Lauter and colleagues have isolated two cDNAs from a tomato root-hair specific library (Lauter et al., 1996). Although the corresponding protein shares 65% identity with AtNRT1.1, their role in NO3 − uptake remains to be demonstrated. Corresponding homologous genes have also been identified in N. plumbaginifolia (Fraisier et al., 2001).
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1.2.5.2 The NRT2 family of transporters Chlorate has also been used to screen for mutants affected in the HATS, but to date this has only been successful in fungi. In Aspergillus nidulans, the chlorate resistant crna mutant was shown to be defective in NO3 − uptake. The CRNA cDNA encodes a transport protein of 507 amino acid containing 12 membrane-spanning domains with two groups of 6 segments separated by a central loop (Unkles et al., 1991). Two CRNA-related genes have since been isolated from Chlamydomonas reinhardtii: CrNRT2.1, which encodes a high affinity NO3 − /NO2 − bi-specific transporter, and CrNRT2.2, which encodes a high affinity NO3 − specific transporter. The presence of a third protein, Nar2, was found to be necessary to form an active NO3 − transport system (Quesada et al., 1994; Galvan & Fernandez, 2001). In higher plants, the first NRT2 genes were cloned in barley (Trueman et al., 1996) and N . plumbaginifolia (Quesada et al., 1997) by PCR amplification using degenerate primers corresponding to conserved motifs found in a subgroup of the major facilitator superfamily (MSF) transporters. Independently, the AtNRT2.1 gene was subsequently isolated using differential display (Filleur & DanielVedele, 1999) and PCR amplification (Zhuo et al., 1999) techniques. NRT2 genes have since been identified in many other plants species (Fig. 1.2). The complete genome sequence of Arabidopsis has revealed the presence of seven NRT2 genes, distributed across three chromosomes (Orsel et al., 2002a). AtNRT2.1/AtNRT2.2 and AtNRT2.3/AtNRT2.4 are arranged in tandem at the top of chromosome 1 and the bottom of chromosome 5, respectively, whilst AtNRT2.6 and AtNRT2.7 are located on chromosomes 3 and 5. Using the amino acid sequence of AtNRT2.1 as a reference, AtNRT2.2, AtNRT2.3, AtNRT2.4, AtNRT2.5, AtNRT2.6 and AtNRT2.7 proteins exhibit 91%, 77%, 88%, 69%, 77% and 57% similarity, respectively. A phylogenetic tree (Fig. 1.2) of all Arabidopsis and other higher plant sequences show that AtNRT2.1, AtNRT2.2, AtNRT2.3, AtNRT2.4 and AtNRT2.6 proteins are similar, whilst AtNRT2.5 and AtNRT2.7 are closer to lower eukaryotic (alga or fungi) than to other plant proteins. In contrast to NRT1, the only NRT2 cDNAs that have been shown to mediate active NO3 − uptake following injection into Xenopus oocytes are CRNA or CrNRT2. Further, the co-injection of Nar2 with CrNRT2.1 is required to obtain active NO3 − uptake (Zhou et al., 2000). Reverse genetics is a valuable part of the functional genomics toolkit since it allows the function of specific genes to be disrupted (Bouchez & Hofte, 1998). In Arabidopsis, extensive populations mutagenised with an insertion element (transposon or T-DNA) have recently become available (Bouche & Bouchez, 2001). A T-DNA mutant affected in both AtNRT2.1 and AtNRT2.2 genes has been identified, in which the HATS but not the LATS activities are disrupted (Filleur et al., 2001). This mutant could be used to determine the function of NRT2 genes in global NO3 − transport processes in plants. The organ specificity
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YNT1 Crna NarK CrNrt2.1 Crnrt2.2 Crnrt2.3 AtNrt2.7 AtNrt2.5 OsNrt2.1 HvNrt2.2 HvNrt2.4 TaNrt2.1 HvNrt2.1 HvNrt2.3 AtNrt2.3 AtNrt2.6 GmNrt2.1 LjNrt2.1 NpNrt2.1 AtNrt2.4 AtNrt2.2 AtNrt2.1
Figure 1.2 Unrooted tree of NRT2 proteins. Sequences are from Hansenula polymorpha (YNT1, NCBI protein number CAA93631), Aspergillus nidulans (Crna, NCBI AAA62125), Escherichia coli (NarK, NCBI CAA34126), Chlamydomonas reinhardtii (CrNrt2.1, NCBI CAA80925 ; CrNrt2.2, NCBI CAA80926; CrNrt2.3, NCBI CAA11238), Arabidopsis thaliana (AtNrt2.1, NCBI ACC64170; AtNrt2.2, NCBI AAC35884; AtNrt2.3, NCBI BAB10099; AtNrt2.4, NCBI BAB10098; AtNrt2.5, NCBI AAF78499; AtNrt2.6, NCBI CAB89321; AtNrt2.7, NCBI CAB87624), Oryza sativa (OsNrt2, NCBI BAA33382), Hordeum vulgare (HvNrt2.1, NCBI AAC49531; HvNrt2.2, NCBI AAC49532; HvNrt2.3, NCBI AAD28363; HvNrt2.4, NCBI AAD28364), Triticum aestivum (TaNrt2, NCBI AAK19519), Glycine max (GmNrt2, NCBI AAC09320), Lotus japonicus (LjNrt2.1, NCBI CAC35729), and Nicotiana plumbaginifolia (NpNrt2.1, NCBI CAA69387).
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of expression also indicates the possible roles of NRT2. In higher plants, most NRT2 genes isolated thus far are expressed preferentially in roots. In tomato, LeNRT2 expression is not observed in whole shoots or leaves (Ono et al., 2000) whilst in N. plumbaginifolia, NpNRT2.1 transcripts are detectable at low levels in leaves, petioles, buds flowers or seeds (Quesada et al., 1994). In Arabidopsis, Orsel et al. (2002b) have demonstrated variation in the expression levels between the seven genes within the NRT2 gene family. However, although most of the NRT2 genes are expressed more in roots than in shoots, AtNRT2.7 showed a greater expression in the aerial tissues, which could indicate a role in NO3 − fluxes within the leaves. 1.2.6 Regulation of nitrate influx and the role of NRT1 and NRT2 genes The regulation of NO3 − uptake is highly complex and it has been the subject of several reviews (Crawford & Glass, 1998; Daniel-Vedele et al., 1998; Forde & Clarkson, 1999; Forde, 2000; Galvan & Fernandez, 2001; Glass et al., 2001; Williams & Miller, 2001). Both environmental factors and internal signals control NO3 − uptake mediated by HATS and LATS. As indicated previously, NO3 − itself is an inducer, which discriminates between constitutive (cHATS and cLATS) and inducible (iHATS) NO3 − uptake systems (Behl et al., 1988). As opposed to NO3 − , addition of reduced N sources such as NH4 + or amino acids to the culture medium inhibits NO3 − uptake (Muller & Touraine, 1992; Kronzucker et al., 1999). Nitrate uptake is also regulated by diurnal cycles and light intensity, which may be due to the transport of photosynthates to the root (Delhon et al., 1995). Internal signals are thought to match the rate of N acquisition to the demand for N (Glass & Siddiqi, 1995). During N starvation, plants increase their capacity to absorb NO3 − transiently, which may be a consequence of de-repression of NO3 − transport due to N metabolites accumulating under non-limiting conditions. After NO3 − is re-supplied, feedback regulation takes place (Siddiqi et al., 1989), but the signals responsible for the decrease in NO3 − influx have not yet been identified. How does NO3 − influx and gene expression correlate? In Arabidopsis, the expression of AtNRT2.1 and regulation of NO3 − influx are tightly linked. For example, AtNRT2.1 is induced by low levels of NO3 − to a transient maximum. Further, AtNRT2.1 expression transiently induced by N starvation (Filleur & Daniel-Vedele, 1999; Zhuo et al., 1999) is strictly correlated to the influx during a day/night cycle and it is inducible by sugars (Lejay et al., 1999). The regulation of AtNRT2.1 may depend on the C flux from glycolysis (Lejay et al., 2003). These correlations, together with defects of the regulation of iHATS activities (NO3 − inducible, starvation de-repressible and NH4 + repressible high affinity uptake) in the atnrt2a mutant (Cerezo et al., 2001) strongly support the hypothesis that the AtNRT2.1/AtNRT2.2 genes play a major role in the NO3 − uptake mediated by the iHATS. The role(s) of other AtNRT2 genes remains to
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Figure 1.3 Regulation of gene expression within the NRT2 gene family in Arabidopsis. Data are from Orsel et al. (2002a, b), Okamoto et al. (2003), or Orsel & Krapp (personal communication).
be established. Figure 1.3 illustrates the specificity in responses of gene expression to different environmental factors. Some members, such as AtNRT2.4 and AtNRT2.5, show a constant increase during NO3 − starvation, and a downregulation after NO3 − re-supply (Okamoto et al., 2003). Although there is no evidence of a signal peptide for tonoplast localisation, these proteins may have a role in the mobilisation of NO3 − from a storage pool (vacuole) to the cytoplasm during long-term starvation. The role of AtNRT2.7 is particularly astonishing since it is preferentially and constitutively expressed in leaves. It is possible that, since the concentrations of leaf apoplastic NO3 − may decline from millimolar to micromolar concentrations under N deficiency, AtNRT2.7 may be involved in scavenging apoplastic NO3 − and thus facilitating the re-absorption of NO3 − by leaf cells (Okamoto et al., 2003). Based on its constitutive expression, it has also been suggested that AtNRT2.7 could play a role in the cHATS. A mutant affected in cHATS has been identified, but the corresponding gene has not been isolated so far (Wang & Crawford, 1996). The role of AtNRT1.1 in mediating NO3 − uptake is less clear than AtNRT2.1. The LATS was originally thought to be constitutive with respect to NO3 − , yet the expression of AtNRT1.1 is NO3 − inducible, even at very low concentrations (Tsay et al., 1993; Filleur & DanielVedele, 1999). This result could be partially explained by the dual affinity of AtNRT1.1 for NO3 − and by the presence of other members of the NRT1 family,
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which are constitutively expressed (AtNRT1.2 and AtNRT1.4) or repressed (AtNRT1.3) by NO3 − starvation (Okamoto et al., 2003). However, these correlations between NO3 − influx and the expression of AtNRT should be treated with caution since transcriptional regulation is not the only mechanism which impacts on NO3 − uptake. For example, the dual affinity of AtNRT1.1 is mediated by protein phosphorylation and such a post-transcriptional regulatory process may explain why NO3 − influx is down-regulated by NH4 + in transgenic plants expressing NpNRT2.1 cDNA under the control of a constitutive root specific promoter (Fraisier et al., 2000). 1.3 Nitrogen assimilation A global overview of N assimilation in plants is shown in Fig. 1.1. Nitrogen assimilation requires the reduction of NO3 − to NH4 + , followed by NH4 + assimilation into amino acids. The following section describes the genomic aspects of that metabolic pathway. 1.3.1 Nitrate reduction Nitrate reduction is catalysed by two enzymes, NR, which reduces NO3 − to NO2 − and NiR, which reduces NO2 − to NH4 + . NR is a homodimer, comprising two identical monomers. Each monomer is constituted from three polypeptides, each of which is associated with a different prosthetic group; flavin adenine dinucleotide (FAD), a haem and a Mo cofactor. The characterisation of mutants deficient in NR in Nicotiana spp., barley and Arabidopsis (Pelsy & Caboche, 1992; Crawford & Arst, 1993) has led to the conclusion that there are two classes of genes, the Nia genes encoding the apoenzyme, and the Cnx (cofactor for nitrate reductase and xanthine dehydrogenase) genes encoding the Mo cofactor. These mutants were screened for resistance to chlorate in the nutrient medium. The number of Nia genes varies between species, for example there is one member in N. plumbaginifolia, and two in tobacco, barley and Arabidopsis (Meyer & Stitt, 2001). In Arabidopsis, the two Nia genes (Nia1 and Nia2) are genetically redundant, although there is specialisation since the Nia2 gene is responsible for 90% of the NR activity in the plant, whereas Nia1 encodes for only 10% of the activity. Surprisingly, the low NR activity due to the Nia1 gene is sufficient for normal growth of the plant, as shown with mutants where Nia2 has been deleted (Wilkinson & Crawford, 1993). One explanation for this observation is a post-translational regulation of NR by phosphorylation and binding of a 14-3-3 protein which inactivates it, especially in conditions of darkness, water stress, or low CO2 levels (Kaiser et al., 1999; MacKintosh & Meek, 2001). The hypothesis is that in the wild-type plant, the protein activity is constantly modulated by this mechanism, rarely being at its full catalytic rate. Thus, in the mutant, since the NR level is very low, there would be less inactivation of the protein (Scheible et al., 1997; Crawford & Forde, 2002).
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In Arabidopsis, six Cnx proteins are involved in catalysing the three stages necessary for the synthesis of the Mo-cofactor (Mendel, 1997). Many studies have focused on the regulation of NR gene expression (Crawford, 1995; Meyer & Stitt, 2001; Crawford & Forde, 2002). NR genes are expressed in roots and leaves in proportion to the amount of NO3 − which is reduced in the different parts of the plant (Pate, 1973). NR genes are positively regulated by several factors including NO3 − , light, carbohydrates, and hormones (e.g. cytokinins). Nia expression is induced by NO3 − within minutes and at micromolar concentrations, suggesting that NO3 − can act as a signal (Crawford, 1995). Light also induces Nia expression, but this response can also be seen in response to carbohydrates, for example sucrose (Vincentz & Caboche, 1991). Cytokinins also increase NR activity by inducing Nia expression (Yu et al., 1998). Conversely, products of the NO3 − assimilation pathway downregulate Nia and Cnx expression, although there are conflicting hypotheses about the regulatory metabolites. Glutamine may be the signal, although other amino acids or NH4 + may be responsible (Meyer & Stitt, 2001). Moreover, Nia genes also respond to circadian rhythms. Molecular genetic insights into NR activity have been used to decrease the pool of NO3 − in the plant. For example, the overexpression of Nia fused with strong promoters in tobacco resulted in a 30 to 40% decrease in NO3 − content in leaves compared to the wild type (Quillere et al., 1994). In Arabidopsis, the same result was obtained with transgenic lines overexpressing NR, which had half the NO3 − content of the wild type due to the inhibition of nitrate uptake, and not to enhanced NO3 − reduction (Gojon et al., 1998). Mutants deficient in NiR are more difficult to obtain than NR deficient mutants, since the accumulation of nitrite is toxic. However, the NiR gene Nii has been cloned from several species including Arabidopsis, birch, maize, spinach, tobacco, and rice (Meyer & Caboche, 1998; Meyer & St¨ohr, 2002). Arabidopsis appears to contain a single Nii gene, whilst other species contain two copies per haploid genome. There is a high degree of sequence conservation between plant species (75–80% similarity). The regulation of Nii genes shares several features with Nia genes, for example NO3 − (although it is difficult to know whether NO3 − or NO2 − is the inducing factor) and light can increase the expression of NiR genes, whilst Nii genes are downregulated by NH4 + and amides (Vincentz et al., 1993). It is not yet known if post-translational regulation of NiR occurs. The data indicate that NR and NiR gene expression is highly coordinated, which is perhaps unsurprising since NO3 − assimilation requires that there are no gaps along the metabolic pathway. 1.3.2
Ammonium assimilation
1.3.2.1 The GS/GOGAT cycle The NH4 + arising from NO3 − reduction by NR and NiR is first fixed on a glutamate molecule to form glutamine, a step which is catalysed by glutamine
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synthetase (GS). Glutamine reacts subsequently with an acid from the Krebs cycle, 2-oxoglutarate, leading to the formation of two molecules of glutamate. This step is catalysed by glutamine 2-oxoglutarate amino transferase (GOGAT). GS and GOGAT function in a cycle, since one of the two glutamate molecules goes back to GS, whilst the other enters in the pathway for amino acid synthesis through transamination. GS is present in the plant as two isoforms, GS1, which is cytosolic, and GS2, which is chloroplastic (Lea et al., 1990). GS2 is the predominant form in leaves, where it is thought to be involved in the assimilation of NH4 + coming from nitrate reduction or from photorespiration (Lea & Ireland, 1999). GS1 is the predominant form in roots, although it is also present in leaves. GS1 is therefore thought to be primarily responsible for the assimilation of NH4 + arising from NO3 − reduction (Oaks & Hirel, 1985). Chloroplastic GS2 is encoded by a single nuclear gene GLN2, whereas multiple genes (named GLN1.x) encode cytosolic GS1. The number of GLN1 genes varies from three to six. There are three genes in pea and Arabidopsis, and four in maize and canola (Hirel & Lea, 2001, 2002; Coruzzi, 2003). Gene expression studies show that GLN2 is expressed predominantly in leaves and GLN1 in roots, which is consistent with their proposed metabolic roles. GLN1 expression in roots seems to relate to the fact that glutamine is one of the main N compounds which is exported to the shoot. A barley mutant defective in GS2 is unable to survive in photorespiratory conditions, thus confirming the role of that isoform in the reassimilation of NH4 + derived from photorespiration (Wallsgrove et al., 1987). Curiously, no mutants defective in GS have yet been found in Arabidopsis. The expression of GS2 is increased by light, and it is also differentially regulated by C and N metabolites. For example, sucrose increases GS2 expression, whereas glutamate and glutamine repress it. Despite conflicting results, it seems that NH4 + enhances GS2 gene expression (Hirel & Lea, 2002). These results are consistent since NH4 + assimilation is enhanced when NH4 + itself is present together with C backbones, and it is repressed when glutamine content reaches high values. In contrast to GS2, multiple isoforms of cytosolic GS1 appear to exist and genes are expressed in different plant parts, and in different tissues, according to the developmental stage and the physiological status of the plant. For example, GS1 genes are expressed in the vascular tissues of leaves, in particular in the phloem (Sakurai et al., 1996), whereas GS2 is expressed predominantly in mesophyll cells, and thus the two enzymes do not overlap. Further, GS1 gene expression is induced during leaf senescence, which implies a role for GS1 in the remobilisation of protein N in the cytosol (Brugiere et al., 2000; Masclaux et al., 2000). More generally, GS1 gene expression is triggered by metabolic changes associated with leaf ageing, including environmental or pathogenic stresses (Hirel & Lea, 2002). Overexpressing a GS1 gene in Lotus corniculatus was even able to induce a premature remobilisation of N in leaves (Vincent et al., 1997). Cytosolic GS1 is also involved in the assimilation of NH4 + resulting from atmospheric N2 fixation in the nodules of legume plants
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associated with Rhizobium (Hirel et al., 1993). It is likely that the different GS1 genes encode different polypeptides, which can assemble in multiple ways, thus leading to various isoforms of the enzyme, each being specific of an organ or a physiological condition. There are two forms of GOGAT in plants, the Fd-GOGAT and the NADHGOGAT, which both catalyse the synthesis of glutamate from glutamine coming from GS activity by fixing glutamine on 2-oxoglutarate. It has been established that Fd-GOGAT is predominantly localised in leaf chloroplasts (Lea, 1993), but it is also present, at very low levels, in non-photosynthetic tissues (Suzuki & Rothstein, 1997). Fd-GOGAT activity accounts for 95% of the total GOGAT activity in the leaves, NADH-GOGAT accounting for the remaining 5% (Sommerville & Ogren, 1980). NADH-GOGAT is primarily located in plastids of non-photosynthetic tissues, for example in roots, in nodules of N-fixing plants, and in etiolated leaf tissues (Hirel & Lea, 2001). The study of Arabidopsis mutants deficient in leaf Fd-GOGAT, which have normal growth only in non-photorespiratory conditions, has shown that FdGOGAT is mainly involved in the re-assimilation of NH4 + coming from photorespiration (Sommerville & Ogren, 1980). Two genes encoding Fd-GOGAT have been identified in Arabidopsis; GLU1, which is expressed in leaves, and GLU2, which is expressed mainly in roots and at low levels in leaves (Coschigano et al., 1998). GLU1 expression is highly up-regulated by light, but GLU2 is much less responsive. In a GLU1-mutant viable in non-photorespiratory conditions (high CO2 or low O2 ), Fd-GOGAT (represented by a low level of GLU2 expression) was able to ensure primary N assimilation (Coruzzi, 2003). Other species, such as grapevine and soybean, may also have two Fd-GOGAT genes (Hirel & Lea, 2002). Further, an interaction between light and N source has been observed in etiolated maize seedlings. Following transfer of seedlings to NO3 − or NH4 + in the light, there was an increase in Fd-GOGAT mRNA (Suzuki et al., 1996). NADH-GOGAT is encoded by a single gene, GLT1, which is expressed at low levels in leaves and at higher levels in roots (Lam et al., 1996; Lancien et al., 2002). NADH-GOGAT gene expression is enhanced by NH4 + (Hirose et al., 1997). A knockout mutant in the GLT1 gene has been studied in Arabidopsis (Lancien et al., 2002), which is unable to grow normally in nonphotorespiratory conditions, confirming a role for NADH-GOGAT in primary N assimilation. In summary, Fd-GOGAT encoded by GLU1 is responsible for the assimilation of NH4 + released by photorespiration in the leaves. Further, Fd-GOGAT encoded by GLU2 may also participate in primary N assimilation in leaves and roots. NADH-GOGAT is involved in primary N assimilation, especially for the synthesis of glutamate in roots and in the nodules of N-fixing legumes. 1.3.2.2 Glutamate dehydrogenase (GDH) Glutamate dehydrogenase (GDH), an enzyme that is well known in animals, is also able to catalyse the assimilation of NH4 + by fixation of an –NH2 group
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to 2-oxoglutarate to form a molecule of glutamate. GDH can also catalyse the reverse reaction, which deaminates glutamate into NH4 + and 2-oxoglutarate. For years, it was debated whether GDH functioned as an anabolic or catabolic enzyme. GDH was discovered before the GS/GOGAT pathway, and since it appeared to be a more direct route to synthesise glutamate, it was thought to be the main enzyme for NH4 + assimilation. However, the high Km of GDH for NH4 + is an argument against such a role. Conversely, GS has a high affinity for NH4 + , and after more than 20 years of research, it is now clear that at least 95% of the NH4 + is assimilated by the GS/GOGAT pathway in plants (Lea & Ireland, 1999). Nevertheless, the role of GDH in plants is still a matter for debate. For example, some studies have demonstrated that GDH can ensure NH4 + assimilation, especially when [NH4 + ]ext is high in the root medium, whilst other studies show that GDH operates in the direction of deamination (Hirel & Lea, 2001). Since GDH activity is induced during leaf senescence and thus N remobilisation, this favours the argument for a catabolic role (Masclaux et al., 2000; Bechtold et al., 1998). Two genes have been isolated, GDH1 and GDH2, which encode mitochondrial proteins (Melo-Oliveira et al., 1996; Turano et al., 1997). High homology with animal genes has been noted. The expression of these genes is up-regulated by NH3 and dark treatment, but down-regulated by light or sucrose. This is in accordance with the stimulation of GDH during senescence, which is a period when carbohydrate concentration decreases and NH3 levels increase in the plant tissues. The expression of these two genes is not co-ordinated, which suggests that they have distinct roles in N metabolism, and more generally in C/N relationships. The isolation of mutants lacking one or the other gene should help to elucidate these roles (Coruzzi, 2003). Interestingly, GDH has been overexpressed in tobacco using a bacterial GDH gene, and transgenic plants had a higher biomass and an improved tolerance to water stress compared to the wild type (Ameziane et al., 2000). More research work on GDH is necessary to better understand its physiological role and genomics will surely help to improve our knowledge. 1.4 Concluding remarks: the search for new genes This chapter has reviewed genes that participate in different steps of the Nassimilation pathway in higher plants. However, our knowledge of the regulatory elements as well as their transduction pathways is still far from complete. The recent progress in genomics allows the use of several novel approaches to identify new regulatory genes. 1.4.1 Search for homologues of genes from different organisms Once a candidate gene is identified in an organism such as bacteria, yeast or even humans, its sequence or the deduced amino acid sequence can be characterised very quickly using Basic Local Alignment Search Tool (BLAST) queries of the
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complete sequence of the Arabidopsis genome (e.g., see http://signal.salk.edu, http://atensembl.arabidopsis.info or http://www.arabidopsis.org). When an Arabidopsis protein shares similarities with a candidate gene, a classical functional genomics study can be performed, including analysis of gene expression, physiological characterisation of a knockout mutant, cellular or sub-cellular localisation and the search for protein partners. For example, homologues of the human glutamate receptor have been found in Arabidopsis and functional studies have led to the hypothesis that this protein could act as a global regulator of C and N metabolism in plants (Kang & Turano, 2003). In the same manner, the PII protein has been shown to play a central role in bacteria and cyanobacteria. In these species, the PII protein acts as a sensor of C/N metabolism as it can be modified (urydilated or phosphorylated in bacteria or cyanobacteria, respectively) in response to variation of the 2-oxoglutarate:glutamine ratio. There is one homologue of PII in Arabidopsis and it has recently been demonstrated that PII is localised in the chloroplast. Transgenic plants overexpressing cDNA were altered in C/N sensing (Hsieh et al., 1998). An ortholog of the yeast TOR (target of rapamycin) gene was also recently identified in Arabidopsis. The yeast protein regulates cellular expansion in response to nutrient availability and a loss of function Arabidopsis mutant, isolated from a T-DNA insertion library, was blocked at the embryo globular stage (Menand et al., 2002). 1.4.2 Searches for candidate genes using high throughput screening Transcriptomic and proteomic methods allow plant responses to developmental or environmental variations to be studied at the whole genome level, by analysing regulation of gene expression, protein synthesis and/or protein modifications. Both methods have been used to systematically identify N-regulated genes, for example using subtractive hybridisations (Zhang & Forde, 1998), differential display techniques (Filleur & Daniel-Vedele, 1999), or by two-D gel protein analyses (Ni & Beevers, 1994). A more powerful method has recently emerged in Arabidopsis with the availability of a complete DNA microarrays, carrying oligonucleotides specific for all the 25 000 genes (see also Chapter 8). Analyses performed with total RNA extracted from plants re-supplied with NO3 − has led to the identification of new genes highly inducible by NO3 − . These genes potentially encode novel metabolic and potential regulatory proteins. In many cases, only a few genes from specific gene families were found to be induced suggesting a non-redundant role of each of these genes (Wang et al., 2003). 1.4.3 Naturally occurring variation In addition to the variation artificially generated by mutants in model species, naturally occurring variation is extensively found in most species (see also Chapters 9, 10 and 12). This variation is of particular use in studying the N-assimilation pathway. For example, N-use efficiency is a complex character that can be dissected into agronomic (yield, seed N content, biomass) or
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physiological (enzymatic activities, N-metabolites contents) traits. Using quantitative trait loci (QTL) mapping to identify the genetic factors influencing the value of a complex quantitative trait, the relationships between processes corresponding to different levels of organisation have been approached through an analysis of the colocalisation between QTL. For example, coincidences have been detected in maize between QTL for yield and QTL for GS enzyme activity, both of which colocalise with genes encoding cytosolic GS (Hirel et al., 2001). Further, coincident GS and GOGAT related QTL have also been mapped in rice (Obara et al., 2001). Since fine mapping QTL is not easy in species with large genomes, QTL for N-related traits have recently been conducted in Arabidopsis (Loudet et al., 2003). QTL specific to different N environments have been detected for shoot biomass (Fig. 1.4), and for N, NO3 − or free amino acid contents. From four loci, corresponding to one or more QTL, two regions colocalised with known genes involved in N metabolism. In Arabidopsis, the rapid developments in the construction of near isogenic lines (NILs) and other fine mapping approaches indicate that further gene(s) impacting on variation in the N-assimilation pathway will be identified in the near future. The functional
Figure 1.4 QTL detected for biomass production in the Arabidopsis Bay-0 X Shahdara mapping population. Each QTL is represented by a triangle located at its most probable position. QTL on the left side and on the right side of the chromosomes are those detected in non-limiting or limiting N environments, respectively. The size of the triangle is proportional to the QTL contribution. The sign of the allelic effect is indicated for each QTL. The framework genetic map is from Loudet et al. (2002).
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analyses of these new genes and their interactions with N metabolism will undoubtedly improve our understanding of whole-plant physiology.
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Daniel-Vedele, F., Filleur, S. & Caboche, M. (1998) Nitrate transport: a key step in nitrate assimilation. Curr. Opin. Plant Biol., 1, 235–239. Delhon, P., Gojon, A., Tillard, P. & Passama, L (1995) Diurnal regulation of NO3 − uptake in soybean plants .1. Changes in NO3 − influx, efflux, and N utilization in the plant during the day night cycle. J. Exp. Bot., 46, 1585–1594. der Leij, M., Smith, S.J. & Miller, A.J. (1998) Remobilisation of vacuolar stored nitrate in barley root cells. Planta, 205, 64–72. Devienne, F., Mary, B. & Lamaze, T. (1994) Nitrate transport in intact wheat roots. I. estimation of 15 NO− cellular fluxes and NO− 3 distribution using compartmental analysis from data of 3 efflux. J. Exp. Bot., 45, 667–676. Dubois, E. & Grenson, M. (1979) Methylamine/ammonia uptake systems in Saccharomyces cerevisiae: multiplicity and regulation. Mol. Gen. Genet., 175, 67–76. Filleur, S. & Daniel-Vedele, F. (1999) Expression analysis of a high-affinity nitrate transporter isolated from Arabidopsis thaliana by differential display. Planta, 207, 461–469. Filleur, S., Dorbe, M.F., Cerezo, M., Orsel, M., Granier, F., Gojon, A. & Daniel-Vedele, F. (2001) An Arabidopsis T-DNA mutant affected in Nrt2 genes is impaired in nitrate uptake. FEBS Lett., 489, 220–224. Forde, B.G. & Clarkson, D.T. (1999) Nitrate and ammonium nutrition in plants: physiological and molecular perspectives. Adv. Bot. Res., 30, 1–90. Forde, B.G. (2000) Nitrate transporters in plants: structure, function and regulation. Biochim. Biophys. Acta Biomembr., 1465, 219–235. Fraisier, V., Dorbe, M.F. & Daniel-Vedele, F. (2001) Identification and expression analyses of two genes encoding putative low-affinity nitrate transporters from Nicotiana plumbaginifolia. Plant Mol. Biol., 45, 181–190. Fraisier, V., Gojon, A., Tillard, P. & Daniel-Vedele, F. (2000) Constitutive expression of a putative high-affinity nitrate transporter in Nicotiana plumbaginifolia: evidence for post-transcriptional regulation by a reduced nitrogen source. Plant J., 23, 489–496. Franco, A.R., Cardenas, J. & Fernandez, E. (1987) A mutant of Chlamydomonas reinhardtii altered in the transport of ammonium and methylammonium. Mol. Gen. Genet., 206, 414–418. Galvan, A. & Fernandez, E. (2001) Eukaryotic nitrate and nitrite transporters. Cell. Mol. Life Sci., 58, 225–233. Gazzarrini, S., Lejay, L., Gojon, A., Ninnemann, O., Frommer, W.B. & von Wiren, N. (1999) Three functional transporters for constitutive, diurnally regulated, and starvation-induced uptake of ammonium into Arabidopsis roots. Plant Cell, 11, 937–947. Glass, A.D.M. & Siddiqi, M.Y. (1995) Nitrogen absorption by plants roots. In Nitrogen Nutrition in Higher Plants. Associated Publishing Co., New Delhi, pp. 21–56. Glass, A.D.M., Brito, D.T., Kaiser, B.N., Kronzucker, H.J., Kumar, A., Okamoto, M., Rawat, S.R., Siddiqi, M.Y., Silim, S.M., Vidmar, J.J. & Zhuo, D. (2001) Nitrogen transport in plants, with an emphasis on the regulation of fluxes to match plant demand. J. Plant Nutr. Soil Sci., 164, 199–207. Glass, A.D.M., Shaff, J.E. & Kochian, L.V. (1992) Studies of the uptake of nitrate in barley. IV. Electrophysiology. Plant Physiol., 99, 456–463. Godon, C., Krapp, A., Leydecker, M.T., Daniel Vedele, F., & Caboche, M. (1996) Methylammonium resistant mutants of Nicotiana plumbaginifolia are affected in nitrate transport. Mol. Gen. Genet., 250, 357–366. Gojon, A., Dapoigny, L., Lejay, L., Tillard, P. & Rufty, T.W. (1998) Effects of genetic modification of nitrate reductase expression on (NO3 − )−15 N uptake and reduction in Nicotiana plants. Plant Cell Environ., 21, 43–53. Guo, F.Q., Young, J. & Crawford, N.M. (2003) The nitrate transporter AtNRT1.1 (CHL1) functions in stomatal opening and contributes to drought susceptibility in Arabidopsis. Plant Cell, 15, 107– 117. Heller, R., Esnault, R. & Lance, C. (1998) Physiologie V´eg´etale. Dunod, Paris.
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Hirel, B. & Lea, P. (2002) The biochemistry, molecular biology and genetic manipulation of primary ammonia assimilation. In Photosynthetic Nitrogen Assimilation and Associated Carbon and Respiratory Metabolism (eds C.Foyer & G. Noctor), Kluwer Academic Publishers, Dordrecht, pp. 71–92. Hirel, B. & Lea, P.J. (2001) Ammonia assimilation. In Plant Nitrogen (eds P.J. Lea & J.F. Morot-Gaudry) Springer-Verlag, Berlin, pp. 79–99. Hirel, B., Bertin, P., Quillere, I., Bourdoncle, W., Attagnant, C., Dellay, C., Gouy, A., Cadiou, S., Retailliau, C., Falque, M. & Gallais, A. (2001) Towards a better understanding of the genetic and physiological basis for nitrogen use efficiency in maize. Plant Physiol., 125, 1258–1270. Hirel, B., Miao, G. & Verma, D. (1993) Metabolic and developemental control of glutamine synthetase genes in legume and non-legume plants. In Control of Plant Gene Expression (ed. D. Verma), CRC Press, Boca Raton, FL, pp. 443–458. Hirose, N., Hayakawa, T. & Yamaya, T. (1997) Inducible accumulation of mRNA for NADH-dependent glutamate synthase in rice roots in response to ammonium ions. Plant Cell Physiol., 38, 1295–1297. Hole, D.J., Emran, A.M., Fares, Y. & Drew, M.C. (1990) Induction of nitrate transport in maize roots, and kinetics of influx, measured with nitrogen-13. Plant Physiol., 93, 624–647. Howitt, S.M. & Udvardi, M.K. (2000) Structure, function and regulation of ammonium transporters in plants. Biochim. Biophys. Acta Biomembr., 1465, 152–170. Hsieh, M.H., Lam, H.M., van de Loo, F.J. & Coruzzi, G. (1998) A PII-like protein in Arabidopsis: putative role in nitrogen sensing. Proc. Natl. Acad. Sci. USA, 95, 13965–13970. Huang, N.C., Chiang, C.S., Crawford, N.M. & Tsay, Y.F. (1996) CHL1 encodes a component of the low-affinity nitrate uptake system in Arabidopsis and shows cell type-specific expression in roots. Plant Cell, 8, 2183–2191. Kaiser, W.M., Weiner, H. & Huber, S.C. (1999) Nitrate reductase in higher plants: a case study for transduction of environmental stimuli into of catalytic activity. Physiol. Plantarum, 105, 385–390. Kang, J. & Turano, F.J. (2003) The putative glutamate receptor 1.1 (AtGLR1.1) functions as a regulator of carbon and nitrogen metabolism in Arabidopsis thaliana. Proc. Natl. Acad. Sci. USA, 100, 6872–6877. Kronzucker, H.J., Glass, A.D.M. & Siddiqi, M.Y. (1999) Inhibition of nitrate uptake by ammonium in barley. Analysis of component fluxes. Plant Physiol., 120, 283–291. Kronzucker, H.J., Siddiqi, M.Y. & Glass, A.D.M. (1995) Kinetics of NO− 3 influx in spruce. Plant Physiol., 109, 319–326. Lam, H., Coshigano, K., Oliveira, I.C., Melo-Oliveira, R. & Coruzzi, G. (1996) The molecular genetics of nitrogen assimilation into amino acids in higher plants. Annu. Rev. Plant Physiol. Plant Mol. Biol., 47, 569–593. Lancien, M., Martin, M., Hsieh, M.H., Leustek, T., Goodman, H. & Coruzzi, G.M. (2002) Arabidopsis glt1-T mutant defines a role for NADH-GOGAT in the non-photorespiratory ammonium assimilatory pathway. Plant J., 29, 347–358. Lauter, F.R., Ninnemann, O., Bucher, M., Riesmeier, J.W. & Frommer, W.B. (1996) Preferential expression of an ammonium transporter and of two putative nitrate transporters in root hairs of tomato. Proc. Natl. Acad. Sci. USA, 93, 8139-8144. Lea, P. (1993) Nitrogen metabolism. In Plant Biochemistry and Molecular Biology (eds P.J. Lea & R.C. Leegood), Wiley, New York, pp. 155–180. Lea, P., Robinson, S. & Stewart, G. (1990) The enzymology and metabolism of glutamine, glutamate, and asparagine. In The Biochemistry of Plants (eds B.J. Miflin & P.J. Lea), Academic Press, New York, pp. 121-159. Lea, P.J. & Ireland, R.J. (1999) Nitrogen metabolism in higher plants. In Plant Amino Acids (ed. B.K. Singh), Marcel Dekker, New York, pp. 1–47. Lejay, L., Gansel, X., Cerezo, M., Tillard, P., Muller, C., Krapp, A., von Wiren, N., Daniel-Vedele, F. & Gojon, A. (2003) Regulation of root ion transporters by photosynthesis: functional importance and relation with hexokinase. Plant Cell, 15, 2218–2232.
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Lejay, L., Tillard, P., Lepetit, M., Olive, F., Filleur, S., Daniel-Vedele, F. & Gojon, A. (1999) Molecular and functional regulation of two NO− 3 uptake systems by N- and C-status of Arabidopsis plants. Plant J., 18, 509–519. Limami, A. & Ameziane, R. (2001) Nitrogen nutrition and distribution of carbon in the plants. In Nitrogen Assimilation by Plants (ed. J. Morot-Gaudry), Plymouth Science Publishers, Plymouth, U.K., pp. 285–296. Liu, K.H. & Tsay, Y.F. (2003) Switching between the two action modes of the dual-affinity nitrate transporter CHL1 by phosphorylation. EMBO J., 22, 1005–1013. Liu, K.H., Huang, C.Y. & Tsay, Y.F. (1999) CHL1 is a dual-affinity nitrate transporter of Arabidopsis involved in multiple phases of nitrate uptake. Plant Cell, 11, 865-874. Loudet, O., Chaillou, S., Camilleri, C., Bouchez, D. & Daniel-Vedele, F. (2002) Bay-0 x Shahdara recombinant inbred line population: a powerful tool for the genetic dissection of complex traits in Arabidopsis. Theor. Appl. Genet., 104, 1173–1184. Loudet, O., Chaillou, S., Merigout, P., Talbotec, J. & Daniel-Vedele, F. (2003) Quantitative trait loci analysis of nitrogen use efficiency in Arabidopsis. Plant Physiol., 131, 345–358. MacKintosh, C. & Meek, S.E. (2001) Regulation of plant NR activity by reversible phosphorylation, 14-3-3 proteins and proteolysis. Cell. Mol. Life Sci., 58, 205–214. Marini, A.M. & Andre, B. (2000) In vivo N-glycosylation of the mep2 high-affinity ammonium transporter of Saccharomyces cerevisiae reveals an extracytosolic N-terminus. Mol. Microbiol., 38, 552–564. Marini, A.M., Vissers, S., Urrestarazu, A. & Andre B. (1994) Cloning and expression of the MEP1 gene encoding an ammonium transporter in Saccharomyces cerevisiae. EMBO J., 13, 3456–3463. Marschner, M. (1995) Mineral Nutrition of Higher Plants, 2nd edn, Academic Press, London. Masclaux, C., Valadier, M.H., Brugiere, N., Morot-Gaudry, J.F. & Hirel, B. (2000) Characterization of the sink/source transition in tobacco (Nicotiana tabacum L.) shoots in relation to nitrogen management and leaf senescence. Planta, 211, 510–518. Matt, P., Geiger, M., Walch-Liu, P., Engels, C. & Stitt, M. (2001) Elevated carbon dioxide increases nitrate uptake and nitrate reductase activity when tobacco is growing on nitrate, but increases ammonium uptake and inhibits nitrate reductase activity when tobacco is growing on ammonium nitrate. Plant Cell Environ., 24, 1119–1137. Melo-Oliveira, R., Oliveira, I.C. & Coruzzi, G.M. (1996) Arabidopsis mutant analysis and gene regulation define a nonredundant role for glutamate dehydrogenase in nitrogen assimilation. Proc. Natl. Acad. Sci. USA, 93, 4718–4723. Menand, B., Desnos, T., Nussaume, L., Berger, F., Bouchez, D., Meyer, C. & Robaglia, C. (2002) Expression and disruption of the Arabidopsis TOR (target of rapamycin) gene. Proc. Natl. Acad. Sci. USA, 99, 6422–6427. Mendel, R.R. (1997) Molybdenum cofactor of higher plants: biosynthesis and molecular biology. Planta, 203, 399–405. Mengel, K. & Kirkby, E. (1987) Principles of Plant Nutrition. International Potash Institute, Bern. Meyer, C. & Caboche, M. (1998) Manipulation of N metabolism. In Transgenic Plant Research (ed. K. Lindsey), Harwood Academic Publishers, London, pp. 125–133. Meyer, C. & Stitt, M. (2001) Nitrate reduction and signalling. In Plant Nitrogen (eds P.J. Lea & Morot-Gaudry J.F.), Springer-Verlag, Berlin, pp. 37–59. Meyer, C. & St¨ohr, C. (2002) Soluble and plasma membrane-bound enzymes involved in nitrate and nitrite metabolism. In Photosynthetic Nitrogen Assimilation and Associated Carbon and Respiratory Metabolism (eds C. Foyer & G. Noctor), Kluwer Academic Publishers, Dordrecht, pp. 49–62. Meynard, J.M., Cerf, M., Guichard, L., Jeuffroy, M.H. & Makowski, D. (2002) Which decision support tools for the environmental management of nitrogen? Agronomie, 22, 817–829. Muller, B. & Touraine, B. (1992) Inhibition of NO− 3 uptake by various phloem-translocated amino acids in soybean seedlings. J. Exp. Bot., 43, 617–623.
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Muller, B., Tillard, P. & Touraine, B. (1995) Nitrate fluxes in soybean seedling roots and their response to amino acids: an approach using 15 N. Plant Cell Environ., 18, 1267–1279. Ni, M. & Beevers, L. (1994) Nitrate-induced polypeptides in membranes from corn seedling roots. J. Exp. Bot., 45, 355–365. Ninnemann, O., Jauniaux, J.C. & Frommer, W.B. (1994) Identification of a high affinity NH4 + transporter from plants. EMBO J., 13, 3464–3471. Oaks, A. & Hirel, B. (1985) Nitrogen assimilation in roots. Annu. Rev. Plant Physiol., 36, 345–365. 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. Okamoto, M., Vidmar, J.J. & Glass, A.D.M. (2003) Regulation of NRT1 and NRT2 gene families of Arabidopsis thaliana: responses to nitrate provision. Plant Cell Physiol., 44, 304–317. Ono, F., Frommer, W.B. & von Wiren, N. (2000) Coordinated diurnal regulation of low-and high-affinity nitrate transporters in tomato. Plant Biol., 2, 17–23. Orsel, M., Filleur, S., Fraisier, V. & Daniel-Vedele, F. (2002a) Nitrate transport in plants: which gene and which control? J. Exp. Bot., 53, 825–833. Orsel, M., Krapp, A. & Daniel-Vedele, F. (2002b) Analysis of the NRT2 nitrate transporter family in Arabidopsis. structure and gene expression. Plant Physiol., 129, 886–896. Pate, J. (1973) Uptake, assimilation and transport of nitrogen compounds by plants. Soil Biol. Biochem., 5, 109–119. Pelsy, F. & Caboche, M. (1992) Molecular genetics of nitrate reductase in higher plants. In Advances in Genetics, Vol 30 (eds J.G. Scandalios & T.R.F. Wright), Academic Press Inc., San Diego, CA, pp. 1–40. Quesada, A., Galvan, A. & Fernandez, E. (1994) Identification of nitrate transporter genes in Chlamydomonas reinhardtii. Plant J., 5, 407–419. Quesada, A., Krapp, A., Trueman, L.J., Daniel-Vedele, F., Fernandez, E., Forde, B.G. & Caboche, M. (1997) PCR-identification of a Nicotiana plumbaginifolia cDNA homologous to the high-affinity nitrate transporters of the crnA family. Plant Mol. Biol., 34, 265–274. Quillere, I., Dufosse, C., Roux, Y., Foyer, C.H., Caboche, M. & Morotgaudry, J.F. (1994) The effects of deregulation of NR gene expression on growth and nitrogen metabolism of Nicotiana plumbaginifolia plants. J.Exp. Bot., 45, 1205–1211. Rawat, S.R., Silim, S.N, Kronzucker, H.J., Siddiqi, M.Y. & Glass, A.D. (1999) AtAMT1 gene expression and NH4 + uptake in roots of Arabidopsis thaliana: evidence for regulation by root glutamine levels. Plant J., 19, 143–152. Rentsch, D., Laloi, M., Rouhara, I., Schmelzer, E., Delrot, S. & Frommer, W.B. (1995) NTR1 encodes a high affinity oligopeptide transporter in Arabidopsis. FEBS Lett., 370, 264–268. Rochat, C. & Boutin, J.P. (1991) Metabolism of phloem-borne amino acids in maternal tissues of fruit of nodulated or nitrate-fed pea plants (Pisum sativum L.). J. Exp. Bot., 235, 207–214. Saier, M.H., Eng, B.H., Fard, S., Garg, J., Haggerty, D.A., Hutchinson, W.J., Jack, D.L., Lai, E.C., Liu, H.J., Nusinew, D.P., Omar, A.M., Pao, S.S., Paulsen, I.T., Quan, J.A., Sliwinski, M., Tseng, T.T., Wachi, S. & Young, G.B. (1999) Phylogenetic characterization of novel transport protein families revealed by genome analyses. Biochim. Biophys. Acta Rev. Biomembr.,1422, 1–56. Sakurai, N., Hayakawa, T., Nakamura, T. & Yamaya, T. (1996) Changes in the cellular localisation of cytosolic glutamine synthetase protein in vascular bundles of rice leaves at various stages of development. Planta, 200, 306–311. Scheible, W.R., Lauerer, M., Schulze, E.D., Caboche, M. & Stitt, M. (1997) Accumulation of nitrate in the shoot acts as a signal to regulate shoot-root allocation in tobacco. Plant J., 11, 671–691. Schjoerring, J.K., Husted, S., Mack, G. & Mattsson, M. (2002) The regulation of ammonium translocation in plants. J. Exp. Bot., 53, 883–890.
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Shelden, M.C., Dong, B., de Bruxelles, G.L., Trevaskis, B., Whelan, J., Ryan, P.R., Howitt, S.M. & Udvardi, M.K. (2001) Arabidopsis ammonium transporters, AtAMT1;1 and AtAMT1;2 have different biochemical properties and functional roles. Plant and Soil, 231, 151–160. Siddiqi, M.Y., Glass, A.D.M., Ruth, T.J. & Fernando, M. (1989) Studies of the regulation of nitrate influx by barley seedlings using 13 NO3 − . Plant Physiol., 90, 806–813. Sommerville, C. & Ogren, W. (1980) Inhibition of photosynthesis in Arabidopsis mutants lacking leaf glutamate synthase activity. Nature, 286, 257–259. Song, W., Steiner, H.Y., Zhang, L., Naider, F., Stacey, G. & Becker, J.M. (1996) Cloning of a second Arabidopsis peptide transport gene. Plant Physiol., 110, 171–178. Sonoda, Y., Ikeda, A., Saiki, S., von Wiren, N., Yamaya, T. & Yamaguchi, J. (2003) Distinct expression and function of three ammonium transporter genes (OsAMT1;1-1;3) in rice. Plant Cell Physiol., 44, 726–734. Suenaga, A., Moriya, K., Sonoda, Y., Ikeda, A., von Wiren, N., Hayakawa, T., Yamaguchi, J. & Yamaya, T. (2003) Constitutive expression of a novel-type ammonium transporter OsAMT2 in rice plants. Plant Cell Physiol., 44, 206–211. Suzuki, A. & Rothstein, S. (1997) Structure and regulation of ferredoxin-dependent glutamate synthase from Arabidopsis thaliana: Cloning of cDNA, expression in different tissues of wild type and gltS mutant strains, and light induction. Eur. J. Bioch., 243, 708–718. Suzuki, A., Burkhart, W. & Rothstein, S. (1996) Nitrogen effects on the induction of ferredoxindependent glutamate synthase and its mRNA in maize leaves under the light. Plant Sci., 114, 83–91. Touraine, B. & Glass, A.D.M. (1997) NO3 − and ClO3 − fluxes in the chl1-5 mutant of Arabidopsis thaliana—Does the CHL1-5 gene encode a low-affinity NO3 − transporter? Plant Physiol., 114, 137–144. Trueman, L.J., Richardson, A. & Forde, B.G. (1996) Molecular cloning of higher plant homologues of the high-affinity nitrate transporters of Chlamydomonas reinhardtii and Aspergillus nidulans. Gene, 175, 223–231. Tsay, Y.F., Schroeder, J.I., Feldmann, K.A. & Crawford, N.M. (1993) The herbicide sensitivity gene CHL1 of Arabidopsis encodes a nitrate-inducible nitrate transporter. Cell, 72, 705–713. Turano, F.J., Thakkar, S.S., Fang, T. & Weisemann, J.M. (1997) Characterization and expression of NAD(H)-dependent glutamate dehydrogenase genes in Arabidopsis. Plant Physiol., 113, 1329–1341. Unkles, S.E., Hawker, K.L., Grieve, C., Campbell, E.I., Montague, P. & Kinghorn, J.R. (1991) cnrA encodes a nitrate transporter in Aspergillus nidulans. Proc. Natl. Acad. Sci. USA, 88, 204–208. Van Diest, A. (1986) Means of preventing nitrate accumulation in vegetable and pasture plants. In Fundamental, Ecological and Agricultural Aspects of Nitrogen Metabolism in Higher Plants (eds H. Lambers, J.J. Neeteson & I. Stulen), Martinus Nijhoff, Dordrecht, pp. 455–471. Venegoni, A., Moroni, A., Gazzarini, S. & Marr`e, M.T. (1997) Ammonium and methylammonium transport in Erigia densa leaves in conditions of different H+ pump activity. Botanica Acta, 110, 369–377. Vincent, R., Fraisier, V., Chaillou, S., Limami, M.A., Deleens, E., Phillipson, B., Douat, C., Boutin, J.P. & Hirel, B. (1997) Overexpression of a soybean gene encoding cytosolic glutamine synthetase in shoots of transgenic Lotus corniculatus L. plants triggers changes in ammonium assimilation and plant development. Planta, 201, 424–433. Vincentz, M. & Caboche, M. (1991) Constitutive expression of nitrate reducatse allows normal growth and development of Nicotiana plumbaginifolia plants. EMBO J., 10, 1027–1035. Vincentz, M., Moureaux, T., Leydecker, M.T., Vaucheret, H. & Caboche, M (1993) Regulation of nitrate and nitrite reductase expression in Nicotiana plumbaginifolia leaves by nitrogen and carbon metabolites. Plant J., 3, 315–324. von Wiren, N., Gojon, A., Chaillou, S. & Raper, D. (2001) Mechanisms and regulation of ammonium uptake in higher plants. In Plant Nitrogen (eds P.J. Lea & J.F. Morot-Gaudry), Springer-Inra, Berlin.
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von Wiren, N., Lauter, F.R., Ninnemann, O., Gillissen, B., Walch-Liu, P., Engels, C., Jost, W. & Frommer, W.B. (2000) Differential regulation of three functional ammonium transporter genes by nitrogen in root hairs and by light in leaves of tomato. Plant J., 21, 167–175. Wallsgrove, R., Turner, J., Hall, N., Kendall, A. & Bright, S (1987) Barley mutants lacking chloroplast glutamine synthetase. Biochemical and genetic analyses. Plant Physiol., 83, 155–158. Wang, M.Y., Siddiqi, M.Y., Ruth, T.J. & Glass, A.D.M. (1993) Ammonium uptake by rice roots II. Kinetics of 13 NH4 + influx across the plasmalemma. Plant Physiol., 103, 1259–1267. Wang, R. & Crawford, N.M. (1996) Genetic identification of a gene involved in constitutive, highaffinity nitrate transport in higher plants. Proc. Natl. Acad. Sci. USA, 93, 9297–9301. Wang, R., Okamoto, M., Xing, X. & Crawford, N.M. (2003) Microarray analysis of the nitrate response in Arabidopsis roots and shoots reveals over 1,000 rapidly responding genes and new linkages to glucose, trehalose-6-phosphate, iron, and sulfate metabolism. Plant Physiol., 132, 556–567. Wang, Y.H., Garvin, D.F. & Kochian, L.V. (2001) Nitrate-induced genes in tomato roots. Array analysis reveals novel genes that may play a role in nitrogen nutrition. Plant Physiol., 127, 345–359. Wilkinson, J.Q. & Crawford, N.M. (1993) Identification and characterization of a chlorate-resistant mutant of Arabidopsis thaliana with mutations in both nitrate reductase structural genes NIA1 and NIA2. Mol. Gen. Genet., 239, 289–297. Williams, L.E. & Miller, A.J. (2001) Transporters responsible for the uptake and partitioning of nitrogenous solutes. Annu. Rev. Plant Physiol. Plant Mol. Biol., 52, 659–688. Wolt, J. (1994) Soil Solution Chemistry: Applications to Environmental Science and Agriculture. Wiley, New York. Yu, X.D., Sukumaran, S. & Marton L. (1998) Differential expression of the Arabidopsis Nia1 and Nia2 genes. Cytokinin-induced nitrate reductase activity is correlated with increased Nia1 transcription and mRNA levels. Plant Physiol., 116, 1091–1096. Zhang, H. & Forde, B.G. (1998) An Arabidopsis MADS box gene that controls nutrient-induced changes in root architecture. Science, 279, 407–409. Zhou, J.J., Fernandez, E., Galvan, A. & Miller, A.J. (2000) A high affinity nitrate transport system from Chlamydomonas requires two gene products. FEBS Lett., 466, 225–227. Zhou, J.J., Theodoulou, F.L., Muldin, I., Ingemarsson, B. & Miller, A.J. (1998) Cloning and functional characterization of a Brassica napus transporter that is able to transport nitrate and histidine. J. Biol. Chem., 273, 12017–12023. Zhuo, D.J., Okamoto, M., Vidmar, J.J. & Glass, A.D.M (1999) Regulation of a putative high-affinity nitrate transporter (Nrt2;1At) in roots of Arabidopsis thaliana. Plant J., 17, 563–568.
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Potassium Sabine Zimmermann and Isabelle Ch´erel
2.1 Introduction The essential macronutrient K+ is the most abundant cation within the plant cell, typically comprising 3–5% of its dry weight (Marschner, 1995). Potassium is accumulated in the cytosol of plant cells at high concentrations (steady-state [K+ ]cyt concentrations of 60–150 mM; Leigh & Jones, 1984; Schroeder et al., 1994; Walker et al., 1996). A high cytosolic K+ :Na+ ratio contributes to salinity tolerance (Chow et al., 1990; Zhu et al., 1998), whilst K+ also protects the plant during water deficiency (Gupta et al., 1989). Potassium uptake into cells is driven by the negative membrane potentials of plant cells (–120 to –220 mV; Sussmann & Harper, 1989), and by the activity of (i) inwardly rectifying K+ channels at high (millimolar) external K+ concentration ([K+ ]ext ) and (ii) transporters that are energised by the electro-chemical gradient of a counterion at low (micromolar) [K+ ]ext . Within the plant cell, compartments in addition to the cytosol impact on K nutrition, including the vacuole(s) which occupies a substantial proportion of the cell volume, as well as the chloroplasts, mitochondria and nuclei. Vacuoles play an important role in buffering the cytosolic K+ concentration ([K+ ]cyt ) in addition to storing K+ and other solutes (Hedrich & Schroeder, 1989). The K+ concentration differs between cellular compartments, cell types, tissues and plant species. For example, [K+ ]cyt varies between 73 and 448 mM in stomatal complexes of the leaf epidermis of Commelina communis, which provides an important gradient between the epidermis and the guard cell which inverts upon stomata closure (Penny & Bowling, 1974). The apoplast may also play a role in the transport of nutrients, including K+ , which should not be neglected (Sattelmacher, 2001). Potassium is a key factor in the osmoregulation of cell turgor, and the interplay between cell turgor and the surrounding cell wall maintains the rigidity of plant tissues. During growth, the uptake of K+ is the basic driving force for increases in cell volume, and thus, K+ dominates the control of plant water relations (Davies & Zhang, 1991). Within the plant, K+ is redistributed from older to younger tissues (Mengel & Kirkby, 1987) indicating its important role in growth. For example, the activation of inwardly rectifying K+ channels contributes to an increase in cell turgor leading to cell elongation during the growth of root hairs (Lew, 1991). However, by studying the uptake of K+ in growing leaf mesophyll cells, Stiles and Van Volkenburgh (2004) have recently suggested
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that the primary role of K+ is to provide electrical counterbalance to H+ efflux, not to drive solute accumulation. Apart from sustained osmotic changes, plant cells have to adapt their turgor and volume rapidly, and reversibly, in response to the environment. Ionic fluctuations and their associated changes in water fluxes bring about these changes. For example, stomatal movements represent an important regulatory mechanism for CO2 and water exchange of plants, which are mainly driven by K+ fluxes across the plasma membrane (Fisher, 1968; Tallman, 1992). Leaf movements in Samanea saman, Dionaea muscipula or Mimosa pudica are mediated by a rapid volume change of specialised cells, which is triggered by K+ channel activities (Iijima & Hagiwara, 1987; Moran et al., 1988). Within the cytosol, K+ is essential for charge balance and to maintain hydration and conformation of proteins, and thus, for the proper functioning of enzymes (Leigh & Jones, 1984; Walker et al., 1998). Potassium also plays a role in membrane transport processes and phloem translocation of assimilates (Patrick et al., 2001). Further, changes in the cytosolic and/or apoplastic distribution of K+ play an important role in the anion neutralisation needed for the maintenance and/or modulation of the membrane potential (Maathuis & Sanders, 1996; Leigh, 2001). In turn, this membrane potential is involved in the control of the activity of a broad range of transport and signalling processes. Variations of the [K+ ]cyt are not only coupled with K+ fluxes across the plasma membrane but also with fluxes between cytosol and vacuole(s). Within this context, it is not surprising that transport systems mediating K+ fluxes across the different cellular membranes are represented in the genomes of plants by a high number of different families and functional transporter types. This chapter provides an overview of the physiology of K+ transport in plants and the regulation of K+ transport at the molecular level. The chapter will describe some of the techniques used to study K+ nutrition, and it will outline how knowledge obtained from studies of model species such as Arabidopsis might be useful in an applied context. 2.2 Physiology of K+ transport All plants require K for their growth, and K+ transport has been well studied at a physiological level. The following section provides a review of physiological studies, which have led to a functional identification of K+ transport systems, particularly in roots, the xylem and phloem, and guard cells. 2.2.1 Functional identification of K+ currents The first analyses of ionic currents across plant plasma membranes were made by Cole and Curtis (1938), who studied action potentials on Nitella, during
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winter months when it was not possible to work on squid axons. A ‘dual mechanism’ of K+ uptake by plant roots, which proposed the joint action of highand low-affinity transport processes, was described in the early 1960s (Epstein et al., 1963). However, modern electrophysiological methods and the development of subsequent molecular techniques have since advanced our knowledge of K+ transport significantly. In principle, one has to distinguish between different components of K+ transport mechanisms, such as (i) pumps, which transport substrates against their electrochemical gradient and which are energised by metabolites, (ii) carriers, proteins that undergo conformational changes during the transport of a substrate against its concentration gradient by coupling the transport to a second ion with an energy gradient more favourable and finally (iii) ion channels, often highly regulated proteins mediating a high turnover of ions through a pore (106 –108 ions s−1 ). Significant progress in studying ionic currents across plant membranes was made in the mid 1980s (Moran et al., 1984; Schroeder et al., 1984) by applying the high-resolution, biophysical, ‘patchclamp’ method (Hamill et al., 1981). In particular, inwardly and outwardly rectifying K+ channel currents were detected across the plasma membrane in most of the species and cell types studied (including guard cell, mesophyll, xylem, stem and root tissues), which have since been analysed intensively (Hedrich & Schroeder, 1989; Lew, 1991; Blatt, 1992; Wegner & Raschke, 1994; Maathuis et al., 1997). These inwardly and outwardly rectifying K+ channels are highly selective for K+ . In addition to voltage-dependent K+ channels, non-selective cation conductances have also been measured suggesting the presence of other types of channel (Demidchik et al., 2002). For example, tonoplast cation transport seems to be dominated by non-selective channels. In the plasma membrane, voltageindependent, non-selective cation channels (NSCCs) conduct K+ with the highest permeability for a broad range of monovalent cations characterised thus far, for example, in Arabidopsis root protoplasts (Demidchik & Tester, 2002). The physiological roles of NSCCs are not yet clear. Plasma membrane K+ channels are the best characterised plant transport system so far, because of (i) the better accessibility of the plasma membrane compared with internal membranes and (ii) their stronger currents compared with those mediated by different transporter types. However, ion fluctuations by other transporters and across all other cellular membranes are presumably of the same physiological importance as channel-mediated transport across the plasma membrane. 2.2.2 Potassium uptake by roots This section focuses on the functional analyses of channels and transporters, which are responsible and necessary for K+ uptake from the soil and its distribution throughout the plant. Sessile plants are very dependent on their environment,
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so they must develop specialised organs and tissues and adapt to various environmental conditions. Plant roots are well suited to exploit the soil for mineral nutrients and to supply them to the whole plant. The level of soil K+ is one of the limiting factors determining the uptake of this ion by the roots. Availability of K+ to the plant depends on its form, and decreases in the order: solution > 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 (L¨uttge & 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; Br¨uggemann 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; V´ery & 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 (M¨aser 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; Lain´e 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 (V´ery 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. <100 M, and in the presence of NH4 + in the external medium, the growth of akt1-1 was affected, indicating that AKT1 might be (surprisingly) involved in K+ uptake in the low concentration range (Hirsch et al., 1998; Spalding et al., 1999). More recently, Desbrosses et al. (2003) observed that root hair length was also affected in this mutant. Intriguingly, in the akt1-1 mutant the T-DNA insertion deletes the entire C-terminal KHA domain, which has been shown to mediate channel clustering (Ehrhardt et al., 1997) and thus perhaps directing (together with other interacting proteins) channel localisation within the membrane (Gomperts, 1996; Ponting et al., 1997). The number of properly localised K+ channels might have a significant influence on K+ uptake efficiency since the root meets thermodynamically unfavourable conditions at [K+ ]ext of <0.3–1 mM (Kochian & Lucas, 1993; Maathuis & Sanders, 1993; Schroeder et al., 1994). Ion channel clustering might also prevent a fast internalisation of this membrane protein in the wild type (Hille, 1992), thus rendering a stable and long lasting expression of the channel protein within the plasma membrane less favourable in the akt1-1 mutant. High-affinity K+
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uptake by this ion channel underlines the weakening of the former distinction between channel and transporter activity. SKOR, the stelar K+ outward rectifier, was the first plant outwardly rectifying channel identified (Gaymard et al., 1998), making the understanding of inward or outward gating of channels with very similar sequences even more puzzling. The expression of SKOR in pericycle and xylem parenchyma cells in roots surrounding xylem vessels evoked the hypothesis that SKOR is involved in K+ loading of the xylem. SKOR activity corresponded to the outward K+ conductance found in xylem parenchyma cells characterised as KORC (De Boer, 1999) and phenotypic analyses of the corresponding knockout mutant skor confirmed this role for K+ release into the xylem sap. Further, treatment with abscisic acid (ABA) decreased SKOR transcript abundance markedly, suggesting a control of K+ translocation from roots towards shoots during drought. However, other transport proteins must also be implicated in this process because the SKOR deletion only partially reduces xylem K+ concentrations. A further functional type of the plant Shaker channels in Arabidopsis is AKT2/3 (the compound name is due to the finding of different transcript length by different authors), which shows weak rectification upon expression in heterologous systems (Marten et al., 1998). This channel, and its homologues in bean and maize, might be responsible for phloem unloading (Bauer et al., 2000; Lacombe et al., 2000; Ache et al., 2001). Interestingly, loss of this gene in the corresponding knockout mutant affects sugar loading into the phloem, underlining the tight connection between different transport processes (Deeken et al., 2002). Other Shaker channel proteins from Arabidopsis participate in guard cell currents (the inward rectifier KAT2, Pilot et al., 2001; and the outward rectifier GORK, Hosy et al., 2003) and in pollen tube growth (SPIK, Mouline et al., 2002; [=AKT6, M¨aser et al., 2001]). The disruption of SPIK results in strongly decreased inwardly rectifying K+ currents as well as in impaired pollen tube growth in in vitro pollen germination assays. This suggests that SPIK plays a crucial role in K+ uptake during growth of the pollen tube. As a consequence, the disruption of the ion channel gene in the spik mutant decreased the probability of fertilisation by mutant pollen by a factor of ca. 1.6 compared with wild-type pollen. AtKC1 [=KAT3, M¨aser et al., 2001] has recently been found to function as a regulatory subunit of AKT1, which is silent upon sole expression in heterologous systems (Reintanz et al., 2002; Duby & Sentenac, unpublished observations). The function of AKT5, which is expressed in flowers, remains to be elucidated. Similarly, functions of Shaker-like channels have also been characterised from other plant species (Pilot et al., 2003b) such as potato (KST1, M¨uller-R¨ober et al., 1995; SKT1, Zimmermann et al., 1998), tomato (LKT1, Hartje et al., 2000), maize (ZmK1 and ZmK2, Philippar et al., 1999), carrot (KDC1, Downey et al., 2000) and Samanea saman (Moshelion et al., 2002). Homologues have been identified also in rice (OsKAT1), wheat (TaAKT1) and grapevine (SOR and SIRK). Clearly, the family of plant Shaker channels plays
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a significant role in sustained K+ transport involved in nutrition, growth and cellular movements. 2.3.2 KCO channel family The first member of the family of ‘two-pore channels’, KCO1, was discovered via the conserved K+ -specific pore region amino acid sequence GYGD (Czempinski et al., 1997a), pointing to a second K+ channel family in planta. Further, homologous genes have been identified in Arabidopsis and potato (Czempinski et al., 1997b). The Arabidopsis KCO family comprises six members, five of which have a structure containing four transmembrane spanning domains embedding two pore motifs and a single member (KCO3), which has only two transmembrane domains embedding one pore motif. By sequence comparison, it seems that KCO3 is deleted in the middle of the protein and therefore its structure resembles the animal inwardly rectifying K+ (IRK)-type channels (Czempinski et al., 1999). The structure of the five other KCOs and comparison to the Shaker channels indicate that two subunits might be sufficient to form a complete pore. The presence of Ca2+ -binding motifs (EF-hands) in the C-terminus of KCOs is a unique feature of plant ‘two-pore’ K+ channels compared with animal homologues. Functionality of the EF-hands has been proven by Ca2+ -dependent mobility shift assays (Czempinski et al., 1999). KCO1elicited, Ca2+ -dependent, outwardly directed K+ currents, observed during electrophysiological studies on Baculovirus-infected insect cells, remain to be confirmed by independent experiments (Czempinski et al., 1997a). Unfortunately, heterologous expression of KCO1 in Xenopus oocytes provokes a reduced viability of the injected oocytes. Interestingly, localisation experiments in planta demonstrating expression of AtKCO1 in the vacuolar membrane (Czempinski et al., 2002; Sch¨onknecht et al., 2002) suggest that KCOs play a role, different from that of the Shaker-like channels, in K+ transport processes across intracellular membranes. Analysis of corresponding knockout mutants will provide more insight into their physiological role in the near future. 2.3.3 KUP/HAK/KT family The largest gene family of K+ transporters (KTs) was originally described in bacteria (KUPs = K+ uptake permeases; Schleyer & Bakker, 1993) and in the soil-borne fungus Schwanniomyces occidentalis (HAKs = high-affinity K+ transporters; Ba˜nuelos et al., 1995). It was later discovered that these transporters include large multigene families in both dicot and monocot plants (Arabidopsis, barley, maize, rice, soybean, tomato, cotton, onion, poplar, ice plant; Quintero & Blatt, 1997). Thirteen members have been found in the Arabidopsis genome (Ahn et al., 2004). Arabidopsis mutants have been identified for two KUP genes, AtKUP4 (trh = tiny root hair; Rigas et al., 2001) and AtKUP2 (shy3-1 = short hypocotyl; Elumalai et al., 2002). Both mutations affect cell
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elongation indicating a role for KUP transporters in plant growth and development. Expression of the KUPs in roots (Kim et al., 1998; Rubio et al., 2000; Ba˜nuelos et al., 2002; Wang et al., 2002) and their function in high-affinity K+ uptake upon heterologous expression in yeast and Arabidopsis cells suggest a role in root K+ uptake (Fu & Luan, 1998). Evidence has also been obtained for their role in salt stress (Su et al., 2002), and TRH1 has been demonstrated to be required for root hair tip growth (Desbrosses et al., 2003). Recently, results from a large study by Ahn et al. (2004) to determine the spatial and temporal expression pattern of each AtKT/KUP gene and functional characterisation in Escherichia coli, have supported the hypothesis that the KUPs play an important role in K+ uptake by root hairs. Ten members of the family have been found in root hairs and one (AtHAK5) is up-regulated upon K+ shortage. In the root hair mutant trh1-1 the biomass is decreased only at low K+ concentrations but uptake rates, measured with Rb+ as a tracer for K+ , are lower in the mutant at all Rb+ concentrations. Functional complementation of a yeast strain or an E. coli mutant deficient in K+ uptake by the transporters AtKT/KUP1, 2, 4 and AtHAK5 had previously provided evidence that these proteins transported K+ (Quintero & Blatt, 1997; Fu & Luan, 1998; Kim et al., 1998; Rubio et al., 2000; Rigas et al., 2001; Elumalai et al., 2002). Ahn et al. (2004) have shown K+ transport in E. coli for five further members (AtKT/KUP5-7, 10, 11). The membrane localisation of the KUPs remains unknown so far, although plasma membrane as well as vacuolar localisations have been proposed (Senn et al., 2001). The K+ transporters of the KUP-family clearly play an essential role in high-affinity K+ uptake in plant nutrition and even single gene mutants cause phenotypic changes. However, the high number of transporters expressed in root hairs remains puzzling but might indicate functional redundancy to ensure vital K+ uptake. 2.3.4 K+ /H+ antiporters Within Arabidopsis, a large superfamily of cation antiporters has been identified, encoding proteins with 10–14 predicted transmembrane domains (M¨aser et al., 2001). These are only just beginning to be characterised. Amongst these cation transporters, one family called CPA2 (= cation:proton antiporter) has been identified by homology with K+ exchange antiporters harbouring six genes in Arabidopsis (KEA1-6). In bacteria, transporters of this type are involved in defence against toxic electrophiles by mediating acidification of the cytosol. In planta, their role is speculated to be in K+ homeostasis by loading K+ into vacuoles or other acidic compartments. To support such hypotheses, functional characterisation in heterologous systems as well as localisation of these proteins in planta are needed. 2.3.5 Trk/HKT The family of K+ permeable transporters of the Trk/HKT type has been characterised in detail in yeast (Trk1 and 2, Gaber et al., 1988; Ko & Gaber, 1991).
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The prediction of the structure of proteins of this family has been corrected during the past few years, abandoning the old model of 10 to 12 transmembrane domains. Resolution of the crystal structure of bacterial K+ channels has led to the assumption that Trk/HKT proteins are formed by four repetitive motifs, each composed of two transmembrane domains surrounding one pore-like domain (Durell & Guy, 1999; Durell et al., 1999; Zeng et al., 2004). In plants, homologues of the yeast Trk have been identified, called HKTs and described as K+ –Na+ co-transporters (see Chapter 6). This family is represented in Arabidopsis by only a single gene (AtHKT1) that functions as Na+ transporter. Analyses of Arabidopsis mutants sas2-1 and sas2-2, which are affected in salt sensitivity, have demonstrated that AtHKT1 is involved in the re-circulation of Na+ from shoot to root (M¨aser et al., 2002; Berthomieu et al., 2003). The wheat HKT1 was the first plant K+ transporter to be cloned (Schachtman & Schroeder, 1994). The role of HKT1 in high-affinity K+ uptake in wheat and barley has been inferred from its localisation in root cortical cells and from expression studies showing an up-regulation of HKT1 upon K starvation together with an increase of activity (Wang et al., 1998). Functional characterisation in yeast and oocytes demonstrated Na+ -coupled K+ transport (Rubio et al., 1995). Other HKT homologues may be energised by H+ rather than Na+ (Schachtmann & Liu, 1999). In the tall Eucalyptus tree (whose height can exceed 50 m), the substantial demand for K+ is reflected by the finding of two HKT proteins, EcHKT1 and EcHKT2, both complement the K+ -limited growth of an E. coli K+ -uptake-deficient triple mutant (Fairbairn et al., 2000). 2.3.6 CNGC family The family of plant ion channels with a similar structure to that of the Shaker-like channels, but harbouring overlapping cyclic nucleotide-binding and calmodulinbinding domains within their C-termini, has been named by analogy to their animal counterparts CNGCs (cyclic nucleotide-gated channels). The first members were found in barley (Schuurink et al., 1998) and in Arabidopsis (K¨ohler & Neuhaus, 1998). Sequencing of the Arabidopsis genome revealed that Arabidopsis possesses 20 genes encoding CNGCs. In contrast to the Shaker-like and KCO channels, CNGCs have no signature of a K+ selective pore and are presently assumed to function as NSCCs, taking part in signalling processes (reviewed by Talke et al., 2003). So far, there is no obvious specific role for CNGCs in K nutrition, although some Arabidopsis CNGCs have been shown to be permeable to K+ . 2.3.7 Redundancy and specificity The identification of many genes encoding proteins involved in K+ transport raises the question: why does a plant need so many transporters for one specific ion? First, there is the need to transport K+ from the soil into the plant
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and to distribute it throughout the plant, crossing both cellular and organellular membranes. Different transporters, which are regulated differently and which are localised to a variety of membranes, offer various substrate specificity and affinity and can therefore contribute to this role. Various transporters allow differential transcriptional responses to signals, including the plant/cell content of the transported mineral element. It can be speculated that ‘redundancy for security’ can operate for a function (gene/protein) in case of need. In addition, composition of multimeric channels by different subunits will multiply regulatory possibilities by heteromerisation. Overall, the high number of transport proteins allows each cell and organelle to be optimally equipped and adapted to a specific membrane protein composition enabling fine-tuning of transport processes. Regulation of all these different K+ conductances by potential, ionic conditions, pH, nucleotides, hormones, other interacting proteins, or by control of their expression level have been reported extensively indicating their putative involvement in physiological functions. 2.3.8 From Arabidopsis to grapevine: potassium transport and wine quality Potassium plays an essential role for grapevine growth and yield as well as in wine quality (reviewed by Mpelasoka et al., 2003). Grape berries function as a strong sink for K+ , especially during the onset of ripening (‘veraison’) and volume increase, thus their juice contains this ion as a major cation. However, too high a K+ concentration decreases free acids, increases overall pH, lowers the tartrate:malate ratio, increases the tartrate precipitation during wine-making and reduces the overall wine quality (Somers, 1977). The K+ concentration is generally higher in the skin of the berry, where the anthocyanins are located, than in the fleshy pericarp (pulp) (Iland & Coombe, 1988). Thus, K+ concentrations are more critical for the fermentation of red wine than of white wine because of longer fermentation times of the berry skins to extract the anthocyanins for the red colour. An optimal pH of red wine should be in the order of 3.3–3.7, requiring careful calibration of vineyard management options to manipulate the best K+ concentration. Research has been made recently to understand molecular mechanisms intrinsic to the plant for this important equilibrium of K+ . The sink strength of grape berries for K+ and other solutes during ripening is probably controlled at the phloem unloading step (Coombe, 1992). The only molecular mechanism of K+ transport described to date in grape berries is an ion channel, SIRK (Shaker-like inwardly rectifying K+ channel; Pratelli et al., 2002). SIRK is expressed in several tissues, but it is predominantly (and timedependently) expressed in berries before veraison; thus implying a role in berryripening. In addition, the expression of two KUP transporters in grape berries has recently been reported (Mpelasoka et al., 2003). These two transporters are also highly expressed before veraison and their expression is restricted to the
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PLANT NUTRITIONAL GENOMICS
berry skin where the highest K+ concentrations have been found. However, the detailed role of these transporters has still to be ascertained. More knowledge about the molecular mechanisms regulating K distribution in vine plants and in their grape berries will provide insight for a better management of fertilisation and irrigation, selection of rootstock/scion combination, breeding or even for harvest and the following vinification process. Genetic improvements targeting the production of K+ use efficient varieties having high yields with low K+ concentration in berries, might also be beneficial. 2.4 Regulation of K+ transport In addition to the cellular complement of different K+ transporters, all of these transporters are regulated in a complex manner, thereby maximising the capacity of the cell to fine-tune ion transport. Adjustment of the transcript level is followed by precise control of the protein targeting and finally complemented by modulation of the protein function throughout a number of cytosolic factors and/or interactions with other proteins. The regulation of ion transport processes has been the focus of interest for a long time, especially at the functional level (reviewed by: Czempinski et al., 1999; Zimmermann et al., 1999; V´ery & Sentenac, 2003; Ch´erel, 2004). Recently, due to the development of efficient methods and techniques, knowledge has been accumulated on the expression of genes and cellular localisation of proteins. The study of protein trafficking and targeting mechanisms will be one of the most exciting fields in the near future and will certainly complement our knowledge about K+ transport regulation (Hurst et al., 2004). This section will summarise the recent data on transcriptional and post-translational regulation of K+ transporters (Fig. 2.1). 2.4.1 Transcriptional regulation Members of different Arabidopsis K+ transporter families have been analysed according to their expression pattern in different plant organs and tissues (Table 2.1). In addition to the different ion channels and transporters, a putative regulatory subunit of Shaker channels, KAB1 (Tang et al., 1996, 1998) has been included to search for a possible co-regulation with channel subunits. Expression patterns of these transport proteins have been classically studied in experiments by Northern blot, in situ hybridisation, RT-PCR or by promoter-GUS (-glucuronidase) fusion. Recently, high-throughput microarray techniques (see Chapter 8) and transcript counting techniques have allowed results to be obtained rapidly for global gene expression patterns depending on tissue, developmental stage and/or environmental conditions. Serial sequencing experiments of small but sufficient tags to identify the individual genes within the Arabidopsis genome (SAGE = serial analysis of gene expression, Fizames et al., 2004;
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POTASSIUM
Table 2.1 Localisation of Arabidopsis genes selected for an in silico expression analysis. References are given by numbers. 1: http://allometra.com/ath fasta mpss shtml (data >4 transcripts per million); 2: Talke et al., 2003; 3: Lagarde et al., 1996; 4: Marten et al., 1999; 5: Lacombe et al., 2000; 6: Gaymard et al., 1998; 7: Ache et al., 2000; 8: Deeken et al., 2000; 9: Reintanz et al., 2002; 10: Pilot et al., 2001; 11: Pilot et al., 2003a; 12: Sch¨onknecht et al., 2002; 13: Rus et al., 2001; 14: Mouline et al., 2002; 15: Nakamura et al., 1995; 16: Philippar et al., 2004; 17: Ivashikina et al., 2003; 18: Szyroki et al., 2001; 19: Ivashikina et al., 2001; 20: Becker et al., 2003; 21: Czempinski et al., 2002; 22: Ahn et al., 2004; 23: Berthomieu et al., 2003; 24: Kim et al., 1998; 25: Fu et al., 1998; 26: Uozumi et al., 2000; 27: K¨ohler et al., 2001; 28: Leonhardt et al., 2004. s: shoot ; r: root ; l: leaf; st: stem; fl: flower; sil: silique; gc: guard cells; mc: mesophyll cells; rh: root hairs. Organ-specific expression MPSS
Tissue-specific expression
Northern blot, RT-PCR
GUS, in situ hybridisation, RT-PCR, microarray
r, low in leaves (3, 5)
r (cortex + epiderm), rh (19), hydathodes (3) high in phloem tissues (4, 5), companion cells (17) low in leaf epiderm, mc, gc (5) pollen (14)
Name
AGI code
AKT1
At2g26650
AKT2
At4g22200
fl, l, sil (1)
l, st, fl (4, 5, 8)
SPIK AKT5 KAT1
At2g25600 At4g32500 At5g46240
sil, fl (1) fl, l, sil (1)
fl (5) fl (5) l, st, fl (5, 10)
KAT2 AtKC1
At4g18290 At4g32650
SKOR
At3g02850
r (1)
r (5, 6)
GORK
At5g37500
sil, fl, r, cal (1)
st (5, 7), l, fl (7)
KCO1
At5g55630
r, l, sil, fl, cal (1)
l, r (12)
KCO2 KCO3 KCO4 KCO5 KCO6
At5g46370 At5g46360 At1g02510 At4g01840 At4g18160
fl, cal, r, sil, l (1) r, sil, fl, cal, l (1)
AtHKT1 AtKup1
At4g10310 At2g30070
cal, sil, fl, l, r (1)
AtKup2
At2g40540
cal, fl, sil, l, r (1)
AtKup3
At3g02050
fl, cal, sil, l, r (1)
AtKup4
At4g23640
fl, sil, r, cal, l (1)
fl, l, st (5, 10) r (5, 9), l (5)
gc (15, 16, 28), etiolated hypocotyl, st (16) phloem companion cells (17) gc, phloem (18, 10) r (endoderm + cortex + epiderm + rh) (9, 11) trichomes, hydathodes (11) xylem parenchyma, pericycle (6), pollen (14) gc (7, 20), rh (19), whole root (20) leaf vasculature (20) mc, gc (12, 28), root stele, sepals, pollen (21)
r (12)
l (12) l, r (12) r (mainly), s (13, 26) r, st (25); l, fl, st, r (24); sil, l, fl, r (22) sil, fl, l, r (22), st, l fl, r (24) sil, l, fl, r (22), low and ubiquitous (24) sil, l, fl, r (22), low and ubiquitous (24)
mc, gc (12), phloem companion cells (17) phloem (23)
(Continued)
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November 5, 2004
PLANT NUTRITIONAL GENOMICS (Continued) Organ-specific expression
Name
AGI code
MPSS
AtKup5 AtKup6 AtKup7 AtKup8 AtKup9 AtKup10 AtKup11 AtKup12 AtHAK5 KEA1 KEA2 KEA3 KEA4 KEA5 KEA6 CNGC1 CNGC2
At4g33530 At1g70300 At5g09400 At5g14880 At4g19960 At1g31120 At2g35060 At1g60160 At4g13420 At1g01790 At4g00630 At4g04850 At2g19600 At5g51710 At5g11800 At5g53130 At5g15410
l, r, fl, sil, cal (1) r (1) cal, r, sil, l, fl (1) r, fl, l, sil (1)
CNGC3 CNGC4 CNGC5 CNGC6 CNGC7 CNGC8 CNGC9 CNGC10 CNGC11 CNGC12 CNGC13 CNGC14 CNGC15 CNGC16 CNGC17 CNGC18 CNGC19 CNGC20 KAB1
At2g46430 At5g54250 At5g57940 At2g23980 At1g15990 At1g19780 At4g30560 At1g01340 At2g46440 At2g46450 At4g01010 At2g24610 At2g28260 At3g48010 At4g30360 At5g14870 At3g17690 At3g17700 At1g04690
fl, l, sil (1) r, l, fl (1) l, sil, fl (1) cal, fl, r, l, sil (1)
Northern blot, RT-PCR
Tissue-specific expression GUS, in situ hybridisation, RT-PCR, microarray
l, sil, r (22) l, sil, r, fl (22) l, sil, r, fl (22) l, fl, r, sil (22) l, sil, fl (22) l, r (22) old leaves (low) (22) l (22) l, sil, fl (low) (22)
mc (28) r, fl, sil, cal, l (1) fl, sil, r, l, cal (1) cal, fl, r, sil, l (1) fl, sil, s, r (2) l, fl, sil, r (1) l, cal, r (1) fl, l (1) l, fl (1) ubiquitous (1) not detected (2) fl (1), ubiquitous (2) fl, cal, sil (1) r, cal, l (1) l (>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
BY037-Broadley-v1.cls
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.
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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)
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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)
BY037-Broadley-v1.cls
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
Ca starvation (29-r) microarray
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
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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
BY037-Broadley-v1.cls
Northern, RT-PCR
Table 2.2
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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)
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(−) 4.2x, 37-wp) (−) (4.3x, 37-wp) (−) (28x, 37-wp)
nsc (28-gc), (+) (28-mc) non rep (28-gc)
(−) (4.2x, 37-wp)
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CNGC2
nsc (30-r)
KEA5
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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)
Ca starvation (29-r) microarray
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
BY037-Broadley-v1.cls
Table 2.2
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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
BY037-Broadley-v1.cls
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.
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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; V´ery & Sentenac, 2003; Ch´erel, 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 (Ch´erel 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 & D¨orffling, 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 (Ba˜nuelos 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 Herv´e 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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Armstrong, C.M. & Hille, B. (1998) Voltage-gated ion channels and electrical excitability. Neuron, 20, 371–380. Ba˜nuelos, M.A., Klein, R.D., Alexander-Bowman, S.J. & Rodr´ıguez-Navarro, A. (1995) A potassium transporters of the yeast Schwanniomyces occidentalis homologous to the Kup system of Escherichia coli has a high concentrative capacity. EMBO J., 14, 3021–3027. Ba˜nuelos, M.A., Garciadeblas, B., Cubero, B. & Rodr´ıguez-Navarro, A. (2002) Inventory and functional characterization of the HAK potassium transporter of rice. Plant Physiol., 130, 784–795. Bauer, C.S., Hoth, S., Haga, K., Philippar, K., Aoki, N. & Hedrich, R. (2000). Differential expression and regulation of K+ channels in the maize coleoptile: molecular and biophysical analysis of cells isolated from cortex and vasculature. Plant J., 24, 139–145. Becker, D., Hoth, S., Ache, P., Wenkel, S., Roelfsema, M.R., Meyerhoff, O., Hartung, W. & Hedrich, R. (2003) Regulation of the ABA-sensitive Arabidopsis potassium channel gene GORK in response to water stress. FEBS Lett., 554, 119–126. Berthomieu, P., Con´ej´ero, 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., V´ery, 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. Birnbaum, K., Shasha, D.E., Wang, J.Y., Jung, J.W., Lambert, G.M., Galbraith, D.W. & Benfey, P.N. (2003) A gene expression map of the Arabidopsis root. Science, 302, 1956–1960. Blatt, M.R. (1990) Potassium channel currents in intact stomatal guard cells. Rapid enhancement by abscisic acid. Planta, 180, 445–455. Blatt, M.R. (1991) Ion channel gating in plants: physiological implications and integration for stomatal function. J. Membr. Biol., 124, 95–112. Blatt, M.R. (1992) K+ channels of stomatal guard cells. Characteristics of the inward rectifier and its control by pH. J. Gen. Physiol., 99, 615–644. Blatt, M.R. & Thiel, G. (1993) Hormonal control of ion channel gating. Annu. Rev. Plant Physiol. Plant Mol. Biol., 44, 543–567. Blum, D.E., Elzenga, J.T.M., Linnemeyer, P.A. & Van Volkenburgh, E. (1992) Stimulation of growth and ion uptake in bean leaves by red and blue light. Plant Physiol., 100, 1968–1975. Bohra, J.S. & D¨orffling, K. (1993) Potassium nutrition of rice (Oryza sativa L.) varieties under NaCl salinity. Plant Soil, 152, 299–303. Brenner, S., Johnson, M., Bridgham, J., Golda, G., Lloyd, D.H., Johnson, D., Luo, S., McCurdy, S., Foy, M., Ewan, M., Roth, R., George, D., Eletr, S., Albrecht, G., Vermaas, E., Williams, S.R., Moon, K., Burcham, T., Pallas, M., DuBridge, R.B., Kirchner, J., Fearon, K., Mao, J. & Corcoran, K. (2000) Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nat. Biotechnol., 18, 630–634 Br¨uggemann, L., Dietrich, P., Becker, D., Dreyer, I., Palme, K. & Hedrich, R. (1999) Channel-mediated high-affinity K+ uptake into guard cells from Arabidopsis. Proc. Natl. Acad. Sci. USA, 96, 3298– 3302. Buschmann, P.H., Vaidyanathan, R., Gassmann, W. & Schroeder, J.I. (2000) Enhancement of Na+ uptake, time-dependent inward-rectifying K+ currents, and K+ channel transcripts by K+ starvation in wheat root cells. Plant Physiol., 122, 1387–1397. Chalot, M., Javelle, A., Blaudez, D., Lambilliotte, R., Cooke, R., Sentenac, H., Wipf, D. & Botton, B. (2002) An update on nutrient transport process in ectomycorrhizas. Plant Soil, 244, 165–175. Ch´erel, I. (2004) Regulation of K+ channel activities in plants: from physiological to molecular aspects. J. Exp. Bot., 55, 337-351. Ch´erel, I., Michard, E., Platet, N., Mouline, K., Alcon, C., Sentenac, H. & Thibaud, J.-B. (2002) Physical and functional interaction of the Arabidopsis K+ channel AKT2 and phosphatase AtPP2CA. Plant Cell, 14, 1133–1146. Chow, W.S., Ball, M.C. & Anderson, J.M. (1990) Growth and photosynthetic responses of spinach to salinity: implications of K+ nutrition for salt tolerance. Aust. J. Plant Physiol., 17, 563–578.
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Claussen, M., L¨uthen, H., Blatt, M. & B¨ottger, M. (1997) Auxin-induced growth and its linkage to potassium channels. Planta, 201, 227–234. Cole, K.S. & Curtis, H.J. (1938) Electric impedance of Nitella during activity. J. Gen. Physiol., 22, 37–64. Coombe, B.G. (1992) Research on development and ripening of the grape berry. Am. J. Enol. Vitic., 38, 120–127. Cosgrove, D.J. & Hedrich, R. (1991) Stretch-activated chloride, potassium and calcium channels coexisting in plasma membranes of guard cells of Vicia faba L. Planta, 186, 143–153. Czempinski, K., Zimmermann, S., Ehrhardt, T. & M¨uller-R¨ober, B. (1997a) New structure and function in plant K+ channels: KCO1, an outward rectifier with a steep Ca2+ -dependency. EMBO J., 16, 2565–2575. Czempinski, K., Zimmermann, S., Thomine, S. & M¨uller-R¨ober, B. (1997b) Two-pore K+ channels in plants: structure and function. Pfl¨ugers Arch., 434, R98. Czempinski, K., Gaedeke, N., Zimmermann, S. & M¨uller-R¨ober, B. (1999) Molecular mechanisms and regulation of plant ion channels. J. Exp. Bot., 50, 955–966. Czempinski, K., Frachisse, J.M., Maurel, C., Barbier-Brygoo, H. & M¨uller-R¨ober, B. (2002) Vacuolar membrane localization of the Arabidopsis ‘two-pore’ K+ channel KCO1. Plant J., 29, 809–820. Davenport, R. (2002) Glutamate receptors in plants. Ann. Bot., 90, 549–557. Davies, W. & Zhang, J. (1991) Root signals and the regulation of growth and the development of plants in drying soil. Annu. Rev. Plant Physiol. Plant Mol. Biol., 42, 55–76. De Boer, A.H. (1999) Potassium translocation into the root system. Plant Biol., 1, 36–45. Deeken, R., Geiger, D., Fromm, J., Koroleva, O., Ache, P., Langenfeld-Heyser, R., Sauer, N., May, S.T. & Hedrich, R. (2002) Loss of the AKT2/3 potassium channel affects sugar loading into phloem of Arabidopsis. Planta, 216, 334–344. Deeken, R., Sanders, C., Ache, P. & Hedrich R. (2000) Developmental and light-dependent regulation of a phloem-localised K+ channel of Arabidopsis thaliana. Plant J., 23, 285–290. Demidchik, V. & Tester, M. (2002) Sodium fluxes through nonselective cation channels in the plasma membrane of protoplasts from Arabidopsis roots. Plant Physiol., 128, 379–387. Demidchik, V., Davenport, R.J. & Tester, M. (2002) Nonselective cation channels in plants. Annu. Rev. Plant Biol., 53, 67–107. Desbrosses, G., Josefsson, C., Rigas, S., Hatzopoulos, P. & Dolan, L. (2003) AKT1 and TRH1 are required during root hair elongation in Arabidopsis. J. Exp. Bot., 54, 781–788. Downey, P., Szabo, I., Ivashikina, N., Negro, A., Guzzo, F., Ache, P., Hedrich, R., Terzi, M. & Lo Schiavo, F. (2000) Kdc1, a novel carrot root K+ channel: cloning, characterisation and expression in mammalian cells. J. Biol. Chem., 275, 39420–39426. Doyle, D.A., Cabral, J.M., Pfuetzner, R.A., Kuo, A., Gulbis, J.M., Cohen, S.L., Chait, B.T. & MacKinnon, R. (1998) The structure of the potassium channel: molecular basis of K+ conduction and selectivity. Science, 280, 69–77. Drew, M.C. & Saker, L.R. (1984) Uptake and long-distance transport of phosphate, potassium and chloride in relation to internal ion concentration in barley: evidence for nonallosteric regulation. Planta, 160, 500–507. Dreyer, I., Antunes, S., Hoshi, T., M¨uller-R¨ober, B., Palme, K., Pongs, O., Reintanz, B. & Hedrich, R. (1997) Plant K+ channel ␣-subunits assemble indiscriminately. Biophys. J., 72, 2143–2150. Dreyer, I., Becker, D., Bregante, M., Gambale, F., Lehnen, M., Palme, K. & Hedrich, R. (1998) Single mutations strongly alter the K+ pore of the Kin channel KAT1. FEBS Lett., 430, 370–376. Dreyer, I., Horeau, C., Lemaillet, G., Zimmermann, S., Bush, D.R., Rodr´ıguez Navarro, A., Schachtman, D.P., Spalding, E.P., Sentenac, H. & Gaber, R.F. (1999) Identification and characterization of plant transporters using heterologous expression systems. J. Exp. Bot., 50, 1073–1087. Durell, S.R. & Guy, H.R. (1999) Structural models of the KtrB, TrkH, and Trk1, 2 symporters based on the structure of the KcsA K+ channel. Biophys. J., 77, 789–807.
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Durell, S.R., Hao, Y., Nakamura, T., Bakker, E.P. & Guy, R. (1999) Evolutionary relationship between K+ channels and symporters. Biophys. J., 77, 775–788. Edwards, M.C., Smith, G.N. & Bowling, D.J.F. (1988) Guard cells extrude protons prior to stomatal opening—a study using fluorescence microscopy and pH sensitive microelectrodes. J. Exp. Bot., 39, 1541–1547. Ehrhardt, T., Zimmermann, S. & M¨uller-R¨ober, B. (1997) Association of plant K+ in channels is mediated by conserved C-termini and does not affect subunit assembly. FEBS Lett., 409, 166–170. Elumalai, R.P., Nagpal, P. & Reed, J.W. (2002) A mutation in the Arabidopsis KT2/KUP2 potassium transporter gene affects shoot cell expansion. Plant Cell, 14, 119–131. Engels, C. & Marschner, H. (1992) Adaptation of potassium translocation into the shoot of maize (Zea mays) to shoot demand: evidence for xylem loading as a regulation step. Physiol. Plantarum, 86, 263–268. Epstein, E., Rains, D.W. & Elzam, O.E. (1963) Resolution of dual mechanisms of potassium absorption by barley roots. Proc. Natl. Acad. Sci. USA, 49, 684–692. Fairbairn, D.J., Liu, W., Schachtman, D.P., Gomez-Gallego, S., Day, S.R. & Teasdale, R.D. (2000) Characterisation of two distinct HKT1-like potassium transporters from Eucalyptus camaldulensis. Plant Mol. Biol., 43, 515–525. Fang, Z., Kamasani, U. & Berkowitz, G.A. (1998) Molecular cloning and expression characterization of a rice K+ channel beta subunit. Plant Mol. Biol., 37, 597–606. Felle, H.H., Hanstein, S., Steinmeyer, R. & Hedrich, R. (2000) Dynamics of ionic activities in the apoplast of the sub-stomatal cavity of intact Vicia faba leaves during stomatal closure evoked by ABA and darkness. Plant J., 24, 297–304. Fisher, R.A. (1968) Stomatal opening: role of potassium uptake by guard cells. Science, 160, 784–785. Fizames, C., Munos, S., Cazettes, C., Nacry, P., Boucherez, J., Gaymard, F., Piquemal, D., Delorme, V., Commes, T.S., Doumas, P., Cooke, R., Marti, J., Sentenac, H. & Gojon, A. (2004) The Arabidopsis root transcriptome by serial analysis of gene expression. Gene identification using the genome sequence. Plant Physiol., 134, 67–80. Fu, H.-H. & Luan, S. (1998) AtKUP1: a dual-affinity K+ transporter from Arabidopsis. Plant Cell, 10, 63–73. Gaber, R.F., Styles, C.A. & Fink, G.R. (1988) TRK1 encodes a plasma membrane protein required for high-affinity potassium transport in Saccharomyces cerevisiae. Mol. Cell. Biol., 8, 2848–2859. Gadsby, D.C. (2004) Spot the difference. Nature, 427, 795–796. Garciadeblas, B., Senn, M.E., Ba˜nuelos, M.A. & Rodr´ıguez-Navarro, A. (2003) Sodium transport and HKT transporters: the rice model. Plant J., 34, 788–801. Gaymard, F., Pilot, G., Lacombe, B., Bouchez, D., Bruneau, D., Boucherez, J., Michaux-Ferri`ere, N., Thibaud, J.-B. & Sentenac, H. (1998) Identification and disruption of a plant Shaker-like outward channel involved in K+ release into the xylem sap. Cell, 94, 647–655. Glass, A.D.M. (1976) Regulation of potassium absorption in barley roots: an allosteric model. Plant Physiol., 58, 33–37. Goff, S.A., Ricke, D., Lan, T.-H., Presting, G., Wang, R., Dunn, M., Glazebrook, J., Sessions, A., Oeller, P., Varma, H., Hadley, D., Hutchinson, D., Martin, C., Katagiri, F., Lange, B.M., Moughamer, T., Xia, Y., Budworth, P., Zhong, J., & Miguel, T. (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science, 296, 92–100. Golldack, D., Su, H., Quigley, F., Kamasani, U.R., Munoz-Garay, C., Balderas, E., Popova, O.V., Bennett, J., Bohnert, H.J. & Pantoja, O. (2002) Characterization of a HKT-type transporter in rice as a general alkali cation transporter. Plant J., 31, 529–542. Gomperts, S.N. (1996) Clustering membrane proteins: it’s all coming together with the PSD-95/SAP90 protein family. Cell, 84, 659–662. Gulbis, J.M., Mann, S. & MacKinnon, R. (1999) Structure of a voltage-dependent K+ channel beta subunit. Cell, 97, 943–952.
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Seki, M., Ishida, J., Narusaka, M., Fujita, M., Nanjo, T., Umezawa, T., Kamiya, A., Nakajima, M., Enju, A., Sakurai, T., Satou, M., Akiyama, K., Yamaguchi-Shinozaki, K., Carninci, P., Kawai, J., Hayashizaki, Y. & Shinozaki, K. (2002b) Monitoring the expression pattern of around 7,000 Arabidopsis genes under ABA treatments using a full-length cDNA microarray. Funct. Integr. Genom., 2, 282–291. Senn, M.E., Rubio, F., Ba˜nuelos, M.A. & Rodr´ıguez-Navarro, A. (2001) Comparative functional features of plant potassium HvHAK1 and HvHAK2 transporters. J. Biol. Chem., 276, 44563–44569. Sentenac, H., Bonneaud, N., Minet, M., Lacroute, F., Salmon, J.-M., Gaymard, F. & Grignon, C. (1992) Cloning and expression in yeast of a plant potassium ion transport system. Science, 256, 663–665. Shabala, S. (2003) Regulation of potassium transport in leaves: From molecular to tissue level. Ann. Bot., 92, 627–634. Sherlock, G., Hernandez-Boussard, T., Kasarskis, A., Binkley, G., Matese, J.C., Dwight, S.S., Kaloper, M., Weng, S., Jin, H., Ball, C.A., Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D. & Cherry, J.M. (2001) The Stanford microarray database. Nucleic Acids Res., 29, 152–155. Somers, T.C. (1977) A connection between potassium levels in the harvest and relative quality in Australien red wines. Aust. Wine, Brewing Spirit Rev., 24, 32–34. Spalding, E.P., Hirsch, R.E., Lewis, D.E., Qi, Z. & Sussman, M.R. (1999) Potassium uptake supporting plant growth in the absence of AKT1 channel activity. J. Gen. Physiol., 113, 909–918. Sparks, D.L. (1987) Potassium dynamics in soils. Adv. Soil Sci., 6, 1–63. Steingrobe, B. & Claassen, N. (2000) Potassium dynamics in the rhizosphere and K deficiency of crops. J. Plant Nutr. Soil Sci., 163, 101–106. Stiles, K.A. & Van Volkenburgh, E. (2004) Role of K+ in leaf growth: K+ uptake is required for light-stimulated H+ efflux but not solute accumulation. Plant Cell Environ., 27, 315–325. Su, H., Golldack, D., Katsuhara, M., Zhao, C. & Bohnert, H.J. (2001) Expression and stress-dependent induction of potassium channel transcripts in the common ice plant. Plant Physiol., 125, 604–614. Su, H., Golldack, D., Zhao, C. & Bohnert, H.J. (2002) The expression of HAK-type K+ transporters is regulated in response to salinity stress in common ice plant. Plant Physiol., 129, 1482–1493. Sussmann, M.R. & Harper, J.F. (1989) Molecular biology of the plasma membrane of higher plants. Plant Cell, 1, 953–960. Szyroki, A., Ivashikina, N., Dietrich, P., Roelfsema, M.R.G., Ache, P., Reintanz, B., Deeken, R., Godde, M., Felle, H., Steinmeyer, R., Palme, K. & Hedrich, R. (2001) KAT1 is not essential for stomatal opening. Proc. Natl. Acad. Sci. USA, 98, 2917–2921. 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. Tallman, G. (1992) The chemiosmotic model of stomatal opening revisited. Crit. Rev. Plant Sci., 11, 35–57. Tang, H., Vasconcelos, A.C. & Berkowitz, G.A. (1995) Evidence that plant K+ channel proteins have two different types of subunits. Plant Physiol., 109, 327–330. Tang, H., Vasconcelos, A.C. & Berkowitz, G.A. (1996) Physical association of KAB1 with plant K+ channel alpha subunits. Plant Cell, 8, 1545–1553. Tang, H., Vasconcelos, A.C., Ma, J. & Berkowitz, G.A. (1998) In vivo expression pattern of a plant K+ channel beta subunit protein. Plant Sci., 134, 117–128. Tang, X.D. & Hoshi, T. (1999) Rundown of the hyperpolarization-activated KAT1 channel involves slowing of the opening transitions regulated by phosphorylation. Biophys. J., 76, 3089–3098. Tchieu, J.H., Fana, F., Fink, J.L., Harper, J., Nair, T.M., Niedner, R.H., Smith, D.W., Steube, K., Tam, T.M., Veretnik, S., Wang, D. & Gribskov, M. (2003) The PlantsP and PlantsT functional genomics databases. Nucleic Acids Res., 31, 342–344. Tester, M. & Leigh, R.A. (2001) Partitioning of nutrient transport processes in roots. J. Exp. Bot., 52, 445–457. Thiel, G., MacRobbie, E.A.C. & Blatt, M.R. (1992) Membrane transport in stomatal guard cells: the importance of voltage control. J. Membr. Biol., 126, 1–18.
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Uozumi, N., Kim, E.J., Rubio, F., Yamaguchi, T., Muto, S., Tsuboi, A., Bakker, E.P., Nakamura, T. & Schroeder, J.I. (2000) The Arabidopsis HKT1 gene homolog mediates inward Na+ currents in Xenopus laevis oocytes and Na+ uptake in Saccharomyces cerevisiae, Plant Physiol., 122, 1249–1259. V´ery, A.-A., Gaymard, F., Bosseux, C., Sentenac, H. & Thibaud, J.B. (1995) Expression of a cloned plant K+ channel in Xenopus oocytes: analysis of macroscopic currents. Plant J., 7, 321–332. V´ery, A.-A. & Sentenac, H. (2002) Cation channels in the Arabidopsis plasma membrane. Trends Plant Sci., 7, 168–175. V´ery, A.-A. & Sentenac, H. (2003) Molecular mechanisms and regulation of K+ transport in higher plants. Annu. Rev. Plant Biol., 54, 575–603. Walker, D.J., Leigh, R.A. & Miller, A.J. (1996) Potassium homeostasis in vacuolate plant cells. Proc. Natl. Acad. Sci. USA, 93, 10510–10514. Walker, D.J., Black, C.R. & Miller, A.J. (1998) The role of cytosolic potassium and pH in the growth of barley roots. Plant Physiol., 118, 957–964. Wang, T.-B., Gassmann, W., Rubio, F., Schroeder, J.I. & Glass, A.D.M. (1998) Rapid up-regulation of HKT1, a high-affinity K+ transporter gene, in roots of barley and wheat following withdrawal of K+ . Plant Physiol., 118, 651–659. Wang, X.Q., Ullah, H., Jones, A.M. & Assmann, S.M. (2001) G protein regulation of ion channels and abscisic acid signaling in Arabidopsis guard cells. Science, 292, 2070–2072. 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-mediate signals. Plant Physiol., 130, 1361–1371. Webb, A.A.R. & Baker, A.J. (2002) Stomatal biology: new techniques, new challenges. New Phytol., 153, 365–370. Wegner, L.H. & De Boer, A.H. (1997) Properties of two outward-rectifying channels in root xylem parenchyma cells suggest a role in K+ homeostasis and long-distance signalling. Plant Physiol., 115, 1707–1719. Wegner, L.H. & Raschke, K. (1994) Ion channels in the xylem parenchyma of barley roots. A procedure to isolate protoplasts from this tissue and a patch-clamp exploration of salt passageways into xylem vessels. Plant Physiol., 105, 363–379. Wilkinson, S. & Davies, W.J. (2002) ABA-based chemical signalling: the coordination of responses to stress in plants. Plant Cell Environ., 25, 195–210. Wipf, D., Benjdia, M., Tegeder, M. & Frommer, W.B. (2002) Characterization of a general amino acid permease from Hebeloma cylindrosporum. FEBS Lett., 528, 119–124. Wipf, D., Benjdia, M., Rikirsch, E., Zimmermann, S., Tegeder, M. & Frommer, W.B. (2003) An expression cDNA library for supression cloning in yeast mutants, complementation of a yeast his4 mutant, and EST analysis from the symbiotic basidiomycete Hebeloma cylindrosporum. Genome, 46, 177–181. Yu, J., Hu, S., Wang, J., Wong, G.K.-S., Li, S., Liu, B., Deng, Y., Dai, L., Zhou, Y., Zhang, X., Cao, M., Liu, J., Sun, J., Tang, J., Chen, Y., Huang, X., Lin, W., Ye, C., Tong, W., & Cong, L. et al. (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science, 296, 79–92. Yifrach, O. & MacKinnon, R. (2002) Energetics of pore opening in a voltage-gated K+ channel. Cell, 111, 231–239. Zei, P.C. & Aldrich, R.W. (1998) Voltage-dependent gating of single wild-type and S4 mutant KAT1 inward rectifier potassium channels. J. Gen. Physiol., 112, 679–713. Zeng, Q. & Brown, P.H. (2000) Soil potassium mobility and uptake by corn under differential soil moisture regimes. Plant Soil, 221, 121–134. Zeng, G.F., Pypaert, M. & Slayman, C.L. (2004) Epitope tagging of the yeast K+ carrier TRK2p demonstrates folding that is consistent with a channel-like structure. J. Biol. Chem., 279, 3003– 3013.
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Zhang, X., Ma, J. & Berkowitz, G.A. (1999) Evaluation of functional interaction between K+ channel alpha- and beta-subunits and putative inactivation gating by co-expression in Xenopus laevis oocytes. Plant Physiol., 121, 995–1002. Zhu, J.-K., Liu, J. & Xiong, L. (1998) Genetic analysis of salt tolerance in Arabidopsis thaliana: evidence for a critical role of potassium nutrition. Plant Cell, 10, 1181-1191. Zimmermann, S., Talke, I., Ehrhardt, T., Nast, G. & M¨uller-R¨ober, B. (1998) Characterization of SKT1, an inwardly rectifying potassium channel from potato, by heterologous expression in insect cell. Plant Physiol., 116, 879–890. Zimmermann, S. & Sentenac, H. (1999) Plant ion channels: from molecular structures to physiological functions, Curr. Opin. Plant Biol., 2, 477–482. Zimmermann, S., Ehrhardt, T., Plesch, G. & M¨uller-R¨ober, B. (1999) Ion channels in plant signalling, Cell. Mol. Life Sci., 55, 183–203.
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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
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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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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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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 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 (V´ery & 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; V´ery & 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; V´ery & 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 (V´ery & 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; M¨aser 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 (Catal´a 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 (Catal´a 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 (Catal´a 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; Malh´o 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’.
References Alonso-Blanco, C., Peeters, A.J.M., Koornneef, M., Lister, C., Dean, C., van den Bosch, N., Pot, J. & Kuiper, M.T.R. (1998) Development of an AFLP based linkage map of Ler, Col and Cvi Arabidopsis thaliana ecotypes and construction of a Ler/Cvi recombinant inbred line population. Plant J., 14, 259–271. Andersen, A.J. (1967) Investigations on the plant uptake of fission products from contaminated soils. I. Influence of plant species and soil types on the uptake of radioactive strontium and caesium. Ris¨o Report, 170, 1–32. Danish Atomic Energy Commission Research Establishment Ris¨o, Roskilde, Denmark. Axelsen, K.B. & Palmgren, M.G. (2001) Inventory of the superfamily of P-type ion pumps in Arabidopsis. Plant Physiol., 126, 696–706.
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Becker, D., Hoth, S., Ache, P., Wenkel, S., Roelfsema, M.R.G., Meyerhoff, O., Hartung, W. & Hedrich, R. (2003) Regulation of the ABA-sensitive Arabidopsis potassium channel gene GORK in response to water stress. FEBS Lett., 554, 119–126. Broadley, M.R., Bowen, H.C., Cotterill, H.L., Hammond, J.P., Meacham, M.C., Mead, A. & White, P.J. (2003) Variation in the shoot calcium content of angiosperms. J. Exp. Bot., 54, 1431–1446. Broadley, M.R., Bowen, H.C., Cotterill, H.L., Hammond, J.P., Meacham, M.C., Mead, A. & White, P.J. (2004) Phylogenetic variation in the shoot mineral concentration of angiosperms. J. Exp. Bot., 55, 321–336. Caines, A.M. & Shennan, C. (1999) Growth and nutrient composition of Ca2+ use efficient and Ca2+ use inefficient genotypes of tomato. Plant Physiol. Biochem., 37, 559–567. Carpita, N. & McCann, M. (2000) The cell wall. In Biochemistry and Molecular Biology of Plants (eds B.B. Buchanan, W. Gruissem & R.L. Jones), American Society of Plant Physiologists, Rockville, MD, pp. 52–108. Catal´a, R., Santos, E., Alonso, J.M., Ecker, J.R., Mart´ınez-Zapater, J.M. & Salinas, J. (2003) Mutations in the Ca2+ /H+ transporter CAX1 increase CBF/DREB1 expression and the cold-acclimation response in Arabidopsis. Plant Cell, 15, 2940–2951. Chan, C.W.M., Schorrak, L.M., Smith, R.K., Bent, A.F. & Sussman, M.R. (2003) A cyclic nucleotidegated ion channel, CNGC2, is crucial for plant development and adaptation to calcium stress. Plant Physiol., 132, 728–731. Cheng, N., Pittman, J.K., Shigaki, T. & Hirschi, K.D. (2002a) Characterization of CAX4, an Arabidopsis H+ /cation antiporter. Plant Physiol., 128, 1245–1254. Cheng, N.-H., Pittman, J.K., Barkla, B.J., Shigaki, T. & Hirschi, K.D. (2003) The Arabidopsis cax1 mutant exhibits impaired ion homeostasis, development, and hormonal responses and reveals interplay among vacuolar transporters. Plant Cell, 15, 347–364. Cheng, S.-H., Willmann, M.R., Chen, H.-C. & Sheen, J. (2002b) Calcium signaling through protein kinases. The Arabidopsis calcium-dependent protein kinase gene family. Plant Physiol., 129, 469–485. Chiu, J.C., Brenner, E.D., DeSalle, R., Nitabach, M.N., Holmes, T.C. & Coruzzi, G.M. (2002) Phylogenetic and expression analysis of the glutamate-receptor-like gene family in Arabidopsis thaliana. Mol. Biol. Evol., 19, 1066–1082. Clark, G.B., Sessions, A., Eastburn, D.J. & Roux S.J. (2001) Differential expression of members of the annexin multigene family in Arabidopsis. Plant Physiol., 126, 1072–1084. Clark, R.B. (1983) Plant genotype differences in the uptake, translocation, accumulation, and use of mineral elements required for plant growth. Plant Soil, 72, 175–196. Clarkson, D.T. (1993) Roots and the delivery of solutes to the xylem. Philos. Trans. R. Soc. Lond. B Biol. Sci., 341, 5–17. Clemens, S., Antosiewicz, D.M., Ward, J.M., Schachtman, D.P. & Schroeder, J.I. (1998) The plant cDNA LCT1 mediates the uptake of calcium and cadmium in yeast. Proc. Natl. Acad. Sci. USA, 95, 12043–12048. De Boer, A.H. (1999) Potassium translocation into the root xylem. Plant Biol., 1, 36–45. De Silva, D.L.R., Hetherington, A.M. and Mansfield, T.A. (1996) Where does all the calcium go? Evidence of an important regulatory role for trichomes in two calcicoles. Plant Cell Environ., 19, 880–886. Davenport, R.J. (2002) Glutamate receptors in plants. Ann. Bot., 90, 549–557. Davenport, R.J., Kiegle, E.A. & Tester, M. (2000) Molecular and functional diversity of glutamate receptor orthologues in plants. J. Exp. Bot., 51S, 30. Demidchik, V., Bowen, H.C., Maathuis, F.J.M., Shabala, S.N., Tester, M.A., White, P.J. & Davies, J.M. (2002a) Arabidopsis thaliana root non-selective cation channels mediate calcium uptake and are involved in growth. Plant J., 32, 799–808. Demidchik, V.V., Davenport, R.J. & Tester, M.A. (2002b) Non-selective cation channels in plants. Annu. Rev. Plant Biol., 53, 67–107.
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Demidchik, V., Shabala, S.N., Coutts, K.B., Tester, M.A. & Davies, J.M. (2003) Free oxygen radicals regulate plasma membrane Ca2+ - and K+ -permeable channels in plant root cells. J. Cell Sci., 116, 81–88. English, J.E. & Barker, A.V. (1987) Ion interactions in calcium-efficient and calcium-inefficient tomato lines. J. Plant Nutr., 10, 857–869. Foreman, J., Demidchik, V., Bothwell, J.H.F., Mylona, P., Miedema, H., Torres, M.A., Linstead, P., Costa, S., Brownlee, C., Jones, J.D.G., Davies, J.M. & Dolan, L. (2003) Reactive oxygen species produced by NADPH oxidase regulate plant cell growth. Nature, 422, 442–446. Franceschi, V.R. & Horner, H.T. (1980) Calcium oxalate crystals in plants. Bot. Rev., 46, 361–427. Furuichi, T., Cunningham, K.W. & Muto, S. (2001) A putative two pore channel AtTPC1 mediates Ca2+ flux in Arabidopsis leaf cells. Plant Cell Physiol., 42, 900–905. Garten, C.T. (1976) Correlations between concentrations of elements in plants. Nature, 261, 686–688. Gaxiola, R.A., Li, J., Undurraga, S., Dang, L.M., Allen, G.J., Alper, S.L. & Fink, G.R. (2001) Droughtand salt-tolerant plants result from overexpression of the AVP1 H+ -pump. Proc. Natl. Acad. Sci. USA, 98, 11444–11449. Gaymard, F., Pilot, G., Lacombe, B., Bouchez, D., Bruneau, D., Boucherez, J., Michaux-Ferri`ere, N., Thibaud, J.-B. & Sentenac, H. (1998) Identification and disruption of a plant shaker-like outward channel involved in K+ release into the xylem sap. Cell, 94, 647–655. Geisler, M., Axelsen, K.B., Harper, J.F. & Palmgren, M.G. (2000) Molecular aspects of higher plant P-type Ca2+ -ATPases. Biochim. Biophys. Acta, 1465, 52–78. Graham, R.D., Welch, R.M. & Bouis, H.E. (2001) Addressing micronutrient malnutrition through enhancing the nutritional quality of staple foods: principles, perspectives and knowledge gaps. Adv. Agron., 70, 77–142. Hirschi, K. (2001) Vacuolar H+ /Ca2+ transport: who’s directing the traffic? Trends Plant Sci., 6, 100– 104. Hirschi, K.D., Miranda, M.L. & Wilganowski, N.L. (2001) Phenotypic changes in Arabidopsis caused by expression of a yeast Ca2+ /H+ antiporter. Plant Mol. Biol., 46, 57–65. Hochmuth, G.J. (1984) Variation in calcium efficiency among strains of cauliflower. J. Am. Soc. Horticult. Sci., 109, 667–672. Horak, O. & Kinzel, H. (1971) Typen des Mineralstoffwechsels bei den h¨oheren Pflanzen. ¨ Osterreichische Botanische Gezelleschaft, 119, 475–495. Islam, A.K.M.S., Asher, C.J. & Edwards, D.G. (1987) Response of plants to calcium concentration in flowing solution culture with chloride or sulphate as the counter-ion. Plant Soil, 98, 377–395. Jefferies, R.L. & Willis, A.J. (1964) Studies on the calcicole-calcifuge habit. II. The influence of calcium on the growth and establishment of four species in soil and sand cultures. J. Ecol., 52, 691–707. Kang, J. & Turano, F.J. (2003) The putative glutamate receptor 1.1 (AtGLR1.1) functions as a regulator of carbon and nitrogen metabolism in Arabidopsis thaliana. Proc. Natl. Acad. Sci. USA, 100, 6872–6877. Karley, A.J., Leigh, R.A. & Sanders, D. (2000) Where do all the ions go? The cellular basis of differential ion accumulation in leaf cells. Trends Plant Sci., 5, 465–470. Kiegle, E., Gilliham, M., Haseloff, J. & Tester, M. (2000) Hyperpolarisation-activated calcium currents found only in cells from the elongation zone of Arabidopsis thaliana roots. Plant J., 21, 225–229. Kim, S.A., Kwak, J.M., Jae, S.-K., Wang, M.-H. & Nam, H.G. (2001) Overexpression of the AtGluR2 gene encoding an Arabidopsis homolog of mammalian glutamate receptors impairs calcium utilization and sensitivity to ionic stress in transgenic plants. Plant Cell Physiol., 42, 74–84. Kinzel, H. (1982) Pflanzen¨okologie und Mineralstoffwechsel, Ulmer, Stuttgart. Kinzel, H. & Lechner, I. (1992) The specific mineral metabolism of selected plant species and its ecological implications. Botanica Acta, 105, 355–361. ¨ Weinl, S., Blazevic, D., Batistic, O. & Kudla, J. (2004) Calcium sensors and their Kolukisaoglu, U., interacting protein kinases: Genomics of the Arabidopsis and rice CBL-CIPK Signaling networks. Plant Physiol., 134, 43–58.
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Lahner, B., Gong, J., Mahmoudian, M., Smith, E.L., Abid, K.B., Rogers, E.E., Guerinot, M.L., Harper, J.F., Ward, J.M., McIntyre, L., Schroeder, J.I. & Salt, D.E. (2003) Genomic scale profiling of nutrient and trace elements in Arabidopsis thaliana. Nat. Biotechnol., 21, 1215–1221. Lee, J.A. (1999) The calcicole-calcifuge problem revisited. Adv. Bot. Res., 29, 1–30. Loneragan, J.F. & Snowball, K. (1969) Calcium requirements of plants. Aust. J. Agric. Res., 20, 465–478. Loneragan, J.F., Snowball, K. & Simmons, W.J. (1968) Response of plants to calcium concentration in solution culture. Aust. J. Agric. Res., 19, 845–857. Longin, J. & Neirinckx, L. (1977) Essai de typologie physiologique des plantes, base´e sur leur m´etabolisme calcique foliaire. Bull. Soc. R. Bot. Belg., 110, 228–238 Luan, S., Kudla, J., Rodr´ıguez-Concepci´on, M., Yalovsky, S. & Gruissem, W. (2002) Calmodulins and calcineurin B-like proteins: Calcium sensors for specific signal response coupling in plants. Plant Cell, 14, S389–S400. Maathuis, F.J.M., Filatov, V., Herzyk, P., Krijger, G.C., Axelsen, K.B., Chen, S., Green, B.J., Li, Y., Madagan, K.L., S´anchez-Fern´andez, R., Forde, B.G., Palmgren, M.G., Rea, P.A., Williams, L.E., Sanders, D. & Amtmann, A. (2003) Transcriptome analysis of root transporters reveals participation of multiple gene families in the response to cation stress. Plant J., 35, 675– 692. Malh´o, R., Moutinho, A., van der Luit, A. & Trewavas, A.J. (1998) Spatial characteristics of calcium signalling: the calcium wave as a basic unit in plant cell calcium signalling. Philos. Trans. R. Soc. Lond. B Biol. Sci., 353, 1463–1473. Marschner, H. (1995) Mineral Nutrition of Higher Plants. 2nd edn, Academic Press, London. M¨aser, P., Thomine, S., Schroeder, J.I., Ward, J.M., Hirschi, K., Sze, H., Talke, I.N., Amtmann, A., Maathuis, F.J.M., Sanders, D., Harper, J.F., Tchieu, J., Gribskov, M., Persans, M.W., Salt, D.E., Kim, S.A. & Guerinot, M.L. (2001) Phylogenetic relationships within cation transporter families of Arabidopsis. Plant Physiol., 126, 1646–1667. McCormack, E. & Braam, J. (2003) Calmodulins and related potential calcium sensors of Arabidopsis. New Phytol., 159, 585–598. McLaughlin, S.B. & Wimmer, R. (1999) Calcium physiology and terrestrial ecosystem processes. New Phytol., 142, 373–417. Miedema, H., Bothwell, J.H.F., Brownlee, C. & Davies, J.M. (2001) Calcium uptake by plant cells – channels and pumps acting in concert. Trends Plant Sci., 6, 514–519. Murashige, T. & Skoog, F. (1962) A revised medium for rapid growth and bioassays with tobacco tissue cultures. Physiol. Plantarum, 15, 473–497. Persson, S., Wyatt, S.E., Love, J., Thompson, W.F., Robertson, D. & Boss, W.F. (2001) The Ca2+ status of the endoplasmic reticulum is altered by induction of calreticulin expression in transgenic plants. Plant Physiol., 126, 1092–1104. Reddy, A.S.N. (2001) Calcium: silver bullet in signaling. Plant Sci., 160, 381–404. Sage, R.F. (2004) The evolution of C4 photosynthesis. New Phytol., 161, 341–370. Sanders, D., Brownlee, C. & Harper, J.F. (1999) Communicating with calcium. Plant Cell, 11, 691–706. Sanders, D., Pelloux, J., Brownlee, C. & Harper, J.F. (2002) Calcium at the crossroads of signaling. Plant Cell, 14, S401–S417. Sayed, O.H. (2001) Crassulacean Acid Metabolism 1975-2000, a check list. Photosynthetica, 39, 339– 352. Shigaki, T. & Hirschi, K. (2000) Characterisation of CAX-like genes in plants: implications for functional diversity. Gene, 257, 291–298. Snedden, W.A. & Fromm, H. (2001) Calmodulin as a versatile calcium signal transducer in plants. New Phytol., 151, 35–66. Sunkar, R., Kaplan, B., Bouch´e, N., Arazi, T., Dolev, D., Talke, I.N., Maathuis, F.J.M., Sanders, D., Bouchez, D. & Fromm, H. (2000) Expression of a truncated tobacco NtCBP4 channel in transgenic plants and disruption of the homologous Arabidopsis CNGC1 gene confer Pb2+ tolerance. Plant J., 24, 533–542.
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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. V´ery, 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. V´ery, 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 Br¨uhl et al., 1996; Nakayama et al., 2000 Hell et al., 1994; Hesse & H¨ofgen, 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 & K¨omives, 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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Ravanel, S., Gakiere, B., Job, D. & Douce, R. (1998) The specific features of methionine biosynthesis and metabolism in plants. Proc. Natl. Acad. Sci. USA, 95, 7805–7812. Ravina, C.G., Chang, C-I., Tsakraklides, G.P., McDermott, J.P., Vega, J.M., Leustek, T., Gotor, C. & Davies, J.P. (2002) The sac mutants of Chlamydomonas reinhardtii reveal transcriptional and posttranscriptional control of cysteine biosynthesis. Plant Physiol., 130, 2076–2084. Rennenberg, H., Schmitz, K. & Bergmann, L. (1979) Long-distance transport of sulfur in Nicotiana tabacum. Planta, 147, 57–62. Renosto, F., Patel, H.C., Martin, R.L., Thomassian, C., Zimmerman, G. & Segel, I.H. (1993) ATP sulfurylase from higher plants. Kinetic and structural characterization of the chloroplast and cytosol enzymes from spinach leaf. Arch. Biochem. Biophys., 307, 272–285. Rotte, C. & Leustek, T. (2000) Differential subcellular localization and expression of ATP sulfurylase and 5’-adenylylsulfate reductase during ontogenesis of Arabidopsis leaves indicates that cytosolic and plastid forms of ATP sulfurylase may have specialized functions. Plant Physiol., 124, 715– 724. Saito, K., Inoue, K., Fukushima, R. & Noji, M. (1997) Genomic structure and expression analysis of serine acetyltransferase gene in Citrullus vulgaris (watermelon). Gene, 189, 57–63. Schroder, G., Eichel, J., Breinig, S. & Schroder, J. (1997) Three differentially expressed Sadenosylmethionine synthetases from Catharanthus roseus: Molecular and functional characterization. Plant Mol. Biol., 33, 211–222. Shen, B., Li, C.J. & Tarczynski, M.C. (2002) High free-methionine and decreased lignin content result from a mutation in the Arabidopsis S-adenosyl-L-methionine synthetase 3 gene. Plant J., 29, 371–380. Shibagaki, N., Rose, A., McDermott, J.P., Fujiwara, T., Hayashi, H., Yomeyama, T. & Davis J.P. (2002) Selenate-resistant mutants of Arabidopsis thaliana identify Sultr1;2, a sulfate transporter required for efficient transport of sulfate into roots. Plant J., 29, 475–486. Smith, I.K. (1975) Sulfate transport in cultured tobacco cells. Plant Physiol., 55, 303–307. Smith, F.W., Ealing, P.M., Hawkesford, M.J. & Clarkson, D.T. (1995a) Plant members of a family of sulfate transporters reveal functional subtypes. Proc. Natl. Acad. Sci. USA, 92, 9373–9377. Smith, F.W., Hawkesford, M.J., Prosser, I.M. & Clarkson, D.T. (1995b) Isolation of a cDNA from Saccharomyces cerevisiae that encodes a high affinity sulphate transporter at the plasma membrane. Mol. Gen. Genet., 247, 709–715. Smith, F.W., Hawkesford, M.J., Ealing, P.M., Clarkson, D.T., Vandenberg, P.J., Belcher, A. & Warrilow, A.G.S. (1997) Regulation of expression of a cDNA from barley roots encoding a high affinity sulphate transporter. Plant J., 12, 875–884. Suter, M., von Ballmoos, P., Kopriva, S., den Camp, R.O., Schaller, J., Kuhlemeier, C., Schurmann, P. & Brunold, C. (2000) Adenosine 5 -phosphosulfate sulfotransferase and adenosine 5 -phosphosulfate reductase are identical enzymes. J. Biol. Chem., 275, 930–936. Takahashi, H., Asanuma, W. & Saito, K. (1999a) Cloning of an Arabidopsis cDNA encoding a chloroplast localizing sulphate transporter isoform. J. Exp. Bot., 50, 1713–1714. Takahashi, H., Sasakura, N., Kimura, A., Watanabe, A. & Saito, K. (1999b) Identification of two leafspecific sulfate transporters in Arabidopsis thaliana (Accession No. AB012048 and AB004060). (PGR99-154). Plant Physiol., 121, 686. Takahashi, H., Sasakura, N., Noji, M. & Saito, K. (1996) Isolation and characterization of a cDNA encoding a sulfate transporter from Arabidopsis thaliana. FEBS Lett., 392, 95–99. Takahashi, H., Watanabe-Takahashi, A., Smith, F.W., Blake-Kalff, M., Hawkesford, M.J. & Saito, K. (2000) The roles of three functional sulphate transporters involved in uptake and translocation of sulphate in Arabidopsis thaliana. Plant J., 23, 171–182. Takahashi, H., Watanabe-Takahashi, A. & Yamaya, T. (2003) T-DNA insertion mutagenesis of sulphate transporters in Arabidopsis. In 5th Workshop on Sulfur Transport and Assimilation: Regulation, Interaction, Signalling (eds J-C. Davidian, D. Grill, L.J. de Kok, I. Stulen, M.J. Hawkesford, E. Schnug & H. Rennenberg), Backhuys Publishers, Leiden, The Netherlands, pp. 339–340.
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Takahashi, H., Yamazaki, M., Sasakura, N., Watanabe, A., Leustek, T., de Almeida Engler, J., Engler, G., van Montagu, M. & Saito, K. (1997) Regulation of sulfur assimilation in higher plants: a sulfate transporter induced in sulfate-starved roots plays a central role in Arabidopsis thaliana. Proc. Natl. Acad. Sci. USA, 94, 11102–11107. Tausz, M. Weidner, W., Wonisch, A., De Kok, L.J. & Grill, D. (2003) Uptake and distribution of 35 Ssulfate in needles and roots of spruce seedlings as affected by exposure to SO2 and H2 S. Environ. Exp. Bot., 50, 211–220. Thompson, J.D., Higgins, D.G. & Gibson, T.J. (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res., 22, 4673–5680. Tsakraklides, G., Martin, M., Chalam, R., Tarczynski, M.C., Schmidt, A. & Leustek, T. (2002) Sulfate reduction is increased in transgenic Arabidopsis thaliana expressing 5 -adenylylsulfate reductase from Pseudomonas aeruginosa. Plant J., 32, 879–889. Vauclare, P., Kopriva, S., Fell, D., Suter, M., Sticher, L., von Ballmoos, P., Kr¨ahenb¨uhl, U., Op den Camp, R. & Brunold, C. (2002) Flux control of sulphate assimilation in Arabidopsis thaliana: adenosine 5 -phosphosulphate reductase is more susceptible than ATP sulphurylase to negative control by thiols. Plant J., 31, 729–740. Vidmar, J.J., Schjoerring, J.K., Touraine, B. & Glass, A.D.M. (1999) Regulation of the hvst1 gene encoding a high-affinity sulfate transporter from Hordeum vulgare. Plant Mol. Biol., 40, 883– 892. Vidmar, J.J., Tagmount, A., Cathala, N., Touraine, B. & Davidian, J-C. (2000) Cloning and characterization of a root specific high-affinity sulfate transporter from Arabidopsis thaliana. FEBS Lett., 475, 65–69. Wang, K.L.C., Li, H. & Ecker, J.R. (2002) Ethylene biosynthesis and signaling networks. Plant Cell, 14, S131–S151. Wang, R., Okamoto, M., Xing, X. & Crawford, N.M. (2003) Microarray analysis of the nitrate response in Arabidopsis roots and shoots reveals over 1,000 rapidly responding genes and new linkages to glucose, trehalose-6-phosphate, iron, and sulfate metabolism. Plant Physiol., 132, 556–567. Warrilow, A.G.S. & Hawkesford, M.J. (1998) Separation, subcellular location and influence of sulphur nutrition on isoforms of cysteine synthase in spinach. J. Exp. Bot., 49, 1625–1636. Warrilow, A.G.S. & Hawkesford, M.J. (2000) Cysteine synthase substrate specificities classify the mitochondrial isoform as a cyanoalanine synthase. J. Exp. Bot., 51, 985–993. Warrilow, A.G.S. & Hawkesford, M.J. (2002) Modulation of cyanoalanine synthase and O-acetylserine (thiol) lyases A and B activity by ß-substituted alanyl and anion inhibitors. J. Exp. Bot., 53, 439–445. Westerman, S., De Kok, L.J., Stuiver, C.E.E. & Stulen, I. (2000) Interaction between metabolism of atmospheric H2 S in the shoot and sulfate uptake by the roots of curly kale (Brassica oleracea). Physiol. Plantarum, 109, 443–449. Williams, J.S., Hall, S., Hawkesford, M.J., Beale, M.H., & Cooper, R.M. (2002) Elemental sulfur and thiol accumulation in tomato and defense against a fungal vascular pathogen. Plant Physiol., 128, 150–159. Yamaguchi, Y., Nakamura, T., Kusano, T. & Sano, H. (2000) Three Arabidopsis genes encoding proteins with differential activities for cysteine synthase and beta-cyanoalanine synthase. Plant Cell Physiol., 41, 465–476. Yamaguchi, Y., Nakamura, T., Harada, E., Koizumi, N. & Sano, H. (1999) Differential accumulation of transcripts encoding sulfur assimilation enzymes upon sulfur and or nitrogen deprivation in Arabidopsis thaliana. Biosci. Biotechnol. Biochem., 63, 762–766. Yildiz, F.H., Davies, J.P. & Grossman, A. (1996) Sulfur availability and the SAC1 gene control adenosine triphosphate sulfurylase gene expression in Chlamydomonas reinhardtii. Plant Physiol., 112, 669–675.
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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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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; N´atr, 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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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; Garciadebl´as 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+ (Garciadebl´as 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 (M¨aser 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 (M¨aser 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 (M¨aser 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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Ishitani, M., Liu, J., Halfter, U., Kim, C.-S., Shi, W. & Zhu, J.K. (2000) SOS3 function in plant salt tolerance requires N-myristoylation and calcium-binding. Plant Cell, 12, 1667–1677. Johnson, L.N., Noble, M.E. & Owen, D.J. (1996) Active and inactive protein kinases: structural basis for regulation. Cell, 85, 149–158. Kim, K.N., Cheong, Y.H., Gupta, R. & Luan, S. (2000) Interaction specificity of Arabidopsis calcineurin B-like calcium sensors and their target kinases. Plant Physiol., 124, 1844–1853. Kochian, L.V. & Lucas, W.J. (1988) Potassium transport in roots. Adv. Bot. Res., 15, 93–178. Laurie, S., Feeney, K.A., Maathuis, F.J., Heard, P.J., Brown, S.J. & Leigh, R.A. (2002) A role for HKT1 in sodium uptake by wheat roots. Plant J., 32, 139–149. Lawson, M.A. & Maxfield, F.R. (1995) Ca2+ - and calcineurin-dependent recycling of an integrin to the front of migrating neutrophils. Nature, 377, 75–79. Lehr, A., Kirsch, M., Viereck, R., Schiemann, J. & Rausch, T. (1999) cDNA and genomic cloning of sugar beet V-type H+ -ATPase subunit A and c isoforms: evidence for coordinate expression during plant development and coordinate induction in response to high salinity. Plant Mol. Biol., 9, 463–475. Liu, J. & Zhu, J.K. (1997) An Arabidopsis mutant that requires increased calcium for potassium nutrition and salt tolerance. Proc. Natl. Acad. Sci. USA, 94, 14960–14964. Liu, J. & Zhu, J.K. (1998) A calcium sensor homolog required for plant salt tolerance. Science, 280, 1943–1945. Liu, J., Ishitani, M., Halfter, U., Kim, C.S. & Zhu, J.K. (2000a) The Arabidopsis thaliana SOS2 gene encodes a protein kinase that is required for salt tolerance. Proc. Natl. Acad. Sci. USA, 97, 3730– 3734. Liu, W., Schachtman, D.P. & Zhang, W. (2000b) Partial deletion of a loop region in the high affinity K+ transporter HKT1 changes ionic permeability leading to increased salt tolerance. J. Biol. Chem., 275, 27924–27932. Lohaus, G., Hussmann, M., Pennewiss, K., Schneider, H., Zhu, J.J. & Sattelmacher, B. (2000) Solute balance of a maize (Zea mays L.) source leaf as affected by salt treatment with special emphasis on phloem retranslocation and ion leaching. J. Exp. Bot., 51, 1721–1732. Maathuis, F., Verlin, D., Smith, F.A., Sanders, D., Fernandez, J.A. & Walker, N.A. (1996) The Physiological Relevance of Na+ -Coupled K+ -Transport. Plant Physiol., 112, 1609–1616. M¨aser, P., Eckelman, B., Vaidyanathan, R., Horie, T., Fairbairn, D.J., Kubo. M., Yamagami, M., Yamaguchi, K., Nishimura, M., Uozumi, N., Robertson, W., Sussman, M.R. and Schroeder, J.I. (2002a) Altered shoot/root Na+ distribution and bifurcating salt sensitivity in Arabidopsis by genetic disruption of the Na+ transporter AtHKT1. FEBS Lett., 531, 157–161. M¨aser, P., Hosoo, Y., Goshima, S., Horie, T., Eckelman, B., Yamada, K., Yoshida, K., Bakker, E.P., Shinmyo, A., Oiki, S. and Schroeder, J.I. & Uozumi, N. (2002b) Glycine residues in potassium channel-like selectivity filters determine potassium selectivity in four-loop-per-subunit HKT transporters from plants. Proc. Natl. Acad. Sci. USA, 99, 6428–6433. Matsushita, N. & Matoh, T. (1991) Characterization of Na+ exclusion mechanisms of salt-tolerant reed plants in comparison with salt-sensitive rice plants. Physiol. Plantarum, 83, 170–176. McCormick, D.B. & Chen, H. (1999) Update on interconversions of vitamin B-6 with its coenzyme. J. Nutr., 129, 325–327. Mendoza, I., Rubio, F., Rodriguez-Navarro, A. & Pardo, J.M. (1994) The protein phosphatase calcineurin is essential for NaCl tolerance of Saccharomyces cerevisiae. J. Biol. Chem., 269, 8792– 8796. Modrek, B. & Lee, C. (2002) A genomic view of alternative splicing. Nat. Genet., 30, 13–19. Moriau, L., Michelet, B., Bogaerts, P., Lambert, L., Michel, A., Oufattole, M. & Boutry, M. (1999) Expression analysis of two gene subfamilies encoding the plasma membrane H+ -ATPase in Nicotiana plumbaginifolia reveals the major transport functions of this enzyme. Plant J., 19, 31–41. Munns, R., Tonnet, L., Shennan, C. & Gardner, P.A. (1988) Effect of high external NaCl concentration on ion transport within the shoot of Lupinus albus. II. Ions in phloem sap. Plant Cell Environ., 11, 291–300.
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Nakamura, T., Liu, Y., Hirata, D., Namba, H., Harada, S., Hirokawa, T. & Miyakawa, T. (1993) Protein phosphatase type 2B (calcineurin)-mediated, FK506-sensitive regulation of intracellular ions in yeast is an important determinant for adaptation to high salt stress conditions. EMBO J., 12, 4063–4071. Nass, R. & Rao, R. (1999) The yeast endosomal Na+ /H+ exchanger, Nhx1, confers osmotolerance following acute hypertonic shock. Microbiology, 145, 3221–3228. Ohta, M., Guo, Y., Halfte, U. & Zhu, J.K. (2003) A novel domain in the protein kinase SOS2 mediates interaction with the protein phosphatase 2C ABI2. Proc. Natl. Acad. Sci. USA, 100, 11771– 11776. O’Keefe, S.J., Tamura, J., Kincaid, R.L., Tocci, M.J. & O’Neill, E.A. (1992) FK-506- and CsA-sensitive activation of the interleukin-2 promoter by calcineurin. Nature, 57, 692–694. Perez-Alfocea, F., Balibrea, M.E., Alarcon, J.J. & Bolarin, M.C. (2000) Composition of xylem and phloem exudates in relation to the salt-tolerance of domestic and wild tomato species. J. Plant Physiol., 156, 367–374. Pitts, R.J., Cernac, A. & Estelle, M. (1998) Auxin and ethylene promote root hair elongation in Arabidopsis. Plant J., 16, 553–560. Portillo, F. (2000) Regulation of plasma membrane H+ -ATPase in fungi and plants. Biochim. Biophys. Acta Biomembr., 1469, 31–42. Qiu, Q.S., Barkla, B.J., Vera-Estrella, R., Zhu, J.K. & Schumaker, K.S. (2003) Na+ /H+ exchange activity in the plasma membrane of Arabidopsis. Plant Physiol., 132, 1041–1052. Qiu, Q.S., Guo, Y., Dietrich, M.A., Schumaker, K.S. & Zhu, J.K. (2002) Regulation of SOS1, a plasma membrane Na+ /H+ exchanger in Arabidopsis thaliana, by SOS2 and SOS3. Proc. Natl Acad. Sci. USA, 99, 8436–8441. Qiu, Q.S., Guo, Y., Quintero, F.J., Pardo, J.M., Schumaker, K.S. & Zhu, J.K. (2004) Regulation of vacuolar Na+ /H+ exchange in Arabidopsis thaliana by the salt-overly-sensitive (SOS) pathway. J. Biol. Chem., 279, 207–215. Quintero, F.J., Blatt, M.R. & Pardo, J.M. (2000) Functional conservation between yeast and plant endosomal Na+ /H+ antiporters. FEBS Lett., 471, 224–228. Quintero, F.J., Ohta, M., Shi, H., Zhu, J.K. & Pardo, J.M. (2002) Reconstitution in yeast of the Arabidopsis SOS signaling pathway for Na+ homeostasis. Proc. Natl. Acad. Sci. USA, 99, 9061–9066. Ralevic, V. & Burnstock, G. (1998) Receptors for purines and pyrimidines. Pharmacol. Rev., 50, 413– 492. Rubio, F., Gassmann, W. & Schroeder, J.I. (1995) Sodium-driven potassium uptake by the plant potassium transporter HKT1 and mutations conferring salt tolerance. Science, 270, 1660–1663. Rubio, F., Schwarz, M., Gassmann, W. & Schroeder, J.I. (1999) Genetic selection of mutations in the high affinity K+ transporter HKT1 that define functions of a loop site for reduced Na+ permeability and increased Na+ tolerance. J. Biol. Chem., 274, 6839–6847. Rus, A., Yokoi, S., Sharkhuu, A., Reddy, M., Lee, B.H., 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. Schachtman, D.P. & Schroeder, J.I. (1994) Structure and transport mechanism of a high-affinity potassium uptake transporter from higher plants. Nature, 370, 655–658. Shi, H. & Zhu, J.K. (2002a) Regulation of expression of the vacuolar Na+ /H+ antiporter gene AtNHX1 by salt stress and ABA. Plant Mol. Biol., 50, 543–550. Shi, H., & Zhu, J.K. (2002b) SOS4, a pyridoxal kinase gene, is required for root hair development in Arabidopsis. Plant Physiol., 129, 585–593. Shi, H., Ishitani M., Kim C.S. & Zhu, J.K. (2000) The Arabidopsis thaliana salt tolerance gene SOS1 encodes a putative Na+ /H+ antiporter. Proc. Natl. Acad. Sci. USA, 97, 6896–6901. Shi, H., Kim, Y.S., Guo, Y., Stevenson, B. & Zhu J.K. (2003a) The Arabidopsis SOS5 locus encodes a cell surface adhsion protein and is required for normal cell expansion. Plant Cell, 15, 19–32. Shi, H., Lee, B., Wu, S.J. & Zhu, J.K. (2003b) Overexpression of a plasma membrane Na+ /H+ antiporter improves salt tolerance in Arabidopsis. Nat. Biotechnol., 21, 81–85.
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Shi, H., Quintero, F.J., Pardo, J.M. & Zhu, J.K. (2002a) The putative plasma membrane Na+ /H+ antiporter SOS1 controls long-distance Na+ transport in plants. Plant Cell, 14, 465–477. Shi, H., Xiong, L., Stevenson, B., Lu, T. & Zhu, J.K. (2002b) The Arabidopsis salt overly sensitive 4 mutants uncover a critical role for vitamin B6 in plant salt tolerance. Plant Cell, 14, 575–588. Trewavas, A.J., Rodrigues, C., Rato, C. & Malho, R. (2002) Cyclic nucleotides: the current dilemma! Curr. Opin. Plant Biol., 5, 425–429. Uozumi, N., Kim, E.J., Rubio, F., Yamaguchi, T., Muto, S., Tsuboi, A., Bakker, E.P., Nakamura, T. & Schroeder, J.I. (2000) The Arabidopsis HKT1 gene homolog mediates inward Na+ currents in xenopus laevis oocytes and Na+ uptake in Saccharomyces cerevisiae. Plant Physiol., 122, 1249–1259. Venema, K., Quintero, F.J., Pardo, J.M. & Donaire, J.P. (2001) The Arabidopsis Na+ /H+ exchanger AtNHX1 catalyzes low affinity Na+ and K+ transport in reconstituted liposomes. J. Biol. Chem., 277, 2413–2418. Vitart, V., Baxter, I., Doerner, P. & Harper, J.F. (2001) Evidence for a role in growth and salt resistance of a plasma membrane H+ -ATPase in the root endodermis. Plant J., 27, 191–201. Wang, T.B., Gassmann, W., Rubio, F., Schroeder, J.I. & Glass, A.D. (1998) Rapid Up-regulation of HKT1, a high-affinity potassium transporter gene, in roots of barley and wheat following withdrawal of potassium. Plant Physiol., 118, 651–659. Winter, E. (1982) Salt tolerance of Trifolium alexandrinum L. III. Effects of salt on ultrastructure of phloem and xylem transfer cells in petioles and leaves. Aust. J. Plant Physiol., 9, 227–237. Wu, S.J., Lei, D. & Zhu, J.K. (1996) SOS1, a genetic locus essential for salt tolerance and potassium acquisition. Plant Cell, 8, 617–627. Yokoi, S., Quintero, F.J., Cubero, B., Ruiz, M.T., Bressan, R.A., Hasegawa, P.M. & Pardo, J.M. (2002) Differential expression and function of Arabidopsis thaliana NHX Na+ /H+ antiporters in the salt stress response. Plant J., 30, 529–539. Zhang, H.X. & Blumwald, E. (2001) Transgenic salt-tolerant tomato plants accumulate salt in foliage but not in fruit. Nat. Biotechnol., 19, 765–768. Zhang, H.X., Hodson, J.N., Williams, J.P. & Blumwald, E. (2001) Engineering salt-tolerant Brassica plants: characterization of yield and seed oil quality in transgenic plants with increased vacuolar sodium accumulation. Proc. Natl. Acad. Sci. USA, 98, 12832–12836. Zhu, J.K. (2000) Genetic analysis of plant salt tolerance using Arabidopsis. Plant Physiol., 124, 941– 948. Zhu, J.K. (2003) Regulation of ion homeostasis under salt stress. Curr. Opin. Plant Biol., 6, 441–445. Zhu, J.K., Liu, J. & Xiong, L. (1998) Genetic analysis of salt tolerance in Arabidopsis thaliana: evidence of a critical role for potassium nutrition. Plant Cell, 10, 1181–1192.
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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 (M¨aser 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 (M¨aser 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; V´ery & 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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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 <0.5 cM (Hazen & Kay, 2003). Because FN mutagenesis produces deletions between 2 and −4 kb (Li et al., 2001), and assuming the deletion is within an exon, bulk segregant analysis of pooled F2 plants using genomic DNA as a probe should also provide a rapid strategy for the identification of mutant genes. The emerging high-density Arabidopsis genome tiling arrays should greatly facilitate the identification of genomic polymorphisms, allowing the rapid identification of deletions produced by FN mutagenesis in a single F2 bulk segregant analysis experiment. Such an approach could revolutionize forward genetics in Arabidopsis making it possible to identify a mutation within 3–4 months. Positional cloning of the genes responsible for ion-profile changes in several of the ionomic mutants (Lahner et al., 2003) is underway in our laboratory. Outcrosses to Ler have been performed, hybrid F1 allowed to self and F2 screened for homozygous mutants. These F2 mapping population are now being screened for cosegragating genetic polymorphisms. The alternative to using positional cloning to map a mutation of interest is to use a mutagen that creates an easily identified genetic tag, the most common of which is T-DNA (Azpiroz-Leehan & Feldmann, 1997; Krysan et al., 1999). Based on the known sequence of the T-DNA insert, PCR and plasmid rescue strategies have been devised to rapidly clone DNA sequences flanking the TDNA insertion site (Feldmann, 1992; Gasch et al., 1992; Liu et al., 1995). With the availability of the Arabidopsis genome insertion site sequence is easily associated with a gene. Such a strategy can be complicated by the fact that 35–40% of mutants identified from T-DNA populations are not tagged (Azpiroz-Leehan & Feldmann, 1997), probably due to abortive integration events. Because of the potentially rapid identification of mutant genes this approach has become very popular. However, due to the low mutation frequency saturation screening requires analysis of 200 000–400 000 plants, making such an approach unrealistic for ionomics at the present rate of sample throughput (Lahner et al., 2003). 7.6.2 Exploiting natural variation As an alternative to laboratory-induced mutations, genetic variation occurring among and within natural populations of Arabidopsis can be used (AlonsoBlanco & Koornneef, 2000). Since Arabidopsis shows a wide geographic distribution many Arabidopsis ecotypes or accessions have been collected and are available from the Arabidopsis Biological Resource Center (ABRC) and the Nottingham Arabidopsis Stock Centre (NASC). Considerable variations for such traits as resistance to biotic and abiotic stress, development and metabolic traits have been described (review see Alonso-Blanco & Koornneef, 2000). Observed variation between accessions can either be qualitative, defined by phenotypic distributions that fall into discrete classes and is caused by one or two major loci, or quantitative, defined by a continuous phenotypic distribution and caused by
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the combined effect of multiple loci. Natural variation in seed and shoot phosphate accumulation is known to exist between the Landsberg erecta (Ler) and Cape Verde Island (Cvi) accessions (Bentsink et al., 2003). Recent data from our laboratory have revealed that significant variation exists in the shoot and seed ionomes between various Arabidopsis accessions. For example, shoots of the Arabidopsis accession Cvi-0 contained significantly lower concentrations of Mg, Na, Ca, and Fe and higher Mo when compared to Col-0. The robust and precise quantification of such variation holds the promise of allowing either mendelian or quantitative trait loci mapping to identify genes involved in regulating the ionome in Arabidopsis. 7.6.3 Reverse genetic approaches An alternative to the forward genetic approach described is the opposite strategy of starting with a mutation in a known gene and asking the question ‘does this mutation have an ionomic phenotype?’ Such an approach switches the focus from one of high throughput screening and mapping to an approach that requires in depth biochemical and physiological analyses of the mutant. As discussed above a reverse genetic approach, in the context of ionomics, requires solutions to a different set of analytical problems – reverse genetics is a CAP approach whereas forward genetics is for the Speeders. For reverse genetics to be a viable functional genomics approach, mutations in all genes in the genome ideally need to be available. A close to saturation collection of Arabidopsis T-DNA insertional mutants, with sequenced borders, curated at Salk Institute Genomic Analysis Laboratory (SIGnAL), made this approach attractive in Arabidopsis. High-throughput screening for induced point mutations (targeting induced local lesions in genomes; TILLING) also makes it possible to identify alternative alleles in genes of interest in numerous species including maize, black cottonwood, Brassica oleracea, and Lotus japonicus. The use of genomic DNA pooling and PCR also makes the identification of mutants in specific genes possible in other species using fast neutron deletion mutagenesis (Li et al., 2001). As part of the ionomics project in our laboratory we are in the process of quantifying the shoot ionome in approximately 1000 homozygous T-DNA insertional lines of Arabidopsis. Mutants have been chosen with insertions in exons of genes thought to be involved in regulating the ionome, including ion transporters and various regulatory proteins. At present, we have analyzed the shoot ionome in approximately 12 000 plants, representing insertions in approximately 600 genes. This data is available in a searchable form in the Arabidopsis Ionomic Database (AID) housed at Purdue University (http://hort.agriculture.purdue.edu/ionomics/database.asp). Our reverse genetic screens are continuing and we will be periodically updating the database with new data.
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Acknowledgments This project is part of a larger collaborative effort funded by the National Science Foundation (NSF) Plant Functional Genomics program (0077378-DBI) awarded to Mary Lou Guerinot, David Eide, Jeff Harper, David E Salt, and Julian Schroeder. More details about the collaborators and project can be found at http://plantst.sdsc.edu/. We also thank Venugopal Naga Venkata Gudimetla for his help in developing the online searchable database.
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Kang, D., Amarasiriwardena, D. & Goodman, A.H. (2004) Application of laser ablation-inductively coupled plasma-mass spectroscopy (LA-ICP-MS) to investigate trace metal distributions in human tooth enamel and dentine growth layers and pulp. Anal. Bioanal. Chem., 378, 1608–1615. Koller, A., Washburn, M.P., Lange, B.M., Andon, N.L., Deciu, C., Haynes, P.A., Hays, L., Schielts, D., Ulaszek, R., Wei, J., Wolters, D., & Yates, J.R. (2002) Proc. Natl. Acad. Sci. USA, 99, 11969– 11974. Koornneef, M., Dellaert, L.W.M. & van der Veen, J.H. (1982) EMS- and radiation-induced mutation frequencies at individual loci in Arabidopsis thaliana (L.) Heynh. Mutation Res., 93, 109– 123. Krysan, P.J., Young, J.C. & Sussman, M.R. (1999) T-DNA as an insertional mutagen in Arabidopsis. Plant Cell, 11, 2283–2290. Lahner, B., Gong, J., Mahmoudian, M., Smith, E.L., Abid, K.B., Rogers, E.E., Guerinot, M.L., Harper, J.F., Ward, J.M., McIntyre, L., Schroeder, J.I. & Salt, D.E. (2003) Genomic scale profiling of nutrient and trace elements in Arabidopsis thaliana. Nat. Biotechnol., 21, 1215–1221. Leonhardt, N., Kwak, J.M., Robert, N., Waner, D., Leonhardt, G. & Schroeder, J.I. (2004) Microarray expression analysis of Arabidopsis guard cells and isolation of a recessive abscisic acid hypersensitive protein phosphatase 2C mutant. Plant Cell, 16, 596–615. Li, X., Song, Y., Century, K., Straight, S., Ronald, P., Dong, X., Lassner, M. & Zhang, Y. (2001). A fast neutron deletion mutagenesis-based reverse genetics system for plants. Plant J., 27, 235–242. 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. Maathuis, F.J.M., Filatov, V., Herzyk, P., Krijger, G.C., Axelsen, K.B., Chen, S.X., Green, B.J., Li, Y., Madagan, K.L., Sanchez-Fernandez, R., Forde, B.G., Palmgren, M.G., Rea, P.A., Williams, L.E., Sanders, D. & Amtmann, A. (2003) Transcriptome analysis of root transporters reveals participation of multiple gene families in the response to cation stress. Plant J., 35, 675–692. Marschner, H. (1995) Mineral Nutrition of Higher Plants. 2nd edn, Academic Press, New York. Martzivanou, M. & Hampp, R. (2003) Hyper-gravity effects on the Arabidopsis transcriptome. Physiol. Plantarum, 118, 221–231. M¨aser, P., Thomine, S., Schroeder, J.I., Ward, J.M., Hirschi, K., Sze, H., Talke, I.N.,Amtmann, A., Maathuis, F.J.M., Sanders, D., Harper, J.H., Tchieu, J., Gribskov, M., Persans, M.W., Salt, D.E., Kim, S.A. & Guerinot, M.L. (2001) Phylogenetic relationships within cation-transporter families of Arabidopsis thaliana. Plant Physiol., 126, 1646–1667. Narewski, U., Werner, G., Schulz, H., & Vogt, C. (2000) Application of laser ablation inductively coupled mass spectrometry (LA-ICP-MS) for the determination of major, minor, and trace elements in bark samples. Fresenius J. Anal. Chem., 366, 167–70 Negishi, T., Nakanishi, H., Yazaki, J., Kishimoto, N., Fujii, F., Shimbo, K., Yamamoto, K., Sakata, K., Sasaki, T., Kikuchi, S., Mori, S., & Nishizawa, N.K. (2002) cDNA microarray analysis of gene expression during Fe-deficiency stress in barley suggests that polar transport of vesicles is implicated in phytosiderophore secretion in Fe-deficient barley roots. Plant J., 30, 83–94. Outten, C.E. & O’Halloran, T.V. (2001) Femtomolar sensitivity of metalloregulatory proteins controlling zinc homeostasis. Science, 292, 2488–2492. Parinov, S., Sevugan, M., Ye, D., Yang, W.-C., Kumaran, M. & Sundaresan, V. (1999) Analysis of flanking sequences from Dissociation insertion lines: A database for reverse genetics in Arabidopsis. Plant Cell, 11, 2263–2270. Pickering, I.J., Hirsch, G., Prince, R.C., Yu, E.Y., Salt, D.E. & George, G.N. (2003) Imaging of selenium in plants using tapered metal capillary optics. J. Synchrotron Radiat., 10, 289–290. Pickering, I.J., Prince, R.C., Salt, D.E. & George, G.N. (2000) Quantitative chemically-specific imaging of selenium transformation in plants. Proc. Natl. Acad. Sci. USA, 97, 10717–10722. Rausch, C., & Bucher, M. (2002) Molecular mechanisms of phosphate transport in plants. Planta, 216, 23–37.
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Rea, P.A. (2003) Ion genomics. Nat. Biotechnol., 21, 1149–1151. Sanders, D., Pelloux, J., Brownless, C. & Harper, J.F. (2002) Calcium at the crossroads of signaling. Plant Cell, 14, S410–S417. Stark, G.R. & Gudkov, A.V. (1999) Forward genetics in mammalian cells: functional approaches to gene discovery. Human Mol. Genet., 8, 1925–1938. Sundaresan, V. (1996) Horizontal spread of transposon mutagenesis: new uses for old elements. Trends Plant Sci., 6, 184–190. Szpunar, J. (2004) Metallomics: a new frontier in analytical chemistry. Anal. Bioanal. Chem., 378, 54–56. Thimm, O., Essigmann, B., Kloska, S., Altmann, T. & Buckhout, T.J. (2001) Response of Arabidopsis to iron deficiency stress as revealed by microarray analysis. Plant Physiol., 127, 1030–1043. V´ery, A.-A. & Sentenac H (2003) Molecular mechanisms and regulation of K+ transport in higher plants. Annu. Rev. Plant Biol., 54, 575–603. Wang, R., Okamoto, M., Xing, X. & Crawford, N.M. (2003) Microarray analysis of the nitrate response in Arabidopsis roots and shoots reveals over 1,000 rapidly responding genes and new linkages to glucose, trehalose-6-phosphate, iron, and sulfate metabolism. Plant Physiol., 132, 556–567. Weigel, D., Ahn, J.H., Blazquez, 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., Dixon, R.A., Harrison, M.J., Lamb, C.J., Yanofsky, M.F. & Chory, J. (2000) Activation tagging in Arabidopsis. Plant Physiol., 122, 1003–1013. Williams, R.J.P. (2001) Chemical selection of elements by cells. Coord. Chem. Rev., 216–217, 583–595. Wintz, H., Fox, T., Wu, Y.Y., Feng, V., Chen, W., Chang, H.S., Zhu, T. & Vulpe, C. (2003) Expression profiles of Arabidopsis thaliana in mineral deficiencies reveal novel transporters involved in metal homeostasis. J. Biol. Chem., 278, 47644–47653. Zhu, J.-K. (2003) Regulation of ion homeostasis under salt stress. Curr. Opin. Plant Biol., 6, 441–445.
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Transcriptional profiling of membrane transporters Frans J.M. Maathuis and Anna Amtmann
8.1 Introduction The functioning of living organisms depends crucially on information processing, for example during the perception of and response to the environment, or during the progression through different developmental stages. In living cells, genetic information flow is normally unidirectional, from DNA to RNA to proteins. The concerted activity of proteins defines the shape and characteristics of an organism, i.e. its phenotype, but ultimately this is to a large extent controlled by the genetic code of the DNA sequence. It is therefore not surprising that transcription of this sequence, i.e. the expression of particular genes and its regulation, has been extensively studied. The advent of microarray technology allows the simultaneous investigation of the expression of thousands or tens of thousands of genes. This chapter deals with general aspects of microarray technology and its applications, particularly regarding the study of membrane transporters and their role in plant mineral nutrition. Approaches that assess the expression of single or small numbers of genes have helped significantly in assigning functions to gene products. The underlying assumption in such studies is that changes in gene expression per se may reflect functional properties of the gene product, the protein. For example, up-regulation of genes after a wounding experiment may suggest that the encoded proteins are involved in the wounding response. However, genomes of multicellular organisms typically contain tens of thousands of genes and a fair proportion of these are expressed at any particular time to encode an equal or even larger number of proteins. A main challenge for biologists and bioinformaticians is therefore to reveal how the expression of such complex sets of genes and proteins leads to particular phenotypes and how these patterns vary in time and space as organisms develop and interact with their environments. Complete genome sequences are available for an increasing number of organisms and, at least in theory, we therefore know all genes in these organisms. Thus, if the appropriate methodology is present, the expression of each gene in space and time can be evaluated to provide a comprehensive picture of how gene expression and protein expression lead to integrated responses to environmental and developmental clues.
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There are various approaches which can be used to detect gene expression and to measure its changes. Most are based on the hybridisation between a gene-specific probe and the RNA or cDNA of the gene of interest. The classical ‘Northern’ technique (e.g. Kozian & Kirschbaum, 1999; Weigel & Glazebrook, 2002) consists of separating RNAs through gel electrophoresis, blotting the RNAs on a cellulose filter and subsequent probing of the filter with a probe complementary to the gene of interest. The greater the abundance of the queried RNA, the more hybridised probe will ‘stick’ to the filter which can be quantified easily if the probe had been labelled (for example with radioactive P). In addition to hybridisation and blotting based methods, polymerase chain reaction (PCR) based methods such as competitive PCR (e.g. Callaway et al., 2002) and real time PCR (e.g. Bohm et al., 1999) are becoming increasingly popular. These methods consist of reverse-transcribing mRNA, or transcript, into a cDNA template of which the amplification is measured against a standard. The more transcript, the more template and hence the more PCR product. Semi-quantitative approaches to follow expression of single genes can also include fusion of reporter genes such as -glucuronidase (GUS) and green fluorescent protein (GFP) (Weigel & Glazebrook, 2002), to promoter or coding regions whereby visualised reporter expression directly correlates with expression of the gene under study. These approaches are adequate to study expression of a single gene or a small number of genes. To assess changes in the expression of large numbers or of all transcripts, high throughput technologies are required. Initially, cellulose based filter arrays (macroarrays, dot blots) were used for this purpose (Baldwin et al., 1999). Such filters contained hundreds to thousands of spots of immobilised nucleic acid that could be assayed by hybridising the filter with complex mixtures of labelled cDNA. The disadvantages of this method are, first, the large size of individual spots and, by consequence, of the filters; second, the high level of autofluorescence of the cellulose membrane, precluding the use of multiplex fluorescent probes. Only the development of microarrays (Schena et al., 1998) capable of carrying thousands of microspots on a solid, non-fluorescent, support allowed the simultaneous analysis of the expression of large numbers of transcripts or of the entire transcriptome (e.g. transcripts of all the expressed coding regions in a genome). Consequently, the study of entire transcriptomes or ‘transcriptomics’ became feasible.
8.2 An overview of microarray technology In essence, a microarray is a small solid support that contains thousands of tiny spots consisting of immobilised nucleic acid attached to the surface (Plate 1 – see colour plate section; Schena, 2000; Worley et al., 2000). Each spot represents a particular gene, being made up of thousands of copies of oligomers,
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encoding a gene specific ‘probe’ sequence. The probe can be derived from cDNA making it several hundred bases long, or from synthesised oligomers which are typically in the order of 40 to 70 bases. The array with its probes can be hybridised with one or more nucleic acid samples of interest, the ‘targets’ (note that in some literature the nomenclature of ‘probes’ and ‘targets’ is used in the opposite way). Target samples are usually cDNAs obtained through reverse transcription of RNA samples. They will often consist of a reference sample and an experimental sample (e.g. wild type versus mutant or non-treated versus treated) that have been differentially labelled and are simultaneously hybridised to one array. There are several ways by which cDNA samples can be labelled, but a convenient way to hybridise more than one sample in parallel is the use of fluorescent dyes that show different wavelengths for maximum emission (Mujumdar et al., 1996), thus allowing the quantification of each label after hybridisation and washing steps, by scanning the array at the appropriate wavelengths. The respective fluorescence intensities in each probe are proportional to the amount of complementary cDNA in the tested samples, thereby yielding a relative expression for each gene represented on the array (Randolph & Wagoner, 1997). 8.2.1 What microarray studies can do The cost of conducting microarray experiments is still relatively high. This is partly due to the purchase of arrays themselves (e.g. between 50 and 300 pounds for genome covering arrays) and partly due to the required consumables. In particular, the fluorescence compounds, such as cyanine dyes, used to label the nucleic acid samples are expensive and can add 20 to 40 pounds to a single array hybridisation. Nevertheless, the ‘price:information’ ratio for microarrays can be extremely low since expression data can be obtained for thousands or tens of thousands of genes in a single experiment. The overall cost of transcriptomics studies based on the use of microarrays will depend on the experimental design, such as the type of array used, the method of labelling, the number of conditions tested and the number of experimental replicates required to reach adequate levels of confidence. Ultimately, these choices will be intricately linked to the questions one wishes to answer. Before dealing with some appropriate examples, it is useful to point out what type of studies are less suitable. (i) Studying changes in expression for a small number of genes (i.e. <10): Technically this can be done using microarrays but generally it may not be very cost effective in comparison to, for example PCR-based methods. However, a potential option is the manufacturing of customer specified arrays that can be obtained relatively cheaply. (ii) Quantitative determination of expression changes: Although it is now generally accepted that microarrays are excellent tools to assess general changes in expression, i.e. transcript levels are going down, up or are not changed, the deduced ratios are sensitive to many interferences, especially at low signal intensities. Therefore,
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the deduced expression ratios should generally be considered semi-quantitative and for very accurate comparisons of gene expression real-time PCR-based methods are preferable. (iii) Expression changes in single or small numbers of cells: Microarray hybridisations require a relatively large amount of cDNA (usually derived from between 10 and 100 g total RNA). This means that normally milligrams of tissue are required. Currently, protocols are being developed and implemented (e.g. Wang et al., 2000b; Iscove et al., 2002) that include sophisticated RNA/cDNA amplification steps to generate enough probe from small numbers of cells (or even single cells) to carry out microarray analysis. However, since linearity of the amplification needs to be maintained, such methods are still technically very challenging and success is yet to be achieved with plant tissues. (iv) Comparison of absolute expression levels: In many biological contexts, it is not only of interest how the expression of particular genes changes but also how the absolute level of expression for one gene compares to another. For example, within a family of closely related genes, it could be of interest to see which isoforms are expressed strongly and which are not. There are two reasons why microarray approaches are less suitable to establish absolute levels of gene expression. First, since the produced cDNA target sequences will have different lengths they will contain different amounts of (fluorescent) label. Absolute levels of fluorescence therefore cannot be compared from one probe (gene) to another. Second, probe hybridisation will depend on homology between probe and target sequence. This can lead to risks of cross hybridisation for highly similar genes. In addition, probe sequences may have different G:C contents affecting hybridisation and, in the case of cDNA based arrays can have different lengths. Thus, the simple assumption that two different probe/target pairs hybridise to the same extent (given they are equally abundant) is not generally valid. However, microarray based approaches can contribute greatly to answering transcriptomics questions. The versatility of the technique is born out by the increasing number of publications based on this technology. Microarrays do not simply replace previous techniques but, due to their accessibility to statistical and combinatorial analysis methods, add a new dimension of information to expression studies that was formerly not available. This allows us to acquire answers to new questions regarding gene expression, genome analysis, drug discovery etc. 8.2.2 Gene expression studies With the increasing amount of sequence data, a primary question regards knowledge about gene function, which in many cases is insufficient or even totally absent. By exposing cells or organisms to defined and controlled conditions, one can test which genes are affected in their expression. Often these are open ended, non-hypothesis driven, experiments to characterise annotated and new genes. Many examples of such studies in plants are available in the literature
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and at various publicly available websites (see below). Experimental approaches include plant treatment with pathogens (Cartieaux et al., 2003), hormones (Rabbani et al., 2003), abiotic stresses (Desikan et al., 2001; Kawasaki et al., 2001; Seki et al., 2002; Thimm et al., 2001; Ozturk et al., 2002; Maathuis et al., 2003) and biotic stress (Schenk et al., 2000; Narusaka et al., 2003). Studies have been carried out on different plant species including Arabidopsis, rice, barley and Medicago (Maathuis et al., 2003; Kawasaki et al., 2001; Faccioli et al., 2002; Fedorova et al., 2002). Another frequent application of transcriptomics studies is the comparison between wild-type and mutant organisms (e.g. Duque & Chua, 2003). This approach can greatly help in explaining observed phenotypes by assessing how a mutation in one gene affects the expression of other genes. For example, a lack of function of an enzyme in a metabolic pathway may have repercussions for the functioning and expression of enzymes downstream in that pathway through substrate dependent feedback mechanisms. Many mutants, particularly those isolated through reverse genetics, do not show obvious phenotypes. This often originates from a functional redundancy of genes. Transcriptomics studies can show how the regulation of alternative transcripts, either related or non-related, compensates for the altered function of the mutant gene and thereby prevents the occurrence of an apparent phenotype. 8.2.3 Genomic analyses Microarray studies can be invaluable in identifying how gene transcription is regulated. Potential regulatory networks can be identified on the basis that expression profiles of genes within a network are likely to be similar. Computer algorithms can sift through large amounts of expression data to extract gene clusters that show similar expression patterns (e.g. Brazma & Vilo, 2000; Huang et al., 2003), across a time course, a range of treatments or amongst a collection of mutants. This not only leads to the identification of co-regulated genes, but further analysis of the promoter regions of co-regulated genes can be carried out to search for promoter cis-elements, signature sequences within promoter regions that may bind transcriptional regulators like transcription factors (Chen et al., 2002; Soman et al., 2003). Target sequences of putative open reading frames can be tested for hybridisation to identify new genes. By using ‘mis-match analysis’, i.e. comparing hybridisation between 100% homologous sequences with hybridisation of sequences that are mismatched in one or a few bases, microarrays can also be used to identify splice variants (Hu et al., 2001), single nucleotide polymorphisms (Tillib et al., 2001) and genomic mutations (Sapolski et al., 1999; Narusaka et al., 2003). 8.3 General aspects of microarray technology Microarray assays are routinely employed in an increasing number of academic and industrial institutions. Nevertheless, this still entails a technically
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complicated and by no means foolproof technology. Therefore, the many facets of this approach will be dealt with in some more detail in the following sections. 8.3.1 Microarray manufacturing Two general methods exist for the deposition of the gene-specific probes on the solid support which typically consists of a (treated) microscope glass slide (Hegde et al., 2000; Worley et al., 2000). Nucleic acid probes can be synthesised base by base directly onto the glass surface by using photolithographic masks. Synthesis is initiated by attaching a linker to the glass surface that contains a light sensitive protective group. By using masks that have small holes in the appropriate spot positions, light can be used to deactivate the protective group thereby making it available for covalent binding of a nucleotide. By using different masks for every round of nucleotide addition, the oligomer synthesis can be exactly controlled for every position, creating arrays with small and very accurate spots and spot densities of up to 300 000 per cm2 . Alternatively, probes in the form of cDNAs (e.g. through PCR amplification of a cDNA library) or synthesised oligonucleotides (40 to 70mers) are spotted on chemically modified glass slides. This typically proceeds through the action of a robot that collects the probes from a microtitre plate into an array of pens (e.g. 8 × 12). The pen array, holding 96 different probes, contains very fine tips that allow the release of picolitre amounts of probe. The pen array spots the probes onto the surface of the required number of slides, is thoroughly washed and subsequently starts a new round of spotting at a new position on the slide with a different set of probes. Microarrays will often contain several spots per gene and also more than one probe sequence per gene. By providing multiple spots per gene, an internal replica is made for every hybridisation, guarding against possible artefacts such as high background areas or different hybridisation efficiency across the array surface. Multiple probes will have different sequences for the same gene. This greatly limits the possibility of cross hybridisation and therefore improves gene specificity. 8.3.2 Experimental design Expression of genes is regulated by many factors and can be sensitive to the smallest variations in external conditions, developmental stages etc. which are not necessarily under the control of the experimenter. In addition, the measured expression ratios will be sensitive to experimental errors such as differences in labelling between samples and variation in hybridisation. These considerations impose limitations to the amount of reliable information that can be obtained from experiments. Proper experimentation requires that growth and treatment conditions and development are controlled as much as possible (e.g. Lee et al., 2000). RNA
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from more than one individual should be pooled to minimise biological variation and experiments should preferably be carried out in several replicates to assess biological variation and allow the calculation of averages and standard deviations. Replica probe spots on arrays can help identify potential problems during the hybridisation whereas control spots for spiked RNA and blanks can reveal whether labelling reactions were adequate and if background signals are acceptable. 8.3.3 RNA isolation and labelling Most conventional RNA isolation methods can be applied to microarray analyses (e.g. Weigel & Glazebrook, 2002 and references therein). However, the information obtained from microarray experiments depends crucially on the quality of the RNA preparation. This implies that utmost care must be taken to avoid RNA contamination with DNA, proteins, lipids or carbohydrates, and to remove chemicals used during the isolation procedure, for example phenol. RNA quality (e.g. Gruffat et al., 1996) can be assessed for degradation by visualising it on a gel. Discreet banding patterns indicate the presence of intact RNA whereas a smear points to degraded RNA. Further quality checks can reveal the purity of the sample, for example by measuring an absorbance spectrum of the sample over the 200 to 350 nm range. The absorbance at 260 nm can be used to quantify the amount of RNA in clean preparations. Contaminating proteins will absorb at 280 nm, phenol residuals will produce an absorption shoulder at around 270 nm, whereas many of the carbohydrates and other contaminants show up as an absorbance peak near 230 nm. Nowadays, high-throughput RNA analysers combining gel electrophoresis and spectrometry are commercially available. Only a few per cent of the total RNA in a cell is made up of mRNA that carries the genetic information encoded in the genes. These transcripts will vary enormously in both size (from a few hundred to over 10 000 nucleotides) and abundance (from a few to thousands of copies per cell). The labelled nucleic acid samples used for hybridisation must accurately reflect the identities and abundance of transcripts (Schena, 2000). Labelling can take place during reverse transcription (direct labelling) by substituting one of the nucleotides that is used for the synthesis of cDNA with a base that is linked to an easily detectable label such as a fluorescent dye. Thus, the reverse transcription polymerase will synthesise cDNA that contains a fluorescent form of one of the bases. By contrast, indirect labelling is carried out after reverse transcription has taken place using an alkylated form of one of the nucleotides. The cDNA with incorporated alkylated nucleotide is subsequently covalently linked to activated dye compounds. The advantage of the latter procedure is a higher labelling efficiency (mainly because reverse transcriptase enzymes are not very good at incorporating the large, dye containing nucleotides). When fluorescent labels are used, the efficiency of incorporation can be determined spectrophotometrically by measuring
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the amount of cDNA (260 nm) and absorption at the appropriate wavelengths of the chromophores. Post hybridisation fluorescence measured at the two characteristic wavelengths should reflect the original transcript composition and abundance for both the reference sample and the experimental sample. Fluorescence intensity is typically measured with a scanner that employs lasers to excite the label fluorophores, and light detecting devices such as photomultiplier tubes to measure the amount of emission light. Scanners must also have an adequate spatial resolution to resolve signal from each spot and from the background area. By using preset colours for each fluorescence signal a false colouring image is obtained for each spot of the array (Plate 2 – see colour plate section) with each colour being correlated to an expression ratio and the colour intensity correlating to the hybridisation signal for each spot (i.e. gene). 8.4 Transcriptomics data analysis and interpretation Analysis of microarray data involves the following steps: (i) image analysis, (ii) signal normalisation, (iii) identification of differentially expressed genes, (iv) gene clustering and (v) biological interpretation. Each of these steps comes with its own challenges. Indeed, the number of methods available for the analysis of microarray data is dazzling and, for most biologists, it is impossible to critically assess the rationales behind the individual algorithms. It should be noted that several excellent, non-commercial, analysis packages are available from the web (e.g. Bioconductor, dChip, see Appendix). We will briefly describe the procedures and applications concerned with each of the above listed steps of microarray analysis. For a more detailed overview of analysis methods consult Speed (2003) and Baldi and Hatfield (2002). 8.4.1 Image analysis Image analysis consists of collecting raw data for fluorescence signal intensity in each spot on the array (or probe set for Affymetrix chips) and for the background. Most scanners are equipped with software for this purpose. Alternatively, image analysis software can be obtained from the web (e.g. Spot and ScanAlyze, see Appendix). To accurately obtain signal data the exact position of spots and background needs to be defined either manually or through spot finding algorithms. Correct spot delimitation is important especially with spotted arrays where spots can vary considerably in position and size. After spots and their surrounding areas have been defined signal and background data are collected. Image analysis is also used to flag array areas and spots containing unreliable signals. For example, inadequate wash procedures may leave ‘smears’ of unhybridised label and specks of dust may generate strong artefactual fluorescence signals.
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8.4.2 Normalisation Microarray experiments are by nature comparative and therefore normalisation is crucial. Arrays that contain different labels (e.g. Cy3/Cy5) require normalisation of the individual dye signals to each other. In case signals from different arrays are compared, normalisation has to be carried out between the arrays. Signal variation that is not due to biological changes in transcript levels may derive from many sources such as different labelling efficiency, divergence in quantum yield of dyes and variation in hybridisation reactions. A prerequisite for any normalisation is an adequate set of invariant genes. This can consist of known control genes (e.g. spiking and ‘housekeeping’ genes) or can be mathematically extracted from the entire dataset. The first approach consists of comparing signals or signal ratios of all individual spots with those of reference spots. References can be genes that are absent in the endogenous RNA isolate (e.g. E. coli genes on plant arrays) and have been ‘spiked’ into the samples in known amounts. Experimental signals can then be normalised to the signals derived from the spiking controls. Alternatively, constitutive or ‘housekeeping’ genes known to have largely stable expression levels can be used for normalisation of ratios. Both strategies have drawbacks: for example the exact amount of added spiking RNA can be difficult to control and in many cases the ‘behaviour’ of ‘housekeeping’ genes is unknown for the specific condition tested in the experiment. As a result very few investigators rely on spiking or constitutive controls alone for normalisation. As an alternative the signal output from all probe/target pairs can be used to normalise dye signals or arrays to each other. In the simplest case this entails a linear scaling procedure (e.g. division of each ratio by the median of all ratios). However, in most cases normalisation requires a non-linear correction and therefore more complex algorithms have to be employed (e.g. Schadt et al., 2000; Li & Wong, 2001; 2002; Dudoit et al., 2002; Irizarry et al., 2003). A critical assessment of several of these approaches favoured the quantile normalisation method (Bolstad et al., 2003). Quantile normalisation has been combined with methods for background correction and log-transformation to build a highly useful normalisation tool (robust multi-array analysis, RMA; Irizarry et al., 2003), which can be obtained from Bioconductor (see Appendix). 8.4.3 Identifying differentially expressed genes The identification of differentially expressed genes is the main goal of most microarray experiments. Methodologies for achieving this goal have shifted over recent years from simple intuitive approaches such as determination of foldchanges (FC) to more sophisticated, t-test based, statistical methods such as Significance Analysis of Microarrays (SAM, Tusher et al., 2001, see Appendix). In the FC approach each signal obtained for the treated sample is divided by the signal obtained for the control sample (for ‘log ratios’ the logarithms of
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each signal are obtained prior to division) and it is assumed that large changes in expression are biologically more significant than small changes. The cut-off for what is considered a ‘significant change’ is often arbitrary (e.g. transcript level changes that exceed twofold). Although statistical methods give the investigator more confidence in the reliability of the results it sometimes appears that statistical rigour can eliminate biologically relevant results. Therefore, the choice of method should reflect the experimental rationale: to extract a ‘safe’ set of differentially expressed genes from highly replicated experiments statistical methods are appropriate whereas for a screening experiment to identify possible candidate genes involved in a biological response, a simple FC approach might suffice. A novel method for the identification of differentially expressed genes has recently been proposed, which is based on a combination of signal strength and statistical validation (Breitling et al., 2004c). This approach ranks genes according to their signal ratio for both up- and down-regulation. By calculating rank products (RP) for each gene a proportionally higher weighting is given to genes that show a consistently high ranking across replicate experiments. Thus, RPs will represent both strong transcriptional change and statistical significance. E-values and false discovery rates can be obtained by calculating RPs for a large number of random experiments and statistical cut-offs can be based on acceptable rates of false-positives. A comparison of the performance of the RP method with the SAM method found that it yields more robust, sensitive and reliable results when evaluated by a variety of criteria. In particular, it was shown that when assessing increasing numbers of replicate experiments the RP method converged to the ‘true’ set of responsive genes more rapidly than the SAM method. Thus, this method is ideally suited when economic and experimental constraints limit the number of replica experiments. A simple Microsoft Excelbased manual for the RP method can be obtained from the Sir Henry Wellcome Functional Genomics Facility (SHWFGF), at the University of Glasgow (see Appendix). 8.4.4 Gene clustering One of the major attractions of microarray technology is the potential to combine results from many different experiments to identify sets of genes that are co-expressed across a number of different treatments and/or time points. Methods for group analysis (see below) can fulfil this task, but traditionally, the identification of sets of co-regulated genes is based on hierarchical clustering methods (Eisen et al., 1999). Based on its response to each experimental condition, every gene is assigned a position in an n-dimensional space (n being the number of conditions) and distances or correlations between the positions can be calculated using various formulae (e.g. Alter et al., 2000; Getz et al., 2000). Distance measurements can be performed in an unsupervised way (where all
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genes are compared to each other), or in a supervised manner where previous knowledge serves as a reference (Brazma & Vilo, 2000). Subsequently, a tree is constructed which organises the genes with respect to their distance within the ‘experiment space’. The gene tree is depicted as an expression matrix with genes in rows and experiments in columns where each entity of the matrix contains a number (or colour code) that quantifies the expression change of the particular gene in the particular experiment. As an alternative to trees, expression profiles can be drawn to depict the behaviour of individual genes over the entire set of array experiments. Genes with similar profiles are grouped together using algorithms such as k-means or self-organised maps (for review see Quackenbush, 2001). The number of profiles, into which the entire set of genes is divided, is pre-determined by the investigator. The result of such analyses is a number of ‘boxes’ each of which depicts the average profile and the range within which profiles of the individual genes vary from the average profile. Genes of a particular profile or cluster might be co-regulated and therefore a logical consequence of clustering is the search for common regulatory target sequences within the upstream regions of clustered genes. The assumption is that relevant regulatory (cis-acting) elements are significantly over-represented in the upstream regions of the gene cluster. Statistical methods can now be employed that compare the occurrence of all possible sequence motifs of a given length within promoter regions of clustered genes with their occurrence in a random background set of promoter regions (e.g. the entire complement of promoter regions). Two commonly used software packages for the detection of over-represented motifs within upstream regions are SPEXS and Bioprospector (see Appendix). The analysis requires prior generation of libraries containing upstream sequences of all genes within cluster and background (see, for example, Maathuis et al., 2003). 8.4.5 Biological interpretation of data The analyses described above will identify differentially expressed genes and establish whether gene expression profiles are similar across different treatments. Most commonly, differentially expressed genes will be grouped according to their annotation, which is sometimes based on functional characterisation but more often only originates from sequence homology. Gene annotation can be obtained from collective databases such as The Arabidopsis Information Resource (TAIR), or The Institute for Genomic Research (TIGR; see Appendix) or from individual researchers. As a result many publications present data sets organised according to annotations such as ‘storage proteins’, ‘photosynthesis’, ‘transport’, ‘transcriptional regulation’ and ‘signalling’. Although such headers are useful for demarcating sets of differentially regulated genes they contain the risk of creating a bias within the ‘data mining process’, due to the size and quality of annotation of the underlying groups. Thus, the attention may be drawn to particularly large or well-studied gene families.
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To avoid group size bias, the number of group-regulated genes must be considered in relation to the total number of group genes present on the array. This approach was used in a study by Maathuis et al. (2003) where the transcriptional response of approximately 1000 putative transporter genes to various nutritional stresses was assessed in Arabidopsis. The procedure included the following steps: (i) Functional or structural annotation of all probes according to homology to known transporters and topological predictions. The categories obtained were exclusive, i.e. no gene appeared in more than one category. (ii) Determination of how many genes within each category were differentially expressed in response to the treatments. By comparing the percentage of regulated genes in each category to the average number of differentially expressed genes for all categories it became apparent that certain types of transporters were more responsive to nutritional stress than others. (iii) Subsequently, regulated genes were sorted according to whether their response was treatment specific or occurred in all treatments. The latter analysis revealed particular transporter categories that displayed higher or lower than average numbers of differentially expressed members in response to a specific treatment (Plate 3, see colour plate section). The above approach has proved useful in identifying certain gene families that are involved in specific nutrient stress responses and others that respond to general stress (Maathuis et al., 2003). However, disadvantages of the method also became apparent. First, both the total number of genes and the number of regulated genes in a category need to be large enough to allow statistical treatment. Second, the procedure only works for lists of regulated genes that have a fixed number and therefore relies on cut-off values for what is considered ‘differentially expressed’. Third, the analysis does not take into account the magnitude of expression changes. The iterative group analysis method (Breitling et al., 2004a, PERL script available from SHWFGF at the University of Glasgow, see Appendix) avoids these drawbacks by providing an automated functional annotation of microarray results together with a statistical confidence level for each annotation feature. Annotation is based on a comprehensive binomial statistics calculation detecting concerted changes in ‘functional classes’ of genes. The ‘functional classes’ can be of diverse origin, such as GeneOntology assignments, key words derived from Basic Local Alignment Search Tool (BLAST) queries of sequence databases, literature extracts, or expression data from other microarray experiments. The detection algorithm will automatically identify genes in each class that are most likely to be genuinely altered in expression level. In short, the algorithm performs the following steps. For each functional class the search for regulated genes progresses along a list of genes ranked by their expression change (e.g. according to fold change, t-statistics or rank product) and counts the number of genes belonging to the class. Every time the algorithm finds a gene belonging to the class it assigns a probability value to the subset of genes so far encountered. Within each class there will be a subset of genes for which this probability value is lowest (minimum P-value). The subsets can then
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be ordered according to their minimumP-value and the most significant groups will represent a meaningful biological interpretation of the entire dataset. As an automated procedure based on statistics, iGA avoids any kind of bias. Further, iGA does not rely on pre-delimited lists of ‘significantly changed’ genes and can be used on any ranked list, even if derived from a single experiment. In fact, by focusing on groups instead of single genes, it is possible to determine statistical significance without experimental replication, with the group members serving as ‘internal replicates’. At the same time, since the iGA approach does not require a significance cut-off in the ranked gene list, it can enhance the sensitivity of gene detection in small, noisy data sets. Biological interpretation will further benefit from establishing the relationships between members of significantly regulated functional gene groups. For this purpose software has been developed to map microarray data onto biological pathways (e.g. GenMapp; Dahlquist et al., 2002; see Appendix). The described iGA method can also be extended into the analysis of microarray data with respect to relational networks which can be depicted in the form of graphs, for example metabolic or signalling pathways, protein interaction maps, shared GeneOntology annotations or literature co-citation relations (evidence graphs). For each evidence network, the graphics-based extension of iGA (GiGA, Breitling et al., (2004b); PERL script available from SHWFGF at the University of Glasgow, see Appendix) identifies a ‘subgraph’ showing the most significant changes in gene expression. Cluster analysis can also help revealing relationships between significantly regulated genes on the basis of similarity between their expression profiles. Moreover, it can provide insights into transcriptional regulation of particular gene groups through the identification of promoter cis-acting elements. For example, Maathuis et al. (2003) used k-means and hierarchical clustering, to identify genes that responded specifically to Ca2+ stress. Genes were further categorised into clusters of up- and down-regulated genes and according to early and late response times. Promoter regions of the obtained clusters were then subjected to analysis using SPEXS and Bioconductor software to extract several sequence motifs that were significantly over-represented. The identified motifs may form targets for, Ca2+ -stress induced, transcriptional regulators. Thus, these types of analyses not only identify functionally relevant groups of genes in datasets but also reveal information about the nature of physiological links between differentially expressed genes and on how transcription itself is controlled.
8.5 Transporter transcriptomics To sustain life, cells must maintain highly ordered, low entropy, compartments. The boundaries which are ultimately responsible for the integrity of these
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compartments are the lipid-protein membranes that demarcate cellular life from the external environment or, within cells, delimit metabolic and signalling functions to specific organelles. A vast array of traffic occurs across membranes associated with signalling events, waste disposal, storage and nutrient flux, that is mediated by specific integral membrane proteins, the membrane transporters. 8.5.1 The role of membrane transporters in plant nutrition and stress General properties of plant membrane transport systems and their energisation are well understood (e.g. Maathuis & Sanders, 1999; M¨aser et al., 2001 and references therein): H+ -transporting adenosine triphosphatases (H+ -ATPases) and proton-pumping inorganic pyrophosphatases (H+ -PPases) establish a proton motive force (PMF) across membranes that consists of an electrical term (membrane potential) and a chemical term (pH gradient). The PMF serves to energise secondary transport mediated by carrier systems for a wide range of organic (e.g. sugars, amino acids) and inorganic (e.g. NO3 − , SO4 2− , K+ ) solutes. In addition, ion channels are located in membranes forming regulated pores that allow dissipative fluxes of ions down their electrochemical potential. This complement of transporters is essential with regard to plant nutrition and stress: Large quantities of soil minerals have to be accumulated to sustain plant growth (Marschner, 1995). Often these minerals are present in low concentrations in the environment and uptake takes place through high affinity carrier systems that are energised by coupling to the PMF. Examples include uptake of SO4 2− and phosphate at the root soil boundary. Inside cells, minerals, sugars and toxic substances are partitioned in specific cellular organelles. Typically, surplus inorganic nutrients and harmful ions are stored in the central vacuole involving specialised transporters located at the tonoplast. Such processes include vacuolar sequestration of Na+ , which is toxic in high cytoplasmic concentrations, mediated by H+ /Na+ antiport systems (e.g. Apse et al., 1999), deposition of heavy metals by cation diffusion facilitators (CDFs; e.g. Blaudez et al., 2003) and of xenobiotics by ATP fuelled ABC transporters (e.g. Rea et al., 1998). Long distance transport between root and shoot or between source and sink tissues also relies on effective transport: xylem parenchyma cells contain ion channels that release K+ into the xylem for transport to green tissue (de Boer & Volkov, 2003), and H+ -coupled sucrose transport in photosynthesising cells is responsible for loading of sugars into phloem vessels to supply non-green tissues (Knop et al., 2004). 8.5.2 Membrane transporter genes Many families of transporters have now been identified and functionally annotated, for example as ‘NO3 − transporters’, ‘Ca2+ exchangers’ or ‘Cl− channels’. This annotation is sometimes based on ‘hard’ data from functional
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characterisation but more often on sequence homology with, for example mammalian genes. Though great care has to be taken in ascribing gene functions solely on the basis of sequence homology, we do nevertheless have a reasonably complete description of the principal membrane transporter gene families and their broad functional context. Many nutrients are transported by more than one family of transporters. For example, K+ is transported in Arabidopsis by carriers of the KUP/HAK (K+ uptake permease/high-affinity K+ ) transporter family, as well as by channels of the KAT/AKT (K+ channel from Arabidopsis thaliana/Arabidopsis K+ transporter) family and the KCO (outward rectifying K+ channel) family (M¨aser et al., 2001). Like the majority of Arabidopsis genes, most transporters are members of multigene families with the largest formed by ABC transporters with over 150 genes (Rea et al., 1998). There is a large variability in the availability of functional data for various families and their constituent isoforms: for example the GLR (glutamate receptor-like) family of genes in Arabidopsis encodes non-selective cation channels (NSCC), hypothesised to be involved in cation transport and Ca2+ signalling (Davenport, 2002). Nevertheless, it has yet to be shown that any GLR functions as a transporter. Other genes, 200 to 300 in Arabidopsis, do bear the hallmarks of integral membrane proteins (e.g. alpha helical trans membrane domains) but lack significant homology to other proteins and are thus merely annotated as ‘unknown’ or ‘putative’ protein. Some, or many, of these may function as transporter and be involved in plant nutrition. To complicate matters further, the extent to which systems exhibit membrane specificity, cell-type specificity or functional redundancy is also largely unknown. 8.5.3 Questions that need an answer The picture of genetic diversity, unknown gene functions and unknown locations of gene products leads to a number of obvious questions to which transcriptomics studies can contribute an answer. (i) Which gene families are involved in the transport of particular nutrients and minerals? By applying different nutrient regimes we can test if particular gene families are affected in transcription, pointing to a possible involvement of those transporters. (ii) Which specific isoforms within a gene family are involved? The occurrence of large gene families indicates there may be a high degree of functional redundancy within families. However, family members may have temporal and spatial specificity in their expression and, most importantly, differ in their functional characteristics. For example, some members of the cyclic nucleotide gated channel (CNGC) family are permeable to Na+ whereas others are not (Hua et al., 2003). Such a relatively minor difference in transporter characteristics can have significant implications for the physiological role of a transporter. (iii) What is the physiological function of the many ‘unknown’ and hypothetical genes? By manipulating
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environmental conditions or by comparing gene transcription in different tissues, valuable clues can be obtained pointing to potential roles of unknown genes. (iv) How is gene transcription regulated? For agricultural exploitation of genomic data, for example to improve nutrient efficiency, we need to know how gene expression is controlled by stress. Does nutrient or heavy metal stress lead to co-ordinate regulation of multiple gene families or to selective regulation of individual genes within or across families and what are the controlling factors in this process? Further outstanding questions on plant nutrition relate to nutrient sensing and signal transduction. It is a long-standing observation that for many nutrients, starvation results in the de-repression of transport activity. Yet the pathways by which low nutrient availability activates gene expression remain little understood. The latter will require detailed knowledge about regulatory and signalling components that act on transporters. We will use results from two generalised approaches based on microarray analysis to illustrate how transcriptomics can contribute in providing some of the answers to these questions. The first strategy starts from a desire to functionally characterise particular genes or gene families, whereas the second approach queries global expression changes in response to particular treatments. 8.5.4 A gene family-based transcriptomics study Many researchers have their ‘pet’ genes or gene families, and are therefore specifically interested in the function of those genes and not necessarily all others. For example, one may study a group of genes that is transcriptionally regulated in response to a specific pathogen and subsequently want to know whether and how the same genes are affected by a range of other pathogens. An alternative question might be how NO3 − transporters respond transcriptionally to concentration changes in NO3 − and NH4 + supply. Microarray technology can be efficiently applied to study transcriptional regulation of such relatively small numbers of genes by spotting or purchasing custom made arrays that contain all genes of interest plus the required control probes (e.g. Swidzinski et al., 2002). In bulk numbers, such arrays are relatively cheap and allow an in depth study into transcriptional changes for a particular group of genes across many conditions. Several gene families were analysed by Maathuis et al. (2003) with respect to the transcriptional response of their individual members. The results showed that although many transporters appear to have similar transport characteristics when analysed in artificial systems (e.g. heterologous expression systems), their transcription is responsive to different external environmental conditions. A particularly interesting case of differential regulation among family members was the transcriptional response of genes for the vacuolar H+ -ATPase (Plate 4 – see colour plate section). This protein complex is formed by 12 subunits, some of which are encoded by several genes, resulting in a total number of 26 genes
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in Arabidopsis (Sze et al., 2002). It was found that individual V-ATPase genes differed in their response to nutritional stress with some genes being more responsive and more selective for a particular stress than others (Plate 4). For example, salt stress led to an up-regulation in many V-ATPase encoding genes, particularly during the latter stages of the stress. In contrast, Ca2+ deficiency generated lower transcript levels for most V-ATPase genes, whereas K+ deficiency only affected a small number of V-ATPase genes. This observation reflects the high complexity of regulation of a single functional protein complex, where transcriptional regulation of individual subunits must be in tune with post-transcriptional regulation as well as protein turnover (Kluge et al., 2003). To exemplify intrafamily variation in transcript levels, the response of the CNGC gene family to different biotic and abiotic stresses such as exposure to jasmonate, heavy metal regimes and different monovalent cations was analysed. CNGCs are putative NSCC for most of which relatively little is known regarding their functions in plants (see Talke et al., 2003 for review). One of the treatments that evoked considerable changes in transcript levels is salinity stress. Plate 5 (see colour plate section) shows post-analysis derived expression ratios for all CNGCs in shoots and roots in response to 80 mM NaCl added to the growth medium during a time course of 2 to 96 hours. The treatment leads to an overall down regulation of CNGCs but more so in shoot tissue than in root tissue. There are large differences between isoforms in their regulation. In shoot tissue, CNGC3 and CNGC8 are particularly affected by the treatment showing twoto sixfold reductions in expression, whereas transcript levels of other isoforms such as CNGC13 are not significantly changed. There are isoform specific differences between root and shoot tissues: CNGC8 is consistently down-regulated in response to the treatment in both shoot and root tissue but more so in shoots. In contrast, the signal for CNGC3 in shoots is considerably different from that in roots where no consistent change is observed. The data also suggest that, in both shoot and root tissues, all CNGC isoforms are expressed. However, for a number of conditions, for example, roots CNGC9 at 2 h and CNGC17 at 5 h, no data were obtained because the signal/background ratio for the fluorescence signal was too low, possibly indicating very low expression levels. Although the absolute expression levels of particular isoforms are unknown and may differ greatly, comparisons between tissues can be made. For example, overall normalised fluorescence signals for CNGC4 are 5140 ± 1640 (arbitrary units) in mature shoot tissue whereas they are 1206 ± 375 in roots indicating that expression is roughly fourfold higher in shoot tissue. Tissue comparisons based on microarray hybridisations suffer from low spatial resolution, due to the relatively large amount of tissue that is needed. However, gene expression patterns derived from microarray data can provide a good indication of average expression levels and provide complementary data to other high throughput methods to record expression levels such as the Massively Parallel Signature Sequencing project (MPSS, see below).
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To make any well-founded claims about the exact roles of particular genes, transcriptomics data on their own are usually not sufficient. Nevertheless, the results depicted in Plate 5 suggest that some CNGC isoforms may be involved in salinity stress and this provides a good starting point for more detailed subsequent studies. For example, it can be hypothesised that CNGC8 mediates Na+ uptake from the environment and its down-regulation at the transcriptional level serves to limit Na+ influx. Reduced long distance translocation of Na+ mediated by CNGC3 and/or CNGC8 in shoot tissues might also require downregulation during salinity and water stress. Alternatively, these transporters may be involved in maintaining K+ homeostasis by participating in the re-allocation of K+ between various tissues.
8.6 Treatment based studies Rather than focusing on a particular sub-set of genes, or a gene family, the experimental rationale in many cases will be to ascertain how environmental conditions and plant development affect global changes in transcripts. In plant physiology, the effect of nutrient regimes and abiotic stress on the transcriptome are of great interest and several reports have described the effect of varying nutrient supply of, for example, NO3 − , phosphate and SO4 2− (Wang et al., 2000a; Wang et al., 2001, 2002; Hammond et al., 2003; Hirai et al., 2003; Nikiforova et al., 2003; Wu et al., 2003). To study the involvement and interaction of membrane transporters in plant nutrition and ionic stress a microarray was used that contains probes for more than 1000 known and putative transporters (Plate 2). This array was employed to study the effect of K+ and Ca2+ deficiency as well as Na+ stress on the Arabidopsis root transporter transcriptome (Maathuis et al., 2003). Perception of external nutrient concentrations and communication of cellular nutrient status between different tissues is a prerequisite for whole-plant nutrient homeostasis. Although K+ is one of the most important macronutrients we have very little knowledge about the regulatory events involved in K+ perception and homeostasis. In the study of Maathuis et al. (2003), K+ deficiency resulted in a very weak transcriptional response. One reason for this observation might have been that relatively old plants were used in the experiments, which already had accumulated sufficient K+ . Indeed, plant tissue K+ remained virtually unchanged after removal of external K+ . In a more recent study (Blake, Armengaud & Amtmann, unpublished observations, 2004) the transcriptional response of membrane transporters to external K+ in 2-week old seedlings was therefore examined. Seedlings show rapid growth requiring large amounts of K+ for osmotic pressure. Since the particular focus of this study was on K+ perception it was necessary to assess transcriptional changes immediately after the onset of a clearly defined change of the external K+ concentration. Rather
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than removing K+ from the growth medium, which is slow due to exchange with cell wall K+ , plants were grown in K+ -deficient conditions and subsequently exposed to a medium containing 10 mM KCl (K+ re-supply) or a K+ -free control medium. To test the specificity of the observed response two additional experiments were carried out. First, 50 mM KCl was added to seedlings that had been grown in K+ -sufficient conditions (2 mM K+ ). Second, K+ starved seedlings were subjected to addition of 10 mM NaCl instead of KCl. RNA from shoots and roots of the treated seedlings was hybridised with the transporter array (Maathuis et al., 2003). It was found that 37 transporter genes responded specifically to re-supplying K+ after K+ deficiency. These genes did not react to additional K+ if K+ was already present in the growth medium and they were not ‘fooled’ by adding NaCl instead of KCl (Fig. 8.1). Although only shortterm responses (within 6 h after re-supply) were assessed the set of responsive genes again included several different types of transporters (Table 8.1). The identified K+ -responsive genes represent useful tools for further study of K+ signalling since they can be used as markers for the specific perception of K+ in the environment. The phytohormone abscisic acid (ABA) is well known to be involved in whole-plant signalling of several environmental stresses including salt and +K (in –K)
+K (in +K)
1 37
3 3 5
1
20
+Na (in –K) Figure 8.1 Numbers of transporter genes affected by the following treatments: Addition of 10 mM KCl to K+ starved plants: ‘+K (in –K)’. Addition of 10 mM NaCl to K+ starved plants: ‘+Na (in –K)’. Addition of 50 mM KCl to K+ sufficient plants: ‘+K (in +K)’. Note that 37 transporters responded specifically to K+ re-supply after starvation (see Table 8.1).
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Table 8.1 Transcriptional response of membrane transporters to K+ nutrition and ABA Functional class of transporter1 ABC transporter Pump Metal transporter Sugar transporter Amino acid transporter Auxin transporter NH4 + transporter NO3 − transporter Phosphate transporter Peptide transporter SO4 2− transporter Anion transporter K+ channel Cl− channel CNGC Putative Ca2+ channel K+ -transporter Antiporter Mg2+ transporter Aquaporin Putative transporter Control 1 2
Responsive2 to K+ 6 2 4 3
1 1
1 2 1 4 11 1 37
Responsive2 to ABA 32 14 9 11 19 4 3 1 3 8 3 3 3 2 2 3 4 7 1 16 57 18 223
Responsive2 to ABA and K+ 4 1 1 1
1 1 1 3 3 16
As assigned by Maathuis et al. (2003). More than 1.7-fold change of signal in both replica spots.
drought stress. Since both of these stresses are closely linked to ion transport and in particular to the availability of K+ the transcriptional response of ion transporters to ABA was characterised and compared to the results from K+ treatment studies. After application of exogenous ABA or salt stress, endogenous ABA levels rise and reach a maximum after 3–8 h (Moons et al., 1997; Goh et al., 2003). When assessing the transcriptional response of transporter genes to 50 M ABA we found that a 3 h treatment evoked the strongest response (as compared to 2 h and 7 h, Blake, Armengaud & Amtmann, unpublished observations, 2004). In addition to several known ABA responsive control genes on the array a large number of transporter genes displayed changes in transcript level upon ABA treatment both in roots and shoots (Table 8.1). When this set of genes was compared to K+ responsive genes it became apparent that approximately 40% of transporters that responded specifically to the K+ treatment were also regulated by ABA (Fig. 8.2). Two preliminary conclusions can be drawn from this result. First, ABA responsive genes are involved in transport processes related to K+ homeostasis. Second, plant response to K+
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+K (in –K)
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Figure 8.2 Numbers of transporter genes affected by the following treatments: Addition of 10 mM KCl to K+ starved plants: ‘+K (in –K)’. Addition of 50 M ABA to K+ starved or K+ sufficient plants: +ABA. Note that more than 40% of the K+ responsive genes also responded to ABA (see Table 8.1).
stress might involve both ABA-dependent and ABA-independent signalling pathways. Further analysis of the response of membrane transporters to ABA revealed even more interesting results. Separate evaluation of ABA responsive genes in K+ -sufficient and K+ -deficient seedlings identified three classes of genes with distinct expression profiles: (i) genes that responded to ABA independently of the K+ status, (ii) genes that responded to ABA only in K+ sufficient plants and (iii) genes that responded to ABA only in K+ deficient plants. Surprisingly only about one fifth of all ABA responsive transporters belonged to the first class whereas the remaining genes were more or less equally distributed between the last two classes (Fig. 8.3). ABA (in –K)
ABA (in +K)
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Figure 8.3 Dependence of the ABA effect on transporter genes on plant K+ status. 50 M ABA was added to K+ sufficient (‘+ABA in –K’) or K+ starved (‘+ABA in –K’) plants: Note that only 23% of the ABA-responsive genes responded to ABA independently of K+ . For the remaining transporters a change in transcript occurred either only in K+ sufficient (44% of genes) or only in K+ starved plants (33% of genes).
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Plate 6 (see colour plate section) shows an example of two members of the same gene family, the multidrug and toxin efflux (MATE) carrier family, which contains at least 50 genes in Arabidopsis, most of which are functionally uncharacterised. The transcripts of the two MATE transporters shown in Plate 6 display fundamentally different expression profiles with respect to ABA and K+ . One of them is strongly up-regulated by ABA in roots and shoots in both K+ starved and K+ sufficient plants, the other one shows down-regulation by ABA, which is specific for roots of K+ starved plants. Thus, ABA-related expression profiles vary considerably not only between different transporters but within gene families. Even more surprising was the finding that several transporters modulate their individual response to ABA depending on plant K+ status, both with respect to the direction of the change (up- or down-regulation) and with respect to the tissue (roots or shoots). We identified a group of genes, which showed up-regulation upon ABA in the roots when K+ was available in the medium, but responded to the same treatment with down-regulation in the shoots when K+ was not present (Plate 7, see colour plate section). This group predominantly contains aquaporins indicating a role for the modulation of their expression during drought and salinity, both stresses well known to be linked to ABA signalling (Luan, 2002). We are currently investigating whether K+ -dependence of gene expression is indeed observed when K+ -sufficient and K+ -starved plants are subjected to hyper-osmotic stress. The above examples of microarray experiments not only reveal interesting and novel facts about K+ -perception in plants but also exemplify experimental designs that are potentially useful for the study of other nutrients. Thus, in addition to comparing plants in two different nutrient conditions, it can be fruitful to assess how plants that have been grown in different nutritional conditions respond to a particular stress. The latter treatment can consist of the application of potential signalling molecules such as described here and could also involve a change in supply of other nutrients or subjecting plants to a non-nutrient stress (e.g. drought, cold or pathogens). Only by using such extended experimental protocols we will be able to exploit the full potential of microarray-based studies.
8.7 Using publicly available transcriptomics data An alternative way to assess gene function and the regulation of gene expression is through analysing data obtained in independent studies (e.g. studies carried out in different laboratories). Many transcriptomics data are deposited in publicly accessible databases. Data can take the form of supplemental results annexed to a publication or form part of publicly funded genomic projects such as those provided by the Stanford Microarray Database (SMD, see below) and
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the Nottingham Arabidopsis Stock Centre (NASC, see below) where individual researchers can apply for microarray services. These databases now have results from hundreds of microarray experiments that can be searched directly at the SMD and NASC websites or through links that are provided at genomics resources such as TAIR (see below). Searches can be done on the basis of entries such as particular experimental conditions to yield expression data for all available genes in response to that treatment. In the SMD, experiments with particular relevance to plant nutrition include Zn treatment (exp. id 7311), Cd treatment (id 10849), KCl vs KNO3 (id 7304), Al stress (id 7304) and Fe deficiency (id 7115). Alternatively, the ‘spot history’ of individual genes can be queried across all experiments showing which treatment affected transcription of the gene(s) of interest. For example, a query across all CNGC genes at the NASC database generates a spreadsheet type file with raw data (only partially reproduced in Fig. 8.4). The first columns identify spots and probes and give gene annotations and description. Across the top, identifiers link to experiments, which can be checked for details such as type of material, growth conditions and treatments. The bold numbers (our modification) show where experiments resulted in a more than
SpotID ProbeNamGeneName Decription Short_A1-Short_A2 A8-CampA8-Cam Bramk Bramk −403350 248250_a At5g53130 CNGC1 148.6 135.1 135.4 96.4 140 225 52.3 75.3 −401610 246510_a At5g15410 CNGC2 144 80.7 272 331 24.4 57.7 2.3 17.7 62.2 123 −403253 248153_a At5g54250 CNGC4 54.5 87.4 137.1 58 89.7 227 −402990 247890_a At5g57940 CNGC5 −421620 266520_a At2g23980 CNGC6 40.4 45.6 71.5 52.8 59.6 99.4 −416886 261786_a At1g15990 CNGC7 12.2 6.5 2.6 3.5 1.6 4.1 1 0.8 1.8 6.6 1.9 1.9 −416242 261142_a At1g19780 CNGC8 −408722 253622_a At4g30560 CNGC9 11.7 13 19.1 15.9 15.5 20.3 −416129 261027_a At1g01340 CNGC10 62.7 21.8 4.5 4.1 3.6 23.6 −418876 263776_sA t2g46440 CNGC11 39.5 7.4 21.5 123 53.1 155 77.4 51.7 123.6 54.5 212 140 −418877 263777_a At2g46450 CNGC12 −410699 255599_a At4g01010 CNGC13 23.8 32 35.8 28.7 22 101.4 −418895 263795_a At2g24610 CNGC14 39 50.7 34.8 41.8 26.3 14.8 −420644 265544_a At2g28260 CNGC15 6.5 20 26.3 26 22.3 20.9 −407501 252399_a At3g48010 CNGC16 8.2 12.4 1.5 12.3 16.3 5 57.7 56.5 114.4 59.2 57.5 50.3 −408715 253615_a At4g30360 CNGC17 13.8 19 18.4 16.7 19.4 19.6 −401690 246590_a At5g14870 CNGC18 32 1.1 14.9 3.2 1.1 15.3 −413477 258377_a At3g17690 CNGC19 −413451 258351_a At3g17700 CNGC20 55.6 12.4 27.8 4.5 22.2 50.2
Figure 8.4 Microarray data for the Arabidopsis Cyclic Nucleotide Gated Channel (CNGC) gene family. Data file was obtained from the NASC database (see text) and only a small part of the entire dataset is reproduced. Values depict raw transcript ratios between treatment and control samples (usually adjacent columns) with bold values denoting more than twofold changes.
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twofold change in transcript level. For example, the ‘Short’ experiment (ozone treatment in column 1 and control in column 2) hybridisations show that CNGC2 is down-regulated by a factor of approximately 3, whereas CNGC13 is upregulated around 4 times in response to the ozone treatment. (There are some higher signal ratios present but generally gene expression is deemed too low to generate reliable results when signals are smaller than 100 for both control and treatment). By the nature of their size, public databases can also be extremely useful to identify co-regulated genes (cluster analysis). For example, one could amalgamate all results of nutrient stress type experiments and search for co-regulated genes across all datasets. Such analyses could identify transcripts that are affected by all or many of the treatments and are therefore likely to represent common stress responsive genes. After obtaining sequence data for 5 upstream regions (e.g. from the FTP server at Munich Information centre for Protein Sequences, MIPS, see below) gene clusters can be searched for common motifs in their promoter regions (Chen et al., 2002; Maathuis et al., 2003). Such motifs may point to regulatory sites that are involved in generating integrated responses to the applied treatment. When interpreting data from public depositories, some caution is required. Experimental conditions may diverge extensively, making a direct comparison between datasets difficult. For example, one treatment may have been carried out on cultured cells, another on seedlings and yet another on mature shoot tissue. Growth conditions, treatment times and time of harvesting material may vary greatly and are all likely to have significant effects on gene transcription. In addition, these service providers generally restrict the number of available arrays to researchers and therefore the experimental outcome is often based on only one or two hybridisations, a factor that will greatly reduce one’s ability to attribute statistical significance to the data.
8.8 Outlook Transcriptomics studies have added greatly to our understanding in many areas of plant biology. It has helped gene identification and annotation, explained mutant phenotypes and delivered many insights into gene function, including regulatory aspects of gene transcription. However, there are two major caveats that limit the contribution of transcriptomics results. First, a phenotype is predominantly determined by the concerted action of proteins but transcriptional regulation does not necessarily correspond with protein activity. This may be either due to a lack of correlation between transcript abundance and protein abundance and/or or due to the predominance of post-transcriptional and posttranslational regulation of protein activity. Post-transcriptional modifications
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such as alternative splicing and RNA editing are unlikely to be detected in most microarray studies whereas post-translational phosphorylation and glycosylation events will remain entirely undetected. Therefore, the absence of a transcriptional response for a particular condition cannot be taken to signify a lack of protein function and this may be particularly pertinent to enzymes whose activity has to change rapidly such as membrane proteins that are involved in ionic signalling. Second, transcriptomics methods do not provide a direct link to protein location. This confounds interpretation of expression studies because the function of many transporters is intricately associated with their subcellular and membrane location which is unknown in most cases. The development of cellular and subcellular transcriptomics will go some way in addressing this problem, but ideally, transcriptomics data will be complementary to results from physiological analyses, transport assays, reporter gene studies etc. Especially integration with other high throughput approaches would generate great added value and some of the ‘-omics’ that are now available are described elsewhere in this volume. For example, the combination of transcriptomics and proteomics approaches would allow an assessment of how regulation of the trancriptome compares to that at the translational and post-translational level. Subcellular proteomics can also directly address the question where proteins are expressed. Integration of transcriptomics and metabolomics data would provide an evaluation of how gene transcription affects output parameters such as metabolite production. Acknowledgements We would like to thank Pawel Herzyk for critical reading of the manuscript. Experimental work in our laboratories referred to in this chapter was funded by the BBSRC (17/P17237 and 17/G17898) and the Nuffield Foundation (NAL/00562/G). References Alter, O., Brown, P.O. & Botstein, D. (2000) Singular value decomposition for genome-wide expression data processing and modelling. Proc. Natl. Acad. Sci. USA, 97, 10101–10106. 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. Baldi, P. & Hatfield, G.W. (2002) DNA Microarrays and Gene Expression. From Experiments to Data Analysis and Modelling. University Press, Cambridge, UK. Baldwin, D., Crane, V & Rice, D. (1999) A comparison of gel-based, nylon filter and microarray techniques to detect differential RNA expression in plants. Curr. Opin. Plant Biol., 2, 96–103. Blaudez, D., Kohler, A., Martin, F., Sanders, D. & Chalot, M. (2003) Poplar metal tolerance protein 1 confers zinc tolerance and is an oligomeric vacuolar zinc transporter with an essential leucine zipper motif. Plant Cell, 15, 2911–2928.
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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. Thimm, O., Essigmann, B., Kloska, S., Altmann, T. & Buckhout, T.J. (2001) Response of arabidopsis to iron deficiency stress as revealed by microarray analysis. Plant Physiol., 127, 1030– 1043. Tillib, S.V., Strizhkov, B.N. & Mirzabekov, A.D. (2001) Integration of multiple PCR amplifications and DNA mutation analyses by using oligonucleotide microchip. Anal. Biochem., 292, 155– 160. Tusher, V.G., Tibshirani, R. & Chu, G. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA, 98, 5116–5121. Wang, R.C., Guegler, K., LaBrie, S.T. & Crawford, N.M. (2000a) Genomic analysis of a nutrient response in Arabidopsis reveals diverse expression patterns and novel metabolic and potential regulatory genes induced by nitrate. Plant Cell, 12, 1491–1509. Wang, E., Miller, L.D., Ohnmacht, G.A., Liu, E.T. & Marincola, F.M. (2000b) High-fidelity mRNA amplification for gene profiling. Nat. Biotechnol., 18, 457–459. Wang, Y.H., Garvin, D.F. & Kochian, L.V. (2001) Nitrate-induced genes in tomato roots. Array analysis reveals novel genes that may play a role in nitrogen nutrition. Plant Physiol., 127, 345–359. 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. Weigel, D. & Glazebrook, J. (2002) Arabidopsis, A Laboratory Manual. Cold Spring Harbour Laboratory Press, New York. Wu, P., Ma, L.G., Hou, X.L., Wang, M.Y., Wu, Y.R., Liu, F.Y. & Deng, X.W. (2003) Phosphate starvation triggers distinct alterations of genome expression in Arabidopsis roots and leaves. Plant Physiol., 132, 1260–1271. Worley, J., Bechtol, K., Penn, S., Roach, D., Hanzel, D., Trounstine, M. & Barker, D. (2000) A systems approach to fabricating and analyzing DNA microarrays. In Microarray Biochip Technology (ed. M. Schena), Biotechniques Books, Eaton Publishing, Natick, MA, pp. 65–85.
APPENDIX I
Useful websites
General genomics resources Biobase at http://www.gene-regulation.com/index.html contains many databases with promoter regions, eukaryotic transcription factors, their genomic binding sites and DNA-binding profiles. MIPS (Munich Information centre for Protein Sequences) at http://mips.gsf.de contains spliced and unspliced sequences, gene annotation, protein sequences and functional domains, mRNAs and ESTs and promoter regions. It also collates information from, and links to, useful external services regarding protein domains and protein targeting. MPSS (Massively Parallel Signature Sequencing) at http://mpss.udel.edu produces short sequence tags from a defined, gene-specific position within an mRNA. The relative abundance of these tags in tissue specific libraries represents a quantitative estimate for the expression of that gene. TargetP at http://www.cbs.dtu.dk/services/TargetP/ predicts the subcellular location of eukaryotic protein sequences based on the predicted presence of any
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of the N-terminal presequences chloroplast transit peptide, mitochondrial targeting peptide or secretory pathway signal peptide. TAIR (The Arabidopsis Information Resource) at http://arabidopsis.org contains genomic data such as sequence, loci, function, number of available ESTs and links to other databases such as the Stanford Microarray database database. Microarray databases EBI (European Bioinformatics Institute) at http://www.ebi.ac.uk/arrayexpress provides ArrayExpress, a microarray service and a searchable public repository for microarray data. It also contains many useful links. NASC (Nottingham Arabidopsis Stock Centre) at http://nasc.nott.ac.uk/home .html is a service to obtain seeds of wild-type and mutant Arabidopsis and has a microarray service. Data of the microarray studies are publicly accessible and can be downloaded by experiment type, by gene or for batches of genes. SMD (Stanford Microarray Database) at http://genome-www5.stanford.edu like NASC runs a microarray service. Results of over 500 experiments can be searched for expression data or used to do cluster analyses. Publicly available microarray software Eisen lab at http://rana.lbl.gov/ provides free downloadable software for basic microarray analysis, for cluster analysis and visualisation, and for Combined Expression and Sequence Analysis. Spot: (Image analysis) at http://experimental.act.cmis.csiro.au/Spot/index.php SMD (Stanford Microarray Database) at http://genome-www5.stanford.edu software for datamining and clustering. EBI (European Bioinformatics Institute) at http://www.ebi.ac.uk/arrayexpress provides software for analysis and clustering of gene expression data. GEPAS (Gene Expression Patterns Analysis Suite) at http://gepas.bioinfo.cnio .es/tools.html where you can download spreadsheet type files with raw data for filtering, clustering and datamining, for example with gene ontology. Bioconductor (Microarray data analysis) at http://www.bioconductor.org dChip (Microarray data analysis) at http://www.dchip.org SAM (Microarray data analysis) at http://www-stat.stanford.edu/ tibs/SAM/ SHWGFG (Microarray data analysis) at http://www.gla.ac.uk/ functionalgenomics/rp/affy analysis.html GenMaPP (Microarray data analysis) at http://www.genmapp.org/ Motif samplers to identify promoter ciselements Alignace at http://atlas.med.harvard.edu/ allows software download or can be used to run motif searching algorithm on line. Geared towards yeast genomics but works fine with any data.
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SPEXS (Sequence Pattern EXhaustive Search) at http://www.ebi.ac.uk/vilo/ SPEXS/ BioProspector at http://bioprospector.stanford.edu/ uses a Gibbs sampling strategy, to examine upstream region of genes for regulatory sequence motifs. ZLAB at http://zlab.bu.edu/zlab/gene.shtml provides a very useful collection of links to software and services to do with genomics, microarrays, analysis etc.
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Exploring natural genetic variation to improve plant nutrient content Dick Vreugdenhil, Mark G.M. Aarts and Maarten Koornneef
9.1 Introduction Genetic variation is probably the most important basic resource for plant biology. It occurs within species which grow naturally, or which are cultivated. This genetic variation allows the adaptation of a plant species to multiple and changing environments, and it is indispensable for plant breeders. Genetic variation present in nature is restricted by selection, which acts against genotypes that perform poorly under specific conditions and, therefore, generally concerns either traits for which selection pressure is low, or traits that allow accessions to adapt to local conditions, which can differ between accessions. For example, the need for an efficient mineral uptake system depends upon the soil in which the plants grow. This is evidently true in the case of extreme environments, such as soils which are polluted by heavy metals, or in soils which are deficient for specific minerals. Only species or accessions that are tolerant to these extreme conditions can thrive on such sites. Other examples of adaptive traits are developmental traits such as seed dormancy and flowering time. The requirement to respond to factors such as temperature and day length, which vary with latitude, will depend on the latitude from which the plants originate. Since many plant species grow in multiple environments, genetic variation can be expected and is indeed found. For similar reasons variation can be expected in crop plants, where specific constraints due to agricultural practice are generally present. However, even traits that have not been selected for in the past may still show considerable variation in crop plants. Mineral content and mineral uptake are examples of such traits, to which little attention has been paid in breeding programs to date, mainly for practical reasons. Although Arabidopsis is not a target species in plant breeding, the analysis of natural genetic variation in this species is becoming increasingly important, due to the relative ease in identifying genes underlying the variation. This is supported by the availability of the complete and annotated genomic DNA sequence (AGI, 2000), and also because of the genetic resources such as knockout and activation mutants, promoter trap lines and whole genome microarrays (e.g. see www.arabidopsis.org; see Chapter 8). However, the approaches for the analysis of natural variation in Arabidopsis do not differ from those for crop plants such as rice (Yano, 2001, see Chapter 10). This chapter provides an outline of
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the methods used for the analysis of natural variation and demonstrates this for mineral uptake and accumulation both in model species and crop plants. As well as discussing general principles for the analysis and exploitation of natural variation, it presents examples of the natural variation in micronutrient content, in particular, Fe and Zn, since these are relevant not only for the plant, but also from the point of view of human nutrition.
9.2
The genetic and molecular analysis of natural variation
Natural genetic variation present among accessions is often multigenic and influenced by the environment. This results in continuous quantitative phenotypic distributions of traits in segregating populations, which are not easily interpreted. Historically this has hampered its analysis. However, the exploitation of this resource down to the molecular level has now become feasible, especially in model species like Arabidopsis and rice, where several genes accounting for natural variation have already been identified (Yano, 2001, Koornneef et al., 2004). Dissecting this variation first requires a quantitative trait locus (QTL) analysis, which estimates the number and genome positions of the segregating quantitative trait loci. The basic principle of QTL mapping is to find those molecular markers for which allelic variation is correlated with a certain character that is often expressed quantitatively (e.g. length in cm, content in mg g−1 d. wt, etc.). The association of marker allelic variation with trait values is due to linkage of the marker with a gene or genes controlling the quantitative trait (Fig. 9.1). QTL analysis requires (i) the generation of an experimental mapping population; (ii) its genotyping with markers well distributed over the entire genome; (iii) the phenotyping for the trait of interest and (iv) the association analysis between phenotypic values of the trait and genotypic classes of the polymorphic markers. For the latter step specific software is available (e.g. Van Ooijen et al., 2000), which calculates the number and genetic positions of loci controlling the trait variation in that population, their relative additive effect, the contribution of genetic interactions between loci (epistasis) and, depending on the population type, the mode of action of each QTL (dominance effects). Although in principle, any segregating population can be used, QTL mapping has proven to be very effective when using so-called immortal mapping populations such as recombinant inbred lines (RILs) and near igogenic lines (NILs), also named introgression lines (ILs) or backcross inbred lines (BILs) (Fig. 9.1). Such populations are easily maintained by selfing since they are effectively homozygous. Therefore, determination of phenotypic values can be based on multiple replicates, which reduces the environmental effects and increases the power to detect QTL. Further, the same genotyped population can be analysed in multiple environments and thus the effects of each QTL in different environments can be precisely estimated and tested for QTL x environment interactions.
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X
Parent 1
203
Parent 2
F1
Recombinant inbred lines (RILs)
10
12
5
4
13
5
Quantitative trait data
Figure 9.1 Scheme showing the production of a set recombinant inbred lines (RILs) and their use for QTL analysis. For the sake of simplicity only one chromosome per line is shown and only six RILs are represented. Two parental lines (accessions), indicated with black and grey chromosomes, respectively, are crossed. The resulting F1 is selfed to generated F2 lines. RILs are generated by successive selfing of the F2 lines until homozygosity is achieved at the F8 generation or higher. Black and grey horizontal dashed lines indicate positions and genotype of molecular markers. In this example, the quantitative trait data as given at the bottom of the figure, indicate a QTL at the lower end of the chromosome; the presence of alleles of parent 2 at the lowest marker is associated with a high value of the trait, whereas alleles from the other parent result in a low trait value.
In Arabidopsis, an increasing number of RIL populations are, or will become, publicly available (listed in http://www.inra.fr/qtlat/NaturalVar/index.htm). Mapping is performed with molecular markers, of which many are available, especially in genetically well-studied species. Recently, the potential of an alternative strategy for the identification and mapping of loci accounting for natural variation, the so-called linkage disequilibrium (LD) mapping, is being theoretically and empirically evaluated (Flint-Garcia et al., 2003). LD mapping aims to exploit the linkage disequilibrium between very close loci naturally persisting in a population as a consequence of their shared ancestry. For this purpose, a collection of unrelated accessions is thoroughly genotyped with markers at a very high density, and phenotyped for the trait of interest. Then, marker-trait associations are searched for directly, with the expectation that markers closely linked to a QTL will show significant association. Once a QTL has been mapped, the identification of the molecular variation underlying this QTL is a major challenge. This includes the identification of the particular gene and the discovery of the nucleotide polymorphisms within
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the gene that determine the phenotypic differences. The difficulties behind this are shown by the very limited number of QTL that have been cloned from plant species (summarised in Paran & Zamir, 2003). The positional cloning of QTL, following similar principles as for map based cloning of genes identified by induced mutations, is an efficient strategy. It basically consists of the identification of closely linked recombination events, requiring the analysis of a large number of segregating progeny with molecular markers covering the critical region at high density. As for mutants, unambiguously inferring the genotype of each plant from the phenotype is the main requirement to avoid misinterpretations. This is accomplished by analysing monogenic fine mapping populations, which can be derived from RILs or NILs (Alonso-Blanco & Koornneef, 2000). The phenotyping should be done as accurately as possible, often requiring the analysis of a large number of progeny from particular individuals. In addition to fine mapping, several functional strategies are available in species where a genome sequence is available or where, due to micro-synteny with a model species, genes are expected to be present in a specific genomic region and provide so-called candidate genes. A strong point for QTL analysis is the unbiased way of searching for loci affecting a trait. Thus, a QTL might be due to variation in a gene coding for a structural protein, but it might also be caused by alteration of a regulatory gene or even in a non-coding regulatory sequence. Another resource to find candidate genes is the analysis of the expression of genes in the region where the QTL is mapped. This will only be successful if the QTL effect is due to considerable difference in gene expression. This analysis can be done using standard assays for a limited number of candidate genes, or using high-throughput genome-wide techniques, such as using microarrays (Borevitz & Chory, 2004). To circumvent the effect of distantly located genes on the QTL gene, one should preferentially use NILs or perform QTL mapping for gene expression (Jansen & Nap, 2001). Further, the identification of an artificially induced mutant showing a phenotypic effect in the trait of interest provides a unique functional argument to select a candidate gene. The availability of T-DNA insertion mutants for almost all Arabidopsis genes (Alonso et al., 2003) and the efficiency of high-throughput screening for induced point mutation (targeting induced local lesions in genomes; TILLING) procedures to identify mutations in numerous candidate genes (Henikoff & Comai, 2003) provide strategies to analyse knockout phenotypes of (nearly) all genes in a QTL region. Ultimately, the proof for the identification of a QTL gene should come from complementation experiments by plant transformation. The transfer of an allele from one parent to the other, and vice versa, or the transfer of either allele into a null background, should show the predicted effects of the QTL. Further, sequencing of both alleles of the corresponding QTL gene will identify DNA polymorphisms. However, to find the precise nucleotide polymorphism underlying the QTL, the so-called quantitative trait nucleotide (QTN), requires further
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work, since some nucleotide polymorphisms might be neutral with respect to the trait under investigation (El-Assal et al., 2001). In other (i.e. non-Arabidopsis) species, useful genetic tools, for example rice T-DNA insertion lines are rapidly becoming available, allowing similar approaches.
9.3 Genetic variation for nutrient content and related traits in model species 9.3.1 Arabidopsis Mutant studies in Arabidopsis have led to the identification and functional characterisation of a wide range of genes involved in nutrient uptake, transport and accumulation in plants (Knappe et al., 2003; Loudet et al., 2003b; MaruyamaNakashita et al., 2003; Nikiforova et al., 2003). The various classes of cation transporters involved in uptake, distribution and sequestration of cations in Arabidopsis have been reviewed by M¨aser et al. (2001) and Hall and Williams (2003). The availability of the complete genomic sequence of Arabidopsis has allowed a genome-wide search for genes putatively involved in nutrient transport. Based on homologies and motifs, M¨aser et al. (2001) concluded that approximately 5% of the Arabidopsis genome encodes membrane transport proteins. This figure does not take into account the regulatory genes involved in transport, which are less easily recognised. The combination of knowledge on candidate genes and the availability of genomic resources allows the analysis of the importance of these genes by testing their phenotype in knockout mutants that are available for most genes (Alonso et al., 2003). However, since paralogues are present for many genes (implying redundancy) and since different genes may encode proteins with related function, strong phenotypes are not always found. Further, genes that are not candidates (e.g. regulatory factors) may escape detection in this approach. Therefore, forward genetic screens, where small-effect mutants might be found (Lahner et al., 2003; see Chapter 7), and the use of natural genetic variation are important tools to discover the genes involved in mineral nutrition. A new powerful tool to unravel networks of genes involved in a certain process is the genome-wide profiling of gene expression using microarrays (Borevitz & Chory, 2004; see Chapter 9) or cDNA-AFLP (Bachem et al., 1996). In transcriptome analysis, gene expression is quantified at the mRNA level to compare two or more experimental conditions (Van Hal et al., 2001; Wu et al., 2001). This approach has been used in Arabidopsis to study up- or downregulated genes under various types of biotic and abiotic stresses, including Fe deficiency, salt stress, NO3 − application and Al toxicity. Up-to-date lists of microarray experiments can be found at http://affymetrix.arabidopsis.info/ and at www.arabidopsis.org.
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A further source of genetic information is the use of natural variation between ecotypes or accessions. The principles and achievements of QTL analysis in Arabidopsis have been briefly outlined above and are reviewed in more detail by Koornneef et al. (2004). Here, we will only mention studies involved in natural variation for traits related to nutrient uptake and accumulation. QTL studies have been performed in Arabidopsis for a range of macro- and micronutrients, including studies on tolerance to high levels of Al or salt (NaCl). Usually, a wide range of variation for the trait under study is reported, often exceeding the variation between the parental lines from which the populations were derived, a phenomenon called ‘transgression’. This indicates that the traits are polygenic and that both parents contribute positive and negative alleles. This is usually substantiated by subsequent QTL analysis, and by quantifying the additive effects of alleles. Per trait, up to eight QTL are reported, as summarised in Table 9.1. Table 9.1 QTL found for nutrient-related traits in various Arabidopsis mapping populations Trait
Range of variation
Mapping population
Number of QTL
Reference
P Phosphate in leaves Phosphate in seeds Phosphate in shoot
5x 5x 10 x
Ler x Cvi Ler x Cvi Bay x Sha
1 4 6
Bentsink et al. (2003) Bentsink et al. (2003) Loudet et al. (2003a)
N N in shoot NO3 − content in shoot
1.25–2.2 x1 1.7 x
Bay x Sha Bay x Sha
72 8
Loudet et al. (2003b) Loudet et al. (2003a)
Fe Seed content
Ler x Cvi
Vreugdenhil et al. (2004)
Zn Seed content
Ler x Cvi
Vreugdenhil et al. (2004)
Cl Content in shoot
1.7 x
Bay x Sha
6–81
Loudet et al. (2003a)
Other cationic minerals (Mn, Mg, Ca, K) Seed content
3–4 x
Ler x Cvi
1–7
Vreugdenhil et al. (2004)
Al Tolerance (roots) Tolerance (roots)
2.5–5 x3 4.4 x
Ler x Col Ler x Col
2 2
Hoekenga et al. (2003) Kobayashi and Koyama (2002)
NaCl Tolerance to salt Salt germination
2–3 x3 0–79%
Ler x Col Ler x Sha
5–6 4
Quesada et al. (2002) Clerkx et al. (2004)
1.6 x 3.5 x
Ler x Col Ler x Cvi
4 2
Payne et al. (2004)
Cs Content in shoot 1 2 3
depending on growth conditions. different QTL detected in different conditions. tolerance was measured in various ways.
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The challenge that comes after QTL mapping is the further fine-mapping of the loci and eventually the identification and cloning of the underpinning genes. This has so far only been achieved in a few instances (El-Assal et al., 2001). When QTL for different traits are mapped in the same population, coincidence of the map positions might point to genes having pleiotropic effects. The most widely used mapping populations of Arabidopsis are Landsberg erecta (Ler) x Columbia (Col) (Lister & Dean, 1993) and Ler x Cape Verde Islands (Cvi) (Alonso-Blanco et al., 1998). We used the latter population to decide if accumulation of the polyvalent anion phytate might be linked to accumulation of cationic minerals (Vreugdenhil et al., 2004). QTL were mapped for phytate levels and for Mn, Ca, K, Fe, Zn and Mg contents. Several loci affecting K and Ca levels in seeds were found to coincide, suggesting common mechanisms of uptake and accumulation (pleiotropy) or tightly linked genes controlling these traits. On first glance, the major locus for phytate appeared to coincide with a locus determining Zn, K, Mn and Ca contents. However, further analysis of near isogenic lines carrying a small Cvi introgression in Ler background, including the phytate locus, revealed that the Zn locus could be separated from the phytate locus. The loci for Ca, Mn and K were tightly linked to the phytate locus, suggesting that a single gene controls these traits. The availability of the DNA sequence of the Arabidopsis genome allows the search for putative candidate genes in the region of a QTL. However, due to the limited size of the mapping population, the relatively small number of markers, and the interference of non-genetic variation, it is often not possible to delimit the genomic region covered by a QTL to less than several hundreds of genes. Despite this uncertainty, many candidate genes have been suggested for nutrient related traits, for example, for N assimilation (Loudet et al., 2003b) and Al tolerance (Hoekenga et al., 2003). In the latter case, Al-inducibility of gene expression, as determined by microarrays, was used as a further criterion to reduce the number of candidate genes. 9.3.2 Rice In rice, yield is often limited by sub-optimal growth conditions, for example due to deficiencies in nutrient supply, or by potentially toxic levels of Al or Fe. Toxicities are generally related to the physical conditions of the soil, determined by, for example soil type and composition, anaerobiosis or pH. In order to optimise growth and yield many studies have focused on nutrient uptake and efficiency. These studies have resulted in information on natural variation within and between Oryza species and subsequently QTL have been mapped for growth and yield traits using different mapping populations. The main results of these QTL analyses as related to nutrients are listed in Table 9.2. The identification of QTL for P-efficiency has been taken one step further by Wissuwa and Ae (2001). They have used this knowledge to transfer a locus with a positive effect
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Trait Al tolerance Relative root length Relative root length Relative root length
Range of variation
Mapping population
Number of QTL
Reference
1.7–2.4 x
Indica x japonica (IR1552 x Azecena) O. sativa x O. rufipogon
4
Wu et al. (2000)
5
Nguyen et al. (2003)
>3 x 3x
Indica x japonica (Kasalath x Koshihikari)
3
Ma et al. (2002)
P deficiency Various traits
2.2–22 x
3–4
Wissuwa et al. (1998)
Various traits
-
1–2
Ming et al. (2001)
Various traits
2.7–3.4 x
Indica x japonica (Kaselath x nipponbare) Indica x japonica (ZYQ8 x JX17) IR20 x IR55178
3–4
Ni et al. (1998)
Fe toxicity Various traits
2.2–9 x
Indica x japonica (Kaselath x nipponbare)
1–3
Wan et al. (2003)
N metabolism Enzyme activities
3.5–6.3 x
Indica x japonica (Kaselath x nipponbare)
6–7
Yamaya et al. (2002)
on P-uptake to a low yielding recipient line using marker assisted selection (MAS). This resulted in an increase in grain yield by 250% under P-limiting conditions, illustrating the power of MAS based on QTL mapping (see also Chapter 10). Despite a long breeding history and the dependence of large numbers of people on rice as their sole source of dietary Fe and Zn, rice grains are the lowest among cereal grains for Fe and Zn content (Gregorio et al., 2000). In a set of nearly 1000 lines, the Fe concentration in the seeds was found to range from 7.5 to 24.4 mg kg−1 , averaging at 12.1 mg kg−1 . Glahn et al. (2002) listed one line with a higher level, at 38.6 mg kg−1 . The Zn content in these lines ranged from 13.5 to 58.4 mg kg−1 , the average content being 25.4 mg kg−1 (Graham et al., 1999; Gregorio et al., 2000). It was further reported that a number of the high-Fe lines belonged to the group of aromatic varieties (Graham et al., 1999; Gregorio et al., 2000). However, additional investigations revealed that the correlation between aromaticity and Fe (and Zn) content, although strong, was not absolute. Several studies, performed at the International Rice Research Institute (IRRI) facilities (Graham et al., 1999; Gregorio et al., 2000) indicated that the high Fe and Zn traits are expressed in a wide range of environments, although there is some evidence of genotype x environment interaction, especially in extreme (e.g. saline) conditions. In rice, several RIL populations are available (see Table 9.2) that can be used for QTL analysis of Fe- and Zn-related traits, allowing marker
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assisted breeding in the near future. So far, no QTL studies on micronutrient levels have been published. Post-harvest processing of rice has a large influence on micronutrient contents. Rice is normally polished and during this process the outer layers, including aleurone and embryo are removed. These tissues are relatively rich in micronutrients. However, different rice varieties respond differently on polishing: some lines, including the popular commercial variety IR64 lose more than 30% of Fe after 15 min of polishing, whereas a traditional Chinese variety loses only about 5% during the same treatment (Gregorio et al., 2000). This finding underlines the necessity to include tissue localisation and processing techniques when aiming at improving the nutritional quality of rice. Bioavailability of Fe and Zn in humans has been suggested to be negatively influenced by phytate. The level of phytate in rice grains ranged from 5.7 to 23.1 mg kg−1 (Glahn et al., 2002). However, these authors did not find a correlation between phytate levels and Fe bioavailability in an in vitro digestion system. They suggested that an unknown compound, related to rice grain colour, might be a factor in this process. 9.3.3 Heavy metal hyperaccumulating species A few plant species are able to accumulate exceptional amounts of the heavy metals Zn, Ni, Cd, Pb, Co, Cu, Mn and As (Baker et al., 2000; Ma et al., 2001; see also Chapter 13). Often these metal hyperaccumulators, as they are called, contain 100- to 1000-fold higher concentrations of specific metals in their aboveground tissues. Their hyperaccumulating character might be due to the presence of genes absent in non-hyperaccumulating species and/or to a different expression of genes compared to normal species. The discovery of the mechanism(s) underlying hyperaccumulation is also important for the discovery of the genes that control the uptake of minerals in non-accumulating species. Unfortunately, the genetic nature of these extreme traits has scarcely been investigated. Most is known from analyses of the Zn/Cd hyperaccumulator Arabidopsis halleri and the Zn/Ni/Cd hyperaccumulator Thlaspi caerulescens. Both species belong to the Brassicaceae family and are closely related to A. thaliana (Mitchell-Olds, 2001). Arabidopsis halleri is one of the closest relatives of A. thaliana, with around 94% identity at the DNA level (Becher et al., 2004). There are several known accessions, which have been collected from different sites in Western and Central Europe (Macnair, 2002), but the genetic variation for Zn accumulation or tolerance is limited within the species (Bert et al., 2000, 2002). Arabidopsis halleri is not an easy species for genetic analysis since it is self-incompatible. However, it can be crossed to the non-accumulator A. lyrata ssp. petrea and therefore backcross or intercross F2 populations can be made to study genetic segregation of Zn and Cd tolerance and accumulation (Macnair et al., 1999; Bert et al., 2003). From these studies, it has become clear that tolerance and
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accumulation are independent traits, each controlled by several genes. Cadmium and Zn tolerance or accumulation is correlated, suggesting that at least some loci are involved in the response to both metals (Bert et al., 2003). At the moment, little is known about the identity of the respective genes. Currently a genetic map for the (A. halleri x A. lyrata ssp. petrea) x A. lyrata ssp. petrea backcross population is being made (Willems & Saumitou-Laprade, personal communication, 2004), which will allow the genetic mapping of metal accumulation and tolerance QTL and the possible identification of candidate genes based on co-linearity with A. thaliana. Thlaspi caerulescens is another very useful model species to study metal hyperaccumulation, which is genetically much easier than A. halleri, since it is completely self-fertile and self-compatible (Assun¸ca˜ o et al., 2003c). There are several accessions known with different metal accumulation and tolerance characteristics (Lombi et al., 2000; Zhao et al., 2002; Assun¸ca˜ o et al., 2003a; Fr´erot et al., 2003; Peer et al., 2003; Roosens et al., 2003). Cosegregation analysis of Zn accumulation and tolerance in F2 and F3 populations from a cross between a high accumulation, low tolerance accession and a low accumulation, high tolerance accession indicated that also in T. caerulescens accumulation and tolerance are independent or at least partially independent traits, and controlled by more than one locus (Assun¸ca˜ o et al., 2003b). Construction of a genetic map for several F2 and F3 populations of interaccession crosses of T. caerulescens is in progress (Assun¸ca˜ o, Deniau & Schat, personal communication, 2004), which will be used to identify QTL for Zn, Cd and Ni accumulation and tolerance. Although the DNA similarity with A. thaliana is generally less than for A. halleri, with around 88% identity in coding regions, micro co-linearity seems to be conserved, which might permit a candidate gene approach for QTL cloning (Rigola D. & Aarts M., unpublished observations, 2004). Despite the genetic variation for Zn accumulation, T. caerulescens, like A. halleri, is a constitutive metal hyperaccumulator, which means that even the ‘low’ metal accumulating genotypes are still accumulating much more Zn than non-accumulator species. This means that QTL analysis of Zn accumulation in an intraspecific cross, as is done for T. caerulescens, may not reveal the genes controlling this trait. Another way to unveil interspecific natural variation is to examine and compare gene expressions of orthologous genes in closely related hyperaccumulating and non-accumulating species. Analysis of the expression of several zinc transporter genes showed that the ZNT1, ZNT2 and ZTP1 genes from T. caerulescens are more or less constitutively over-expressed when compared to the expression in the related non-accumulator T. arvense (Pence et al., 2000; Assun¸ca˜ o et al., 2001). With the advent of whole genome A. thaliana microarrays, similar analysis can be performed on a whole-genome scale. Comparison of A. thaliana and A. halleri thus revealed that several other metal homeostasis related genes, such as ZIP-, HMA-, NRAMP- and CDF-type metal transporters and nicotianamine synthases, and also stress-response and signal transduction
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genes are overexpressed in A. halleri compared to A. thaliana grown under similar conditions (Becher et al., 2004; Weber et al., 2004). The combined approach of QTL analysis and gene expression comparison is likely to identify the genes that confer Zn (and Cd or Ni) hyperaccumulation to these adapted species.
9.4 Genetic variation for nutrient content and related traits in crop plants The description and discussion of natural variation for nutrient levels in crop plants will also be limited to micronutrients. Rice has been described above in the paragraph on model species. 9.4.1 Wheat The contents of Fe and Zn in seeds of modern wheat varieties range from 25 to 56 mg kg−1 for Fe, and 25 to 65 mg kg−1 for Zn, and the contents of both micronutrients are positively correlated (Monasterio & Graham, 2000). In a separate study, Graham et al. (2001) also reported a 2.5-fold range for Fe content in wheat grains, although absolute levels were lower. Extending the studies to related Triticum species revealed an even larger variation: T. tauschii and T. boeoticum seeds contained up to nearly 100 mg kg−1 Fe. For Zn content, 142 mg kg−1 was reported for T. dicoccoides, and 177 mg kg−1 for T. boeoticum (Cakmak et al., 2000; Monasterio & Graham, 2000). In a comparison of varieties released between 1950 and 1995, a slightly negative correlation was found between year of release and iron and zinc content (Monasterio & Graham, 2000), suggesting a negative effect of high-yielding varieties, as obtained in the green revolution, on micronutrient quality. However, some of the modern high-yielding varieties are not different in mineral content as compared to old ones, indicating that the benefits of the green revolution in terms of total yield can be combined with maintained or increased quality (Monasterio & Graham, 2000; Graham et al., 2001). 9.4.2 Maize Considerable variation in the micronutrient content in the seeds of maize has also been reported. In 13 field trials in Mexico and Zimbabwe, comprising a total of 1814 lines, the seed Fe content ranged from 10 to 63 mg kg−1 , and seed Zn content varied between 13 and 58 mg kg−1 . However, these differences were not only due to genetic variation, but also environmental conditions (B¨anziger & Long, 2000). The range of genetic variation was about two- to threefold, when lines were tested on the same experimental plot. Welch and Graham (2004) reported lower (1.5-fold) variation in Fe and Zn in maize. A problem in improving
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the nutritional quality of maize might be the reported negative correlation between grain yield and grain Fe and Zn concentrations (B¨anziger & Long, 2000). 9.4.3 Beans Contents of Fe and Zn in common bean (Phaseolus vulgaris) rank among the highest in vegetable sources (Graham et al., 2001). As in cereals, the contents of both minerals are positively correlated. In a screen of over 1000 cultivated varieties, levels for Fe and Zn were found to range from 34 to 89, and from 21 to 54 mg kg−1 , respectively. Across genotypes a correlation coefficient of 0.52 was found (Beebe et al., 2000). Some wild lines were found to have even higher content of Fe, up to 100 mg kg−1 , but higher values for Zn were not found (Beebe et al., 2000). The high-density traits are remarkably stable across environments, but genotype x environment interactions have also been found (Graham et al., 2001; Welch & Graham, 2004). In the progeny of a cross between two contrasting bean varieties, an even higher correlation (r = 0.66) between Fe and Zn was found. For beans, QTL have been mapped for Fe and Zn content. Preliminary results indicate there are 7 and 11 loci for Fe and Zn contents, respectively. Four loci were found to affect the contents of both minerals, confirming the correlation in contents mentioned before (Beebe et al., 2000). In a separate study, only two QTL were found for Fe content in seeds, and one for Zn content, but four QTL were also found for the content of tannin, a possible antinutrient, which can reduce the bioavailability of minerals (Guzman-Maldonado et al., 2003). 9.4.4 Brassica rapa Brassica rapa is another important food crop, especially in East Asia. In contrast to the previous crops the leaves of this plant are eaten rather than the seeds. There are many accessions known for Brassica rapa, both from Western and Eastern origin, which can be separated into many different plant types. Next to leafy vegetable and turnip types, there are also oilseed types with an appearance similar to Brassica napus. In our labs, we study the natural variation for mineral composition and phytate content of B. rapa genotypes. The Zn, Fe and Mn contents were examined in a large set of breeding lines obtained from genetic resource centres in Europe and China. In a set of 111 lines, mainly consisting of chinese cabbage and Pak-choi, and also containing lines for chinese flowering cabbage, flat chinese cabbage, Mizuna and turnip, an average foliar Zn content of 65 g g−1 d. wt was found with a range from 23 to 156 g g−1 d. wt. For Fe and Mn the respective averages found were 133 and 35 g g−1 d. wt, with corresponding ranges of 60–350 g g−1 d. wt and 21–53 g g−1 d. wt. A similar set of lines was exposed to a high zinc concentration in hydroponic culture (800 M), which also revealed great differences, varying from mild toxicity symptoms (8%) to severely affected lines (46%) (Wu, Wang, Sun, Koornneef & Aarts, unpublished observations, 2004). Phosphate and phytate levels in seeds
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and leaves also showed considerable genetic variation between Brassica rapa accessions. Phosphate levels in leaves differed over 10-fold, averaging at 11.5 mg g−1 d. wt and phytate in leaves ranged from 0 to 4 mg g−1 d. wt, being on average 1 mg g−1 d. wt (Zhao, Koornneef & Vreugdenhil, unpublished observations, 2004).
9.5 Physiological processes underlying micronutrient content The amount of nutrients ultimately accumulating in mature seeds depends on a plethora of processes: mobilisation in the soil, uptake into the roots, loading into the xylem, translocation form roots to shoots, complex formation with chelators, remobilisation from leaves/stems to seeds, phloem loading and unloading and storage in the seeds. This list can easily be extended, and not all processes mentioned will be equally important in all species or for each nutrient. When only the final nutritionally relevant trait (e.g. seed content or bioavailability) is taken into account while comparing accessions, it is likely that interesting germplasm is overlooked. If, for instance, a line contains alleles positively affecting one physiological aspect, but other alleles negatively influencing another trait, the final result might be neutral or even negative. A way to overcome this dilemma is to investigate each process separately. An alternative approach is to use QTL analysis of an integrative trait in a segregation population large enough to allow combinations of a large number of favourable alleles for various processes eventually leading to this trait. Evidently, variation for the traits under investigation should be present. The ultimate breeding goal will be to pyramid all favourable alleles. To design an efficient strategy it is crucial not only to describe the processes involved, but also to identify those that pose a major constraint on the final trait. Grusak (2000) described a mutant approach in pea to discriminate various processes involved in Fe accumulation in seeds and their relative importance. For Arabidopsis, many transporter genes have been already described that are supposedly involved in cellular uptake and sequestration (M¨aser et al., 2001), as well as genes involved in metal chelation and mobilisation (Clemens, 2001). At the moment very little is known about the genes controlling signal transduction and transcriptional regulation responsible for the control of the processes mentioned above. Surprisingly little information can be found on the percentage of the total amount of micronutrient in the whole plant that is ultimately stored in the seeds. For Fe partitioning, a figure of 75% is reported for pea, whereas for rice it might be as low as 4% (Grusak, 2000). This will lead to different breeding strategies to improve the Fe content of seeds of these two species. In pea, it would be best to improve total uptake into the plant, whereas for rice, improved partitioning within the plant seems more appropriate. It should also be taken into account that partitioning within the plant may depend on the growth stage of the crop
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(Akman & Kara, 2003) and position of the grain within the plant (Calderini & Ortiz-Monasterio, 2003). However, unwanted interactions between processes may complicate breeding strategies. For instance, altering the plant’s capacity to take up Fe from the soil might result in unwanted side effects, due to toxicity of Fe by generating free radicals. Hence, such changes have to be paralleled by co-ordinated modification of the capacity to sequester Fe (Grusak, 2000).
9.6 Transferring knowledge from model to crop species The genetic analysis of natural variation, which is (and will increasingly be) applied to dissect traits related to mineral nutrition, is expected to give an insight into the number of loci that control these traits. Since studies based on the analysis of the genetic differences between two parental genotypes of a population are by definition limited, a more comprehensive analysis requiring many populations, probably supplemented with LD mapping, will be needed to get an impression of the total number of genes in a species that reflects the total genetic variation in the germplasm of a species. The latter determines the potential for plant breeding to improve plant quality by pyramiding all available favourable alleles. Identification of the loci that show variation in the germplasm of a species is important because, without this knowledge, plant breeding for increased micronutrient content is a cumbersome procedure. To track the favourable alleles in breeding programs, functional markers of the relevant genes are expected to be indispensable in the future (Anderson & Lubberstedt, 2003). For traits such as micronutrient content, MAS is a very useful application because of the variability of the expression of the trait and the costs of the micronutrient assays. The best functional markers are the genes themselves. Especially for the identification of these functional markers the cloning of the genes underlying QTL is crucial. For technical reasons it is expected that this will proceed faster in model plants such as Arabidopsis and rice than in other crop species (Koornneef et al., 2004). The link between model plants and crop species is based on the observation that many plant processes are under control of similar genes and thus genes known to be involved in a certain process in a model species are candidate genes for a similar process in crops. Regulation of flowering time is an example in which it has been shown that genes involved in a complex multigenic trait in a model species are also relevant in crop plants. Some genes controlling flowering time in Arabidopsis were found to have functional orthologues in rice (Izawa et al., 2003). However, an important lesson could also be learned from this study: not all genes important for Arabidopsis are relevant in other plants. For instance the FLC gene controlling the response to vernalisation in Arabidopsis is not found in rice. Cereals that respond to vernalisation, such as wheat, control this process through other genes than in Arabidopsis (Yan et al., 2004).
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An important tool for translating QTL data from one species to another is the synteny of gene order, which may occur along long stretches of chromosomes, especially in closely related species. This will help the identification of candidate genes in a crop species by making use of the knowledge that genes involved in a given process are located in the synthenic region of the model species. This comparison is not restricted to genes identified and cloned as QTL but also for genes identified with other approaches such as reverse and forward genetics. An example of this approach is the finding that in several Brassica species a flowering time QTL co-locates with FLC (Schranz et al., 2002). The importance of this gene was confirmed by complementation studies (Tadege et al., 2001). However, in Brassica nigra flowering time variation was associated with the CO gene (Kruskopf-Osterberg et al., 2002), which is located not far from the FLC gene in Arabidopsis. Hence, a rough map position does not allow the discrimination between both candidate genes and sequencing, gene expression and transformation experiments are required to do so. In addition to MAS, transgenic approaches can also be used. Several strategies have been proposed to increase the level of available Fe in seeds (Lucca et al., 2001). Besides the technical aspects, this approach will have the added complication of more complex regulatory and consumer acceptance issues. Examples of a transfer of knowledge from models to crop plants in relation to micronutrient accumulation have, to our knowledge, not been published. This is mainly due to a lack of fundamental molecular and genetic knowledge of this process both in crops and model plants. The recent focus on micronutrient quality of plants, as being important to prevent human nutritional disorders, and the availability of the basic materials and tools to unravel these processes, are likely to result in a rapid progress in this field.
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El-Assal, S.E.-D., Alonso-Blanco, C., Peeters, A.J.M., Raz, V. & Koornneef, M. (2001) A QTL for flowering time in Arabidopsis reveals a novel allele of CRY2. Nat. Genet., 29, 435–440. Flint-Garcia, S.A., Thornsberry, J.M. & Buckler, E.S. (2003) Structure of linkage disequilibrium in plants. Annu. Rev. Plant Biol., 54, 357–374. Fr´erot, H., Petit, C., Lef`ebre, C., Gruber, W., Collin, C. & Escarr´e, J. (2003) Zinc and cadmium accumulation in controlled crosses between metallicolous and nonmetallicolous populations of Thlaspi caerulescens (Brassicaceae). New Phytol., 157, 643–648. Glahn, R.P., Chen, S.Q., Welch, R.M. & Gregorio, G.B. (2002) Comparison of iron bioavalability from 15 rice genotypes: Studies using an in vitro digestion/caco-2 cell culture model. J. Agric. Food Chem., 50, 3586–3591. Graham, R., Senadhira, D., Beebe, S., Iglesias, C. & Monasterio, I. (1999) Breeding for micronutrient density in edible portions of staple food crops: conventional approaches. Field Crops Res., 60, 57–80. Graham, R.D., Welch, R.M. & Bouis, H.E. (2001) Addressing micronutrient malnutrition through enhancing the nutritional quality of staple foods: Principles, perspectives and knowledge gaps. Adv. Agron., 70, 77–142. Gregorio, G.B., Senadhira, D., Htut, H. & Graham, R.D. (2000) Breeding for trace mineral density in rice. Food Nutr. Bull., 21, 382–386. Grusak, M.A. (2000) Strategies for improving the iron nutritional quality of seed crops: lessons learned from the study of unique iron-hyperaccumulating pea mutants. Pisum Genet., 32, 1–5. Guzman-Maldonado, S.H., Martinez, O., Acosta-Gallelos, J.A., Guevara-Lara, F. & Paredes-Lopez, O. (2003) Putative quantitative trait loci for physical and chemical components of common bean. Crop Sci., 43, 1029–1035. Hall, J.L. & Williams, L.E. (2003) Transition metal transporters in plants. J. Exp. Bot., 54, 2601–2613. Henikoff, S. & Comai, L. (2003) Single-nucleotide mutations for plant functional genomics. Annu. Rev. Plant Biol., 54, 375–401. Hoekenga, O.A., Vision, T.J., Shaff, J.E., Monforte, A.J. & Lee, G.P. (2003) Identification and characterization of aluminum tolerance loci in Arabidopsis (Landsberg erecta x Columbia) by quantitative trait locus mapping. A physiologically simple but genetically complex trait. Plant Physiol., 132, 936–948. Izawa, T., Takahashi, Y. & Yano, M. (2003) Comparative biology comes into bloom: genomics and genetic comparison of flowering pathways in rice and Arabidopsis. Curr. Opin. Plant Biol., 6, 113–120. Jansen, R.C. & Nap, J.P. (2001) Genetical genomics: the added value from segregation. Trends Genet., 17, 388–391. Knappe, S., Flugge, U.I. & Fischer, K. (2003) Analysis of the plastidic phosphate translocator gene family in Arabidopsis and identification of new phosphate translocator-homologous transporters, classified by their putative substrate-binding site. Plant Physiol., 131, 1178–1190. Kobayashi, Y. & Koyama, H. (2002). QTL analysis of Al tolerance in recombinant inbred lines of Arabidopsis thaliana. Plant Cell Physiol., 43, 1526–1533. Koornneef, M., Alonso-Blanco, C. & Vreugdenhil, D. (2004) Naturally occurring genetic variation in Arabidopsis thaliana. Annu. Rev. Plant Biol., 55, 141–172. Kruskopf-Osterberg, M., Shavorskaya, O., Lascoux, M. & Lagercrantz, U. (2002) Naturally occurring indel variation in the Brassica nigra COL1 gene is associated with variation in flowering time. Genetics, 161, 299–306. Lahner, B., Gong, J., Mahmoudian, M., Smith, E.L., Abid, K.B., Rogers, E.E., Guerinot, M.L., Harper, J.F., Ward, J.M., McIntyre, L., Schroeder, J.I. & Salt, D.E. (2003) Genomic scale profiling of nutrient and trace elements in Arabidopsis thaliana. Nat. Biotechnol., 21, 1215–1221. Lister, C. & Dean, C. (1993) Recombinant inbred lines for mapping RFLP and phenotypic markers in Arabidopsis thaliana. Plant J., 4, 745–750.
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Lombi, E., Zhao, F.-J., Dunham, S.J. & McGrath, S.P. (2000) Cadmium accumulation in populations of Thlaspi caerulescens and Thlaspi goesingense. New Phytol., 145, 11–20. Loudet, O., Chaillou, S., Krapp, A. & Daniel-Vedele, F. (2003a) Quantitative trait loci analysis of water and anion contents in interaction with nitrogen availability in Arabidopsis thaliana. Genetics, 163, 711–722. Loudet, O., Chaillou, S., Merigout, P., Talbotec, J. & Daniel-Vedele, F. (2003b) Quantitative trait loci analysis of nitrogen use efficiency in Arabidopsis. Plant Physiol., 131, 345–358. Lucca, P., Hurrell, R. & Potrykus, I. (2001) Genetic engeneering approaches to improve the bioavailability and the level of iron in rice grains. Theor. Appl. Genet., 102, 392–397. Ma, L.Q., Komar, K.M., Tu, C., Zhang, W., Cai, Y. & Kennelley, E.D. (2001) A fern that hyperaccumulates arsenic. Nature, 409, 579. Ma, J.F., Shen, R.F., Zhao, Z.Q., Wissuwa, M., Takeuchi, Y., Ebitani, T. & Yano, M. (2002) Response of rice to Al stress and identification of quantitative trait loci for Al tolerance. Plant Cell Physiol., 43, 652–659. Macnair, M.R. (2002) Within and between population genetic variation for zinc accumulation in Arabidopsis halleri. New Phytol., 155, 59–66. Macnair, M.R., Bert, V., Huitson, S.B., Saumitou-Laprade, P. & Petit, D. (1999) Zinc tolerance and hyperaccumlation are genetically independent characters. Proc. R. Soc. Lond. B Biol. Sci., 266, 2175–2179. Maruyama-Nakashita, A., Inoue, E., Watanabe-Takahashi, A., Yarnaya, T. & Takahashi, H. (2003) Transcriptome profiling of sulfur-responsive genes in Arabidopsis reveals global effects of sulfur nutrition on multiple metabolic pathways. Plant Physiol., 132, 597–605. M¨aser, P., Thomine, S., Schroeder, J.I., Ward, J.M., Hirschi, K., Sze, H., Talke, I.N., Amtmann, A., Maathuis, F.J.M., Sanders, D., Harper, J.F., Tchieu, J., Gribskov, M., Persans, M.W., Salt, D.E., Kim, S.A., & Guerinot, M.L. (2001) Phylogenetic relationships within cation transporter families of Arabidopsis. Plant Physiol., 126, 1646–1667. Ming, F., Zheng, X.W., Mi, G.H., Zhu, L.H. & Zhang, F.S. (2001) Detection and verification of quantitative trait loci affecting tolerance to low phosphorus in rice. J. Plant Nutr., 24, 1399–1408. Mitchell-Olds, T. (2001) Arabidopsis thaliana and its wild relatives: a model system for ecology and evolution. Trends Ecol. Evol., 16, 693–699. Monasterio, I. & Graham, R.D. (2000) Breeding for trace minerals in wheat. Food Nutr. Bull., 21, 392–396. 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. Nikiforova, V., Freitag, J., Kempa, S., Adamik, M., Hesse, H. and Hoefgen, R. (2003) Transcriptome analysis of sulfur depletion in Arabidopsis thaliana: interlacing of biosynthetic pathways provides response specificity. Plant J., 33, 633–650. Paran, I. & Zamir, D. (2003) Quantitative traits in plants: beyond the QTL. Trends Genet., 19, 303-306. Payne, K.A., Bowen, H.C., Hammond, J.P., Hampton, C.R., Lynn, J.R., Mead, A., Swarup, K., Bennett M.J., White, P.J. & Broadley, M.R. (2004) Natural genetic variation in caesium (Cs) accumulation by Arabidopsis thaliana. New Phytol., 16(162), 535–548. Peer, W.A., Mamoudian, M., Lahner, B., Reeves, R.D., Murphy, A.S. & Salt, D.E. (2003) Identifying model metal hyperaccumulating plants: germplasm analysis of 20 Brassicaceae accessions from a wide geographical area. New Phytol., 159, 421–430. Pence, N.S., Larsen, P.B., Ebbs, S.D., Letgham, D.L.D., Lasat, M.M., Garvin, D.F., Eide, D. & Kochian, L.V. (2000) The molecular physiology of heavy metal transport in the Zn/Cd hyperaccumulator Thlaspi caerulescens. Proc. Natl. Acad. Sci. USA, 97, 4956–4960.
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Quesada, V., Garcia-Martinez, S., Piqueras, P., Ponce, M.R. & Micol, J.L. (2002) Genetic architecture of NaCl tolerance in Arabidopsis. Plant Physiol., 130, 951–963. Roosens, N., Verbruggen, N., Meerts, P., Xim´enez-Emb´un, P. & Smith, J.A.C. (2003) Natural variation in cadmium tolerance and its relationship to metal hyperaccumulation for seven populations of Thlaspi caerulescens from western Europe. Plant Cell Environ., 26, 1657–1672. Schranz, M.E., Quijada, P., Sung, S.-B., Lukens, L., Amasino, R. & Osborn, T.C. (2002) Characterization and effects of the replicated flowering time gene FLC in Brassica rapa. Genetics, 162, 1457–1468. Tadege, M., Sheldon, C.C., Helliwell, C.A., Stoutjesdijk, P., Dennis, E.S. & Peacock, W. J. (2001) Control of flowering time by FLC orthologues in Brassica napus. Plant J., 28, 545–553. Van Hal, N.L., Vorst, O., van Houwelingen, A.M., Kok, E.J., Peijnenburg, A., Aharoni, A., van Tunen, A.J. & Keijer, J. (2001) The application of DNA microarrays in gene expression analysis. J. Biotechnol., 78, 271–280. Van Ooijen, J.W., Boer, M.P., Jansen, R.C. & Maliepaard, C. (2000) MAPQTL version 4.0: software for the calculation of QTL positions on genetic maps. Plant Research International, Wageningen (www.kyazma.nl). Vreugdenhil, D., Aarts, M.G.M., Koornneef, M., Nelissen, H. & Ernst, W.H.O. (2004) Natural variation and QTL analysis for cationic mineral content in seeds of Arabidopsis thaliana. Plant Cell Environ., 27, 828–839. 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. Weber, M., Harada, E., Vess, C., Roepenack-Lahaye, E. & Clemens, S. (2004) Comparative microarray analysis of Arabidopsis thaliana and Arabidopsis halleri roots identifies nicotianamine synthase, a ZIP transporter and other genes as potential metal hyperaccumulation factors. Plant J., 37, 269–281. Welch, R.M. & Graham, R.D. (2004) Breeding for micronutrients in staple food crops from a human nutrition perspective. J. Exp. Bot., 55, 353–364. 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. Wissuwa, M. & Ae, N. (2001) Genotypic variation for tolerance to phosphorus deficiency in rice and the potential for its exploitation in rice improvement. Plant Breed., 120, 43–48. 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, S.-H., Ramonell, K., Gollub, J. & Somerville, S. (2001) Plant gene expression profiling with DNA microarrays. Plant Physiol. Biochem., 39, 917–926. Yamaya, T., Obara, M., Nakajima, H., Sasaki, S., Hayakawa, T. & Sato, T. (2002) Genetic manipulation and quantitative-trait loci mapping for nitrogen recycling in rice. J. Exp. Bot., 53, 917–925. Yano, M. (2001) Genetic and molecular dissection of naturally occurrung variation. Curr. Opin. Plant Biol., 4, 130–135. Yan, L.L., Loukoianov, A., Blechl, A., Transquilli, G., Ramakrisna, W., SanMiguel, P., Bennetzen, J. , Echenique, V. & Dubcovsky, J. (2004) The wheat VRN2 gene is a flower repressor down-regulated by vernalisation. Science, 303, 1640–1644. Zhao, F.-J., Hamon, R.E., Lombi, E., McLaughlin, M.J., McGrath, S.P. (2002) Characteristics of cadmium uptake in two contrasting ecotypes of the hyperaccumulator Thlaspi caerulescens. J. Exp. Bot., 53, 535–543.
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10 Mapping nutritional traits in crop plants Matthias Wissuwa
10.1 Introduction Nutritional traits have been investigated using classical quantitative genetic methods for decades, and results have suggested typically that nutritional traits (for example tolerance to P deficiency) are controlled by multiple genes (Fawole et al., 1982). Although it had not previously been possible to identify individual loci controlling these traits, the availability of DNA markers in the late 1980s has since made it possible to dissect complex nutritional traits into underlying quantitative trait loci (QTL). This chapter provides a review of QTL mapping in rice, with particular emphasis on examples from the author’s own work on tolerance to P deficiency. It briefly reviews past achievements, before discussing technical and procedural aspects of mapping studies. Alternative mapping approaches, such as the use of mutant population screens (e.g. see Chapter 7), which constitute the preferred method of mapping nutritional traits in Arabidopsis, have been discussed in detail in previous chapters. They have not been widely used in crop plants and will therefore be touched upon only briefly here. The concept of QTL mapping using detailed linkage maps based on molecular markers was developed and first applied by Paterson et al. (1988) to map fruit properties in tomato. The earliest QTL mapping experiments for nutritional traits were conducted for P deficiency in maize (Reiter et al., 1991) and Fe deficiency in soybeans (Diers et al., 1992). QTL studies on nutritional disorders then increased in number in the late 1990s, with rice being the crop most commonly used, followed by maize, soybean, and barley (Table 10.1). The traits most commonly investigated were P deficiency (Reiter et al., 1991; Ni et al., 1998; Wissuwa et al., 1998; Kaeppler et al., 2000; Ming et al., 2001), N-use efficiency (Agrama et al., 1999; Obara et al., 2001; Fang et al., 2001; Mickelson et al., 2003), Al toxicity (Bianchi-Hall et al., 2000; Wu et al., 2000; Ninamango-C´ardenas et al., 2003; Nguyen et al., 2003), Fe deficiency or toxicity (Diers et al., 1992; Lin et al., 1997; Wan et al., 2003), and K deficiency (Wu et al., 1998). Most studies identified several loci associated with the trait investigated. However, few subsequent reports have been published that have (i) further characterized identified QTLs, (ii) suggested an application in marker assisted breeding, or (iii) described progress toward cloning QTLs. It therefore appears that, as of now, tangible results are lacking despite the considerable effort devoted to mapping nutritional traits.
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MAPPING NUTRITIONAL TRAITS IN CROP PLANTS Table 10.1 QTLs identified for nutritional traits in crops Trait
Crop
N use efficiency
maize
N absorption/ utilization N recycling N storage/ remobilization P deficiency P deficiency
rice
maize rice
P deficiency P deficiency P deficiency
rice rice maize
Fe deficiency Fe deficiency
soybean soybean
Fe toxicity
rice
Al toxicity Al toxicity
Measurements
Environment
QTL detected (0)1
yield under N field deficiency NO3 − /NH4 + uptake solution
7
glutamine synthetase NO3 − /total N, remobilized N dry weight dry weight, P uptake
pots field
7 (-)2 2–8 (0)
solution solution pots
rice rice
relative tiller number relative dry weight dry weight, mycorrhiza chlorosis chlorosis, chlorophyll dry weight, leaf bronzing relative root length relative root length
Al toxicity
soybean
Al toxicity
K deficiency
2 (-)2
Source Agrama et al. (1999) Fang et al. (2001)
field field
Obara et al. (2001) Mickelson et al. (2003) 6 (1) Reiter et al. (1991) 4 (1) Wissuwa et al. (1998) 3 (2) Ni et al. (1998) 1 (1) Ming et al. (2001) 3 (0) Kaeppler et al. (2000) 0–3 (0–2) Diers et al. (1992) 2–4 (1) Lin et al. (1997)
solution
4 (2)2
Wan et al. (2003)
solution solution
3 (0) 5 (1)
relative root length
solution
4 (0)
maize
relative root length
solution
5 (0)
rice
K uptake, use efficiency
solution
2 (0)
Wu et al. (2000) Nguyen et al. (2003) Bianchi-Hall et al. (2000) NinamangoC´ardenas et al. (2003) Wu et al. (1998)
rice barley
pots field
1 Number in parenthesis refers to number of major QTLs detected, where a major QTL is defined as explaining >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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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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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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kb 0
M18 M22
91 120
M31
165
M38
203
M50
267
M59
333
M69
407
Kasalath
B A C 1 B A C 2 B A C 3 B A C
Pup1 interval
M1
4 B A C
M75
530
Pup1 region sequence assembly
5
Line A Phenotype N Score 1.2
B N 1.1
C K 2.3
D K 2.3
E K 2.2
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
50
IAC 47
100
Pup1 lines
Kasalath
IR 36
150
tolerant local checks
intolerant check
Grain yield (g row−1)
200
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.
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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-C´ardenas, F.E., Guimar˜aes, 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 <1500 kg ha−1 (Johnston, 1994). Today, few of the N, P, K and other atoms leaving a farm in harvested crops ever return. Increasing urbanization worldwide means that nutrients in grain, meat and milk are often transported hundreds of kilometers or more from the farm where the food was produced. For example, the United States exported about 8×1010 kg of grain in 2002, or about 27% of total production (US Department of Agriculture, 2003). The exported grain contained about 1.4×109 kg N and 2.3×108 kg P (based on composition of maize from Table 1 of Loomis & Connor, 1992). Nutrient transfers within countries are also large, as crops are often not consumed (by humans or animals) in the same region where they are grown. Many of these nutrients eventually flow from sewage treatment plants into rivers and eventually to the ocean. Returning nutrients to the farm of origin would be difficult for several reasons. The energy cost of transporting fresh manure (at an 85% moisture content) containing about 5.6 kg N t−1 is about 5000 kcal km−1 , or about 900 kcal kg−1 N km−1 , while the energy cost of synthesizing synthetic N fertilizer (and transporting from factory to farm) is about 12 000 kcal kg−1 N (Pimentel et al., 1984). Thus, transporting manure for more than 13 km uses more energy than it would take to synthesize and transport synthetic N fertilizer. Technological advances or environmental concerns (nutrient loading in particular locations) may someday make the return of nutrients to farms more common, but it will not be easy. Even if future advances in energy technologies make energy cost irrelevant, the labor involved in handling bulky, low-nutrient materials may be an issue for collection, transport and field application. The ratio of N to P, K
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and other elements may not match crop needs. For example, applying poultry manure with a N/P ratio of 3.7 to maize with a grain N/P ratio of 5.8 (both ratios based on data in Loomis & Connor, 1992) may result in net accumulation of P in soil, especially given that N is more easily lost from soil than P is. Timing of mineralization within a season may not coincide with crop needs, but this is less of an issue from a multi-decade perspective. Animal manure and sewage sludge may contain pathogens, weed seeds, or toxic chemicals from industrial or domestic sources. Organic sources of nutrients provide benefits not found in inorganic fertilizers, such as improving soil physical properties. However, these nonnutrient benefits would probably not be sufficient to justify widespread transport of animal manures over great distances, given the alternative of growing green manure crops in situ. The atmosphere contains an unlimited supply of N but global supply of P is limited. This is an important difference between these two major nutrients. We will never ‘run out’ of N fertilizer so long as we are willing to pay the energy cost of reducing atmospheric N2 to ammonia (NH3 ). When natural gas becomes too expensive, H2 gas generated by electrolysis (using electricity from solar panels or maybe nuclear fusion) could do the job, perhaps at a higher cost. So the main issues with N are economic costs, NO3 − pollution of drinking water and the environmental consequences of excess N for natural ecosystems, especially aquatic ones. These may be very serious problems in some areas. For example, NO3 − levels have apparently been increasing in wells in California’s San Joaquin Valley, at the same time that some pesticides have apparently decreased (Burow et al., 1998). In contrast with N, P fertilizers are derived from high-Pcontaining minerals of which there is only a limited global supply. The lifetime of these P reserves is hard to estimate, since it depends on rate of use, the extent of ore deposits not yet discovered, the price we are willing to pay for P fertilizer, and possible advances in technology that might lower the cost of extracting and using these ores. World P reserves extractable at $36 per ton are estimated to last for 88 years, whereas a higher price of $90 per ton would expand their supply for a lifetime of 343 years (Roberts & Stewart, 2002). If advances in the genomics of crop plants and their microbial symbionts result in access to otherwise unavailable sources of fixed P and/or increase the fraction of applied P that is actually taken up and used by crops, then this could decrease the rate at which P reserves are consumed. Eventually, after hundreds of years, it may be necessary for much of the P exported from farms to cities to make the return trip to those farms. We have already mentioned the difficulty of this undertaking. In Brave New World (Huxley, 1932), it was speculated that someday even the P in our bodies will be recovered and reused after death. We will leave consideration of such ultimate solutions to the P problem to those with a very long-term view of sustainability. Elements other than N may be supplied by weathering of soil minerals, but for how long? Elements found in the parent material of the soil may, in some cases,
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be released at sufficient rates to balance removal in harvested crops for long periods of time. However, these nutrients can eventually become depleted. Even natural ecosystems, which export less nutrients than farms, may still require external inputs to maintain productivity. The fertility of Hawaiian rainforests, for example, apparently depends on P supplied as windblown dust from central Asia (Chadwick et al., 1999). Proteoid roots or symbiosis with mycorrhizal fungi, both discussed below, may give plants access to essential elements found in minerals that ordinarily release these elements only very slowly. Do these plant adaptations, in effect, weather minerals more rapidly, increasing the immediate supply of nutrients but accelerating long-term nutrient depletion? Given conservation of matter, what are the prospects for genetic improvement of crop nutrition? Ideally, we would both maximize the ability of crops to acquire nutrients and optimize the use of the nutrients acquired. These two topics will be discussed in order in this section. Then, under Constraint/opportunity 2 (Section 11.3) we will consider the extent to which past natural selection may have missed opportunities to optimize nutrient acquisition and use. Nutrient acquisition based on symbiosis will be discussed under Constraint/opportunity 3 (Section 11.4). Proteoid roots may increase P acquisition temporarily, but is the increase sustainable? Proteoid roots are an example of a sophisticated adaptation that may nonetheless have little potential to enhance crop nutrition over the long term. Members of the family Proteaceae and some other plants, including some lupins (Lupinus spp.), produce ephemeral clusters of roots called proteoid roots, especially in soils poor in P. Roots in these clusters excrete large amounts of organic acids and other materials that increase the availability of relatively insoluble forms of P (Dinkelaker et al., 1995). Significant progress is being made in understanding the physiology and genomics of proteoid roots (Vance et al., 2003), but conservation of matter remains an important constraint on this system. Proteoid roots mainly form in soil containing high amounts of humus, both in the field and in split-root experiments (Dinkelaker et al., 1995). This suggests that their usual role in nature is recycling of P from plant residues. Proteoid roots may also give plants access to mineral forms of P that are otherwise unavailable, but how long will that resource last? We have been unable to find any quantitative analysis of this question, but the short lifespan of individual root clusters (days to months; Dinkelaker et al., 1995) may reflect local exhaustion of P in forms that can be solubilized. Even if proteoid roots are not a long-term substitute for P inputs, transferring this trait to other crops might reduce input requirements somewhat by making a larger fraction of applied P available to the crop. Similarly, improved uptake of N from soils would not eliminate the need for inputs but could reduce losses. A significant fraction of N applied is lost due to NH3 volatilization, NO3 − leaching and denitrification (Alexander, 1977). More active NO3 − uptake by surface roots could reduce the pool of NO3 − available to denitrify or to leach downwards. Deeper roots could capture escaping nitrate
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that would otherwise be lost to groundwater. Natural selection may not have missed many opportunities for improvements in these traits, however, except to the extent that conditions today are different than those under which the wild ancestors of our crops evolved (Denison et al., 2003b). This topic is discussed further below. Increasing the efficiency with which nutrients are used, once taken up, will be difficult. If we define the N-use efficiency (NUE) of a crop as the amount of useful product (e.g. grain) produced per unit of N taken up by the crop, what are the prospects for increasing NUE? We expect increases in NUE to contribute little to world food security, for several reasons. If we consider only the N in the grain, the direct relationship between N and protein, discussed above, limits our ability to get more grain protein from the same amount of grain N. There may be more prospects for increasing the efficiency with which N is used elsewhere in the plant. For example, C4 plants achieve leaf photosynthesis rates as high or higher than those of C3 plants, despite lower leaf N contents (Loomis & Connor, 1992). One can at least imagine similar increases in efficiency in other processes. The problem is that most grain crops already transfer the vast majority of N from the vegetative parts to the grain at the end of growth. In wheat, grain N may account for almost 90% of total plant N (Cassman et al., 1992). This high ‘N harvest index’ is partly the consequence of natural selection for seed production in N-limited environments. Greater NUE might mean leaves could achieve similar photosynthesis with less leaf N, but then less N will be available to transfer from leaf to grain at the end of the season. Then either grain yield will be lower or the crop will have to take up additional N from the soil. Note also that all plants respond to N deficiency by producing lower N biomass. If we define NUE as producing more biomass per unit N taken up, N deficiency inevitably leads to higher NUE, but lower yields. A simple way to get higher NUE, if yield is ignored, is therefore to breed for plants with defective root systems. We hypothesize that increases in NUE will make little or no contribution to increasing yields or to achieving the same yields with lower N uptake. This hypothesis could easily be disproved, if false, by the counterexample of a new cultivar with significantly increased yield shown to be due to higher NUE. We do not expect this to happen. Similar arguments apply to other nutrients as well, although the extent of transfer to grain may be less for some elements. In those cases, it is at least possible that nutrient-use efficiency could be improved with resulting increases in yield. In summary, conservation of matter will always be a significant constraint on crop nutrition. Improved capture of soil NO3 − by crops could reduce NO3 − available for leaching. This would improve the overall N balance of the system so that inputs can be reduced somewhat. To some extent, this might also be true of other nutrients. However, export of nutrients in harvested crops would still need to be balanced by import of the same nutrients. Further, past natural selection may not have left much room for improvement in uptake of soil N from low-N soils,
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although uptake under today’s fertilized conditions might perhaps be improved. Nitrogen can be ‘imported’ from the atmosphere through biological fixation of N2 , and it may be possible to improve or extend this process, as discussed below. Extending the proteoid root trait (or similar soil-mining traits) to new species would probably provide only a temporary benefit. The one-way transfer of P (and other nutrients, aside from N) from farms to cities may be unsustainable in the long-term, irrespective of plant nutrient-acquisition adaptations. Improvements in crop ‘nutrient-use efficiency’ will be of little value for nutrients that are mostly transferred into the harvested portion of the crop already.
11.3 Constraint/opportunity 2: our crops’ legacy of preagricultural evolution Natural selection has already tested many possible solutions to plant nutrition problems. Although even millions of years were not enough to test every possible genotype, natural selection has certainly tested more genotypes than any conceivable human enterprise; and rejected almost all of them. If we simply increase the expression of nutrition-related enzymes at random, we will mostly be reinventing phenotypes that have already failed the test of natural selection. The wild ancestors of our crops were the survivors of a harsh winnowing process that eliminated numerous alternatives. Some of the genotypes rejected by natural selection may be well suited to agriculture, however. We will make faster progress in crop genetic improvement if we focus on those traits for which natural selection has given us suboptimal solutions. To identify these traits, we need to briefly review how natural selection works. Natural selection always favors individual competitiveness over community productivity. Some alleles increase both the competitiveness of individual plants against their neighbors and the collective productivity of a plant community, relative to alternative alleles. Such alleles will almost always increase their representation within a population of plants. For example, an allele coding for an enzyme that uses less ATP to carry out the same reaction will usually displace alleles coding for less efficient versions of the same enzyme, unless the lessefficient forms confer some other advantage (e.g. wider temperature tolerance). There are a few examples of ‘selfish genes’ (Dawkins, 1976) in plants that spread even though they reduce the reproductive success of plants in which they occur, for example, maternally transmitted genes preventing pollen production (Dominguez, 1995), but plant genes affecting nutrient acquisition generally seem to serve the interests of the individual plant well. But when the ability of an individual plant to outcompete its neighbors – strictly speaking, to increase its Darwinian fitness by producing more descendants than others of its species with different genes – conflicts with community productivity, efficiency, or stability, natural selection always favors individual competitiveness (Harper, 1977).
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Humans have often increased crop productivity at the expense of individual plant competitiveness. In fact, we have suggested that most increases in crop yield potential to date have involved reversing the effects of past natural selection for individual competitiveness, in cases where the interests of the individual plant conflict with the collective performance of the plant community (Denison et al., 2003b). An example relevant to crop nutrient uptake is the conflict between individual and community with respect to rooting patterns. Zhang et al. (1999) analyzed rooting patterns from the standpoint of water uptake, but their conclusions seem equally relevant to nutrient uptake. According to their analysis, a plant community (e.g. a crop) will be most productive if it produces just enough roots to capture the available soil resources. Any ‘excess’ root production represents resources that could have been used for seed production. But a population of such ‘optimal’ plants can be invaded and displaced by a mutant that produces more roots than this ‘optimum’. This is because the benefit to the individual plant of taking a larger share of the soil resources exceeds the cost to the individual plant of making more roots. Natural selection could never solve this ‘tragedy of the commons’ problem (Hardin, 1968), even given unlimited time. But humans can select those genotypes that perform well as members of a plant community (typically, a genetically uniform crop). Many of the successes using this approach have involved aboveground traits. For example, shorter rice cultivars have higher yield because they put a larger fraction of available photosynthate into grain, but they are also less competitive for light. If forced to compete with taller, lower yielding cultivars, the short cultivars disappear from the population in a few years (Jennings & Aquino, 1968). We suggest that there may be significant opportunities for progress below ground as well, possibly starting with the analysis by Zhang et al. (1999). Past natural selection may not have optimized crops for present conditions. Natural selection is not necessarily a slow process. Alleles that greatly enhance survival and reproduction can displace alternative alleles in only a few generations. This is seen, for example, in the rapid evolution of herbicide resistance in some weeds (Hill et al., 1994). Some traits evolve more slowly, however. Evolution of traits related to nutrient uptake and use may not have kept pace with changes in soil fertility or in atmospheric CO2 content over the last few decades. For example, in nature or in preindustrial agriculture, usually there would be little available soil N left by the beginning of the grainfill period. There might therefore be room for genetic improvement in the ability of crop plants to take up N specifically during this period, perhaps supplied from aircraft, or in irrigation water. Similarly, natural selection may not have kept up with recent increases in atmospheric CO2 concentration. Increasing CO2 may decrease NO3 − assimilation in leaves of C3 tomato (Searles & Bloom, 2003), but not in C4 maize (Cousins & Bloom, 2003). There may, therefore, be more opportunity for genetic improvement of this trait in C3 than in C4 crops. Similarly, natural selection may not have kept up with recent increases in soil fertility.
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We need to consider both past natural selection and our current goals. Consider rooting patterns. Crops whose ancestors evolved in a climate characterized by periodic or seasonal shortages of water may root more deeply than is necessary for irrigated agriculture. There is considerable phenotypic plasticity for rooting depth, but genotypes with shallower rooting might nonetheless achieve higher yields, where soil resources are increased by fertilization and/or irrigation, by allocating more resources to grain and less to roots. Putting a larger fraction of total root biomass near the surface might also reduce erosion, which can represent a significant loss of P and other nutrients. On the other hand, deeper roots might capture some additional NO3 − that would otherwise be lost. Even if the amount of nitrate captured was not enough to significantly increase yields, it would at least reduce the pollution of groundwater. Natural selection has never had either reducing erosion or reducing nitrate leaching per se as a goal. There may therefore be significant room for genetic improvement in the ability of crop plants to prevent erosion or reducing nitrate leaching. Breeding for root systems to achieve these goals might mean sacrificing some aboveground growth. It is also possible that including existing deep-rooting crops in a rotation would be more practical than genetic improvement of deep-soil nitrate-scavenging ability in a shallow-rooting crop. Similarly, control of erosion depends on tillage practices (which interact with crop competitiveness with weeds, for example) and on spatial patterns of crops on the landscape, not just the rooting patterns of an individual crop. We therefore suggest that greater consideration of the ecological and evolutionary context of crop nutrient acquisition and use should accompany genomic approaches.
11.4 Constraint/opportunity 3: conflicts of interest in nutritional symbioses Microbe–plant symbiosis has the potential to increase the sustainability of cropping systems. However, enhancing the use of symbiosis under field conditions can be difficult. Conceptual discussions of sustainable agriculture tend to include root symbionts as an important component in promoting nutrient cycling and reducing external inputs, even protecting against major disturbances such as disease and erosion. Arbuscular mycorrhizae (AM) are considered to be the critical interface between host plant and soil environment, mediating processes such as P uptake and water relations. Management of biological N2 fixation is historic, dating to 370–285 BC (van Kessel & Hartley, 2000). However, the estimated 40 Mt N y−1 fixation contribution to agricultural systems is still half of the 80 Mt N y−1 supplied by NH3 synthesis (Socolow, 1999). This suggests the harnessing of N2 fixation potential should be an obvious goal of sustainable crop production. However, recognition of fundamental constraints (evolutionary, energetic and economic) is essential in guiding such a research program.
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Our understanding of the legume-rhizobial symbiosis, including its genetics, has been increasing rapidly (Long, 2001). However, general trends suggest our reliance on N2 fixation is decreasing. van Kessel and Hartley (2000) show a significant decline in N derived from fixation in soybean and bean after the mid-1980s, with pea showing a slight increase and lentil showing no significant difference. This may partly reflect increases in the availability of soil N. Replacement of external N fertilizer with biological N2 fixation could result in a more sustainable cropping system, but can high yields be maintained? Traditionally, legume crops were a component of diverse farming systems, increasing fertility in ley rotations, but the economics of such systems limit extensive use. Some cash crops can apparently meet some or all of their own N needs through symbiotic N2 fixation. Selection for a soybean variety grown with zero N fertilizer application has been successful in Brazil. D¨obereiner (1997) states that this zero-N soybean has become the country’s largest export product. We have not seen N budgets that would show whether this system is truly sustainable, or whether it relies on net mineralization of soil organic matter for some fraction of the N needs of the crop. Mycorrhizal research advancements have been less directed, as gains from incorporating the symbiosis into plant breeding programs are not as obvious. The varied contributions of the symbiosis can not be measured by a single nutrient test, or even single-season yield, as some nonnutrient benefits may occur at longer term scales and may affect soil as well as plant components. Although mycorrhizae increase plant P acquisition rate, the symbionts do not extract it from an ‘unlimited’ source, in contrast to rhizobial fixation of atmospheric N2 . Mycorrhizal symbioses increase availability of P to plants through various mechanisms (Koide, 1991) but there will still be a need for some P inputs to balance P removed in the crop. However, discussion of the role of mycorrhizae in sustainable crop production is incomplete without recognizing the various other benefits of the symbiosis, including improved access to organic N (another finite resource), improved water uptake, increased soil aggregation and improved disease resistance (Bethlenfalvay & Linderman, 1992; Newsham et al., 1995; Hodge et al., 2001). In the absence of positive selection in breeding programs for effective symbiosis with mycorrhizal fungi, dependence on mycorrhizae appears to have decreased. It has been demonstrated that modern wheat breeding practices tend to reduce mycorrhizal dependence, as wheat cultivars released before 1950 consistently show increased dependence on mycorrhizae when compared to those released subsequently (Hetrick et al., 1993). In comparing 22 landraces of wheat to 22 modern varieties, Maske (1989) found that, in low P-soils, inoculation with mycorrhizae resulted in a higher increase in average yields of the landraces compared to modern varieties. In high-P soils, mycorrhizal inoculation caused a greater yield depression in the modern varieties than in the land
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races, resulting in slightly greater total yield of the landrace varieties relative to modern varieties (Johnson & Pfleger, 1992). Current benefits to crop plants from nutritional symbionts are often less than ideal. Sorwli and Mytton (1986) noted that ‘. . . N limitations caused by inadequate symbiosis may be more common in temperate agriculture than has previously been suspected’. Rhizobia have been shown to vary considerably in the benefits they provide to the host (Denton et al., 2000; Thrall et al., 2000), with strains fixing little to no N2 common to both natural and agricultural soils (Singleton & Stockinger, 1983; Burdon et al., 1999). The presence of an indigenous population of rhizobial strains has been shown to affect both the magnitude of legume response and the persistence of the introduced strain (McLoughlin et al., 1990; Thies et al., 1995). Indigenous rhizobial populations are noted for their highly competitive nature and their ability to survive for years even in the absence of a legume (Brockwell et al., 1995; Howieson, 1995). Marginal effectiveness is also common in mycorrhizae; 60 of 150 AM isolates tested on cassava were shown to be ineffective or have only slight positive effects (Howeler et al., 1987). Hepper et al. (1988) found that under greenhouse conditions, the addition of certain introduced mycorrhizal fungi in combination with particular indigenous fungi led to a ‘transient reduction’ in total leaf length. Because neither species alone had this effect, the authors hypothesized that there was competitive antagonism in the rhizosphere. Root symbionts do not always give a clear benefit, varying on a continuum from parasitic to mutualistic but we can perhaps manage the selection regime to favor more mutualistic partnerships (Kiers et al., 2002). There are significant conflicts of interest between plants and their symbionts. Although microbial symbionts generally benefit from healthier plant hosts, natural selection favors partners that maximize their own reproductive fitness, even if this imposes a cost on the other partner (Denison, 2000; West et al., 2002a,b). The cost of supporting a rhizobial symbiont is high; nodule production and maintenance consumes as much as 20–30% of net photosynthate production (Pate, 1986; Provorov & Tikhonovich, 2003). Likewise, N2 fixation is an energyintensive process for rhizobia (Gutschick, 1981). Consumption of carbohydrate to respiration in support of N2 fixation competes with hoarding of resources by rhizobia to support their own growth and reproduction (Denison, 2000). Conflicts of interest among symbionts sharing a host may also reduce benefits from symbiosis. The rhizobia infecting a given plant have a shared interest in increasing the overall growth and photosynthesis of their host (by providing the host with N), thereby increasing overall levels of resources available to the rhizobia themselves. However, evolutionary theory predicts that this shared interest selects for cooperation only under very restrictive conditions (Frank, 1998). In particular, there needs to be a low genetic diversity (high relatedness) of the symbionts on a host root system, which is usually not the case (Dowling & Broughton, 1986). Multiple infection results in a ‘tragedy of the commons’
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(Hardin, 1968), in which the individual benefits to rhizobia from increasing host N supply outweigh the individual costs of fixing N2 . This favors reduced investment in symbiosis, analogous to the evolution of virulence (increasing microbial reproduction at the expense of the host) that typically occurs when multiple parasites infect a single host (Williams & Nesse, 1991; Herre, 1993; Frank, 1998). Thus, conflicts of interest among symbionts (i.e. competition) can reduce overall benefits to the host (Frank, 1996; Hoeksema & Bruna, 2000; Bot et al., 2001). Genomics, in combination with an understanding of these conflicts of interest, may lead to significant improvements in the benefits that crop plants derive from symbiosis. Conflicts of interest among symbionts are analogous to conflicts in plants between individual competitiveness and community performance, where natural selection always favors the former (Harper, 1977). However, plant breeders have often reversed this past selection in agronomic crops by selecting for plant adaptations that increase community level traits, such as yield, at the expense of individual competitiveness (Denison et al., 2003b). A similar approach could be useful in breeding or selecting symbiont lineages. Rhizobial strains that maximize the host plant’s fitness at the cost of their own have been identified. Cevallos et al. (1996) found that rhizobial mutants that were unable to synthesize poly--hydroxybutyrate (PHB), an energy-rich storage compound, continued to fix N2 for longer periods than did the PHB-producing parents. This ‘altruistic’ reduction in rhizobial storage PHB could result in overall increased N2 fixation levels at some cost of rhizobial fitness. Similarly, competitive exclusion mechanisms employed by rhizobia, such as bacteriocin production, have been shown to increase saprophytic and nodulation competitive ability (Schwinghamer & Brockwell, 1978; Wilson et al., 1998) but this increase may come at the cost of a reduction in symbiotic potential (Triplett & Sadowsky, 1992). If so, then reducing energy allocated to competitive ability may improve symbiotic performance. Reducing competitive ability may seem counterintuitive but selection for weakening competition in symbionts may be analogous to decreasing the competitive edge of agronomic crops by reducing their height or level of branching (Denison et al., 2003b) – both select for community-level performance above individual fitness. The difference is that humans can easily alter the competitive balance between crops and weeds using tillage or herbicides. Analogous approaches to suppression of indigenous rhizobia that are good competitors but poor symbionts may not be practical. Selection for the benefit of a group is rare in nature but group selection can be imposed by humans. This approach has been successful in laying hens (Muir, 1996). Selection based on the yield of an entire test plot, rather than individual plants, is also a form of group selection. A focus on improving the community-level performance of symbionts could lead to significant advances in nutrient dynamics of cropping systems. For instance, the mycorrhizal hyphal networks connecting plants (Helgason et al., 1998) suggest the possibility of
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resource sharing (Fitter et al., 1998; Perry, 1998) along gradients of resource availability. Although the actual nutrient contribution (Robinson & Fitter, 1999) as well as the evolutionary implications of this type of sharing in nature (Perry, 1998; Wilkinson, 1998) have been questioned, the existence of the networks implies at least the potential to move resources. Whether the resources remain in the fungal hyphae (Fitter et al., 1998) or even if this resource contribution is significant to plant growth (van der Heijden et al., 2003) requires more research. M˚artensson et al. (1998) suggest that between 3 and 50% of the N in a nonfixing receiver plant (chicory) can be derived via mycorrhizal hyphal transfer from a N2 -fixing legume, depending on the particular host–mycorrhizal combination. Breeders could work with this variation to develop host–symbiont combinations to maximize resource sharing across agricultural fields, from points of high fertility to low fertility. Again, competition with indigenous mycorrhizae could be a problem unless strict specificity was established between host and symbiont. If successful, human-mediated group selection of symbiont–host communities could create a biological equivalent to technology-driven precision agriculture where rates of fertilizer are applied according to site-specific requirements. Transfer of nutrients via mycorrhizae might work even if nutrients are patchy on a scale too fine for precision agriculture equipment to detect. On the other hand, long-distance transfer through mycorrhizae would be impractical, and so some version of precision agriculture would still be needed to address large-scale patterns in soil fertility. Altruistic symbionts may be decades away, but research aimed at understanding the genetic code of these symbionts is advancing rapidly. Rhizobia are one of the best-studied microbes, with two rhizobial genomes completely sequenced (Colebatch et al., 2002). Provorov and Tikhonovich (2003) suggest that breeders should concentrate on the bacterial populations with the highest levels of genetic polymorphisms, with the highest frequencies of symbiotic effectiveness, and those isolated from ‘gene centers’ of legume diversity. There have been successes in improving genotypes through curing cryptic plasmids and knocking out negative regulation of symbiotic gene expression (Provorov & Tikhonovich, 2003) but without a consideration of rhizosphere selection pressures, silverbullet genetic manipulation may prove too ephemeral. Rather, success will be maximized if strains with increased symbiotic effectiveness are favored by ‘natural’ selection in an agricultural context. Less is known of the functional genetics of AM. However, an understanding of its genetic foundation is actively sought. Recent evidence suggests that AM spores contain a population of genetically different nuclei (Kuhn et al., 2001). This has strong implications for breeding of agriculturally appropriate mycorrhizae, because conflicts between genes and ‘the individual’ may be even more apparent than in plants. Because of this multinucleic characteristic, subcultured spores derived from one ‘mother spore’ will vary in effectiveness, further increasing the difficulty of selection (Feldman, 1998). In addition, the mobility of
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genetic material raises the possibility that an introduced AM isolate could exchange nuclei via anastomosis with the indigenous community (Sanders, 2002), resulting in potentially both positive and negative outcomes. Internal spore diversity could also lead to differential expression of the phenotype depending on external conditions. Such variation could be useful in breeding schemes but progress will perhaps be marred by all the interacting factors. Several approaches could be utilized in breeding plants that maximize symbiont gains. The successful introduction of an improved symbiont strain would probably require that hosts have an identification-based access system so that only specific strains would be able to initiate a symbiotic relationship. At this point, in spite of elaborate communication signals (Hirsch, 1999), legumes do not seem to exclude parasitic rhizobia that are closely related to their usual symbiotic partners (Amarger, 1981; Hahn & Studer, 1986). This is not surprising, since they would have similar or identical recognition signals. Selection for exclusion of specific indigenous strains could be successful with the appropriate genetic approach. For instance, a nodulation-restricting allele sym2A was identified in Afghan pea that allowed only initiation of the symbiosis with strains harboring the nodX gene. Similarly, the rj1 allele, restricting nodule formation by many indigenous strains (Devine & Weber, 1977), has been identified in soybean. If tighter host exclusion mechanisms were a breeding priority, it would increase the probability that the selected symbiont would successfully enter into the relationship, even in the presence of an indigenous community (Lohrke et al., 1996). Identification-based selection also reduces the amount of carbohydrate that would potentially be used in forming the association. The danger of an identification-based selection system is that the short generation time of rhizobia compared to plants and the ability of rhizobia to modify their own extracellular signals (Roche et al., 1991) could lead to selection in indigenous strains to mimic cooperative strains, possibly favoring parasitic symbionts that produce the same signals as the improved strains. Similarly, mutant versions of the improved strains that retain the recognition signals but allocate resources to their own reproduction rather than to N2 fixation would not be excluded by exclusion systems based on identification signals. Where inoculants are not available, on the other hand, restricting nodulation to only those rhizobia with specific recognition signals might result in no nodules at all. Selection based on actual symbiotic performance, rather than recognition signals, may be a superior breeding strategy. We have proposed (Denison, 2000) and then shown empirically (Kiers et al., 2003) that at least some legumes actively reward cooperation or punish less cooperative behavior. This idea has been termed sanctions and can be defined as the preferential supply of resources (or alternatively the severing of resources) to nodules based on the amount of N2 supplied by the nodules (Denison, 2000; Simms & Taylor, 2002; West et al., 2002a,b; Kiers et al., 2003). Whether there is an analogous sanctioning response in mycorrhizae is not known (Smith & Smith, 1996), but the evolutionary
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persistence of this symbiosis in the face of multiple infection suggests similar sanctions may exist. In legumes, breeders could select to strengthen sanctions so that the host makes the reproductive success of rhizobial strains even more contingent on their ability to export N. Given our ability to control which plants grow in a given field, perhaps we should develop legume crops that selectively enrich the soil with the best strains of rhizobia or mycorrhizal fungi. One way to identify plant genotypes that selectively enrich the soil with the best symbionts would be to grow a genetically diverse population of plants in a standard soil mix (probably in pots), then test their effects on symbiont populations in the soil by growing a genetically uniform test crop in the same pots. Performance-based selection would eliminate opportunities for less mutualistic strains to gain advantages through mimicking signals because rewards would be directly correlated with benefit. The disadvantage of this approach is that, in the first years or two, legumes would invest some carbohydrate in forming the association with rhizobia that turn out to be poor fixers. How current breeding practices are affecting the sanction response is unknown. Dependence on biologically fixed nitrogen in the wild ancestors of agronomic crops was presumably greater with natural selection favoring legumes able to reduce losses to ineffective rhizobia. However, natural selection among legumes may have favored those that continued to support marginally effective rhizobia, rather than giving up the N these strains can provide. Field studies comparing older and newer soybean cultivars are currently underway to evaluate how 70 years of soybean breeding has modified the sanctioning response. One approach to manipulating symbiosis has been the use of hypernodulating mutants that continue to fix N2 even in the presence of NO3 − (Bhatia et al., 2001). Although it might seem that the opposite approach (i.e. selecting for even stricter conditional resource-based fixation) would be more appropriate for sustainable systems, it has been suggested that N2 fixation is often downregulated excessively (Provorov & Tikhonovich, 2003). N2 -fixing intensity is highest under low to moderate fertility (Rabie, 1981). By selecting for fixation under gradients of N fertility, theoretically more N can be derived from fixation (Rys & Mytton, 1985; Phillips & Teuber, 1992). Hypernodulating legumes would allow for fixation under moderate N conditions, thereby maximizing the amount of N derived from symbiotic N. However, field trials of hypernodulating mutants (Wu & Harper, 1991; Pracht et al., 1994) have found yields that are 25% less than normally nodulating cultivars. Use of hypernodulating mutants could be useful in a rotation situation as N carryover effects have been shown when hypernodulating legumes precede subsequent nonfixing grain crops (Song et al., 1995). Coordinated plant–symbiont breeding schemes that identify optimal combinations under various environmental conditions might result in the most durable and long-term symbiotic gains. Success in breeding plants for increased
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N2 fixation is partly marred by the difficulty in combining N2 -fixing ability with other traits (yield, disease resistance, seed quality; Herridge et al., 2001). These external traits, as well as selection for a positive interaction between the host and the symbiont genetics, require a comprehensive breeding program. Increased N2 fixation by selecting for plant and symbiont traits has been suggested simultaneously (Mytton et al., 1977). However, Phillips et al. (1985) advocated selecting plant genes for increased N2 fixation in the presence of only one strain of rhizobia. Selecting for increased N2 fixation in a coordinated plant–symbiont breeding scheme will only be useful if those particular selected symbionts can be maintained in soil. Selecting for plant genes that enhance N2 fixation with all rhizobial strains may therefore be a more practical approach (Phillips & Teuber, 1992). Reviews of breeding for N2 fixation (Brockwell et al., 1995; Herridge & Danso, 1995; Herridge et al., 2001) acknowledge that an interdisciplinary approach is needed. One potential approach would be to dispense with symbiosis and integrate key symbiont genes into the host plant genome. Or perhaps rhizobia could be controlled as a self-reproducing organelle, like mitochondria. The increased host regulation (and dependence on host for symbiont reproduction) would decrease conflicts of interest between host and symbiont. Theoretically, genomic integration between hosts and rhizobia could bring about N2 -fixing grains. The possible extension of N2 fixation into nonlegume crop has been a great motivator of basic N2 fixation research (Colebatch et al., 2002). This research goal is not going to be realized anytime soon, as the genetic code for successful symbiotic fixation involves two genomes; simple modification of one or the other will not result in a successful partnership. Regulation of the symbiosis, including regulation of nodule oxygen by legumes, involves complicated mechanisms of control not easily transferred through genetic manipulation (Denison et al., 2003b). But, with rapid genetic advances (Donwie & Bonfante, 2000), N2 fixing grains could be a reality someday. Would they then fit into a paradigm of sustainable crop nutrition? In the abstract, self-fertilizing crops would be quite advantageous, but the environmental implications of such drastic increases in biological fixation would require substantial investigation. One possible concern is the escape of the N2 -fixation trait to associated weed species. Breeding for increased gain from symbiotic relationships should be a goal for sustainable cropping systems. Incorporating symbionts as a selection factor in laboratory and field trials is the most basic necessity. Designing farming systems that maximize the benefits of the symbiosis is the next step. ‘Neighbor effects’ (Expert et al., 1997), such as sharing resources or scavenging for otherwise excess resources, could be bred for in both mycorrhizal and rhizobial symbiotic systems. The evolutionary conflicts of interests between host and symbiont need to be recognized, but selection can aim to increase benefits to the host at the cost of rhizobial fitness, if successful reestablishment of the symbiont can be assured. This is not a trivial requirement, unfortunately. Human-mediated
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group selection could be useful in reducing competition between symbionts and establishing resource sharing in mycorrhizal hyphal links. Exchange of genetic material, whether through anastomsis or lateral plasmid exchange will affect the genetics of both the introduced and indigenous symbiont strains. For this reason, population-scale studies are necessary to determine the fate of introduced gene complexes. Genetic advances, coupled with an ecological and evolutionary perspective, will lead to maximization of symbiotic potential.
11.5 A fourth constraint/opportunity: complexity The consequences of even ‘simple’ genetic changes are often hard for humans to predict. For example, changing a single photoreceptor gene was expected to modify the response of potato plants to crowding, increasing allocation to tubers (Boccalandro et al., 2003). The actual effects were much more complex than anticipated. This genetic modification changed the light environment of leaves within the crop canopy, not just their response to light. There were also changes in branching and in stomatal conductance. On the other hand, transgenic mice without myoglobin turn out to be physiologically normal (Garry et al., 1998) – an equally surprising result. Genomics will increase our ability to track expression patterns of large numbers of genes, but it seems unlikely that gene expression patterns alone would have been sufficient to predict the phenotype of these transgenic potatoes or mice, even under controlled conditions. Unanticipated interactions with biotic and abiotic factors make it even harder to use genomics to predict how crops will perform under field conditions. Computer models are a possible solution. If we can write equations that accurately describe the key processes that occur in crops and soils, we should be able to predict the effect of genetic changes in crop nutrition on yield and on nitrate leaching, for example. Existing models typically have less detail on some processes than on others (Denison & Loomis, 1988; Beckie et al., 1995; Amthor & Loomis, 1996; Gersani et al., 2001), but the memory and speed of modern computers eliminate hardware limitations as constraints on the development of more comprehensive models. The ability of even improved crop models to correctly predict ‘counterintuitive’ effects of genetic change is uncertain, however. Reasonable agreement between predicted and measured crop yields during model ‘validation’ does not ensure that responses to genetic changes or a different environment will be even qualitatively correct. Discrepancies are often corrected through ‘calibration’, which typically involves adjustment of one or more unmeasured parameters to make yield data fit. ‘Validation’ based on yields in a similar environment is therefore a rather weak test. A more thorough and rigorous approach, based on detailed checks of the qualitative and quantitative accuracy of component submodels, would be expensive and time-consuming. A substantial increase in
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funding for model development and testing seems necessary, but more rigorous testing is equally important. We need to make independent tests of crop models (which are, after all, merely complex hypotheses) as common as independent tests of other hypotheses (Kinraide & Denison, 2003). The ‘open source’ paradigm currently gaining favor for development of more robust computer software (Raymond, 2001) may prove useful. But even with better computer models, complex interactions among crop plants, rapidly evolving microorganisms and their environment may often lead to unexpected results. References Alexander, M. (1977) Introduction to Soil Microbiology, John Wiley and Sons, New York. Amarger, N. (1981) Competition for nodule formation between effective and ineffective strains of Rhizobium meliloti. Soil Biol. Biochem., 13, 475–480. Amthor, J.S. & Loomis, R.S. (1996) Integrating knowledge of crop responses to elevated CO2 and temperature with mechanistic simulation models: model components and research needs. In Carbon Dioxide and Terrestrial Ecosystems (eds G.W. Koch & H.A. Mooney), Academic Press, New York, pp. 317–345. Beckie, H.J., Moulin, A.P., Campbell, C.A. & Brandt, S.A. (1995) Testing effectiveness of four simulation models for estimating nitrates and water in two soils. Can. J. Soil Sci., 75, 135–143. Bethlenfalvay, G.J. & Linderman, R.G. (1992) Mycorrhizae in Sustainable Agriculture, American Society of Agronomy, Madison, WI. Bhatia, C.R. Nichterlein, K. & Maluszynski, M. (2001) Mutations affecting nodulation in grain legumes and their potential in sustainable cropping systems. Euphytica, 120, 415–432. Boccalandro, H.E. Ploschuk, E.L. Yanovsky, M.J. Sanchez, R.A. Gatz, C. & Casal, J.J. (2003) Increased phytochrome B alleviates density effects on tuber yield of field potato crops. Plant Physiol., 133, 1539–1546. Bot, A.N.M., Rehner, S.A. & Boomsma, J.J. (2001) Partial incompatibility between ants and symbiotic fungi in two sympatric species of Acromyrmex leaf-cutting ants. Evolution, 55, 1980–1991. Brockwell, J. Bottomley, P.J. & Thies, J.E. (1995) Manipulation of rhizobia microflora for improving legume productivity and soil fertility: a critical assessment. Plant Soil, 174, 143–180. Burdon, J.J., Gibson, A.H., Searle, S.D., Woods, M.J. & Brockwell, J. (1999) Variation in the effectiveness of symbiotic associations between native rhizobia and temperate Australian Acacia: within-species interactions. J. Appl. Ecol., 36, 398–408. Burow, K.R., Stork, S.V. & Dubrovsky, N.M. (1998) Nitrate and pesticides in ground water in the eastern San Joaquin Valley, California: occurrence and trends. U.S. Geological Survey Water-Resources Investigations Report 98-4040. U.S. Geological Survey, Sacramento. Cassman, K.G., Bryant, D.C., Fulton, A.E. & Jackson, L.F. (1992) Nitrogen supply effects on partitioning of dry matter and nitrogen to grain of irrigated wheat. Crop Sci., 32, 1251–1258. Cevallos, M.A., Encarnacion, S., Leija, A., Mora, Y. & Mora, J. (1996) Genetic and physiological characterization of a Rhizobium etli mutant strain unable to synthesize poly--hydroxybutyrate. J. Bacteriol., 178, 1646–1654. Chadwick, O.A., Derry, L.A., Vitousek, P.M., Huebert, B.J. & Hedin, L.O. (1999) Changing sources of nutrients during four million years of ecosystem development. Nature, 397, 491–497. Colebatch, G., Trevaskis, B. & Udvardi, M. (2002) Symbiotic nitrogen fixation research in the postgenomics era. New Phytol., 153, 37–42. Cousins, A.B. & Bloom, A.J. (2003) Influence of elevated CO2 and nitrogen nutrition on photosynthesis and nitrate photo-assimilation in maize (Zea mays L.). Plant Cell Environ., 26, 1525–1530.
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Dawkins, R. (1976) The Selfish Gene, Oxford University Press, Oxford. Denison, R.F. (2000). Legume sanctions and the evolution of symbiotic cooperation by rhizobia. Am. Nat., 156, 567–576. Denison, R.F., Bryant, D.C. & Kearney, T.E. (2003a) Crop yields over the first nine years of LTRAS, a long-term comparison of field crop systems in a Mediterranean climate. Field Crops Res., 86, 267–277. Denison, R.F. Kiers, E.T. & West, S.A. (2003b) Darwinian agriculture: when can humans find solutions beyond the reach of natural selection? Q. Rev. Biol., 78, 145–168. Denison, R.F. & Loomis, R.S. (1988) An Integrative Physiological Model of Alfalfa Growth and Development, University of California, Oakland, CA. Denton, M.D., Coventry, D.R., Bellotti, W.D. & Howieson, J.G. (2000) Distribution, abundance and symbiotic effectiveness of Rhizobium leguminosarum bv. trifolii from alkaline pasture soils in South Australia. Aust. J. Exp. Agric., 40, 25–35. Devine, T.E. & Weber, D.F. (1977) Genetic specificity of nodulation. Euphytica, 26, 527–535. Dinkelaker, B., Hengeler, C. & Marschner, H. (1995) Distribution and function of proteoid roots and other root clusters. Bot. Acta, 108, 183–200. D¨obereiner, J. (1997) Biological nitrogen fixation in the tropics: social and economic contributions. Soil Biol. Biochem., 29, 771–774. Dominguez, C.A. (1995) Genetic conflicts of interest in plants. Trends Ecol. Evol., 10, 412–416. Donwie, J.A. & Bonfante, P. (2000) Development and good breeding in legume models: poise and peas. New Phytol., 148, 7–9. Dowling, D.N. & Broughton, W.J. (1986) Competition for nodulation of legumes. Annu. Rev. Microbiol., 40, 131–157. Expert, J.M., Jacquard, P., Obaton, M. & L¨uscher, A. (1997) Neighbourhood effect of genotypes of Rhizobium leguminosarum biovar trifolii, Trifolium repens and Lolium perenne. Theor. Appl. Genet., 94, 486–492. Feldman, F. (1998) The strain-inherent variability of arbuscular mycorrhizal effectiveness, II: Effectiveness of single spores. Symbiosis, 25, 131–143. Fitter, A.H., Graves, J.D., Watkins, N.K., Robinson, D. & Scrimgeour, C. (1998) Carbon transfer between plants and its control in networks of arbuscular mycorrhizas. Funct. Ecol., 12, 406– 412. Frank, S.A. (1996) Host-symbiont conflict over the mixing of symbiotic lineages. Proc. R. Soc. Lond. B Biol. Sci., 263, 339–344. Frank, S.A. (1998) Foundations of Social Evolution, Princeton University Press, Princeton, NJ. Garry, D.J., Ordway, G.A., Lorenz, J.N. & Radford, N.B. (1998) Mice without myoglobin. Nature, 395, 905–908. Gersani, M., Brown, J.S., O’Brien, E.E., Maina, G.M. & Abramsky, Z. (2001) Tragedy of the commons as a result of root competition. J. Ecol., 89, 660–669. Greenland, D.J. (1975) Bringing the green revolution to the shifting cultivator. Science, 190, 841–844. Gutschick, V.P. (1981) Evolved strategies in nitrogen acquisition by plants. Am. Nat., 118, 607–637. Hahn, M. & Studer, D. (1986) Competitiveness of a nif- Bradyrhizobium japonicum mutant against the wild-type strain. FEMS Microbiol. Lett., 33, 143–148. Hardin, G. (1968) The tragedy of the commons. Science, 162, 1243–1248. Harper, J.L. (1977) Population Biology of Plants, Academic Press, London. Helgason, T., Daniell, T.J., Husband, R., Fitter, A.H. & Young, J.P.W. (1998) Ploughing up the woodwide web? Nature, 394, 431. Hepper, C.M., Azcon-Aguilar, C., Rosendahl, S. & Sen, R. (1988) Competition between three species of Glomus used as spatially separated introduced and indigenous mycorrhizal inocula for leek (Allium porrum L.). New Phytol., 110, 207–215. Herre, E.A. (1993) Population structure and the evolution of virulence in nematode parasites of fig wasps. Science, 259, 1442–1445.
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Herridge, D.F. & Danso, S.K.A. (1995) Enhancing crop legume N2 fixation through selection and breeding. Plant Soil, 174, 51–82. Herridge, D.F., Turpin, J.E. & Robertson, M.J. (2001) Improving nitrogen fixation of crop legumes through breeding and agronomic management: analysis with simulation modelling. Aust. J. Exp. Agric., 41, 391–401. Hetrick, B.A., Wilson, G.W.T. & Cox, T.S. (1993) Mycorrhizal dependence of modern wheat cultivars and ancestors: a synthesis. Can. J. Bot., 71, 512–518. Hill, J.E., Smith, R.J. & Bayer, D.E. (1994) Rice weed control: current technology and emerging issues in temperate rice. Aust. J. Exp. Agric., 34, 1021–1029. Hirsch, A.M. (1999) Role of lectins (and rhizobial exopolysaccharides) in legume nodulation. Curr. Opin. Plant Biol., 2, 320–326. Hodge, A., Campbell, C.D. & Fitter, A.H. (2001) An arbuscular mycorrhizal fungus accelerates decomposition and acquires nitrogen directly from organic material. Nature, 413, 297– 299. Hoeksema, J.D. & Bruna, E.M. (2000) Pursuing the big questions about interspecific mutualism: a review of theoretical approaches. Oecologia, 125, 321–330. Howeler, R.H., Sieverding, E. & Saif, S. (1987). Practical aspects of mycorrhizal technology in some tropical crops and pastures. Plant Soil, 100, 249–283. Howieson, J.G. (1995) Rhizobial persistence and its role in the development of sustainable agricultural systems in Mediterranean environments. Soil Biol. Biochem., 27, 603–610. Huxley, A. (1932) Brave New World, HarperPerennial, New York. Jennings, P.R. & Aquino, R.C. (1968) Studies on competition in rice, III: The mechanism of competition among phenotypes. Evolution, 22, 529–542. Johnson, N.C. & Pfleger, F.L. (1992) Vesicular-arbuscular mycorrhizae and cultural stresses. In Mycorrhizae in Sustainable Agriculture (eds G.J. Bethlenfalvay & R.G. Linderman), Agronomy Society of America, Madison, WI, pp. 71–100. Johnston, A.E. (1994) The Rothamsted classical experiments. In Long-Term Experiments in Agricultural and Ecological Sciences (eds R.A. Leigh & A.E. Johnston), CAB International, Wallingford, UK, pp. 9–38. Kiers, E.T., Rousseau, R.A., West, S.A. & Denison, R.F. (2003) Host sanctions and the legumerhizobium mutualism. Nature, 425, 78–81. Kiers, E.T., West, S.A. & Denison, R.F. (2002) Mediating mutualisms: farm management practices and evolutionary changes in symbiont co-operation. J. Appl. Ecol., 39, 745–754. Kinraide, T.B. & Denison, R.F. (2003) Strong inference, the way of science. Am. Biol. Teach., 65, 419–424. Koide, R.T. (1991) Nutrient supply, nutrient demand and plant response to mycorrhizal infection. New Phytol., 117, 365–386. Kuhn, G., Hijri, M. & Sanders, I.R. (2001) Evidence for the evolution of multiple genomes in arbuscular mycorrhizal fungi. Nature, 414, 745–748. Lohrke, S.M., Orf, J.H. & Sadowsky, M.J. (1996) Inheritance of host controlled restriction of nodulation by Bradyrhizobium japonicum USDA 110. Crop Sci., 36, 1271–1276. Long, S.R. (2001) Genes and signals in the Rhizobium-legume symbiosis. Plant Physiol., 125, 69–72. Loomis, R.S. & Connor, D.J. (1992) Crop Ecology: Productivity and Management in Agricultural Systems, Cambridge University Press, Cambridge, UK. M˚artensson, A.M., Rydberg, I. & Vestberg, M. (1998) Potential to improve transfer of N in intercropped systems by optimising host-endophyte combinations. Plant Soil, 205, 57–66. Maske, G.G.B. (1989) Genetical analysis of the efficiency of VA mycorrhiza with spring wheat. Agric. Ecosyst. Environ., 29, 273–280. McLoughlin, T.J., Hearn, S. & Alt, S.G. (1990) Competition for nodule occupancy of introduced Bradyrhizobium japonicum strains in a Wisconsin soil with a low indigenous bradyrhizobia population. Can. J. Microbiol., 36, 839–845.
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Muir, W.M. (1996) Group selection for adaptation to multiple-hen cages: selection program and direct responses. Poult. Sci., 75, 447–458. Mytton, L.R., El-Sherbeeny, M.H. & Lawes, D.A. (1977) Symbiotic variability in Vicia faba, 3: Genetic effects of host plant, rhizobium strain and of host × strain interaction. Euphytica, 26, 785–791. Newsham, K.K., Fitter, A.H. & Watkinson, A.R. (1995) Multi-functionality and biodiversity in arbuscular mycorrhizas. Trends Ecol. Evol., 10, 407–411. Pate, J.S. (1986) Economy of symbiotic nitrogen fixation. In On the Economy of Plant Form and Function (ed. T.J. Givnish), Cambridge University Press, Cambridge, UK, pp. 299–326. Perry, D.A. (1998) A moveable feast: the evolution of resource sharing in plant–fungus communities. Trends Ecol. Evol., 13, 432–434. Phillips, D.A., Cunningham, S.D., Bedmar, E.J., Sweeney, T.C. & Teuber, L.R. (1985) Nitrogen assimilation in an improved alfalfa population. Crop Sci., 25, 1011–1015. Phillips, D.A. & Teuber, L.R. (1992) Plant genetics of symbiotic nitrogen fixation. In Biological Nitrogen Fixation (eds G. Stacey, R.H. Burris & H.J. Evans), Chapman & Hall, New York, pp. 625– 647. Pimentel, D., Berardi, G. & Fast, S. (1984) Energy efficiencies of farming wheat, corn, and potatoes organically. In Organic Farming: Current Technology and Its Role in a Sustainable Agriculture (eds D.F. Bezdicek, J.F. Power, D.R. Keeney & M.J. Wright), American Society of Agronomy, Madison, WI, pp. 151–161. Powlson, D.S. & Johnston, A.E. (1994) Long-term field experiments: their importance in understanding sustainable land use. In Soil Resilience and Sustainable Land Use (eds D.J. Greenland & I. Szabolcs), CAB International, Wallingford, UK, pp. 367–394. Pracht, J.E., Nickell, C.D., Harper, J.E. & Bullock, D.G. (1994) Agronomic evaluation of non-nodulating and hypernodulating mutants of soybean. Crop Sci., 34, 738–740. Provorov, N.A. & Tikhonovich, I.A. (2003) Genetic resources for improving nitrogen fixation in legumerhizobia symbiosis. Genet. Res. Crop Evol., 50, 89–99. Rabie, R.K. (1981) Nitrogen nutrition in legumes with special concern of seed yield production. J.Plant Nutr., 4, 175–194. Raymond, E.S. (2001) The Cathedral and the Bazaar, O’Reilly & Associates, Sebastopol, CA. Roberts, T.L. & Stewart, W.M. (2002) Inorganic phosphorus and potassium production and reserves. Better Crops, 86, 7. Robinson, D. & Fitter, A.H. (1999) The magnitude and control of carbon transfer between plants linked by a common mycorrhizal network. J. Exp. Bot., 50, 9–13. Roche, P., Debelle, F., Maillet, F., Lerouge, P., Faucher, C., Denarie, J. & Prome, J.C. (1991) Molecular basis of symbiotic host specificity in Rhizobium meliloti: nodH and nodPQ genes encode the sulfation of lipo-oligosaccharide signals. Cell, 67, 1131–1143. Rys, G.J. & Mytton, L.R. (1985) The potential for breeding white clover (Trifolium repens L.) with improved nodulation and nitrogen fixation when grown with combined nitrogen, 1: Effects of different amounts of nitrate nitrogen on phenotypic variation. Plant Soil, 88, 181–195. Sanders, I.R. (2002) Ecology and evolution of multigenomic arbuscular mycorrhizal furgi. Am. Nat., 160, S128–S141. Schwinghamer, E.A. & Brockwell, J. (1978) Competitive advantage of bacteriocin and phage-producing strains of Rhizobium trifolii in mixed culture. Soil Biol. Biochem., 10, 383–387. Searles, P.S. & Bloom, A.J. (2003) Nitrate photo-assimilation in tomato leaves under short-term exposure to elevated carbon dioxide and low oxygen. Plant Cell Environ., 26, 1247–1255. Simms, E.L. & Taylor, D.L. (2002) Partner choice in nitrogen-fixation mutualisms of legumes and rhizobia. Integr. Comp. Biol., 42, 369–380. Singleton, P.W. & Stockinger, K.R. (1983) Compensation against ineffective nodulation in soybean. Crop Sci., 23, 69–72. Smith, F.A. & Smith, S.E. (1996) Mutualism and parasitism: diversity in function and structure in the ‘arbuscular’ (VA) mycorrhizal symbiosis. Adv. Bot. Res., 22, 1–43.
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Socolow, R.H. (1999) Nitrogen management and the future of food: lessons from the management of energy and carbon. Proc. Natl. Acad. Sci. USA, 196, 6001–6008. Song, L., Carroll, B.J., Gresshoff, P.M. & Herridge, D.F. (1995) Field assessment of supernodulating genotypes of soybean for yield, N2 Fixation and benefit to subsequent crops. Soil Biol. Biochem., 27, 563–569. Sorwli, F.K & Mytton, L.R. (1986) The nitrogen fixing potential of Vicia faba rhizobia (R. leguminosarum) from different agricultural locations. Plant Soil, 92, 249–254. Stanhill, G. (1990) The comparative productivity of organic agriculture. Agric. Ecosyst. Environ., 30, 1–26. Thies, J.E., Woomer, P.L. & Singleton, P.W. (1995) Enrichment of Bradyrhizobium spp. populations in soil due to cropping of the homologous host legume. Soil Biol. Biochem., 27, 633–636. Thrall, P.H., Burdon, J.J. & Woods, M.J. (2000) Variation in effectiveness of symbiotic associations between native rhizobia and temperate Australian legumes: interactions within and between genera. J. Appl. Ecol., 37, 52–65. Triplett, E.W. & Sadowsky, M.J. (1992) Genetics of competition for nodulation of legumes. Annu. Rev. Microbiol., 46, 399–428. US Department of Agriculture (2003) Statistics of grain and feed. In Agricultural Statistics 2003 (ed. F. Chapman), U.S. Government Printing Office, Washington, DC, pp. I1–I50. van der Heijden, M.G.A., Wiemken, A. & Sanders, I.R. (2003) Different arbuscular mycorrhizal fungi alter coexistence and resource distribution between co-occurring plant. New Phytol., 157, 569– 578. van Kessel, C. & Hartley, C. (2000) Agricultural management of grain legumes: has it led to an increase in nitrogen fixation? Field Crops Res., 65, 165–181. Vance, C.P., Uhde-Stone, C. & Allan, D.L. (2003) Phosphorus acquisition and use: critical adaptations by plants for securing a nonrenewable resource. New Phytol., 157, 423–447. West, S.A., Kiers, E.T., Pen, I. & Denison, R.F. (2002a) Sanctions and mutualism stability: when should less beneficial mutualists be tolerated? J. Evol. Biol., 15, 830–837. West, S.A., Kiers, E.T., Simms, E.L. & Denison, R.F. (2002b) Sanctions and mutualism stability: why do rhizobia fix nitrogen? Proc. R. Soc. Lond. B Biol. Sci., 269, 685–694. Wilkinson, D.M. (1998) The evolutionary ecology of mycorrhizal networks. Oikos, 82, 407–410. Williams, G.W. & Nesse, R.M. (1991) The dawn of Darwinian medicine. Q. Rev. Biol., 66, 1–22. Wilson, R.A., Handley, B.A. & Beringer, J.E. (1998) Bacteriocin production and resistance in a field population of Rhizobium leguminosarum biovar viciae. Soil Biol. Biochem., 30, 413–417. Wu, S. & Harper, J.E. (1991) Dinitrogen fixation potential and yield of hypernodulating soybean mutants: a field evaluation. Crop Sci., 31, 1233–1240. Zhang, D.Y., Sun, G.J. & Jiang, X.H. (1999) Donald’s ideotype and growth redundancy: a game theoretical analysis. Field Crops Res., 61, 179–187.
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12 Methods to improve the crop-delivery of minerals to humans and livestock Michael A. Grusak and Ismail Cakmak
12.1 Introduction Humans and other animals are dependent on plant species to provide them with dietary minerals. Plants can contain a broad range of mineral elements, but concentrations in any one plant will vary depending on species, genotype and environmental constraints. In theory, a balanced diet containing several plant food sources will provide an adequate dietary intake of all essential minerals for any given species (Dwyer, 1991; American Dietetic Association, 2002). In practice, however, diets are not always diverse enough, or consumed in sufficient quantities, to assure adequate intake of all minerals. This situation is especially prevalent in low-income populations of the human species throughout the developing world, where total caloric intake is low, and diets are restricted to one or two staple foods that are often a poor source of several minerals (Food and Agriculture Organization of the United Nations, 2000; Underwood, 2000). Similarly, for livestock, mineral needs for optimal growth or productivity are rarely met by plant foods alone; supplemental minerals often are added to animal feeds (Pond et al., 1995). To help provide higher quantities of plant-based dietary minerals, researchers have been working to enhance the mineral density of plant foods. This is not proving to be an easy task, as minerals must be acquired from the rhizosphere, and are partitioned to edible tissues via a complex, integrated series of short- and long-distance transport events (Grusak, 2002a). While some gains have been realized through conventional breeding, especially for micronutrients, continued efforts to understand the molecular mechanisms and regulation of plant mineral nutrition are essential if we wish to make significant improvements in the food supply. Thankfully, genomic studies are beginning to provide us with some of the necessary knowledge and tools to effect these changes. In this chapter, we will discuss the importance of plants in the dietary food chain, why plant mineral research is important and must be expanded, and how our existing genomic (and conventional) technologies can be applied to initiate improvements in plant mineral content. We have attempted to present these issues within a conceptual framework to discuss how nutritional genomics per se can facilitate plant improvement. Several other articles on the role of agriculture and molecular genetics to increase plant mineral content are available (Grusak & DellaPenna,
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1999; Frossard et al., 2000; Cakmak, 2002; Cakmak et al., 2002; Williams, 2003; Poletti et al., 2004; Welch & Graham, 2004).
12.2 Plants as sources of dietary minerals 12.2.1 Mineral nutrition for humans To understand better how plant mineral content might be improved, it is first worth noting what humans require and what plant foods can provide. There are 16 mineral elements deemed essential for humans. These include the macronutrients N, S, K, Ca, P, Cl, Na and Mg, along with the micronutrients Fe, Zn, Mn, Cu, Mo, Cr, I and Se (National Research Council [US] Food and Nutrition Board, 1989) (see Table 12.1). Dietary proteins, peptides and free amino acids are the predominant source of N and S, and in fact, much of the required N and S must be obtained as essential amino acids (Reeds & Beckett, 1996). The other macro- and micronutrients are obtained and absorbed in various organic and inorganic forms, including free ions. Additionally, elements such as B, Ni, Si and V have been suggested as human-essential, but evidence for these remains circumstantial (Nielsen, 1996). Nonetheless, their occurrence in plant foods, Table 12.1 Human mineral requirements and examples of mineral content in plant food sources (per 100 g f. wt food) Required Mineral
Maximum Adult US RDA1
Content in cooked white rice2
Content in cooked black bean2
Content in raw spinach2
Potassium (K) Calcium (Ca) Phosphorus (P) Chloride (Cl) Sodium (Na) Magnesium (Mg) Iron (Fe) Zinc (Zn) Manganese (Mn) Copper (Cu) Molybdenum (Mo) Chromium (Cr) Iodine (I) Selenium (Se)
2000 mg 1200 mg 1200 mg 750 mg 500 mg 350 mg 15 mg 15 mg 2–5 mg 1.5–3 mg 75–250 g 50–200 g 150 g 70 g
10 mg 2 mg 8 mg NA3 5 mg 5 mg 0.14 mg 0.41 mg 0.26 mg 0.05 mg NA NA NA 5.6 g
355 mg 27 mg 140 mg NA 237 mg 70 mg 2.10 mg 1.12 mg 0.44 mg 0.21 mg NA NA NA 1.2 g
558 mg 99 mg 49 mg NA 79 mg 79 mg 2.71 mg 0.53 mg 0.90 mg 0.13 mg NA NA NA 1.0 g
1 Recommended dietary allowances (RDA) are the daily levels of intake of essential nutrients judged to be adequate to meet the known nutrient needs of practically all healthy persons. Values presented are the highest RDA either for male or female adults, excluding pregnant or lactating women. Values are from National Research Council (US) Food and Nutrition Board (1989). 2 Values are from US Department of Agriculture, Agricultural Research Service (2001). 3 NA: not available.
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along with health-beneficial elements like fluoride (for the prevention of dental caries), has dietary significance. Plant foods can provide all of these elements, especially those that have been determined as essential for the growth and reproductive development of plants themselves. The plant-essential elements are N, S, P, K, Ca, Mg, Cl, Fe, Zn, Mn, Cu, B, Mo and Ni (Eskew et al., 1983; Marschner, 1995). Because these 14 are required, all plants acquire them from soils through various transport mechanisms, and they should be found in all plant foods. Elements not identified as plant essential, but which are essential for humans (e.g. Na, Cr, I, Se, Si and V), can enter the plant through various non-selective transport mechanisms when these minerals are available in the soil (Kochian, 1991). Fortunately, for humans and livestock, these other minerals often do make their way into the food supply (Kabata-Pendias & Pendias, 1992). However, for any of these elements (plant essential or non-essential), their content in plant foods can be quite variable (Table 12.1). Mineral concentrations can differ across tissues within a single plant, across genotypes of a given species, or more broadly across species. Thus, although plants have the potential to deliver many dietary minerals, that delivery is not always optimal in any given food source. 12.2.2 Recommended intake versus actual intake in humans Many governments provide dietary recommendations for the daily intake of minerals, vitamins, protein, lipids, carbohydrates, water and other health-beneficial compounds (Harper, 1985, 1987). These recommendations vary across the life span, as well as with different physiological states. Infants and children have lower requirements for most minerals, relative to adults, and pregnant or lactating women have higher needs for some minerals relative to men of the same age (National Research Council [US] Food and Nutrition Board, 1989). Infectious diseases can also promote different mineral requirements, especially in the case of intestinal parasites where nutrient absorption is impaired (Thurnham, 1997; Hoste, 2001). Unfortunately, not all individuals achieve these recommended intakes, either because of personal choice or because of environmental constraints (Anonymous, 1998; Edmonds et al., 2001). For instance, in developed countries, most mineral intake inadequacies exist not because of a lack of food, but rather because of behavior-influenced decisions in food selection (Baranowski et al., 1999). Total caloric intake often exceeds recommended guidelines, as evidenced by the rising global incidence of obesity (R¨ossner, 2002); unfortunately, the prevalence of and preference for processed foods, often with low mineral density, does not provide adequate quantities of all minerals. In the developing world, large segments of the global population have food intakes that are severely low, leading to malnutrition of energy, protein, minerals, or other micronutrients (Food and Agriculture Organization of the United Nations, 2000). Poverty, in conjunction with the high cost of vegetables and animal food
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products, leads many to subsist on a diet predominated by staple foods (e.g. rice, wheat, maize, bean, cassava and sweet potato) that have low concentrations of many minerals. Because of these low intakes of food and/or minerals, several food-related nutrient-deficiency diseases can be found throughout the world. Iron deficiency, which is believed to affect 30–40% of the world’s population, has a deleterious effect on cognitive development in children and on work productivity in adults (Yip & Dallman, 1996). Zinc deficiency, also believed to occur throughout a large percentage of the world’s population (but firm estimates are unavailable owing to the lack of good biomarkers), causes stunting in infancy, delayed maturity of reproductive development and reproductive organs, impairments in brain development and function, and increases susceptibility to various pathogenic diseases (Hambidge, 2000; Sandstead, 2003). Iodine deficiency is another nutritional disease, with an estimated global incidence of 13%, but with 38% of the world’s population currently at risk for iodine deficiency disorders (World Health Organization, 1999). Low I status can lead to irreversible mental retardation in children and goiters in adults (Stanbury, 1996). Besides these micronutrients, a deficiency in Ca can be found in certain regions of the world. The incidence of nutritional rickets, a disease in which insufficient Ca intake leads to stunting and severe bone deformities, is quite high in parts of India and Africa (Thacher, 2003); it also is starting to be observed in developed countries (DeLucia et al., 2003). Finally, Se deficiencies have been seen in parts of China (Fordyce et al., 2000), and are an increasing concern in several European countries (Arthur, 2003). 12.2.3 Bioavailability Although total mineral intake is an important determinant of mineral adequacy (in humans and livestock), not all ingested minerals are completely absorbed and utilized. Bioavailability is a term used to describe the digestion, absorption and subsequent utilization of dietary compounds (Linder, 1991). It encompasses all the physical, chemical and enzymatic processes that contribute to the breakdown of food, and includes the secretory processes and membrane transporters in the gut that facilitate nutrient absorption and trafficking into the body. For minerals within a given food source, their bioavailability depends on the types and quantities of promotive and inhibitory compounds that are found within that food, or that are ingested concurrently from other foods in the meal. Calcium bioavailability, for instance, can be as high as 30% from some foods, or as low as 5% in foods where this mineral is found as Ca-oxalate crystals (Weaver & Heaney, 1991). Dietary phytic acid (phytate) can influence bioavailability of several minerals (e.g. Fe, Zn, Ca), because of its capacity to form insoluble precipitates (Wise, 1995). As with Ca-oxalate crystals, these insoluble components are unavailable to membrane transporters on the surface of enterocytes (absorptive cells of the intestinal epidermis), as food moves through the gut.
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Several organic molecules can also influence mineral bioavailability. These include tannins and various polyphenolics as inhibitors, or ascorbic acid and S-amino acids as promoters (Welch & Graham, 2004). Although efforts have been undertaken to manipulate these compounds in plants, especially the reduction of tannins, the potential also exists to manipulate inorganic constituents directly. A better understanding of how Ca is channeled to and/or sequestered in Ca-oxalate crystals could allow us to reduce levels of this mineral salt in plants (Nakata, 2003), thereby improving Ca absorption. Similarly, a sound knowledge base in P nutrition and phytate metabolism will allow us to lower phytate levels in target tissues, such as has been achieved with low-phytate maize mutants (Raboy, 2001), or has been attempted with the engineering of the phytate-degrading enzyme, phytase (Lucca et al., 2001; Brinch-Pedersen et al., 2003). Clearly, continued research in specific areas of plant mineral nutrition is needed to effect these types of changes. 12.2.4 Mineral nutrition for livestock The essential minerals required by livestock are the same as those needed by humans (Pond et al., 1995). However, the daily requirements for specific minerals are quite different. Not only do these vary considerably from one animal species to the next, but the basis for establishing mineral needs is also somewhat different in livestock than in humans. For people, the nutritional goal is to provide sufficient minerals that will minimize deficiencies in the population and will maintain adequate growth in infants and children, or stable body mass and composition in adults. For livestock, although mineral intakes to prevent deficiency have been studied in the past, current efforts are more focused on the economics of production. Mineral intake levels often are set to maximize growth (e.g. swine, beef cattle, broiler chickens) or to optimize productivity (e.g. dairy cattle, laying hens) (Coleman & Moore, 2003). Thus, with recommended mineral intakes high for certain livestock, it is common for commercial feed to contain supplemental minerals (Pond et al., 1995). Efforts to enhance mineral concentrations in plant foods would reduce the need for some of these additions, and thereby lower costs to the livestock producer. Interestingly, these mineral needs are not static, but are rather a moving target. The production potential and nutritional requirements of animals are also under constant manipulation through conventional breeding and biotechnology (Bonneau & Lavard, 1999). The type of plant material fed to livestock differs, in part, from that consumed by humans. Although cereals and grain legumes are fed widely to many animals, forages are a significant part of some diets, especially for ruminant animals (Pond et al., 1995; Reddy et al., 2003). Both monocots (e.g. Lolium spp., Pennisetum spp.) and dicots (e.g. Medicago sativa, Trifolium spp.) serve as forage, and thus plant scientists should not overlook these species as targets for mineral improvement. In the case of forages, strategies to increase mineral concentration
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would be worthwhile, but efforts to ensure an appropriate balance of minerals would be equally important. Grass tetany, for instance, is a serious and lifethreatening disease caused by an imbalance in plasma Mg that arises when K/Mg ratios are elevated in forage grasses (Robinson et al., 1989; Dua & Care, 1995).
12.3 Conceptual strategies for mineral improvement Plant nutritional science is at an exciting juncture because of the wealth of sequence information available for mineral-related genes in various model and agronomic species, and because of the breadth of technologies available to study the expression and function of relevant genes and gene products (Grusak, 2002b). Scientists have now identified membrane transporters for several mineral ions (Williams et al., 2000; White & Broadley, 2003), and these molecular tools could aid efforts to enhance mineral density in plant foods, either through genetic engineering or through marker-assisted selection (see below). However, although knowledge of specific genes can be useful, it is important to remember that any one protein product functions within a broader whole-plant system, and can only move a mineral from point A to point B, or perhaps can only act to sequester it within one compartment. No protein allows a plant to ‘make’ a mineral, unlike the situation with biosynthetic enzymes that can convert precursors into other molecules of interest. Instead, all minerals must be acquired from the soil matrix, must be moved to the xylem for delivery to transpiring shoot tissues, must be transported across membranes for utilization by cells along the root–shoot pathway, and in most cases must ultimately be partitioned to the phloem pathway for long-distance transport to seeds. Strategies for mineral improvement, therefore, must take into account the role of several short- and long-distance transport processes, as well as the availability of a given mineral in different tissues. One can think of the soil–plant continuum as a series of compartments, in which different transporters provide specific transfer capabilities for each mineral from one compartment to the next, and each compartment has a unique storage capacity (i.e. pool size) for each mineral. Total flux of a mineral from the soil to a terminal tissue in the plant (e.g. leaves or seeds) will be determined both by the rate limitations of transport steps along the pathway and by the size of the mineral pool in each successive compartment. Pool size is an important issue, because it helps identify where increased compartmental flux is needed within the whole-plant system. For instance, Fig. 12.1 shows a distribution of the partitioning of several minerals between shoot vegetative and reproductive tissues in a mature pea plant. It can be seen that the partitioning of some minerals to seeds is quite high. This means that efforts to double the Fe or Cu concentration in pea seeds would require enhanced transport into the plant to increase the vegetative pool of these minerals, and may require elevated rates of transport from leaves to seeds (i.e. via phloem loading). On the
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Seed Fraction
Vegetative Fraction
Fe Zn Cu Mn K Mg 0
20
40
60
80
100
Mineral partitioning Figure 12.1 Partitioning of total shoot minerals between seed and vegetative fractions (stems, leaves, stipules, and pod walls) in a pea plant at harvest maturity. Data for cultivar Sparkle are presented as the percentage of minerals measured in total shoot tissues (M.A. Grusak, unpublished observations).
other hand, a doubling of Zn, Mn, K or Mg concentration in seeds theoretically could be accomplished solely by increasing flux from leaves to seeds. Note that phloem loading and transport are mentioned here, because seeds are surrounded by tissues that block transpiration from the seed surface (e.g. all legumes, maize), and thus there are no minerals imported via the xylem pathway (Grusak, 1994, 2002a). For most minerals, further research is needed to identify and characterize the full inventory of molecular- and tissue-level components that contribute to whole-plant mineral dynamics. Thankfully, nutritional genomic studies are adding to this knowledge, especially in the areas of gene discovery and sequence analysis, gene expression and functional analysis of proteins. More information is also needed on existing variation in mineral concentrations, both in edible and non-edible tissues. Once a complete picture of the rate limitations in any plant–mineral situation is available, one then can design rational approaches to increase mineral concentrations. These approaches can include the use of existing genetic variation, conventional breeding and marker-assisted selection and/or transgenic technologies. 12.4 Exploiting existing genetic variation Significant genotypic differences have been reported for seed concentrations of minerals, especially for micronutrients in staple food crops such as wheat, rice, maize and bean (see also Chapters 9 & 10). Such genotypic variation can be exploited for improving food crops with enhanced levels of minerals, and in fact is essential for a successful breeding approach. Because of the global problem of
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human micronutrient deficiencies, as noted above, a Micronutrient Project was initiated with several partners within the Consultative Group on International Agricultural Research (CGIAR) and other academic and government scientists (Bouis, 1996; Bouis et al., 2000), to screen a large number of plant genotypes for seed or root concentrations of Fe and Zn. Extensive genetic variation for these micronutrient minerals has been reported for wheat (Monasterio & Graham, 2000), rice (Gregorio et al., 2000), maize (B¨anziger & Long, 2000), bean (Beebe et al., 2000) and cassava (Chavez et al., 2000) (Table 12.2). This genetic variation is presently being exploited in the breeding programs at different CGIAR centers in the framework of the HarvestPlus Challenge Program coordinated by IFPRI (International Food Policy Research Institute) and CIAT (International Center for Tropical Agriculture) (Bouis, 2003). Below, several examples are given for the extent of genotypic variation found for mineral nutrients, and the ways that this variation can be used to enhance mineral content in plant foods. 12.4.1 Wheat Throughout much of the world, wheat contributes greatly to energy, protein and mineral intake, and in some regions it is the predominant staple food crop, providing a major portion of daily calories (Fig. 12.2). Because of this extensive consumption, efforts to improve wheat with enhanced levels of micronutrients can play a paramount role in reducing global micronutrient deficiencies. Data on genetic variation for micronutrients in wheat and related species is available from screening studies realized at CIMMYT (International Maize and Wheat Improvement Center). In a germplasm study with 505 genotypes including high-yielding bread wheat and durum wheat genotypes, triticale, synthetic wheats and several other genetic materials grown in different locations, seed concentrations of Fe and Zn ranged from 20 to 59 with an average of 34 mg Fe kg−1 d. wt and from 16 to 67 with an average of 34 mg Zn kg−1 d. wt, respectively (Table 12.2). From this screening study, a subset of genotypes with very low and very high micronutrient concentrations (170 genotypes) was chosen for concurrent planting at the same location. The genetic variation for Zn and Fe in this subset was very similar to the variation found with the 505 genotypes (Table 12.2). In further germplasm studies that included primitive and wild species of wheat, the genetic variation was much larger, especially in the case of the primitive wheat, Triticum dicoccum (Monasterio & Graham, 2000). At CIMMYT, breeders have recently initiated a program using promising genotypes of wild relatives of wheat (Aegilops tauschii), primitive wheat (T. dicoccum) and prebreeding lines, to transfer alleles contributing to elevated seed concentrations of Zn and Fe into high-yielding elite wheat cultivars. In the case of wild wheats, natural variation for micronutrients is more extensive and thus these are more promising for a successful breeding program. Wild wheats and wild relatives of wheat are widespread in different populations
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14 16 15 23 15 15 18 16 – 21 39 4
140 350 250 51 416 100 100 195 119 1031 20 20
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22 25 27 58
42 58 40 50
67 65 92
Max
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29 35
30 21 22 21
24 25 26 32
34 35 41
Mean
62 8
– 34
18 10 27 19
8 8 9 11
20 25 32
Min
155 13
96 89
59 17 57 39
24 17 24 23
59 56 73
Max
94 10
60 55
26 13 32 24
13 11 13 15
34 37 43
Mean
Iron (mg kg−1 d. wt)
Chavez et al., 2000 Chavez et al., 2000
Beebe et al., 2000 Beebe et al., 2000
B¨anziger & Long, 2000 B¨anziger & Long, 2000 B¨anziger & Long, 2000 B¨anziger & Long, 2000
Gregorio et al., 2000 Gregorio et al., 2000 Gregorio et al., 2000 Gregorio et al., 2000
Monasterio & Graham, 2000 Monasterio & Graham, 2000 Monasterio & Graham, 2000
Reference
subset of mixed genotypes selected after initial screening of the 505 genotypes grown at different locations (see Monasterio & Graham, 2000).
16 25 26
505 170 154
Min
Zinc (mg kg−1 d. wt)
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Wheat grown at CIMMYT Mixed genotypes Selected genotypes1 Pre-breeding lines Rice genotypes grown at IRRI Traditional and improved IR breeding Tropical japonicas Aromatic rice Maize grown at CIMMYT Landraces Germplasm pools Breeding germplasms Breeding germplasms Bean grown at CIAT Wild genotypes Cultivated genotypes Cassava grown at CIAT Leaves Roots
Plant species
Number of genotypes
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Table 12.2 Ranges in seed concentrations of zinc and iron in various state food crops grown in field of different Consultative Group on International Agricultural Research (CGIAR) centers
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70% 60% 50% 40% 30% 20% 10%
D
Tu Taj rk ikis m K en tan yr i gh sta A yzs n ze ta A rb n fg a ha ija ni n s A tan lg A eri rm a e Tu nia ni si a K az Sy ak ria U h zb st ek an M ista or n oc co Ira Tu n r G key eo rg In ia di C a hi na
ev e D lop EU ev in el g op C US ed ou A C ntr ou ie nt s ri W es or ld
0%
Figure 12.2 Daily caloric intake from wheat in different countries and regions. Source: Food and Agriculture Organization Database 2003; compiled by H.J. Braun, International Maize and Wheat Improvement Center (CIMMYT), Turkey.
throughout the Fertile Crescent Region (e.g. Iraq, Turkey, Syria, Lebanon, Israel and Jordan), an area where Zn deficiency is a potential problem in soils (Cakmak et al., 1999; Hacisalihoglu & Kochian, 2003). Many of the wild wheats have originated from Turkey, where Zn-deficient soils are common, and it is likely that wild wheats from Turkey have a high genetic capacity for the acquisition of soil Zn. Of the wild wheat species studied, Triticum dicoccoides, a tetraploid wheat, showed substantial variation for micronutrients, and especially for Zn (Cakmak et al., 2000). In different T. dicoccoides accessions collected from the Fertile Crescent region (nearly 800 accessions coming mainly from Israel and Turkey), Fe and Zn concentrations of seeds varied from 15 to 96 mg Fe kg−1 d. wt and from 30 to 118 mg Zn kg−1 d. wt, respectively (Cakmak et al., in press). An analysis of 110 T. dicoccoides accessions subsequently grown in a greenhouse showed genotypic variation for Zn that was particularly high (14–190 mg Zn kg−1 d. wt); a similar range of variation was not found for other mineral nutrients. The accessions having the highest seed Zn concentrations also had higher seed size or seed weight, resulting in the highest measured total Zn contents per seed (up to 7 g per seed). These results indicate that high Zn concentration in seeds is not a consequence of seed size (i.e. not a concentration effect of small seeds). It seems very likely that T. dicoccoides represents a very valuable genetic resource for improving Zn concentration of cultivated wheat, and several breeding efforts are in progress to exploit this species to enhance Zn concentrations in seeds of high-yielding wheat cultivars in Turkey and Israel. This species also could be an important source of alleles for metal-related genes, especially orthologs of Zn transporters in the ZIP family
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(Guerinot, 2000; Gross et al., 2003). In fact, sequence polymorphisms in these genes (between T. aestivum and T. dicoccoides) could provide useful molecular markers for continued breeding efforts. 12.4.2 Rice Rice is a major staple food crop for nearly 3 billion people, and contributes to protein and carbohydrate intake mainly in Asia, but also in parts of Africa (Khush, 1997). Mineral concentrations in rice seeds are generally low, and mineral levels are diminished even further after milling and polishing, which removes the nutrient-rich aleurone layer and embryo. The remaining endosperm contains low levels of most minerals, especially the micronutrient metals. Efforts at the International Rice Research Institute (IRRI) have been conducted to screen a large number of rice germplasm for genetic variation in micronutrient concentrations. A total of 1138 genotypes collected from different countries were analyzed for Zn and Fe concentration after growing them under the same environmental conditions. The genetic variation ranged from 6 to 24 mg kg−1 d. wt, with a mean value of 12 mg kg−1 d. wt for Fe, and from 15 to 58 mg kg−1 d. wt, with a mean value of 25 mg kg−1 d. wt for Zn (Gregorio et al., 2000). Among the rice genotypes tested, new breeding lines and traditional lines had the lowest, and aromatic rice genotypes had the highest levels of micronutrients (Table 12.2). Aromatic rice genotypes contain consistently more Fe and Zn than do the nonaromatic genotypes. Seven aromatic and seven nonaromatic rice genotypes grown under the same conditions demonstrated average Fe and Zn concentrations that were 18 and 32 mg kg−1 d. wt for aromatic and 11 and 21 mg kg−1 d. wt for nonaromatic rice genotypes, respectively (Graham et al., 1997). These results point to aromatic rice germplasm as promising genetic resources for improving micronutrient levels in rice. 12.4.3 Maize Maize is a major staple food crop for many people living in Africa and the Americas, and is an important feed for livestock (Byerlee & Heisey, 1996). Unfortunately, maize-based foods and feeds are very low in micronutrient concentrations and rich in phytic acid, which limits the biological utilization of many minerals. Information on genetic variability for mineral nutrients in maize seed is available from screening studies realized at CIMMYT and at the International Institute of Tropical Agriculture (IITA) (B¨anziger & Long, 2000; Maziya-Dixon et al., 2000; Oikeh et al., 2003). Over 1800 maize genotypes screened for seed micronutrients in field experiments established in Mexico and Zimbabwe showed concentrations ranging from 10 to 63 mg kg−1 d. wt and from 13 to 58 mg kg−1 d. wt, for Fe and Zn, respectively (B¨anziger & Long, 2000) (subsets of these data are presented in Table 12.2). However, the maize genotypes demonstrating elevated levels of Fe and Zn were associated with very low grain yield. According to B¨anziger and Long (2000), environmental factors contributed greatly to the
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genetic variation found for Zn and Fe in maize. Recently, Oikeh et al. (2003) also found significant variation for Fe and Zn concentrations in seeds of 49 maize genotypes grown at different locations in Nigeria. The variation in kernel Fe and Zn concentrations were between 17 and 24 mg kg−1 d. wt and between 17 and 25 mg kg−1 d. wt respectively, and were not influenced by location. The more promising genotypes maintained high levels of seed Fe and Zn across different environments. 12.4.4 Bean Grain legumes, especially common bean, are an important food source for many people living in Latin America and Africa and can provide varying levels of minerals, protein and carbohydrates (Wang et al., 2003). In some regions, beans and cereals are consumed together, but their combined intake still does not meet daily requirements for several minerals. Thus, there is a clear need for genetic improvement of beans with enhanced levels of minerals and/or with reduced levels of antinutrients (i.e. tannins, phytate) (Beebe et al., 2000). Several examples exist of promising genetic variability for minerals in different bean germplasm. Studies conducted at CIAT, using a common bean collection comprising 119 wild and 1031 cultivated genotypes, showed seed Fe concentrations ranging from 34 to 92 mg kg−1 d. wt, with an average of 35 mg kg−1 d. wt, and seed Zn concentrations ranging from 21 to 54 mg kg−1 d. wt, with an average of 35 mg kg−1 d. wt (Graham et al., 1999; Beebe et al., 2000). Interestingly, wild genotypes in these studies were not superior to cultivated genotypes regarding the concentrations and variability of micronutrients (Table 12.2). By contrast, it has been argued that wild and weedy common bean accessions collected in Mexico could be a promising genetic resource for improvements in seed micronutrient levels (Guzman-Maldonado et al., 2000). However, in this study, only two cultivated bean genotypes were compared with 70 accessions of wild and weedy bean genotypes. Relative to the results obtained at CIAT, the genetic variability for Zn in the genotypes from Mexico was smaller (ranging from 24 to 38 mg kg−1 d. wt), but for Fe the variation was greater (from 71 to 180 mg Fe kg−1 d. wt). According to Beebe et al. (2000), although environmental factors affect seed mineral concentrations to some degree, the effects of the genetic components on elevated micronutrient concentrations is sufficiently stable and expressed across different environments, such that breeding efforts for mineral levels in bean should be fruitful. Similarly, studies of Ca levels in bean pods suggest that breeding will be more effective in elevating pod Ca concentration than would soil fertilization be (Miglioranza et al., 1997; Quintana et al., 1999). 12.4.5 Other crops Although much of our discussion has focused on micronutrient metals, this is not meant to imply that genetic diversity for other minerals is lacking. In pea, an important legume both for humans and livestock, seed mineral concentrations
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were found to vary both for micro- and macroelements. Nearly 500 accessions of the Pisum Core Collection, a genetically diverse subset of the entire US Department of Agriculture, Agricultural Research Service (USDA/ARS) pea collection (Western Regional Plant Introduction Station, Pullman, Washington, USA), were grown under uniform, nutrient-replete conditions in a greenhouse (Grusak, unpublished observations, 2003). Concentrations of the microelements Fe, Zn, Mn and Cu varied 4.5-fold, 6.6-fold, 6.8-fold and 10.1-fold, respectively, while concentrations of the macroelements Ca, Mg, K and P varied 9.1-fold, 2.3-fold, 2.8-fold and 3.6-fold, respectively. Specific mineral data for individual accessions are available at the Germplasm Resources Information Network (GRIN) Web site (http://www.ars-grin.gov/cgi-bin/npgs/html/desclist.pl?177). Similarly, for spinach, a leafy vegetable grown throughout the world, genetic diversity for leaf mineral concentrations was shown to be quite broad. Almost the entire USDA/ARS spinach collection (North Central Regional Plant Introduction Station, Ames, Iowa, USA) was grown under uniform, nutrientreplete conditions in a growth chamber (Grusak, unpublished observations, 2003). For 327 accessions, concentrations of the microelements Fe, Zn, Mn and Cu varied 2.7-fold, 12.3-fold, 14.3-fold and 4.9-fold, respectively, while concentrations of the macroelements Ca, Mg, K and P varied 3.8-fold, 2.9-fold, 3.9-fold and 2.6-fold, respectively. Leaf mineral data for individual spinach accessions are also available at the GRIN Web site (http://www.ars-grin.gov/cgibin/npgs/html/desclist.pl?219).
12.5 Integrating genomic technologies for mineral improvement Clearly, there is significant genetic diversity for almost any mineral of interest within existing germplasm collections and/or other unique genetic populations. This diversity offers tremendous opportunities to utilize various genomic resources and technologies, in an effort to manipulate mineral levels in plants. Fortunately, following the successes and advances that came out of the sequencing of the Arabidopsis genome (Arabidopsis Genome Initiative, 2000), most major crop species (e.g. the legumes Medicago truncatula and Lotus japonicus) have had genome projects in operation for several years. While the various projects are currently at different levels of advancement, most are providing crop-specific sequence information (expressed sequence tags [ESTs] from various tissues; genomic sequence), bacterial artificial chromosomes (BACs) for BAC-end sequencing to generate physical maps (and for use in genomic sequencing) and gene indices (tentative consensus [TCs] sequences) compiled from the computational analysis of ESTs. In addition, several genome projects are generating molecular markers for mapping and the construction of genetic maps, are developing comparative maps between the crop of interest and related species (and also often with Arabidopsis), are providing cDNA-based or oligonucleotide-based microarrays for global gene expression studies, or are
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providing mutants for functional studies (e.g. T-DNA lines, or lines identified through high-throughput screening for induced point mutations [targeting induced local lesions in genomes; TILLING]; McCallum et al., 2000; Scholte et al., 2002). In Fig. 12.3, we present a generalized flowchart that includes many of these tools and resources, and in the remainder of this chapter we will discuss how they can impact cultivar development. 12.5.1 The Path to Gene Discovery As noted earlier, mineral accretion in an edible tissue is determined by several transport and/or storage processes, and in many cases, we are still attempting to determine the rate-limiting process or molecular player that most influences final mineral levels. This is where genetic diversity can make a significant impact. When mineral diversity is assessed in populations that are well mapped with molecular markers (e.g. RILs; near introgressed lines [NILs]), the mineral data can be statistically analyzed to identify quantitative trait loci (QTLs) associated with elevated mineral concentrations. This approach has been used to locate QTLs for total P and phytate levels in Arabidopsis seeds (Bentsink et al., 2003), as well as seed Fe and Zn concentrations in bean (Beebe et al., 2000). When QTLs are identified in species that also have extensive genomic sequence information available (e.g. Arabidopsis, rice), fine mapping in the vicinity of the QTL can allow one to fairly quickly identify putative genes that have relevance to the mineral trait. How quickly this can be done will depend on the original density of markers in the QTL region, and how much additional crossing (to generate NILs) and fine mapping will be needed to localize a short candidate region of the genome. Alternatively, if full genome sequence is limited, but BACs are available that are anchored to a physical map, fine-mapping followed by shot-gun sequencing of specific BACs neighboring the QTL can also lead to candidate genes (L´evy et al., 2004). Subsequent verification of the putative mineral-related genes can involve bioinformatic tools and/or functional methodologies (Borevitz & Chory, 2004). For example, Basic Local Alignment Search Tool (BLAST) queries of sequence databases (Altschul & Lipman, 1990) will provide ‘hits’ to similar sequences entered in GenBank, from the same or other species. Although one always must be cautious of gene annotations in GenBank (gene assignments are not refereed), BLAST results can be used to sort through the candidates. For mineral phenomena, hits to genes encoding membrane transporters, metal-binding peptides, or transcription factors, to name a few, might be worth further attention, especially those for which prior molecular information pertinent to a given mineral’s nutritional physiology is available. Protein function can then be verified through heterologous expression of candidate genes in yeast (e.g. for membrane transporters; Lop´ez-Mill´an et al., 2004), or, if available, knockout mutants could be screened for altered mineral physiology. For species in which mutant
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Genetic diversity
QTL analyses
Genomic analyses
Gene discovery
Bioinformatic and functional analyses
Allele discovery
Trait-specific molecular markers
Genetic engineering
Improved cultivar
Figure 12.3 Flowchart depicting the contribution of various nutritional genomics tools and resources that can contribute to cultivar development.
populations have not been developed, one should consider using mutants in another plant system, if lines are available in a putative orthologous gene (Perry et al., 2003). The tremendous strength of our nutritional genomic resources is that we can and should move between different eukaryotic and prokaryotic systems
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to identify the molecular players in any mineral process (DellaPenna, 1999; Delseny, 2004). QTL analysis is not the only route to novel gene discovery. Microarrays can provide comparisons of global gene expression in different tissues, different genotypes, or at various developmental stages (Aharoni & Vorst, 2002; see also Chapter 8). This technology can help identify genes that are up- or downregulated with a mineral process of interest, and these genes then can be critically analyzed through various bioinformatic or functional strategies, as noted above. For success in this approach, the key is to have microarrays with good coverage of the transcriptome (including genes with no known function), and tissues that demonstrate broad diversity for the mineral process of interest. This is where characterized diversity among unique genotypes is important, as it can provide useful, contrasting experimental samples for mRNA extraction. 12.5.2 The path to improved cultivars In the context of this chapter, it is important to remember that an improved cultivar pertains not only to mineral density (or bioavailability), but to an improved mineral trait in combination with the maintenance of other desired qualities (i.e., high yield, disease resistance). Thus, the full impact of nutritional genomics only will be realized when its tools are integrated with conventional breeding methodologies to generate new genotypes, stacked with several useful traits. Gene identification is important to this process, because it provides very direct and targeted tools for marker-assisted selection (MAS) in conventional breeding. MAS usually is performed with molecular markers that are only linked to a relevant QTL (as noted in Fig. 12.3). In other words, the marker helps identify progeny that carry one parent’s locus, but this is done without specific knowledge of the genes at that locus (Gepts, 2002). During the advancement of breeding generations, any recombination that occurs between the molecular marker and the actual allele conferring the improved trait will invalidate the marker as a selection tool in subsequent generations. The further the marker is from the relevant gene, the higher the probability that it can be separated from its original allele by recombination. While MAS has been used successfully (Hash et al., 2003), specific knowledge of the critical gene for a mineral process can facilitate the development of gene-targeted selection approaches that are much more robust (Andersen & L¨ubberstedt, 2003). Selection for molecular markers that reside within a gene/allele of interest should allow an absolute maintenance of that allele as generations are carried forward. Allele discovery, therefore, plays a central role in facilitating gene-targeted selection. Once a critical gene has been identified, specific primers can be developed to clone genomic segments from two diverse genotypes (i.e. that show differences in mineral concentration). Sequencing of genomic DNA would lead to the identification of single-nucleotide polymorphisms (SNPs) or length
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variations within exons or introns, that could be exploited to develop specific molecular markers for the allele of interest (Borevitz & Chory, 2004). Progeny carrying the allele then would need to be assessed for mineral concentration, to confirm the allele’s benefit. In addition to its impact on conventional breeding, gene discovery, of course, is essential for any genetic engineering approach using transformation technologies. Selected alleles of a mineral-related gene can be moved from one species to another; thus, nutritional genomics research in model systems (e.g. Arabidopsis, M. truncatula, rice) will play an important role in providing these genes. Once identified, any number of strategies can be employed to express a transgene in a crop of interest. Ubiquitous, inducible, tissue-specific or development-specific expression can be driven by appropriate promoters (Tomsett et al., 2004). The latter require knowledge of genes and genome regulation that might be broader than those pertaining to mineral physiology alone (e.g. a promoter region for a seed storage protein could be used to drive seed-specific expression of other genes; Lucca et al., 2001). Thankfully, there is much basic research being directed towards gene regulation and promoter discovery that the mineral scientist can draw from (Shah et al., 2003; Qu & Takaiwa, 2004).
12.6 Future needs Although the steps presented in Fig. 12.3 can be viewed in the context of a single crop, we hope the preceding discussion has demonstrated that the genomic resources of several species (crop and model plants; prokaryotic organisms) can (and should) be applied to the goal of crop mineral improvement. Because of the explosion of genome projects, and the continuing advances in technologies to study whole genomes, we are optimistic that additional mineral-dense cultivars are on the horizon, and that nutritional genomic research will play a significant role in this effort. There are, however, several issues that need to be addressed as these improvement efforts move forward, and we hope researchers will give them due attention. r For each mineral-crop situation, assessments should be made about the need for upper limits to an improvement strategy. Some minerals are toxic in excess, and thus projected daily intakes of a crop, in conjunction with bioavailability percentage and mineral concentration must be integrated to determine whether a problem might occur. Interdisciplinary collaboration between plant scientists and human or animal nutritionists is encouraged to address this issue. r More information is needed on mineral distribution and storage within a plant, to assess the capacity of different tissues to serve as repositories of
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minerals prior to subsequent partitioning. Knowledge of possible temporal changes in these compartments is also required. r For seed mineral improvement, a better understanding of source–sink photoassimilate partitioning is needed in individual crops. Because most minerals contribute minimally to phloem turgor pressure, they will be carried along with the predominant flow of carbohydrates from wherever they enter the pathway. Mineral remobilization thus may be highest from strong source regions, such as flag leaves in cereals. r For transgenic strategies, more information is needed on tissue-specific or development-specific promoters, especially in leaf tissues that might serve as sources of minerals for mobilization to seeds. When seed mineral density is the ultimate goal, seed-specific transgene expression will not necessarily impact mineral transport to that sink. r For any mineral improvement effort, changes in the level of other minerals must be monitored to ensure that required minerals are not lowered, and toxic minerals are not accumulated.
Disclaimer The contents of this publication do not necessarily reflect the views or policies of the US Department of Agriculture, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government. Acknowledgements The writing of this chapter was funded in part by the US Department of Agriculture, Agricultural Research Service under Cooperative Agreement Number 58-6250-1-001.
References Aharoni, A. & Vorst, O. (2002) DNA microarrys for functional plant genomics. Plant Mol. Biol., 48, 99–118. Altschul, S.F. & Lipman, D.J. (1990) Protein database searches for multiple alignments. Proc. Natl. Acad. Sci. USA, 87, 5509–5513. American Dietetic Association (2002) Position of the American Dietetic Association: vegetarian diets. J. Am. Diet. Assoc., 97, 1317–1321. Andersen, J.R. & L¨ubberstedt, T. (2003) Functional markers in plants. Trends Plant Sci., 8, 554–560. Anonymous (1998). The state of the world’s children 1998: a UNICEF report. Malnutrition: causes, consequences and solutions. Nutr. Rev., 56, 115–123.
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DeLucia, M.C., Mitnick, M.E. & Carpenter, T.O. (2003) Nutritional rickets with normal circulating 25-hydroxyvitamin D: a call for reexamining the role of dietary calcium intake in North American infants. J. Clin. Endocrinol. Metab., 88, 3539–3545. Dua, K. & Care, A.D. (1995) Impaired absorption of magnesium in the aetiology of grass tetany. Br. Vet. J., 151, 413–426. Dwyer, J.T. (1991) Nutritional consequences of vegetarianism. Annu. Rev. Nutr., 11, 61–91. Edmonds, J., Baranaowski, T., Baranowski, J., Cullen, K.W. & Myres, D. (2001) Ecological and socioeconomic correlates of fruit, juice, and vegetable consumption among African-American boys. Prev. Med., 32, 476–481. Eskew, D.L., Welch, R.M. & Norvell, W.A. (1983) Nickel: an essential micronutrient for legumes and possibly all higher plants. Science, 222, 621–623. Food and Agriculture Organization of the United Nations (2000) The State of Food Insecurity in the World 2000, Food and Agriculture Organization, Rome. Fordyce, F.M., Zhang, G.D., Green, K. & Liu, X.P. (2000) Soil, grain and water chemistry in relation to human selenium-responsive diseases in Enshi District, China. Appl. Geochem., 15, 117–132. Frossard, E., Bucher, M., Machler, F., Mozafar, A. & Hurrell, R. (2000) Potential for increasing the content and bioavailability of Fe, Zn and Ca in plants for human nutrition. J. Sci. Food Agric., 80, 861–879. Gepts, P. (2002) A comparison between crop domestication, classical plant breeding, and genetic engineering. Crop Sci., 42, 1780–1790. Graham, R.D., Senadhira, D., Beebe, S.E., Iglesias, C. & Ortiz-Monasterio, I. (1999) Breeding for micronutrient density in edible portions of staple food crops: conventional approaches. Field Crops Res., 60, 57–80. Graham, R.D., Senadhira, D. & Ortiz-Monasterio, I.A. (1997) Strategy for breeding staple-food crops with high micronutrient density. Soil Sci. Plant Nutr., 43, 1153–1157. Gregorio, G.B., Senadhira, D., Htut, H. & Graham, R.D. (2000) Breeding for trace mineral density in rice. Food Nutr. Bull., 21, 382–386. Gross, J., Stein, R.J., Fett-Neto, A.-G., & Fett, J.P. (2003) Iron homeostasis related genes in rice. Genet. Mol. Biol., 26, 477–497. Grusak, M.A. (1994) Iron transport to developing ovules of Pisum sativum, 1: Seed import characteristics and phloem iron-loading capacity of source regions. Plant Physiol., 104, 649–655. Grusak, M.A. (2002a) Enhancing mineral content and bioavailability in plant food products. J. Am. Coll. Nutr., 21, 178S–183S. Grusak, M.A. (2002b) Phytochemicals in plants: genomics-assisted plant improvement for nutritional and health benefits Curr. Opin. Biotechnol., 13, 508–511. Grusak, M.A. & DellaPenna, D. (1999) Improving the nutrient composition of plants to enhance human nutrition and health. Annu. Rev. Plant Physiol. Plant Mol. Biol., 50, 133–161. Guerinot, M.L. (2000) The ZIP family of metal transporters. Biochim. Biophys. Acta Biomembr., 1465, 190–198. Guzman-Maldonado, S.H., Acosta-Gallegos, J. & Paredes-Lopez, O. (2000) Protein and mineral content of a novel collection of wild and weedy common bean (Phaseolus vulgaris L.). J. Sci. Food Agric., 80, 1874–1881. Hacisalihoglu, G. & Kochian, L.V. (2003) How do some plants tolerate low levels of soil zinc? Mechanisms of zinc efficiency in crop plants. New Phytol., 159, 341–350. Hambidge, M. (2000) Human zinc deficiency. J. Nutr., 130, 1344S–1349S. Harper, A.E. (1985) Origin of Recommended Dietary Allowances – an historic overview. Am. J. Clin. Nutr., 41, 140–148. Harper, A.E. (1987) Evolution of Recommended Dietary Allowances – new directions? Annu. Rev. Nutr., 7, 509–537. Hash, C.T., Bhasker Raj, A.G., Lindup, S., Sharma, A., Beniwal, C.R., Folkertsma, R.T., Mahalakshmi, V., Zerbini, E. & Bl¨ummel, M. (2003) Opportunities for marker-assisted selection (MAS) to improve the feed quality of crop residues in pearl millet and sorghum. Field Crops Res., 84, 79–88.
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Hoste, H. (2001) Adaptive physiological processes in the host during gastrointestinal parasitism. Int. J. Parisitol., 31, 231–244. Kabata-Pendias, A. & Pendias, H. (1992) Trace Elements in Soils and Plants. 2nd edn, CRC Press, Boca Raton, FL. Khush, G.S. (1997) Origin, dispersal, cultivation and variation of rice. Plant Mol. Biol., 35, 25–34. Kochian, L.V. (1991) Mechanisms of micronutrient uptake and translocation in plants. In Micronutrients in Agriculture, 2nd edn (eds J.J. Mordvedt, F.R. Cox, L.M. Shuman & R.M. Welch), Soil Science Society of America, Madison, WI, pp. 229–296. L´evy, J., Bres, C., Geurts, R., Chalhoub, B., Kulikova, O., Duc, G., Journet, E.P. An´e, J.M., Lauber, E., Bisseling, T., D´enari´e, J., Rosenberg, C. & Debell´e, F. (2004) A putative Ca2+ and calmodulindependent protein kinase required for bacterial and fungal symbioses. Science, 303, 1361–1364. Linder MC. (1991) Nutritional Biochemistry and Metabolism: With Clinical Applications. 2nd edn, Elsevier, New York. Lop´ez-Mill´an, A.-F., Ellis, D.R. & Grusak, M.A. (2004) Identification and characterization of several new members of the ZIP family of metal ion transporters in Medicago truncatula. Plant Mol. Biol., 54, 583–596. Lucca, P., Hurrell, R. & Potrykus, I. (2001) Genetic engineering approaches to improve the bioavailability and the level of iron in rice grains. Theor. Appl. Genet., 102, 392–397. Marschner, H. (1995) Mineral Nutrition of Higher Plants, Academic Press, San Diego. Maziya-Dixon, B., Kling, J.G., Menkir, A. & Dixon, A. (2000) Genetic variation in total carotene, iron, and zinc contents of maize and cassava genotypes. Food Nutr. Bull., 21, 419–422. McCallum, C.M., Comai, L., Greene, E.A. & Henikoff, S. (2000) Targeting Induced Local Lesions IN Genomes (TILLING) for plant functional genomics. Plant Physiol., 123, 439–442. Miglioranza, E., Barak, P., Kmiecik, K. & Nienhuis, J. (1997) Comparison of soil and genotypic effects on calcium concentration of snap bean pods. Hortscience, 32, 68–70. Monasterio, I. & Graham, R.D. (2000) Breeding for trace minerals in wheat. Food Nutr. Bull., 21, 392–396. Nakata, P.A. (2003) Advances in our understanding of calcium oxalate crystal formation and function in plants. Plant Sci., 164, 901–909. National Research Council (US) Food and Nutrition Board (1989) Recommended Dietary Allowances, 10th edn, National Academy Press, Washington, DC. Nielsen, F.H. (1996) Other trace elements. In Present Knowledge in Nutrition. 7th edn (eds E.E. Ziegler & L.J. Filer, Jr), International Life Sciences Institute, Washington, DC, pp. 353–377. Oikeh, S.O., Menkir, A., Maziya-Dixon, B., Welch, R. & Glahn, R.P. (2003) Genotypic differences in concentration and bioavailability of kernel-iron in tropical maize varieties grown under field conditions. J. Plant Nutr., 26, 2307–2319. Perry, J.A., Wang, T.L., Welham, T.J., Garder, S., Pike, J.M., Yoshida, S. & Parniske, M. (2003) A TILLING reverse genetics tool and web-accessible collection of mutants of the legume Lotus japonicus. Plant Physiol., 131, 866–871. Poletti, S., Gruissem, W. & Sautter, C. (2004) The nutritional fortification of cereals. Curr. Opin. Biotechnol., 15, 162–165. Pond, W.G., Church, D.C. & Pond, K.R. (1995) Basic Animal Nutrition and Feeding. 4th edn, John Wiley and Sons, New York. Qu, L.Q. & Takaiwa, F. (2004) Evaluation of tissue specificity and expression strength of rice seed component gene promoters in transgenic rice. Plant Biotechnol. J., 2, 113–125. Quintana, J.M., Harrison, H.C., Nienhuis, J., Palta, J.P., Kmiecik, K. & Miglioranza, E. (1999) Comparison of pod calcium concentration between two snap bean populations. J. Am. Soc. Horticult. Sci., 124, 273–276. Raboy, V. (2001) Seeds for a better future: ‘low phytate’ grains help to overcome malnutrition and reduce pollution. Trends Plant Sci., 6, 458–462. Reddy, B.V.S., Sanjana Reddy, P., Bidinger, F. & Bl¨ummel, M. (2003) Crop management factors influencing yield and quality of crop residues. Field Crops Res., 84, 57–77.
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Reeds, P.J. & Beckett, P.R. (1996) Protein and amino acids. In Present Knowledge in Nutrition. 7th edn (eds E.E. Ziegler & L.J. Filer, Jr), International Life Sciences Institute, Washington, DC, pp. 67–86. Robinson, D.L., Kappel, L.C. & Boling, J.A. (1989) Management practices to overcome the incidence of grass tetany. J. Anim. Sci., 67, 3470–3484. R¨ossner, S. (2002) Obesity: the disease of the twenty-first century. Int. J. Obes., 26 (Suppl. 4), S2–S4. Sandstead, H.H. (2003) Zinc is essential for brain development and function. J. Trace Elem. Exp. Med., 16, 165–173. Scholte, M., d’Erfurth, I., Rippa, S., Mondy, S., Cosson, V., Durand, P., Breda, C., Trinh, H., RodriguezLlorente, I., Kondorosi, E., Schultze, M., Kondorosi, A. & Ratet, P. (2002) T-DNA tagging in the model legume Medicago truncatula allows efficient gene discovery. Mol. Breed., 10, 203–215. Shah, N.H., King, D.C., Shah, P.N. & Fedoroff, N.V. (2003) A tool-kit for cDNA microarray and promoter analysis. Bioinformatics, 19, 1846–1848. Stanbury, J.W. (1996) Iodine deficiency and the iodine deficiency disorders. In Present Knowledge in Nutrition. 7th edn (eds E.E. Ziegler & L.J. Filer, Jr), International Life Sciences Institute, Washington, DC, pp. 378–383. Thacher, T.D. (2003) Calcium-deficiency rickets. Endocr. Dev., 6, 105–125. Thurnham, D.I. (1997) Micronutrients and immune function: some recent developments. J. Clin. Pathol., 50, 887–891. Tomsett, B., Tregova, A., Garoosi, A. & Caddick, M. (2004) Ethanol-inducible gene expression: first steps towards a new green revolution? Trends Plant Sci., 9, 159–161. Underwood, B.A. (2000) Overcoming micronutrient deficiencies in developing countries: is there a role for agriculture? Food Nutr. Bull., 21, 356–360. US Department of Agriculture, Agricultural Research Service (2001) USDA Nutrient Database for Standard Reference, Release 14, Nutrient Data Laboratory Home Page: http://www.nal.usda.gov/fnic/ foodcomp. Wang, T.L., Domoney, C., Hedley, C.L., Casey, R. & Grusak, M.A. (2003) Can we improve the nutritional quality of legume seeds? Plant Physiol., 131, 886–891. Weaver, C.M. & Heaney, R.P. (1991) Isotopic exchange of ingested calcium between labeled sources. Evidence that ingested calcium does not form a common absorptive pool. Calcif. Tissue Int., 49, 244–247. Welch, R.M. & Graham, R. (2004) Breeding for micronutrients in staple food crops from a human nutrition perspective. J. Exp. Bot., 55, 353–364. White, P.J. & Broadley, M.R. (2003) Calcium in plants. Ann. Bot., 92, 487–511. Williams, L.E., Pittman, J.K. & Hall, J.L. (2000) Emerging mechanisms for heavy metal transport in plants. Biochim. Biophys. Acta, 1465, 104–126. Williams, P.E.V. (2003) Engineering plants for animal feed for improved nutritional value. Proc. Nutr. Soc., 62, 301–309. Wise, A. (1995) Phytate and zinc bioavailability. Int. J. Food Sci. Nutr., 46, 53–63. World Health Organization (1999) Progress Towards the Elimination of Iodine Deficiency Disorders, World Health Organization, Geneva. Yip, R. & Dallman, P.R. (1996) Iron. In Present Knowledge in Nutrition. 7th edn (eds E.E. Ziegler & L.J. Filer, Jr), International Life Sciences Institute, Washington, DC, pp. 277–292.
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13 Use of plants to manage sites contaminated with metals Steven N. Whiting, Roger D. Reeves, David G. Richards, Mike S. Johnson, John A. Cooke, Fran¸cois Malaisse, Alan Paton, J. Andrew C. Smith, J. Scott Angle, Rufus L. Chaney, Rosanna Ginocchio, Tanguy Jaffr´e, Bob Johns, Terry McIntyre, O. William Purvis, David E. Salt, Henk Schat, Fangjie Zhao and Alan J.M. Baker
13.1 Introduction1 Environmental technologies exploiting metal-tolerant plants draw heavily on the same principles and experimental techniques as both genetics and nutrition. The study of metal tolerance in plants has been directed towards rehabilitation of contaminated soils since the 1950s, at first using selective breeding to generate metal-tolerant cultivars (reviewed by, for example, Bradshaw & Johnson, 1992). This field of study has kept track with plant science, and now cutting-edge biotechnology and genetic techniques are applied to developing tailor-made plants for use in the management of contaminated sites (Karenl¨ampi et al., 2000). A key aspect of maintaining this rate of progress must be the conservation of wild, metal-tolerant plants, which form the resource for many of these environmental technologies. This chapter synthesizes our understanding of the state of the genetic resource, and particular focus is given to the research required to progress the industrial application of plants in the management of metal-contaminated sites. 13.1.1 Defining plants that can be used to manage contaminated sites Key to understanding how plants can be used to manage contaminated sites is a knowledge of which plants are used, and why. Metals are ubiquitous in the environment at concentrations ranging from trace (parts per trillion) through to high (parts per hundred) levels of abundance. The way in which plants respond to these metals depends on a number of factors, including (i) the species/race/ecotype/individual in question, (ii) whether or not the metal is an ‘essential’ nutrient for plant growth, (iii) the concentration of metal, and (iv) the impact of other nutrients, metals and edaphic factors, which can act antagonistically or synergistically with the metal.
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Plants used in the management of metal-contaminated sites are those with adaptive biological mechanisms that allow them to resist or tolerate higher concentrations of metals than do non-adapted races/ecotypes or species. These mechanisms can be divided into two broad groups: avoidance and tolerance. Metal avoidance involves either plant-derived mechanisms to prevent/exclude metals from entering the roots, or symbiotic associations with mycorrhizal fungi that reduce the quantity of metals that reach the roots. Avoidance protects the plants from the disruptive effects of metals ex planta. On the other hand, plants exhibiting metal tolerance have physiological and biochemical mechanisms to detoxify the metals, allowing them to grow despite the potentially toxic metals within their tissues. These mechanisms are reviewed in detail elsewhere (Baker, 1981, 1987; Shaw, 1990; Ernst et al., 1992). For the purposes of clarity in this chapter, we are defining plants used to manage metal-contaminated sites as metallophytes – plants that are able to grow and thrive despite the high concentrations of metals in the soil. This broad term includes both metal ‘avoiders’ and ‘tolerators’. 13.1.2 Evolution of metallophytes on metal-contaminated soils Naturally evolved metallophytes are the result of tens, hundreds and often millions of years of the strong selective pressures that metal-loaded soils exert on plants (Antonovics et al., 1971; Wild & Bradshaw, 1977; Baker, 1987). As seed or propagules are transported onto the site and begin to grow, only those individuals with a degree of resistance to metal toxicity will survive and reproduce. The less resistant are killed or have lower fecundity. This greater contribution of metal-resistant individuals to the next generation is compounded over time. Each successive generation will possess not only greater numbers of tolerant individuals, but also more specialized adaptive mechanisms that are suited to survival of the prevailing toxic metals. This results in the evolution of communities of plants with metal avoidance or tolerance mechanisms, or those that have protective mycorrhizal associations. The length of exposure to metals governs the degree of specialization of the metal resistance trait. Metal resistance can be found in some plant populations after only a few tens of years of exposure to metals. These metal-tolerant races of common plant species have greater abilities to resist metals than do members of the same species from clean soils. Under the strict definition of Lambinon & Auquier, 1964, cited in Baker, 1987, these should be termed pseudometallophytes. As the time of exposure increases, the mechanisms of metal resistance become progressively more specialized. Plants that have evolved on substrates derived from weathered mineral deposits for many thousands or millions of years have the most highly specialized mechanisms of resistance or tolerance. Metal-rich minerals have been present at or near to the surface of the earth for many millions of years and often pre-date the origins of the angiosperms. These millennia of selection for resistance to metals result in true metallophytes.
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Indeed, these metallophytes have often diverged genetically and morphologically to form new taxa endemic to their individual area (‘absolute metallophytes’ sensu Lambinon and Auquier). The majority of these metallophyte taxa maintain low concentrations of metals in their shoots. A few species (circa 500 discovered to date), however, have extremely specialized mechanisms that enable them to accumulate, or even ‘hyperaccumulate’ (Brooks et al., 1977) metals in their shoots at concentrations that can exceed 2% of their dry weight. The ecology and physiology of metal-tolerant and metal-hyperaccumulating plants have been the subject of many detailed reviews (see, e.g. Shaw, 1990; Baker et al., 2000). Understanding the physiology of these natural metallophytes at the biochemical and genetic levels underpins the artificial selection or genetic manipulation of plants to enhance their uses in managing contaminated land. 13.1.3 How are plants exploited in the management of contaminated land? Plants can be used in a number of ways to manage contaminated sites. The type and origin of the plants used is obviously determined by the type of site and the goal of the management strategy. These might be long-term goals, for example the establishment of a vegetative cover to stabilize, rehabilitate or restore an ecosystem on metal-contaminated wastes derived from mining or metal-smelting activities.2 In other cases, the goal might be short term, such as using plants to extract metals from a contaminated site, for example, phytoremediation.3 It is perceived that both environmental and economic benefits might flow from using metallophytes to manage contaminated sites. The primary technologies currently being developed and deployed are detailed below. 13.1.4 Stabilizing metal-contaminated soils with vegetation The most obvious application of metallophytes to managing contaminated sites is their use in establishing vegetation on substrates with high concentrations of metals. Mining and smelting activities, for example, tend to eliminate areas of vegetation either by deliberate vegetation clearance or by toxicity from fugitive metals. These activities also produce large volumes of metal-rich waste (overburden, low-grade ore, tailings, slag) that need to be stabilized to prevent migration of the metals. The obligation of operators to minimize environmental impacts during mining operations, or at decommissioning, means that vegetation must be established on the denuded areas and accumulated wastes. The high concentrations of metals in these areas, however, can impede the establishment of non-tolerant plants. Many attempts have been made to overcome the toxic effects of the metals by capping with clean soil/rock, by adding soil amendments to reduce metal toxicity, via the breeding of metal-tolerant plant cultivars, or by the introduction of metal-tolerant species from other areas. These approaches have had varied success. They often result in the establishment of large areas that lack biodiversity and in the introduction of non-native species. They can
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also be limited by the lack of financial provision to decontaminate or cap the site, or even by the limited availability of clean substrate. Clearly, for mined outcrops, native metallophytes are the optimal choice for revegetating metalliferous substrates because they have naturally evolved tolerance to the suite of toxic metals specific to that location. Indeed, it is likely to be faster to harness such naturally pre-adapted vegetation when multiple toxic metals are considered, compared to the time taken for either natural recolonization or for the screening and breeding of commercial plant cultivars for tolerance to multiple metals. Cataloguing and conservation of metallophytes at a site before operation will therefore be essential for saving these species to be used in future revegetation. 13.1.5 Ex situ ‘biotech’ applications for metallophytes Metallophytes underpin several biotechnologies that can be used to manage or decontaminated metal-loaded soils. One such technology, phytoextraction (the term is used interchangeably with phytoremediation; see Endnote 3), has attracted considerable attention from scientists and industry. Phytoextraction exploits the ability of metal-hyperaccumulating plants to absorb metals from the soil, allowing the metals to be harvested as an agricultural crop (reviewed by, e.g., Baker et al., 2000; Meagher, 2000). Phytoextraction might cost an order of magnitude less than physical remediation methods, but is limited by the years it may take to clean the site, particularly if the site is heavily contaminated (Zhao et al., 2003). A second technology is based on the phytoextraction template – phytomining. Here, the hyperaccumulator plants are used to extract commercially valuable metals from low-grade mineral ores or metalliferous soils; the ash of the harvested plants can be retailed as a metal-rich bio-ore and smelter feedstock (Anderson et al., 1999). Metallophytes might innovate numerous other biotechnological breakthroughs, either in situ (e.g. as bioindicators of mineral deposits) or ex situ via the exploitation of their genetic and chemical composition. There is much to learn from the unique metallo-biology of metallophytes. As we understand more about our own needs, we might realize many new applications for metallophytederived products. For instance, it is now known that nutritional supplements of Zn and Se improve both animal and human health – both are metals for which hyperaccumulator plants have been identified. These hyperaccumulators, or plants modified with their genes, could be the next sensation as ‘functional foods’ or ‘nutraceuticals’ (Guerinot & Salt, 2001). 13.2 Global status of metallophytes – promoting conservation of a genetic resource The use of metallophytes in managing contaminated sites must go hand-in-hand with efforts to conserve the genetic diversity of metallophytes, integrating the
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efforts of scientists, industry and governments (Whiting et al., 2002, 2003). First, as a component of global biodiversity, their fundamental value is their contribution to ‘natural capital’. Natural capital is the stock of natural assets in the world, which include biodiversity and resources such as oil, minerals, clean air and freshwater (Pearce, 1993). As natural capital, biodiversity is more than just a collection of species – it is the current state of the dynamic changes of evolution within any ecosystem. Biodiversity’s ‘value’ therefore extends to the choices or options it carries for future evolution (Pearce, 1993). The principles of sustainable development compel us to make our best efforts to conserve metallophyte biodiversity for the benefit of future generations. Conserving metallophytes will not be a simple task. Ecosystems on metalrich soils shelter very diverse biocoenoses, comprising plants, microbes and fauna that are specialized to tolerate or avoid the toxic effects of the metals. The scope of ecosystem-level field explorations on metal outcrops has been limited but must be encouraged. Understanding the extent of the metallophytes resource will involve cataloguing and conserving the remaining species before any present or future land-clearing activity eliminates them. Three priorities can be identified. 13.2.1 The need for field explorations using an ecological approach It is essential that geobotanical reconnaissance be initiated on metal-rich soils. Good taxonomic skills are crucial, as are the use of robust ecological techniques. Unfortunately, the number of, and fiscal support for, taxonomists and ecologists is dwindling. There is an urgent need for more precise geobotanical explorations, notably to elucidate the specific links between the plants and their native substrates. Interactions among the different biotic components of the ecosystem must be considered, for example, between metallophytes and the microflora in their rhizosphere. A further necessity is that metallophytes be specifically included in ecological surveys that are conducted prior to any potential development of a metal-rich site, such as the opening of a new mine. Both the support and motivation to achieve these goals have been, and remain, weak in the minerals industry. Equal efforts must be made to understand and conserve all metallophytes, from pseudometallophytes, through to the most extreme hyperaccumulator species. Despite the fact that metallophytes that do not accumulate metals in their shoots far outnumber the metal-hyperaccumulating species, hyperaccumulator plants attract far more research into their discovery and biological mechanisms. Much of the enthusiasm for studying hyperaccumulator species is driven by the race to commercialize metal-accumulating plants in phytoextraction (Baker & Whiting, 2002). Additionally, hyperaccumulator species are easier to identify from their phenotype ex situ by chemical analysis of their leaves, or even in situ with colorimetric test kits (Plate 8, see color plate section) or X-ray fluorescence techniques (Salt et al., 1999). Non-accumulating metallophytes, on the
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other hand, can only be recognized as metal resistant by a tacit assumption based on their existence on metal-rich substrates, or by rigorous tolerance testing in the laboratory. Consequently, the exact number of metallophyte species is not known, but there may be hundreds of thousands of species/ecotypes. The extent of metal-hyperaccumulating taxa is better known for Ni (about 330 species), Zn (12), Mn (10), Co (30), Cu (32), Se (20), Pb (14), As (5) and Cd (2), but these numbers are certainly set to increase (Reeves, 1992; Reeves & Baker, 2000). 13.2.2 Metallophyte ‘hotspots’ There are many unexplored ‘hotspots’ with high diversity and endemism of metallophytes, including mineralized outcrops and mine, smelter and other industrial wastes rich in metals. Several regional heavy metal outcrops may be recognized as major foci of metallophyte diversity: Latin America, Southeast Asia, Central and Southern Africa, China, Mediterranean Europe, Cuba and New Caledonia. The South-Central African copper outcrops provide a good example. Here about 550 metallophyte taxa have been identified to date (Brooks & Malaisse, 1985). At least 40 of these taxa are habitat specialists (holoendemics) confined to this small area, even to one or two isolated locations, whilst more than 30 species can hyperaccumulate Cu and/or Co. In the same Zambezian region, serpentine outcrops of the Zimbabwean Great Dyke have yielded 350 metallophytes, 26 endemic taxa and 5 Ni hyperaccumulators. A strong focus on the flora of ultramafic soils exists because they comprise the majority of metalliferous soils. Ultramafic soils have also given rise to the most extensive evolution of new species, which often remain endemic to regions of this soil type (Baker et al., 1992). This restriction of species to localized ultramafic soils means that many metallophytes, whether hyperaccumulators or not, are very rare. Recent fieldwork on serpentinophytes in Brazil (Brooks et al., 1992), Cuba (Reeves et al., 1999), South Africa (Morrey et al., 1992), Australia (Batianoff et al., 1991, 2000) and Turkey (Reeves et al., 2001) has yielded species new to science, which include further instances of hyperaccumulation. Many ultramafic floras, especially in the tropics, remain inadequately investigated. The analysis of herbarium specimens by Reeves (2003) discovered further cases of Ni hyperaccumulation by plants from Puerto Rico, Australia, Indonesia (Sulawesi, Japan) and Sabah. The incomplete identification of three of these tropical Ni hyperaccumulators is symptomatic of the need for greater exploration of the world’s metallophyte-hotspots and more detailed taxonomy. Other tropical ultramafic floras needing study include those of Guatemala, Costa Rica and Venezuela, parts of Brazil beyond Goi´as state, New Guinea, islands of Indonesia (e.g. Waigeo and Gebe), and many of the islands of the Philippines (Luzon, Mindanao, Palawan, Samar, Bicol, Nonoc and Dinagat). Whilst these hotspots deserve detailed investigation, other metallophytes must not be forgotten. There are countless areas or regions of metal-rich soils
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around the globe, an old mine or smelter works for example, where metallophytes are known to exist, but are not considered as especially valuable for conservation. This is not the case however. Much, if not most, of the influential research work on metallophytes to date has been on metal-tolerant plants from these areas, for example the Pb/Zn/Cd-rich wastes from mining (pre-Roman to early 1900s) in the Peak District, UK. There are many small areas of metal-rich soils supporting metallophyte species that can be found in a number of recognized habitats (see Annex 1 of the European Habitats Directive 1992 92/43/EEC). Work to identify and study these better-known areas must continue alongside the study of plants in as-yet unsurveyed hotspot areas.
13.2.3 The need to develop the resource base: databases, germplasm and living collections Concerted efforts must be directed at protecting the native environments of metallophytes. However, this will often conflict with development activities such as mining, logging and agriculture. A critical requirement for conserving metallophytes is therefore assembling collections prior to activities likely to cause disturbance, according to methodologies that account for (i) the diversity within the ecosystem; (ii) genetic diversity within each species/population (e.g. by maintaining ‘core collections’, Diwan et al., 1995); and (iii) species that have sustainable uses in rehabilitation, revegetation, restoration and other phytotechnologies. It might be possible to protect/conserve the species in situ, for example, by leaving representative biotope ‘islands’, or ex situ, for example, in seed gardens, arboreta, botanic gardens or germplasm banks. Databases of metallophyte species and of research on metallophytes are also crucial. Collections of seed or germplasm of metallophytes are vital for basic research, conservation of genetic resources and large-scale breeding activities. The danger of a valuable natural resource becoming extinct before its properties and distribution are properly known is ever present. There is no germplasm facility dedicated to metallophytes, although in the last decade a number of small seed collections of metallophytes have been established as ‘seed banks’. The Universities of Melbourne (Australia) and Oxford (United Kingdom) now manage a seed bank of (largely European) metal-hyperaccumulator plants. The Australian Centre for Mining Environmental Research (http://www.acmer.com.au) has also supported work on the collection and use of native seed for the revegetation of Australian mineral wastes, as have nationally funded programs (e.g. FloraBank and Greening Australia). Initiatives such as the Kew Millennium Seed Bank project might aid preservation of metallophytes. To underpin this activity, however, there is an urgent need for a fuller understanding of storage and viability requirements for seed of metallophytes.
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Geobotanical survey data from mineralized areas must be collated in accessible databases. Attempts to produce such databases have been few and global coverage is patchy. The most extensive are Environment Canada’s PHYTOREM database, which currently supports data on about 800 metallophytes, and the METALS (metal-accumulating plants) database, maintained by ECUS Ltd, UK. A great benefit would be the integration of such databases on a global scale, and their availability through the World Wide Web. These databases must assimilate research into life cycles, nutrient requirements, associated species (microbes, plants, insects), propagation and resistance to disease and herbivory. Often, nothing is known beyond the morphology and location given in the publication where the species was first described. This is clearly inadequate for judging the potential of a species for further cultivation and uses. Integrated global efforts are therefore required to conserve metallophytes. An unknown number of metallophytes are already extinct. The push towards sustainable development and the development of Governmental policies and International treaties such as the Convention on Biological Diversity that encourage the identification and conservation of metallophyte biodiversity must help protect those species that remain, as must the discovery of uses for their unique properties. For companies that operate in areas of metal-rich soils, efforts to protect metallophytes might be incorporated in Environmental Management Systems as guided by ISO 14000 (International Organization for Standardization).
13.3 Using metallophytes for the restoration or rehabilitation of mined and disturbed land Mining is probably responsible for the destruction of the majority of metallophyte habitats and the associated loss of species. The most direct mechanism for ensuring the survival of metallophytes in mined areas is to promote their use in ecological restoration and site reclamation at mine closure. The adoption of sustainable development policies necessitates that land reclamation considerations be incorporated into mine planning such that it becomes a major governing factor in the initiation and management of mining operations, waste disposal and site closure (Johnson et al., 1994). From an environmental perspective, mining causes the destruction of natural ecosystems through removal of soil and vegetation and their burial beneath mullock, tailings and even topsoil stockpiled for rehabilitation at mine closure. Thus, the ecological and sustainable approach to the reclamation of mined land in practice can largely be considered as ecosystem restoration – the re-establishment of the land’s ecological integrity, its structure, function and biodiversity (Cooke & Johnson, 2002; Choi, 2004). Environmental best-practice in mining can, and should, incorporate the goals of both the conservation of metallophytes for future generations, their use in site rehabilitation and the exploitation of their unique genetic properties in
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environmental technology. The use of metallophytes in ecological restoration sensu stricto is likely to be small in comparison to the huge potential they have for site rehabilitation, reclamation and remediation (see Endnote 2). To date, metallophytes have occupied a particular place within revegetation strategies for metalliferous sites, and the practical use of metal-tolerant plant populations (mostly grasses) to stabilize and revegetate waste is well known (e.g. Bradshaw & Johnson, 1992). In particular, the use of ecotypes of the temperate grasses Agrostis capillaris (common bent-grass) and Festuca rubra (red fescue) (both Poaceae) is a proven reclamation technology of over 20 years’ standing for dealing with medium-toxicity Pb, Zn and Cu mine tailings (Smith & Bradshaw, 1979; Johnson et al., 1994). These ecotypes have metal tolerance as a genetically heritable character, and some have been bred on to cultivar status (e.g. F. rubra cv. ‘Merlin’). These metal-tolerant plants can be combined with a minimal covering of a suitable substrate to stabilize the metalliferous wastes. For example, in 1977, the revegetation at Parc Pb/Zn mine in North Wales used 100 mm of quarry shale seeded with a mixture of tolerant F. rubra cv. ‘Merlin’ and the legume Trifolium repens L. (white clover) (Fabaceae), which can naturally increase the nitrogen content of the substrate. The tolerant fescue provided a bioengineering solution to the erosion problems by rooting well through the shale cover and into the tailings, thus giving the site physical stability. This vegetation cover persists to the present day. The use of species indigenous to the metal-contaminated area to be revegetated has been restricted (Tordoff et al., 2000). For true ecological restoration of sites degraded by mining and processing activities, the template developed by the Science and Policy Working Group of the Society for Ecological Restoration should be applied (Society for Ecological Restoration Science and Policy Working Group [SER], 2002). This should not just reinstate the entire ecosystem native to the area, but also reinitiate succession on its historical trajectory (see Endnote 2). There are a number of key biological problems concerning metallophytes and their role in rehabilitation and ecological restoration of mine sites that also need research: r Identifying and understanding metal tolerance in indigenous metallophytes; r Encouraging the commercial production of suitable native species and their seeds; r Overcoming slow growth rates typical of stress-tolerant species and improving sward ground cover in metal-tolerant grasses; r Reducing fertilizer inputs and identifying nitrogen-fixing metallophytes to promote ‘low maintenance’ vegetative cover; r Developing metallophytes with multiple-metal tolerance systems for use on heterogeneous wastes and other chemically complex mining substrates;
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r Developing metal-excluding plants to minimize transfer of metals into the food chain on the restored sites (both livestock and native fauna); r Post-revegetation chemical and ecological monitoring of restored sites to provide case studies of longer term ecosystem development. The minerals industry should understand the importance, potential value and methods of conservation of local indigenous metallophytes as key to the rehabilitation of metalliferous wastes. The recent history of mining in ultramafic areas of New Caledonia illustrates a number of important points. New Caledonia has a very large endemic flora restricted to ultramafic soils and many, if not most, of the 1130 plant species are metallophytes. Yet the past mining practices have not only ignored the biological importance of this hotspot of metallophyte diversity, but also the potential use of these indigenous plants in restoring the 120-year legacy of mining – over 200 km2 of severely degraded mined land. In fact, local metallophytes such as N2 -fixing trees (Gymnostoma spp.) (Casuarinaceae) were ignored until the 1980s (Fig. 13.1). Clearly, the aim in New Caledonia, and globally, should be no less than the restoration of the unique natural vegetation (Bradshaw, 1997; Tordoff et al., 2000; SER, 2002; Choi, 2004; Whiting et al., 2002, 2003, 2004). Such best practices will help to maintain the future of mining within the context of sustainable development as more mining projects occur in remote wilderness areas and fragile ecosystems, where innovative and creative ecological restoration methods will be needed.
13.4 Access to metallophyte genetic resources A template for the conservation and sustainable uses of metallophytes is provided by the Convention on Biological Diversity (CBD), incepted at the 1992 Earth Summit in Rio de Janeiro (http://www.biodiv.org). The CBD entered into force in December 1993 and has been ratified by 188 countries (168 signatures) (http://www.biodiv.org). The CBD is an international treaty and thus a source of international law. The core objectives of the CBD are the conservation of biological diversity, the sustainable use of its components and the fair and equitable sharing of benefits arising out of the use of genetic resources. The CBD has several implications for both the study of metallophytes and for industrial activity in metal-rich areas (not only metallophytes in their natural habitats, but also those on reworked metalliferous surfaces from past mining activity as well as plants found on tailings, settling tanks and wastes, and also in ex situ collections). For example, the CBD calls on parties to identify and monitor biodiversity, identify adverse processes and manage their biological resources; it requires parties to adopt measures relating to the use of biological resources to avoid or minimize adverse impacts on biological diversity. It is relevant to the ex situ commercial applications of metallophytes because concerns surrounding
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Figure 13.1 Restoration trial in New Caledonia with endemic metallophytes including Gymnostoma leucodon (Casuarinaceae) and Schoenus juvenis (Cyperaceae).
alien species have been identified as an issue cutting across many articles and thematic programmes of the CBD. 13.4.1 Access and benefit sharing Countries have sovereign control over access to their genetic resources but are obliged to facilitate access. However, those seeking access must obtain the prior informed consent of the country, by telling those responsible for access to genetic resources what they want, what they are going to do with it and get their consent to proceed. They must also negotiate mutually agreed terms for access to results, benefits sharing (royalties and technology transfer) and, where possible, carry out joint research with the provider country. This access and sharing has several implications that may be relevant to metallophyte research. Given national sovereignty, the CBD recognizes the host country’s authority to determine access to genetic resources and some 50 countries have now developed
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or are developing access laws. These measures govern access to genetic resources, biochemical compounds and traditional knowledge by companies and other collectors. In terms of economics, the markets based on products derived from genetic resources are an estimated US$500–800 billion per annum (ten Kate & Laird, 1999). There are arguments about how much is derived from genetic resources per se and how much by technological developments based on them. The point is that substantial sums are involved and this is a key factor in the decision to draw up access laws. This access and sharing has several implications for use of metallophytes in managing metal-contaminated land. A number of issues mitigate against easy access to metallophytes for research, and against the transportation, distribution or sharing of metallophyte germplasm necessary to pursue research and development. It is not always clear who should be contacted in the provider country to legally gain access to metallophytes, for prior informed consent and negotiation of benefit sharing. Some countries have allocated rights to exploit mining sites to individuals, military officials or multinational companies. Permission from indigenous people or local communities from surrounding areas may also be required. On the other hand, there is a perception within the scientific and research community that genetic resources are the property of no one. This clearly contravenes the CBD. There are increasing pressures to employ, enhance or genetically modify metallophytes for use in remediation, but there is a lack of regulatory oversight, which is important because these plants may be endangered, not commercially available, or they may be invasive in a non-native habitat. This is compounded by a lack of scientifically validated techniques to evaluate sustainability of biodiversity-based processes or products. There are also problems associated with regulation, transport and storage in the transfer of candidate cultivars for phytotechnology that could be considered as contaminated materials or dangerous goods in some jurisdictions. 13.4.2 Action required All these factors might be obstacles to the use of metallophytes in research and the management of contaminated sites. How can a course be steered that facilitates access to metallophytes, ensures the fair and equitable sharing of any resultant benefits, and ensures that researchers and industry act legally? The first step is to clarify the value of metallophytes to science, industry, governments and broader society, perhaps by the establishment of a Metallophytes Working Party. There are two reasons for this. First, conservation of metallophytes in their indigenous habitat will only be assured if interested stakeholders broadly understand their value. Second, it is important to outline the value of metallophytes so that all parties have a clear idea of what benefits might accrue from their study. This will make negotiation of benefit sharing much easier. Benefits do not need to be financial.
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The CBD calls for provider countries to identify a focal point for issues surrounding access and benefit sharing. In countries where the process of gaining legal access to metallophytes is unclear, the minerals industry, which will most likely be working in areas where metallophytes grow, could help encourage governments to identify such a focal point to facilitate access to metallophyte genetic resources. Many companies and researchers may perceive access laws to be restrictive and a barrier to science. However, it is too easy to blame the country of origin if user communities have not been proactive in helping to create a framework that allows access via prior informed consent and benefit sharing. It is in the interests of companies and scientific institutions to work with countries in developing access and benefit sharing agreements. It might therefore be advantageous to concentrate research on metallophytes from one or two countries initially. A coordinated approach from researchers, using case studies to illustrate the value of metallophytes, the benefits from research, and how they can be shared with the provider country, could lead to an access and benefit sharing agreement. This would prevent each individual worker having to contact and negotiate agreements with provider countries independently. Such an agreement could then be used as a template for further research in other countries.
13.5 Metallophytes as a resource base for phytotechnologies Metallophytes offer huge potential for the development of environmental phytotechnologies. Two technologies can immediately be identified: phytostabilization and phytoremediation (Endnote 3). Phytostabilization exploits metaltolerant plants in the reclamation and revegetation of degraded metal-polluted areas. There are many thousands of species of metal-tolerant plants that might be considered for phytostabilization. These species are unified by the fact that they restrict the transfer of metals to their shoots, which will reduce the entry of metals into the food chain. Conversely, phytoextraction exploits metal-accumulating plants to scavenge metals from metalliferous soils and, thus, reducing the concentrations of metal in the substrate. Ideal species for use in phytoextraction ‘hyperaccumulate’ metals in their shoots between 102 and 105 times greater than do ‘normal’ plants (Baker, 1981; Baker et al., 2000). Fewer than 500 metallophytes species have to date been found to have the ability to hyperaccumulate metals. 13.5.1 Phytostabilization Establishing vegetative cover is one of the best ways of preventing metals migrating from contaminated sites via erosion, dust or in leachate and runoff. The plants established during reclamation and revegetation provides long-term stability in terms of preventing metals from leaving the site, and thus this technology
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is often termed phytostabilization. Vegetative cover is also aesthetically pleasing and provides wildlife habitat or can be developed into a resource such as parkland. Phytostabilization has widespread application for rehabilitation of sites contaminated with metals. However, unlike ecological restoration, phytostabilization uses metallophytes that are not indigenous to the site because many metal-contaminated waste sites tend to be recent and consequently do not have naturally adapted ecosystems of metallophytes. The value of establishing a persistent vegetative cover for preventing pollutant movement has long been known. One strategy is to establish highly metaltolerant vegetation directly on the metalliferous substrate. The alternative is to modify the substrate physically and chemically with capping materials or soil amendments to render it less toxic, permitting the growth of plants with limited metal tolerance. A push to understand the chemical dynamics of metals in soils has yielded a highly promising phytostabilization technology. This technology uses ‘tailor-made’ amendments of P- and Fe-rich organic wastes (biosolids or manure/compost) plus alkaline amendments, coupled with metal-tolerant excluder plant species. The technology has been used with demonstrated successes on Pb-, Zn- and Cd-contaminated sites in the United States and Poland, and on Ni-contaminated sites in Canada (Daniels et al., 1998; Brown & Chaney, 2000; Li et al., 2000; Kukier & Chaney, 2001; Brown et al., 2003a,b). Considerable research efforts are needed to continue screening plants for their ability to tolerate metals, ensuring that the metals are not translocated to the aerial parts, and to continue to improve the efficacy of soil amendments. 13.5.2 Phytoremediation Two technologies exploit plants to remove metals from soils – phytoextraction and phytomining (Endnote 3). These hold some promise for removing metals from soils, but a number of hurdles must be leapt before this technology become commercialized (Brooks, 1998; van der Lelie et al., 2001). First, only a few of the wild hyperaccumulator species might be suitable for development into commercially viable phytoextraction technologies. Second, the feasibility of phytoremediation to de-pollute soil depends on both the level of contamination in the soil and the mass of metal that can be extracted by each crop. For many, if not most, mining and smelter wastes, it would be very difficult or even impossible to clean up these sites with phytoremediation (Zhao et al., 2003). Stabilization using metal-tolerant plants is then a more logical choice. Cleaning up metal-contaminated soils by phytoremediation is most feasible on soils with low levels of contaminants such as agricultural land impacted by the application of low-level metal sources, for example sewage sludge or atmospheric deposition. Here, the burden of metals might be extracted by phytoremediation in as few as 3 to 5 years (Zhao et al., 2003). On some metalliferous sites, phytomining to extract Ni, Co, Tl and Au for their economic value may be possible, generating a
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metal-rich smelter feedstock (‘bio-ore’); the driving factor here is the high monetary value of these metals (Brooks et al., 1998; Anderson et al., 1999). Industry has been keenly interested in metal phytoextraction in the past decade (Whiting et al., 2002, 2003). Phytoextraction technology has only had limited successes to date, but phytomining is just beginning to be applied in a largescale manner. However, even when the practical issues in phytoextraction have been solved, there are a number of economic and risk management issues that must be considered, which have to date received scant attention. For instance, phytoremediation is probably cheaper than other commercial methods of cleaning a brownfield site, but might take many years longer to clean the soil and success cannot be guaranteed. Brownfield land often has commercial potential waiting to be unlocked and thus, economically, it might be more advantageous and less risky to take the more expensive but quick option of, for example, ‘dig and dump’ and develop the land in the same year. Similarly, the threat of penalties for not complying with environmental regulations relating to pollution tends to push owners of contaminated sites to take the quicker option with the most guaranteed success rate. However, despite these constraints, there are a number of situations where phytoextraction might be applicable as a low cost, less invasive solution. Key research priorities for phytoextraction are as follows: r Scientific understanding of the physiological, molecular and genetic mechanisms of metal-hyperaccumulating metallophytes. r Screening and breeding hyperaccumulating plants for higher biomass and/or higher metal accumulation. r Development of agronomic practices, for example, planting and harvest dates, methods for planting and harvest, plant density/yield tradeoffs, nutrient additions, light, water, temperature, soil conditions, plant protection, weed and pest control, and determination of annual vs perennial (regrowth) management (Chaney et al., 2000). r Methods for processing the biomass, including incineration, metal extraction from the biomass or its ash, and disposal in landfills. Notably, the energy produced during ashing can be harnessed, and a metal phytoextraction model could be very economical, in particular, in developing countries. r Environmental Risk Assessment of phytoextraction crops, for example, their impact on the food chain (Linacre et al., 2003). 13.5.3 Looking to the future Between phytostabilization and phytoextraction, phytostabilization has the most promise for commercial application. Sites contaminated by many metals can be remediated to prevent erosion and initiate a healthy ecosystem. Inexpensive
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by-product amendments can also be used to improve soil fertility to support phytostabilization and phytoextraction. The low cost of these methods compared to soil removal and replacement could provide great public benefit. Given sufficient funding and working field demonstrations, these phytotechnologies might have the potential to become multi-million dollar industries.
13.6 Genetic modification to ‘design’ metallophytes for use in the remediation of contaminated land The use of metallophytes in managing metal-contaminated land is recognized as an environmentally desirable goal, but there are a number of issues to be resolved before this objective can be realized in practice. One of these is the rarity of most metallophytes, many of which have very limited population sizes and are threatened by industrial development (see above; also Whiting et al., 2002, 2003). For specific, larger scale applications, non-indigenous metal-tolerant plants will need to be considered. These might be wild metallophytes collected from other areas and propagated. Extensive research is also being directed at the selection of genotypes or cultivars with favourable growth characteristics (such as metal-tolerant high-biomass Brassica crops; Salt et al., 1998), or genetically modified plants that can tolerate and hyperaccumulate specific metals (PilonSmits & Pilon 2002). Successful development of improved metallophyte crops will require a detailed understanding of the underlying biological mechanisms. 13.6.1 Unravelling metal tolerance Dissecting the metal-tolerant plants that have evolved on metal-rich soils is the obvious starting point for ‘designer’ metallophytes. Scientists wishing to understand the genetic basis of metal tolerance have made much use of natural variation in tolerance within certain species. The physiological, genetic and biochemical mechanisms of metal tolerance in non-accumulating metal-tolerant plants are becoming well elucidated (e.g. Karenl¨ampi et al., 2000; Clemens, 2001; Hall, 2002), and appear, in certain cases, to be under the control of relatively few genes (Smith & Macnair, 1998). For example, analysis of the progeny of genetic crosses between metallicolous and non-metallicolous forms of Silene vulgaris have shown that metal tolerance is largely metal-specific and under relatively simple genetic control (Schat & Vooijs, 1997). Tolerance to Cu, Zn or Cd appears to be largely controlled by a small number of genes (one or two major genes plus hypostatic modifiers). The genetic loci involved often seem to be identical in geographically isolated metallicolous populations, even when belonging to different subspecies. On the other hand, the molecular identity and functions of these genes are still largely unknown. In cupricolous plants of
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S. vulgaris, a metallothionein gene encoding a metal-binding protein was shown to be expressed at high levels, and appeared to act as a hypostatic enhancer of Cu tolerance (Van Hoof et al., 2001a). The epistatic factors have also not yet been identified, but seem to be involved in active pumping of Cu out of the cell, probably by an ATPase located at the plasma membrane (Van Hoof et al., 2001b). In contrast, Zn tolerance has been shown to be genetically correlated with an enhanced capacity for Zn transport into the cell vacuole (Chardonnens et al., 1999). The mechanism of Cd tolerance, however, remains elusive. A better understanding of the processes of metal tolerance and homeostasis will ultimately be important as a basis for more targeted strategies for developing metal-tolerant crop plants and plants for phytostabilization. 13.6.2 Unravelling metal hyperaccumulation Unlike tolerant plants, the nutrition, physiology and underlying genetic structure of metal-hyperaccumulating metallophytes are far from being revealed (see also Chapter 9). Hyperaccumulator plants have a more complex genetic background because of the many mechanisms of metal transport, homeostasis, binding and sequestration. By analysing each process individually, a model of how these species function is gradually being built. Rapid advances in biophysical and chemical analysis techniques are providing key tools for characterizing the complexation of the metals in situ in cells, for example X-ray atomic spectroscopy (Salt et al., 1999). Similarly, the explosion in the number of molecular biological tools available for dissecting genetic mechanisms is revealing a long list of genes available to improve plants for phytoextraction and phytostabilization in the next decade. 13.6.2.1 Metal acquisition The most striking characteristics of hyperaccumulator plants are their ability to accumulate such exceptional concentrations of metals in their aboveground biomass without suffering from metal toxicity. These plants can evidently combine effective uptake and translocation mechanisms with a high degree of cellular tolerance to metals within the plant. A particular enigma is that metal hyperaccumulators apparently divert considerable energy into the mechanisms of scavenging these potentially toxic metals from the soil. The Zn accumulator species Thlaspi caerulescens, for example, has been shown to accumulate high concentrations of Zn in its shoots even when grown on soils containing low concentrations of bioavailable Zn (McGrath et al., 2001). A number of mechanisms contribute to this trait: Kinetic studies of metal uptake by T. caerulescens have shown that the roots possess high-affinity uptake systems for metals such as Zn and Cd, although less selective, lower affinity transport pathways may also play a role at higher soil metal concentrations (e.g. Assun¸ca˜ o et al., 2001). The root systems of these plants also show positive growth responses to localized
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patches of metals (Whiting et al., 2000), and may exhibit other features such as enhanced mobilization of metals via root exudates that result in efficient metal extraction from the soil (McGrath et al., 2001). 13.6.2.2 Physiological dissection of hyperaccumulators There has been a considerable physiological and biochemical research on the distinctive traits that characterize hyperaccumulator plants. These studies have helped to define the ways in which metal hyperaccumulators differ from nontolerant plants, as well as from plants such as certain grasses, rushes and reeds that tolerate metals more by virtue of excluding them from (or restricting their uptake into) living cells (Baker et al., 2000; Salt & Kr¨amer, 2000). Once inside the roots of hyperaccumulator plants, the metals appear to avoid irreversible sequestration within compartments such as cell vacuoles, and are instead loaded into the xylem for translocation to the shoot. But immediately on entry into the cell cytoplasm, the metal ions must be chelated by ligands to minimize any toxic effects on metabolism. This function may be fulfilled to a large extent by lowmolecular-weight organic acids and amino acids, and by cysteine-rich molecules such as phytochelatins and metallothioneins (Meagher, 2000; Clemens, 2001; Cobbett & Goldsbrough, 2002). However, there may also be more specific responses to particular metals, such as the Ni- and Co-induced production of the amino acid histidine seen in roots of Alyssum spp., which appears to play an important role both in detoxifying the incoming metal ions and in facilitating their export to the shoot (Kr¨amer et al., 1996). Within the shoot tissues, the highest metal concentrations are found in two phases, the cell wall (apoplast) and the cell vacuole, both of which can be regarded as ‘extracytoplasmic’ compartments. However, different hyperaccumulator species seem to apportion the metals differently between their various tissues. In the majority studied so far, including species of Alyssum and Thlaspi, the metals reach their highest concentration in epidermal cells, whereas in Arabidopsis halleri the mesophyll tissue appears to play the more important role in hyperaccumulation of Zn and Cd (K¨upper et al., 2001). A better understanding of these processes will ultimately be important as a basis for more targeted strategies for future selection and development of lines of metal-tolerant and/or accumulating plants. 13.6.3 Strategies to develop plants for phytoremediation and restoration With the recent application of molecular biological tools to the study of metal hyperaccumulation and homeostasis in plants, the list of genes available to improve plants for phytoremediation and land restoration is set to grow rapidly in the next decade. Of particular interest in this regard are genes involved in the transport of metals across the plasma membrane, root exudation of metal chelating compounds, metal chelation in the cytoplasm and vacuolar metal compartmentalization.
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The most widely studied ‘model’ Zn hyperaccumulators T. caerulescens and A. halleri exhibit broad inter- and intra-population variation in metal accumulation and tolerance, providing good opportunities for formal genetic analysis and the molecular cloning of candidate genes (Pollard et al., 2002). For example, two genes from the ZRT, IRT-like proteins (ZIP) gene family, which encode membrane transporters for Zn and other metals, are expressed at high levels in T. caerulescens and may be responsible for enhanced Zn uptake from the soil (Pence et al., 2000; Assun¸ca˜ o et al., 2001). However, ecotypic differences in Zn, Cd and Ni accumulation are not associated with differential expression of these transporters, suggesting that additional transport proteins may also be involved. Another class of putative Zn transporter, from the cation diffusion facilitators (CDF) gene family, was also found to be over-expressed in the leaves of T. caerulescens, especially in the most Zn-tolerant, calamine ecotype, suggesting that this transporter might be involved in the foliar sequestration of metal that generates the distinctive hyperaccumulator phenotype (Assun¸ca˜ o et al., 2001). Soil bioavailability of pollutant trace metals is known to be a major ratelimiting step for both metal exclusion for tolerance and plant uptake for phytoextraction. Recent advances in our understanding of the biochemistry of phytosiderophore, histidine and citrate production and secretion may allow manipulation of rhizosphere trace metal bioavailability in the near future. For example, the recent cloning of genes controlling six critical enzymes in the phytosiderophore biosynthetic pathway, including nicotianamine synthase, nicotianamine aminotransferase (Takahashi et al., 2001) and the phytosiderophore transporter YS1 (Curie et al., 2001), suggest that this exclusively monocotbased pathway might soon be transferred to dicots and allow increased uptake of metals such as Ni, Zn and Cd. In recent years, dramatic progress has been made in our understanding of the molecular biology of ion uptake across the root plasma membrane. Of particular interest is the recent discovery that a change to a single amino acid residue in an Fe transporter (IRT1) can alter its specificity such that it then predominantly transports Cd (Rogers et al., 2000). Such manipulations will be critical if we are to design plants suited to remediation of particular pollutant trace metals. An essential component of any phytoextraction strategy will be the ability to develop plants that are highly tolerant of toxic metals (Salt & Baker, 2001). Both cytoplasmic chelation and vacuolar compartmentalization are known to be essential for metal tolerance at the cellular level. Manipulation of the expression of enzymes such as ␥ -glutamyl-cysteine synthetase or ATP phosphoribosyl synthase, which are known to be involved in the biosynthesis of metal chelates such as phytochelatins and free histidine, respectively, raises the prospect of being able to develop metal-tolerant plants. Over-expression of genes involved in vacuolar compartmentalization, such as members of the CDF and cation/protoncoupled antiporters or exchangers (CAX) families in plants (Persans et al., 2001), also holds great promise for the development of plants that can tolerate and
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accumulate elevated levels of various pollutant trace elements. The sequencing of complete plant genomes and development of new tools in bioinformatics and functional genomics will also greatly facilitate the discovery of genes that can play an important role in the development of efficient phytoextraction crops. 13.6.4 Looking to the future Given the number of research groups focused on metal tolerance and hyperaccumulation, a full understanding of metallophytes may not be a too distant prospect. This must include all of the key processes from the ground up if effective transgenic metallophytes are to be developed for managing contaminated land. The exceptional concentrations of metals in the shoots of hyperaccumulator plants, in particular, demonstrate the linkage of effective metal acquisition mechanisms by the roots with a high degree of cellular tolerance to metals within the plant. The sequencing of complete plant genomes and development of new tools in bioinformatics and functional genomics will greatly facilitate gene discovery. The new ability to rapidly screen entire genomes of species, coupled with the exponential increases in computing power to process and compare data, is driving progress at a phenomenal rate (see also Chapter 7). Future research for biochemical and genetic elucidation of metallophytes include the following: 1. Manipulation of metal bioavailability, avoidance and acquisition – the extent of the production of metal-chelating compounds in the rhizosphere to precipitate, detoxify or mobilize metals (e.g. McGrath et al., 2001; Takahashi et al., 2001); the roles of root size, architecture and metallophilic foraging traits (Schwartz et al., 1999; Whiting et al., 2000); the critical role of root-associated microbes which has typically been overlooked (Kamnev & van der Lelie, 2000; Whiting et al., 2001). 2. Metal trafficking and homeostasis – the critical roles of ion transport across membranes of cells (Assun¸ca˜ o et al., 2001; Persans et al., 2001; Lombi et al., 2002; White et al., 2002). 3. Detoxification – the chemical nature of metal-binding ligands in cells and vascular tissue to detoxify and facilitate transport and storage of metals (Kr¨amer et al., 1996; Salt et al., 1999; Meagher, 2000; Cobbett & Goldsbrough, 2002). 4. Sequestration – the mechanisms controlling the biochemical processes in metal compartmentation in organs and cells, and what roles these play in metal tolerance at the cellular level (K¨upper et al., 1999, 2001). 5. Raison d’etre (sensu Boyd & Martens, 1992) – why some plants hyperaccumulate metals in their shoots at concentrations that would be toxic to other plants?
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13.7 Does the prospect of using metallophytes in site remediation and reclamation raise ethical issues? Little is known about the risks that might be associated with using metallophyte plants for site remediation ex situ from their native environments. There may be public concern over the use of a non-indigenous fast-growing crop that accumulates toxic metals in its shoots. This concern, whether real or imagined, is likely to be magnified if that crop has been genetically modified (Linacre et al., 2003). Perhaps the most significant concern with either of these scenarios is simply the potential for metal-accumulating plants to escape from the site of cultivation and become established as a new weed within ecosystems. The quickest mechanism for the unintended spread of released species/ genes into ecosystems is via the dispersal of seed. Seeds of many metalhyperaccumulating plants, for example, are small and might be carried by the wind, water or animals. The fact that wild hyperaccumulator plants have tended to remain endemic to metalliferous soils suggests that spread by this route could be unsuccessful; hyperaccumulator plants appear less able to survive or establish viable populations on low metal soil, thus posing a reduced hazard for establishment beyond the initial area of planting. This is perhaps because the high metal content of the tissues confers protection against root and shoot pathogens and herbivores (Ghaderian et al., 2000; Pollard et al., 2002). Further research must establish the competitive ability of non-native metallophyte species to be used for remediation projects, and their ability to establish in low metal environments. A number of other potential risks must be considered to enable the acceptance of technologies based on metallophytes or their derivatives. Pollen of natural or genetically modified metallophytes might move via wind or insects with potential for gene-flow or introgression into wild and agronomic relatives. An additional consideration for using metallophytes is the potential for transfer of metals up the food chain if metals are assimilated into the plant tissue. These potential risks have received scant attention, with few studies targeted directly at assessing the scale of the threats, if any, posed (Pilon-Smits & Pilon, 2002; Wolfe & Bjornstad, 2002; Rock, 2003). Any risks must be interpreted in the context of the permanent risk posed by leaving contaminated sites untreated, which may represent a more direct threat to human health. This raises an interesting legislative hurdle that might deter the use of metallophytes for reclamation in the United States, and probably many of the developed countries. Mining and environmental quality laws require that any metalliferous soils left behind must be covered with uncontaminated substrate to avoid the introduction of metals to the food chain, thus obviating the need to develop metallophytes for reclamation (http://www.epa.gov/). This is unfortunate because there are many thousand abandoned hectares of metal-contaminated lands for which there is no funding for cap material, and no revegetation prescription. Consequently, there is a real need to develop integrated risk assessment, management and communication
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strategies for metallophyte-based technologies to be acceptable to the public and to regulators (Linacre et al., 2003).
13.8 Conclusions: the use of metal-tolerant plants to manage contaminated sites Metal-contaminated soils are extensive throughout the world, and plants, whether indigenous, early pioneer species, or introduced by man, have exceptional abilities to cope with the abiotic stresses in these environments. The island-like nature of metalliferous outcrops has assisted the evolution of many endemic metallophyte species, which make an important and disproportionately large contribution to global biological diversity. The vegetation of these metalliferous areas can easily be lost in the early stages of mine site development, and the restricted distribution of these species leads to absolute rather than local extinctions. Concerted efforts must therefore be directed at cataloguing and conserving metallophytes from the onset of operations at any site, with structured methodology for conserving them both in and ex situ. Promoting metallophytes awareness and recognition that they have commercial uses is undoubtedly the vital first stage for achieving this goal. The bottom line is that the extent of our understanding of the biodiversity, biological mechanisms and biotechnological applications for metallophytes is fragmentary. It is clear that plants have a lot to offer for the management of metalcontaminated sites. Site stabilization and rehabilitation, in particular, offer a huge market for commercial techniques of introducing plants to metal-contaminated soils. Biotechnological applications such as phytoremediation and phytomining too hold promise for commercialization. The issues identified in this chapter should be the primary foci for future research and efforts to conserve and exploit plants for the management of contaminated sites.
Endnotes 1
This chapter first appeared in Restoration Ecology, the journal of the Society of Ecological Restoration (Whiting et al., 2004, Vol. 12, No. 1, pp. 107–117, Blackwell Publishing). The Introduction, originally focused on metallophytes and sustainable development from an industry perspective, has been modified to address plant nutritional genomics. 2 A note on definitions: ecological restoration, reclamation and rehabilitation. Formal definitions are set out by the Society for Ecological Restoration (SER) used herein and can be found at http://www.ser.org. The SER defines ecological restoration as the ‘process of assisting the recovery of an ecosystem that has been degraded, damaged or destroyed’ (SER, 2002). In
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other words, ecological restoration aims to return a site to its ‘historic trajectory’, rejuvenating the ‘ecosystem with respect to its health, sustainability and integrity’. The historic conditions are therefore the vital starting point for restoration design. Where the pre-existing conditions of a damaged ecosystem are not comprehensively recorded, the general direction and boundaries of the trajectory can be established by integrating a number of techniques including comparison with an appropriate reference ecosystem. Under this definition, the term (ecological) restoration can be applied to the use of metallophytes in revegetation only where the site (and/or reference site) originally had those metallophyte species. Unfortunately, metallophyte ecosystems have not been rigorously catalogued in the past and thus the ecosystems at many, if not most, mine sites cannot be ecologically restored. The majority of uses for metallophytes on metal-contaminated sites will therefore be reclamation (also called phytostabilisatia revegetation or rehabilitation), where metallophytes’ abilities to tolerate metals are exploited in the establishment of a vegetative cover (not representative of the pre-existing conditions). Note that these processes are not remediation. 3 A note on definitions: phytoremediation. The term phytoremediation is commonly used in scientific literature and the popular press as the generic process of using plants to remediate contaminated soils, where the contaminants can be metals, organic compounds or even water. A good overview is provided by the United States Environmental Protection Agency’s publication ‘Introduction to Phytoremediation’ (Document Reference: EPA/600/R-99/107). Within phytoremediation, more specific terms can be applied to the processes used to clean contaminated environments. Phytoextraction is the process of using metal-accumulating or metal-hyperaccumulating plants to remove metals and metalloids from soils. The plants sequester the metals in their shoots, which can then be harvested. This is a transient process because the vegetative cover of metallophytes is removed when the substrate is clean so that the site can be used for other purposes (e.g. Baker et al., 2000; Chaney et al., 2000). Phytomining exploits metal-accumulating plants to recover commercially valuable metals from metal-loaded substrates. Here, the metals are not necessarily contaminants of the soils since the metals may be naturally present at high concentrations because the soils have developed over mineralized bedrock (e.g. Anderson et al., 1999). Phytovolatilization exploits metabolic pathways in plants (and bacteria) that can chemically reduce mercury and selenium to synthesize volatile organomercury and organoselenium compounds. The contaminants are effectively volatilized out of the soil. These are not discussed in this chapter; for reviews see, for example, Rugh et al. (2000) and Terry et al. (2000). Phytodegradation is the technology that exploits the ability of plants to degrade a large range of organic contaminants. The plants (and bacteria – biodegradation/bioremediation) metabolize the organic compounds, breaking them down into smaller organic compounds and eventually water, CO2 and salts. This technology is being commercially applied around the world (there are many papers but see, for example,
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McCutcheon & Schnoor, 2003). Hydraulic control and evapotranspirative covers (ET covers) are the names given to the use of vegetation to remove water from a substrate. Because ET covers use plants to remove water from systems where it is a problem, this technology can be included within the overarching definition of phytoremediation. One of the most common uses of plants for hydraulic control is on landfills. By enhancing evaporation and transpiration, plants reduce the amount of water that is able to penetrate the landfill. This reduces the volume of leachate produced, which typically has a concentrated burden of metal and organic pollutants. Plants can also be used for hydraulic control of plumes of toxins in groundwater. These are discussed elsewhere; see, for example, Ferro et al. (2001, 2003). Treatment wetlands, constructed wetlands, flow-through wetlands are names used to refer to submerged and emergent plants used to remove contaminants such as metals, organics, nutrients and suspended solids from wastewater. Wetlands are used globally to treat wastewater with huge success. These are not discussed in this chapter; for reviews see, for example, Wetzel (1993), Kadlec and Knight (1996) and Horne (2000).
Acknowledgments The authors are grateful to Blackwell Publishing for permission to reproduce this work from its original appearance in Restoration Ecology (Whiting et al., 2004, Research priorities for conservation of metallophytes and their sustainable uses in ecological restoration and site remediation. Restoration Ecology, 12(1), 107–117). The authors thank Rio Tinto plc for funding the Workshop on metallophytes during which this article was conceived.
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Salt, D.E., Prince, R.C, Baker, A.J.M., Raskin, I. & Pickering, I.J. (1999) Zinc ligands in the metal hyperaccumulator Thlaspi caerulescens as determined using X-ray absorption spectroscopy. Environ. Sci. Technol., 33, 713–717. Salt, D.E., Smith, R.D. & Raskin, I. (1998) Phytoremediation. Annu. Rev. Plant Physiol. Plant Mol. Biol., 49, 643–668. Schat, H., & Vooijs, R. (1997) Multiple tolerance and co-tolerance to heavy metals in Silene vulgaris: a co-segregation analysis. New Phytol., 136, 489–496. Schwartz, C., Morel, J.L., Saumier, S., Whiting, S.N. & Baker, A.J.M. (1999) Root development of the zinc-hyperaccumulator plant Thlaspi caerulescens as affected by metal origin, content and localization in soil. Plant Soil, 208, 103–115. Shaw, A.J. (1990) Heavy Metal Tolerance in Plants: Evolutionary Aspects, CRC Press, Boca Raton, FL. Smith, R.A.H. & Bradshaw, A.D. (1979) The use of metal tolerant plant populations for the reclamation of metalliferous wastes. J. Appl. Ecol., 16, 595–612. Smith, S.E. & Macnair, M.R. (1998) Hypostatic modifiers cause variation in degree of copper tolerance in Mimulus guttatus. Heredity, 80, 760–768. Society for Ecological Restoration Science and Policy Working Group (2002) SER Primer on Ecological Restoration. Available at: http://www.ser.org/. Takahashi, M., Nakanishi, H., Kawasaki, S., Nishizawa, N.K. & Mori, S. (2001) Enhanced tolerance of rice to low iron availability in alkaline soils using barley nicotianamine aminotransferase genes. Nat. Biotechnol., 19, 466–469. ten Kate, K. & Laird, S. (1999) The Commercial Use of Biodiversity: Access to Genetic Resources and Benefit Sharing, Earthscan/Commission of the European Community, London. Terry, N., Zayed, A.M., de Souza, M.P. & Tarun, A.S. (2000) Selenium in higher plants. Annu. Rev. Plant Physiol. Plant Mol. Biol., 51, 401–432. Tordoff, G.M., Baker, A.J.M. & Willis, A.J. (2000) Current approaches to the revegetation and reclamation of metalliferous mine wastes. Chemosphere, 41, 219–228. van der Lelie, D., Schwitzgu´ebel, J.P., Glass, D.J., Vangronsveld, J. & Baker, A.J.M. (2001) Assessing phytoremediation’s progress in the United States and Europe. Environ. Sci. Technol., 35, 446A– 452A. Van Hoof, N.A.L.M., Hassinen, V.H., Hakvoort, H.W.J., Ballintijn, K.F., Schat, H., Verkleij, J.A.C., Ernst, W.H.O., K¨arenlampi, S.O. & Tervahauta, A.I. (2001a) Enhanced copper tolerance in Silene vulgaris (Moench) Garcke populations from copper mines is associated with increased transcript levels of a 2b-type metallothionein gene. Plant Physiol., 126, 1519–1526. Van Hoof, N.A.L.M., Koevoets, P.L.M., Hakvoort, H.W.J., Ten Bookum, W.M., Schat, H., Verkleij, J.A.C. & Ernst, W.H.O. (2001b) Enhanced ATP-dependent copper efflux across the root cell plasma membrane in copper-tolerant Silene vulgaris. Physiol. Plant., 113, 225–232. Wetzel, R.G. (1993) Constructed wetlands: scientific foundations are critical. In Constructed Wetlands for Water Quality Improvement (ed. G.A. Moshiri), Lewis Publishers, Boca Raton, FL, pp. 3–7. White, P.J., Whiting, S.N., Baker, A.J.M. & Broadley, M.R. (2002) Does zinc move apoplastically to the xylem in roots of Thlaspi caerulescens? New Phytol., 153, 199–211. Whiting, S.N, de Souza, M.P. & Terry, N. (2001) Rhizosphere bacteria mobilize Zn for hyperaccumulation by Thlaspi caerulescens. Environ. Sci. Technol., 35, 3144–3150. Whiting, S.N., Leake, J.R., McGrath, S.P & Baker, A.J.M. (2000) Positive responses to Zn and Cd by roots of the Zn and Cd hyperaccumulator Thlaspi caerulescens. New Phytol., 145, 199–210. Whiting, S.N., Reeves, R.D. & Baker A.J.M. (2002) Conserving biodiversity: mining, metallophytes and land reclamation. Mining Environ. Manage., 10, 11–16. Whiting, S.N., Richards, D. & Baker, A.J.M. (2003) Plants with mettle: growing the hard way. Mater. World, (April), 10–12. Whiting, S.N., Reeves, R.D., Richards, D., Johnson, M.S., Cooke, J.A., Malaisse, F., Paton, A., Smith, J.A.C., Angle, J.S., Chaney, R.L., Ginocchio, R., Jaffr´e, T., Johns, R., McIntyre, T., Purvis, O.W.,
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Salt, D.E., Schat, H., Zhao, F.J. & Baker, A.J.M. (2004) Research priorities for conservation of metallophytes and their sustainable uses in ecological restoration and site remediation. Restoration Ecol., 12, 107–117. Wild, H. & Bradshaw, A.D. (1977) The evolutionary effects of metalliferous and other anomalous soils in S. Central Africa. Evolution, 31, 282–293. Wolfe, A.K. & Bjornstad, D.J. (2002) Why would anyone object? An exploration of social aspects of phytoremediation acceptability. Crit. Rev. Plant Sci., 21, 429–438. Zhao, F.J., Lombi, E. & McGrath, S.P. (2003) Assessing the potential for zinc and cadmium phytoremediation with the hyperaccumulator Thlaspi caerulescens. Plant Soil, 249, 37–43.
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Abscisic acid (ABA) 33, 35, 43–52, 131–134, 141, 188–191 Adenylphosphosulphate (APS) 95, 96, 99 APS kinase 96, 97, 99 APS reductase 94, 96–98, 101, 103, 104 Allelic diversity (see Natural genetic variation) Aluminium (Al) 112, 119, 151, 192, 205–208, 220, 221, 223 Amino acids 1, 2, 5–8, 10, 12–14, 1, 87, 88, 90, 96, 97, 99, 101, 102, 183, 189, 266, 269, 304 Ammonium (NH4 + ) Assimilation 1, 4, 6, 12–16 Uptake/transport 3–7, 13, 34, 49, 50, 189, 221 Apoplast (apoplastic pathway) 11, 26, 27, 29–31, 67, 71–73, 79–82, 304 Ca2+ -ATPases (see also Ca) 67, 73, 74, 76, 79, 82, 130 H+ -ATPases 76, 79, 113, 115, 142, 143, 183, 185, 186, 303 Na+ /K+ -ATPases 130 Arabidopsis thaliana Arabidopsis ionomic database 78, 150–166 Arabidopsis as a model species 17, 27, 201, 202, 204, 205, 214, 215, 277 Arabidopsis mutants 1, 5, 7, 8, 10–17, 32, 34–38, 53, 75, 78–81, 90, 104, 117, 121, 128–144, 151–158, 163–166, 168, 172, 174, 199, 201, 204, 205, 220, 238, 278 Arabidopsis transcriptional profiling 17, 103, 104, 119, 150, 158, 170–194, 205, 280 Ecotypes 163–165, 206 Natural genetic variation 17, 163–166, 201–215 Wild relatives 209–211, 304, 305 Arabinogalactans 137 Arsenic (As) (see Heavy metals) ATP sulphurylase 93–96, 98 ATPases
Backcross inbred lines (BILs) 202, 230, 231, 234 Barium (Ba) 70, 71 Bean (Phaseolus spp.) 82, 212, 245, 252, 266, 268, 271–273, 276 Bioavailability of minerals in human diets (see Human health) Biodiversity 289, 291, 294, 296, 298, 308 Biological nitrogen (N2 ) fixation (see microorganisms) Brassica spp. 7, 87, 90–92, 166, 212–215 302 Breeding Marker assisted selection (MAS) (see Molecular markers) Performance-based selection 224, 228, 256, 257 Plant-symbiont breeding (see Symbiosis) Cadmium (Cd) Hyperaccumulation 209–211, 292, 302–305 In soils 293, 300 Uptake/transport 78, 151, 159, 161, 192, 303, 305 Caesium (Cs) 128, 139, 141, 206 Calciotrophe 69, 70, 81 Calcium (Ca) Ca2+ -ATPases 67, 73, 74, 76, 79–82, 130 Ca2+ -binding proteins 36, 52, 79–81, 129, 130, 133, 144 Ca2+ -channels 67, 73–76, 79–81, 184, 189 Ca2+ /H+ antiporters 67, 73, 76, 79–81 Ca2+ -oxalate crystals 66, 69, 70, 81, 268, 269 Ca2+ -signaling 66, 67, 73–76, 79–81, 184 Deficiency 67, 68, 82, 186, 187 Oxalate plants 69, 70, 81 Plant requirements 67–72 Casparian band (see Roots) Cation Exchange Capacity (CEC) 72, 73, 80, 82 Chloride (Cl− ) 31, 128, 151, 183, 189, 206, 266, 267 Chromium (Cr) (see Heavy metals) Cis-acting elements 104, 117, 118, 174, 180, 182, 199
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Cobalt (Co) (see Heavy metals) Community productivity (see Natural selection) Computer databases (see Internet resources) Computer models 158, 243, 260 Conservation of genetic resources (see also Metallophytes) 287, 290–294, 296, 298 Copper (Cu) (see Heavy metals) Crop quality 3, 39, 87, 88, 102, 209, 211, 212, 214, 215, 258 −cyanoalanine synthase 98, 100 Cyclic nucleotide gated channels (CNGCs) 32, 33, 38, 42, 47, 48, 50–52, 75, 76, 82, 134, 184, 186, 187, 189, 192, 193 Cysteine 87, 93–102, 304 Databases (see Internet resources) Developing world (see Human health) Dimethylsulphoniopropionate (DMSP) 88, 100 DNA (see Genomic DNA) Environmental variation 4, 17, 29, 40, 42, 81, 150, 159, 160, 170, 185, 187, 201, 202, 208, 211, 212, 222–224, 227, 228, 237, 238, 243, 257, 259, 260, 265, 275, 276 Ethyl methane sulphonate (EMS) mutants (see mutants) Fast neutron (FN) mutants (see mutants) Fertilisers 3, 40, 87, 88, 102, 245, 246, 252, 255, 295 Fine mapping (see Genetics of mineral nutrition) Genes Cloning genes (see Genetics of mineral nutrition) Co-regulated genes (see Genes) 174, 179, 180, 193 Gene annotation 180–183, 192, 193, 198, 235, 238, 278 Gene arrays (see Microarrays) Gene discovery 238, 271, 278–281, 306 Gene expression 1, 4–6, 10–18, 33–43, 49–54, 73–76, 79–82, 92–99, 101–104, 112–121, 131–145, 162, 170–200, 204–211, 215, 230, 236, 238, 249, 255, 259, 270, 271, 277, 278–282, 305 Genetics of mineral nutrition Fine mapping 18, 204, 207, 227–229, 234–239, 278
Forward genetics 32, 128, 139, 144, 156, 163–166, 205, 215 Gene expression (see Genes) Linkage disequilibrium (LD) mapping 203, 214 Mapping populations 18, 76, 165, 201–211, 220–239 Mutants (see mutants) Natural genetic variation (see Natural genetic variation) Positional cloning 121, 129, 140, 144, 164, 165, 204, 207, 210, 214, 215, 220, 221, 235–238 Reverse genetics 8, 32, 78, 117, 128, 163, 166, 174 Substitution mapping 229, 232–235 Genomes 5, 8, 13, 17, 18, 27, 32, 36, 38, 40, 53, 78, 79, 87, 90, 98, 113, 117, 120, 121, 137, 138, 143, 150–166, 170–173, 201, 202, 204, 205, 207, 210, 228, 234, 235, 255, 258, 277, 278, 281, 306 Genomic DNA 164–166, 280 Germplasm 213, 214, 272–277, 293, 294, 298 Glutamate dehydrogenase (GDH) 15, 16 Glutathione (GSH) 87–89, 93–104 Group selection (see Natural selection) GS/GOGAT cycle 2, 13–16, 18 Heavy metals Arsenic (As) 78, 151, 160, 161, 209, 292 Bioavailability in soils 305, 306 Cadmium (Cd) (see Cadmium) Chromium (Cr) 78, 151, 155, 159, 161, 266, 267 Cobalt (Co) 78, 151, 155, 159–161, 209, 292, 300, 304 Contaminated soils 70, 287–310 Copper (Cu) 78, 151, 155, 159, 209, 266, 267, 270, 271, 277, 292, 295, 302, 303 Hyperaccumulation 209–211, 292, 302–305 Lead (Pb) 78, 151, 209, 292, 293, 295, 300 Metallophyte flora 287–310 Nickel (Ni) 78, 151, 155, 209–211, 266, 267, 292, 300, 304, 305 Phytoextraction 290, 291, 299–309 Phytomining 290, 300, 301, 308, 309 Phytoremediation 150, 289–291, 299–310 Phytostabilisation 299–303 Phytovolatilisation 309 Pollution (see Pollution)
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Restoration 289, 293–310 Soils 70, 71, 112, 160, 161, 201, 214, 224–226, 267, 274, 287–310 Tolerance 201, 206–210, 221–227, 287–310 Uptake/transport 76, 189, 210, 303 Zinc (Zn) (see Zinc) Human health 3, 82, 88, 150, 208, 265–282, 290 Hyperaccumulation (see Heavy metals) Individual competitiveness (see Natural selection) Inductively-coupled plasma-mass spectroscopy (ICP-MS) (see Mineral analyis of plant tissues) In silico techniques (see also Internet resources) 32, 43, 158 Internet resources 17, 153, 157, 158, 166, 167, 198–201, 203, 205, 277, 293, 296, 307, 308 Ion channels and transporters 1–12, 16–18, 26–54, 66, 67, 72–82, 89–98, 101, 103, 104, 112–121, 130, 134, 136, 138–145, 152, 166, 181–192, 205, 210, 213, 236, 238, 270, 274, 278, 305 Ionome (see Ionomics) Ionomics 150–166 Iron (Fe) Deficiency in plants (see Mineral deficiencies in plants) Human nutrition 202, 266–268, 270–278 Natural genetic variation in Fe content of plants 166, 205–215, 220–224, 270–278 Plant nutrition 78, 87, 112, 119, 120, 151, 153, 192, 202, 205–215, 220–224, 267, 268, 270–278, 305 Linkage disequilibrium (LD) mapping (see Genetics of mineral nutrition) Magnesium (Mg) 69, 70–72, 78, 140, 151, 159, 162, 166, 206, 207, 224, 244, 266, 267, 270, 271, 277 Maize (Zea mays) 13–15, 18, 30, 35, 36, 51, 93, 98, 113, 166, 211, 212, 220, 221, 234, 235, 243, 245, 246, 250, 268–276 Manganese (Mn) 78, 151, 155, 159–162, 164, 206, 207, 209, 212, 224, 266, 267, 271, 277, 292 Mapping populations (see Genetics of mineral nutrition)
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Marker assisted selection (MAS) 164, 208, 214, 215, 220, 222, 228–230, 234, 237, 270, 271, 280 MAS (see Molecular markers) Massively Parallel Signature Sequencing (MPSS) 41, 42, 44–48, 186, 198 Metabolomics 102, 150, 151, 158, 194 Methionine 6, 87–89, 96, 97, 99, 100 Microarrays Data analysis and interpretation (see Transcriptomics) Transcriptome analysis (see Transcriptomics) Microorganisms Biological N2 fixation (see Biological N2 fixation) Mycorrhiza (see Mycorrhiza) Rhizobia (see Symbioses) Rhizobium spp. (see Symbioses) Mineral analysis of plant tissues Atomic absorption (AA) 156 Inductively-coupled plasma-mass spectroscopy (ICP-MS) 155, 156, 159, 162, 163 Inductively-coupled plasma optical emission spectroscopy (ICP-OES) 156 X-ray spectroscopy 162, 303 Molybdenum (Mo) 12, 13, 78, 151, 155, 160, 161, 164, 166, 266, 267 MPSS (see Massively Parallel Signature Sequencing) Mutants Arabidopsis thaliana (see Arabidopsis thaliana) Ethyl methane sulphonate (EMS) mutants 121, 163, 164 Fast neutron (FN) mutants 163–165 Forward/reverse genetic strategies for studying mutants (see Genetics of mineral nutrition) Mycorrhiza 53, 54, 116, 117, 221, 243, 247, 251–259, 288 Natural genetic variation (see also Genetics of mineral nutrition) Allelic diversity 18, 76–78, 202–206, 213, 214, 223, 229–239, 249, 250, 272, 274, 279–281 Arabidopsis thaliana 17, 163–166, 201–215 Crop species 239 Tolerance to toxic levels of minerals/heavy metals 67, 76, 80, 81, 127, 128, 144, 201, 206–210, 221, 222, 224, 238, 287–295, 299–310
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Natural selection Group selection 254, 255, 259 Heavy metal tolerance (see Natural genetic variation) Individual competitiveness vs community productivity 249–255 Nutrient-use efficiency 17, 201, 220–239, 243, 248, 249 ‘Tragedy of the commons’ 250, 253 Near isogenic lines (NILs) 18, 204, 207, 227–229, 234–239, 278 Nickel (Ni) (see Heavy metals) NILs (see Near isogenic lines) Nitrate (NO3 − ) Human health 3 Nitrate reductase (see Nitrate reductase) Nitrite reductase (see Nitrite reductase) NO3 − assimilation 1–6, 12–19, 250 NO3 − uptake/transport 1–12, 185, 189, 247 Osmotic role of NO3 − 1, 4 Pollution (see Pollution) Signaling role of NO3 − 1, 13 Nitrate reductase (NR) 1, 2, 4, 7, 12, 13 Nitrite reductase (NiR) 1, 2, 12, 13 Nitrogen (N) Assimilation 1–6, 12–19, 98, 102, 207, 250 Biological N2 fixation (see Microorganisms) Deficiency 11, 87, 102, 119, 152, 221, 245, 248 Fertilisers 3, 102, 245, 246, 252, 295 N/S interactions 87, 95, 98, 101–104, 187 N-use efficiency 17, 201, 220–239, 243, 248, 249 Nitrogen fixation (see Microorganisms) Nitrogen-use efficiency (see Nitrogen) Nutrient-responsive genes (see Transcriptomics) Nutrient-use efficiency 17, 201, 220–239, 243, 248, 249 Organic production systems (see Sustainability) Oryza sativa (see Rice) Oxalate plants (see Calcium) Pathogens 14, 66, 74, 88, 102, 103, 119, 174, 185, 191, 242, 246, 268, 307 PCR (see Polymerase chain reaction) Performance-based selection (see Breeding) Phaseolus spp. (see Bean) Phenotyping strategies 150–166, 202–205, 220–239 Phloem 1, 14, 27, 30, 33, 35, 39, 41, 67, 68, 72, 89, 92, 100, 101, 115, 135, 140, 143, 183, 213, 270, 271, 282
Phosphate (see Phosphorus) Phosphorus (P) Deficiency 43, 112–121, 187, 208, 220–239 Fertilisers 112, 246 Mycorrhiza (see Mycorrhiza) Pollution (see Pollution) Proteoid roots (see Proteoid roots) P-use efficiency 220–239 Uptake/transport 112–121, 208, 221, 231–239, 251 Photosynthesis 6, 10, 15, 30, 69, 112, 127, 135, 180, 183, 248, 250, 253 Phytase 236, 238, 269 Phytate 207, 209, 212, 213, 268, 269, 276, 278 Phylogeny 8, 67–69, 71, 82, 90, 91 Phytochelatin 88, 101, 304, 305 Phytoextraction, Phytomining, Phytoremediation, Phytostabilisation, and Phytovolatilisation (see Heavy metals) Plant ecology and mineral nutrition 69, 251, 259, 289, 291–296, 300, 308, 309 Pollution Fertilisers 3, 242, 244, 246–248, 251, 259 Heavy metals 201, 287–310 Polymerase chain reaction (PCR) 8, 32, 33, 40–48, 98, 120, 121, 164–166, 171–173, 175, 236 Positional cloning (see Genetics of mineral nutrition) Potassium (K) Deficiency K+ homeostasis 37, 50, 51, 129, 136, 140, 143, 144, 182–193 K+ /Na+ selectivity 50, 127–130, 134, 136–144, 187 Potassium plants 69, 70, 81 Uptake/transport 26–54, 129, 134, 136–144, 182–193 Promoters Cis-acting elements (see Cis-acting elements) Trans-acting elements (see Trans-acting elements) Protein kinase (PKS) gene family 131, 133 Protein synthesis 2, 17, 88, 94, 95, 98–103 Proteoid roots 118, 120, 247–249 Proteomics 17, 158, 194 Quantitative trait loci (QTL) analysis 17–19, 76–78, 81, 165, 166, 201–215, 220–239, 278–281
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Recommended dietary allowances (RDA) (see Human health) Rhizosphere 67, 71, 73, 115, 116, 120, 224, 226, 227, 229, 231, 253, 255, 265, 291, 305, 306 Rice (Oryza sativa) 4–6, 13, 18, 32, 35, 36, 50, 52, 53, 82, 90, 91, 92, 113, 116, 119, 138, 139, 144, 152, 174, 201, 202, 205, 207–209, 213, 214, 220–239, 250, 266, 268, 271–273, 275, 278, 281 RNA 6, 15, 17, 32, 93, 94, 96, 98, 119, 130, 139, 144, 164, 170–194, 198, 205, 236, 280 Roots Apoplastic transport of ions in roots (see Apoplast) Casparian band 29, 72, 73 Root cells 4, 29, 34, 67, 72–75, 80, 116 Symbioses with microorganisms (see Microorganisms) Symplastic transport of ions in roots (see Symplast) Ruminants 269 S-adenosylmethionine 88, 96, 100 Salt tolerance/salinity (see Sodium) Selenium (Se) 78, 90, 151, 160–162, 266–268, 290, 292, 309 Single nucleotide polymorphisms (SNPs) 163, 164, 280, 281 Sodium (Na) K+ /Na+ selectivity 50, 127–130, 134, 136–144, 187 Na+ homeostasis 50, 127–145 Na+ tolerance 127–145 Uptake/transport 50, 127–145, 187 SOS genes (SOS1–SOS5) 127–145 Strontium (Sr) 70, 71 Substitution lines (SLs) 229, 232–235 Sulphotransferases 99 Sulphur (S) Assimilation 87–89, 93–104 Deficiency 87, 88, 93, 101, 102, 104 Fertilisers 87, 88, 102 S/N interactions 87, 95, 98, 101–104, 187 Uptake/transport of SO4 2− 87–97, 103, 104 Sulphurtransferases 99 Sustainability 3, 242–260, 291–298, 305, 308, 309 Symbioses Mycorrhiza (see Mycorrhiza) Plant-symbiont breeding 257–259
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Rhizobia 15, 243, 252–259 Symbiotic performance 254, 256, Symplast (symplastic pathway) 29, 30, 52, 67, 72, 73, 80, 135 T-DNA (transfer DNA) 7, 8, 17, 32, 34, 117, 121, 128, 131, 136, 139, 141, 143, 163–166, 204, 205, 278 Thlaspi spp. 209, 210, 303, 304 TILLING (Targeting induced local lesions in genomes) 166, 204, 278 ‘Tragedy of the commons’ (see Natural selection) Trans-acting elements 117, 118 Transcriptome analysis (see Transcriptomics) Transporter genes (see Calcium, Heavy metals, Nitrogen, Phosphorus, Potassium, Sodium, Sulphate, and Transcriptomics) Transcripts 6, 10, 33, 35, 40–44, 50, 51, 61, 114, 136, 138, 141, 171, 172, 174, 176, 178, 179, 186, 187, 189–193, 230 Transcriptomics 17, 103, 104, 119, 150, 158, 170–194, 205, 280 Control genes 178, 189 Data analysis and interpretation (see also Internet resources) 177–182, 199 Nutrient-responsive genes 10–12, 40–53, 76, 95, 103, 104, 118–121, 187–193, 205 Transporters Triticum aestivum (see Wheat) Vacuoles 1, 2, 4, 11, 26, 27, 36, 37, 66, 67, 69, 70, 72, 76, 79–82, 89, 93, 115, 134, 135, 140–144, 183, 185, 303–305 Wheat (Triticum aestivum) 3, 6, 35, 38, 43, 75, 87, 88, 90, 138, 139, 144, 211, 214, 243–245, 248, 252, 268, 271–274 Xylem 1, 3, 4, 27–30, 33, 35, 41, 52, 67, 71–73, 75, 80, 89, 92, 103, 115, 117, 135, 183, 213, 270, 271, 304 Zea mays (see Maize) Zinc (Zn) Human nutrition 202, 208, 209, 211, 212, 266–268, 271, 290 Hyperaccumulation (see Heavy metals) Zn content of plants 78, 151, 152, 159–162, 202, 206–212, 224–227, 266, 271, 272–278, 292, 303–305 Zn2+ transporters in plants 210, 211, 274, 305
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data analysis Plate 1 Schematic overview of microarray technology. (A) cDNA or synthetic oligo derived probes are deposited on a glass slide in a predefined grid pattern, with individual spots representing specific genes. (B) RNA is isolated from control plants and treated plants, and both RNA samples are reverse transcribed and differentially labelled (C) for example with different fluorophores. Differentially labelled samples can be simultaneously hybridised to the array (D). After appropriate wash steps, the array is scanned for fluorescence signals (E) of both control and treatment target sample and data are interpreted through various analysis procedures.
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Plate 2 Partial image of a microarray after hybridisation, washing and scanning procedures. Ratio image of scans at 635 and 532 nm using a false colouring in red to denote high signal strength at 635 nm and green to denote high signal strength at 532 nm. Equal fluorescence signals are depicted in yellow, whereas white spots indicate signal saturation.
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Plate 3 Expression changes of transporter genes in response to Ca2+ and K+ starvation or Na+ stress. The number of all genes in each functional transporter class is set to 100%. The number of transcripts that remained unchanged in all conditions is shown in grey. Transcripts that changed (shown in colour) are shown separately for those that responded to all three conditions (dark blue), and those that responded specifically to one stress, i.e. Na+ (dark green), Ca2+ (light blue), K+ (yellow). All transcripts that showed at least a twofold change in both replica spots of at least one time point were considered ‘responsive’. Note that the total number of genes affected by K+ starvation was too low to be statistically relevant. Based on Table 1 from Maathuis et al. 2003.
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Plate 4 Transcriptional regulation of the Arabidopsis V-ATPase subunit gene family in response to NaCl stress, Ca2+ or K+ starvation over a 2–96 h period. Bar shows colour scale for up- or downregulation. Note the specificity induced by various treatments regarding both the entire family (e.g., NaCl vs Ca2+ deficiency treatment) and for separate subunits (e.g., regulation of VHA-B1 by Ca2+ and K+ deficiency but not by NaCl treatment). Scale bar shows log2 -fold changes, ‘n.d.’ is not detected. Reproduced, with permission, from Maathuis et al. (2003) Transcriptional analysis of root transporters reveals participation of multiple gene families in the response to cation stress. Plant J. 35: 675–692 −3 −2 −1 0
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Plate 5 Transcriptional regulation of the Arabidopsis CNGC gene family in response to NaCl stress over a 2–96 h period in shoots and roots. Bar shows colour scale for up- or downregulation. Note that several isoforms are far more responsive to the treatment than others (e.g., CNGC3, 8 and 19 in shoot), and tissue specificity, showing particular isoforms regulated in shoots but not roots (e.g., CNGC3) and vice versa (CNGC5). Scale bar shows log2 -fold changes, ‘n.d.’ is not detected.
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CE initial: SM/TechBooks
log2ratio
BY037-Broadley-v1.cls
QC: FCH/FFX
>2
5 .0
Roots
October 20, 2004
Shoots
Roots
Shoots
4 .0
3 .0 2 .5 2 .0
MATE1
1 .5 1 .2
1
< −2
1 .0 0 .9
MATE2
0 .8 0 .7 0 .6 0 .5 0 .4 0 .3 0 .2 0 .1 0 .0
+ABA in − K
+ ABA in + K
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’).
Roots
Shoots
Roots
Shoots
Aquaporin isoforms
log2ratio
>2
1
< −2
+ABA in −K
+ABA in +K
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.
4
15:55