Plant Stress Biology: From Genomics to Systems Biology

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Plant Stress Biology Edited by Heribert Hirt

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Plant Stress Biology From Genomics to Systems Biology

Edited by Heribert Hirt

WILEY-VCH Verlag GmbH & Co. KGaA

The Editor Prof. Dr. Heribert Hirt UGRV Plant Genomics 2, rue Gaston Gremieux F-91057 Evry France

& All books published by Wiley-VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate. Library of Congress Card No.: applied for British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. Bibliographic information published by the Deutsche Nationalbibliothek Die Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliograﬁe; detailed bibliographic data are available on the Internet at http://dnb.d-nb.de & 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microﬁlm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not speciﬁcally marked as such, are not to be considered unprotected by law. Printed in the Federal Republic of Germany Printed on acid-free paper Cover design Adam-Design, Bernd Adam, Weinheim Typesetting Macmillan Publishing Solutions, Bangalore, India ¨rlenbach Printing Strauss GmbH, Mo Bookbinding Litges & Dopf GmbH, Heppenheim ISBN:

978-3-527-32290-9

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Contents Preface XI List of Contributors Part I 1

XIII

From Model Systems to Crop Improvement

1

General Stress Response of a Model Bacterium

3

Abram Aertsen, Philipp De Spiegeleer, Laurence Van Melderen, and Chris W. Michiels

1.1 1.2 1.2.1 1.2.2 1.2.3 1.3 1.3.1 1.3.2 1.3.3 1.3.4 1.4 2

Introduction 3 General Stress Response 3 The sS Regulatory Network 4 E. coli Osmotic Shock Resistance 5 E. coli Acid Resistance: An Example of a Differentially Controlled sS Module 6 Regulation of sS 7 Transcriptional Regulation of sS 7 Translational Regulation of sS 8 Post-Translational Regulation of sS 9 Competition for RNAP and Promoters 10 Conclusions 11 Moss as a Model System for Plant Stress Responses Andrew C. Cuming

2.1 2.2 2.3 2.4 2.5 2.6 2.7

Introduction 17 Model Systems 19 Physcomitrella as a Model System 22 Water Stress and Abscisic Acid 24 T. ruralis: A Model for Poikilohydry 28 Cold Stress and Abscisic Acid 29 Future Perspectives 30

Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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Emerging Trends in Functional Genomics for Stress Tolerance in Crop Plants 37 Swatismita Ray, Prasant K. Dansana, Avantika Bhaskar, Jitender Giri, Sanjay Kapoor, Jitendra P. Khurana, and Akhilesh K. Tyagi

3.1 3.2 3.3

3.7

Introduction 37 Abiotic Stresses Encountered by Plants 38 Genome-Wide Investigations to Understand Components Involved in Abiotic Stress Responses 39 Transcriptome Analysis 39 Role of MicroRNAs in Stress 41 Analysis of Abiotic Stress-Responsive Genes using Proteomic Approaches 42 Quantitative Trait Loci for Abiotic Stress Tolerance 44 Networking the Stress Response Gene Function 44 Sensing Systems 44 Calcium and Calcium-Sensing Proteins 45 MAPK Proteins: At the Crossroads of Signaling Pathways 47 Other Pathways 48 Transcription Factors at the Junction 49 Functional Characterization of Stress Response Genes by the Transgenic Approach 51 Conclusions 52

Part II

Stress Responses and Newly Involved Plant Hormones

4

Stress Physiology of Higher Plants: Cross-Talk between Abiotic and Biotic Stress Signaling 67

3.3.1 3.3.2 3.3.3 3.4 3.5 3.5.1 3.5.2 3.5.3 3.5.4 3.5.5 3.6

Miki Fujita, Yasunari Fujita, Fuminori Takahashi, Kazuko Yamaguchi-Shinozaki, and Kazuo Shinozaki

4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8

Introduction 67 Cuticles and Stomata 68 Hormone Signaling Governs Biotic and Abiotic Stress Responses 71 Roles of ROS at Points of Convergence between Biotic and Abiotic Stress Response Pathways 73 Transcription Factors Involved in the Cross-talk between Abiotic and Biotic Stress Signaling 74 Mitogen-Activated Protein Kinase Cascade 76 Effects of Humidity and Temperature on Biotic Stress Responses 78 Conclusions 79

65

Contents

5

Jasmonates in Stress, Growth, and Development

91

Claus Wasternack

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6

Introduction 91 JA Biosynthesis 92 JA Metabolism 95 Bound OPDA – Arabidopsides 97 Mutants of JA Biosynthesis and Signaling 98 COI1–JAZ–JA-Ile-Mediated JA Signaling 101 Transcription Factors Involved in JA Signaling 104 Jasmonates and Oxylipins in Development 106 Conclusions 108 Brassinosteroids Confer Stress Tolerance

119

Uday K. Divi and Priti Krishna

6.1 6.2 6.3 6.3.1 6.3.2 6.3.3 6.3.4 6.3.5 6.4 6.5 6.6 6.7 6.8 7

Introduction 119 BR Signaling 120 BR Increases Stress Tolerance 121 Temperature Stress 121 Salt Stress 123 Drought Stress 123 Pathogen Attack 124 Other Stresses 126 Anticancer and Antiviral Effects 126 Genetic Evidence for a Role of BR in Plant Stress Responses 126 BR-Independent Role of BAK1 in Innate Immunity and Cell Death 127 Systematic Study to Dissect the Role of BR in Abiotic Stress Tolerance 130 Future Directions 131 Cold, Salinity, and Drought Stress

137

Narendra Tuteja

7.1 7.2 7.3 7.3.1 7.3.2 7.3.3 7.3.4 7.4 7.4.1

Introduction 137 Abiotic Stress Response and Stress-Induced Genes 139 Cold Stress 141 Effect of Low-Temperature Stress on Plant Physiology 141 Cold Acclimation 142 Function of Cold-Regulated Genes in Freezing Tolerance 142 Calcium Signaling in Cold Stress Response 144 Salinity Stress 144 Negative Impact of Salinity Stress 146

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| Contents 7.4.2 7.4.3 7.4.4 7.4.5 7.4.6 7.5 7.5.1 7.5.2 7.5.3 7.6 8

Calcium Signaling and SOS Pathways in Relation to Salinity Stress 147 ABA and Transcription Factors in Salinity Stress Tolerance 148 Water Stress due to Salinity 149 Proline and GB in Salinity Stress 149 ROS in Salinity Stress 150 Drought Stress 151 Effect of Drought on Stomata and Photosynthesis 152 Sugars and other Osmolytes in Response to Drought Stress 153 Phospholipid Signaling in Drought Stress 154 Conclusions and Future Prospects 154 Heavy Metal Stress in Plants

161

Ann Cuypers, Karen Smeets, and Jaco Vangronsveld

8.1 8.2 8.3 8.4 8.4.1 8.5 8.6

Introduction 161 Uptake and Distribution of Metals in Plants 162 Metal Stress Affects the Plant’s Physiology 163 Unraveling the Cellular Responses of Metal Stress Metal-Induced Oxidative Stress 166 Signaling Under Metal Stress 167 Conclusions 170

9

Systematic Analysis of Superoxide-Dependent Signaling in Plant Cells: Usefulness and Speciﬁcity of Methyl Viologen Application 179

165

Simone Jacob and Karl-Josef Dietz

9.1 9.1.1 9.1.2 9.2 9.2.1 9.2.2 9.2.3 9.2.4 9.3

Reactive Oxygen Species and Antioxidant Defense 179 Reactive Oxygen Species – Generation and Biological Relevance 179 Detoxiﬁcation of ROS – Antioxidative Network in Plants 181 Methyl Viologen: From Redox Indicator and Herbicide to Application as Effector in Oxidative Stress Investigation 183 General Considerations to Methyl Viologen as Herbicide and Toxin 183 Mechanism of Methyl Viologen Toxicity in Plants and Animals 185 Lipid Peroxidation as a Consequence of Oxidative Stress upon Methyl Viologen Application 186 Requirement of the Antioxidative Network upon Methyl Viologen Application 186 Gaining Insights into Superoxide Anion-Mediated Signaling in Plants – Goals and Limitations of Methyl Viologen Application 187

Contents

9.3.1 9.3.2

9.3.3 9.4

Superoxide Anion and Hydrogen Peroxide Signaling: A Problem of Differentiation? 187 Transgenic Plants as a Powerful Tool towards Understanding the Participation of Superoxide Anion in Signal Transduction Processes 187 Towards Understanding of Superoxide Anion Signaling in Plants 190 Conclusions 191

Part III From Transcriptomics and Proteomics to Signaling Networks 10

Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress Expression Proﬁle Dataset 199 Dierk Wanke, Kenneth W. Berendzen, Joachim Kilian, and Klaus Harter

10.1 10.2 10.3 10.4 10.4.1 10.4.2 10.4.3 10.4.4 10.4.5 10.4.6 10.4.7 10.4.8 10.4.9 10.5 10.6 10.7

Introduction 199 The AtGenExpress Abiotic Stress Experiment 200 General Findings 201 The Nine Stresses 204 UV-B Light Stress 204 Osmotic Stress 206 Salt Stress 208 Cold Stress 209 Drought Stress 210 Heat Stress 211 Wounding Stress 211 Genotoxic Stress 212 Oxidative Stress 213 Signal Integration 213 Novel Approaches and Future Developments 221 Conclusions 221

11

Integrative Approaches to Elucidate and Analyze Protein Interaction and Signaling Networks 227 Sergio de la Fuente van Bentem, Alberto de la Fuente, and Heribert Hirt

11.1 11.2 11.2.1 11.2.2 11.3 11.3.1 11.3.2 11.4 11.4.1 11.4.2

Introduction 227 Protein Networks 228 Introduction to Protein Networks 228 CNA 229 PINs 230 Toward Global Arabidopsis PINs 231 An Arabidopsis PIN of Calmodulin- and Calmodulin-Like-Binding Proteins 240 PSNs 240 Introduction to PSNs 240 From Perturbations and Responses to PSNs

241

197

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| Contents 11.4.3 11.4.4 11.5

High-Throughput Approaches to Create Perturbations and to Measure Responses 242 A NetworKIN Approach to Construct Plant Phosphorylation Networks 243 Future Outlook on Plant Networks 245 Index

249

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Preface There are already a number of excellent books on plant stress biology on the market, so why have another one? This was a question I posed myself when I was asked to edit a book on ‘‘Plant Stress Biology.’’ An obvious answer was to look at topics others had left aside and, moreover, to concentrate on new developments in a larger context than special topic reviews that are published by journals. So what’s different and new? According to my opinion, the plant ﬁeld so far has given too little attention to studies done on other model systems than Arabidopsis. Thus, two chapters begin the book in the hope that plant biologists will gain new insights from Abram Aertsen and colleague’s chapter on ‘‘Stress Responses of Model Bacteria’’ and Andrew Cuming’s chapter on ‘‘Moss as a Model System for Plant Stress Responses.’’ In the ﬁnal chapter in the ﬁrst section, Akhilesh Tyagi and colleagues try to bridge these and other ﬁndings to apply our current knowledge to the realm of ‘‘Emerging Trends in Functional Genomics for Stress Tolerance in Crop Plants.’’ Recent interesting developments in the ﬁeld of stress biology make up the next section of the book. One chapter deals with recent ﬁndings on the existence of extensive cross-talk between abiotic and biotic signaling in ‘‘Stress Physiology of Higher Plants: Cross-Talk between Abiotic and Biotic Stress Signaling’’, which is highlighted by authors around Kazuo Shinozaki’s laboratory. Plant hormones such as abscisic acid are well known to play a role in abiotic stress. However, jasmonic acid and brassinosteroids have received much less attention to their potential roles in abiotic stress in plants. Therefore, Claus Wasternack contributes a chapter on ‘‘Jasmonates in Stress, Growth, and Development’’ and the group of Priti Krishna discusses how ‘‘Brassinosteroids Confer Stress Tolerance.’’ A discussion of ‘‘Cold, Salinity, and Drought Stress’’ by Narendra Tuteja, ‘‘Heavy Metal Stress in Plants’’ by Ann Cuyper’s group, and ‘‘Systematic Analysis of Superoxide-Dependent Signaling in Plant Cells: Usefulness and Speciﬁcity of Methyl Viologen Application’’ by Simone Jacob and Karl-Josef Dietz follows in a suite of chapters dealing with a number of abiotic stresses relevant to agriculture. The ﬁnal section of the book gives an outlook how the latest technologies will shape our understanding and future applications in plant stress biology. Thus, a chapter on ‘‘Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress Expression Proﬁle Dataset’’ by Klaus Harter’s lab discusses the Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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Preface

insights gained by performing transcriptomic analyses of abiotic stresses. Recent developments in the ﬁeld of proteomics are changing our views about how stresses are perceived and how this information is processed by signaling pathways. Thus, a ﬁnal chapter by my group together with Alberto de la Fuente discusses novel ‘‘Integrative Approaches to Elucidate and Analyze Protein Interaction and Signaling Networks.’’ Altogether, this book should stimulate approaches to plant stress biology by new avenues and help to build bridges between scientiﬁc ﬁelds that have so far received little interaction. Heribert Hirt Paris, August 2009

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List of Contributors Abram Aertsen K.U. Leuven Centre for Food and Microbial Technology Department of Microbial and Molecular Systems Faculty of Bioscience Engineering Kasteelpark Arenberg 22 3001 Leuven Belgium and Universite Libre de Bruxelles Genetique et Physiologie Bacterienne IBMM-DBM 12 Rue des Professeurs Jeneer et Brachet 6041 Gosselies Belgium Kenneth W. Berendzen ¨bingen University of Tu Center of Plant Molecular Biology Auf der Morgenstelle 1 ¨bingen 72076 Tu Germany Avantika Bhaskar University of Delhi South Campus Interdisciplinary Centre for Plant Genomics and Department of Plant Molecular Biology Benito Juarez Road New Delhi 110021 India

Andrew C. Cuming Leeds University Centre for Plant Sciences Leeds LS2 9JT UK Ann Cuypers Hasselt University Centre for Environmental Sciences Agoralaan Building D 3590 Diepenbeek Belgium Prasant K. Dansana University of Delhi South Campus Interdisciplinary Centre for Plant Genomics and Department of Plant Molecular Biology Benito Juarez Road New Delhi 110021 India Philipp De Spiegeleer K.U. Leuven Centre for Food and Microbial Technology Department of Microbial and Molecular Systems Faculty of Bioscience Engineering Kasteelpark Arenberg 22 3001 Leuven Belgium

Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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| List of Contributors Karl-Josef Dietz Bielefeld University Biochemistry and Physiology of Plants Faculty of Biology W5 ¨tsstrasse 25 Universita 33501 Bielefeld Germany Uday K. Divi University of Western Ontario Department of Biology London, Ontario N6A 5B7 Canada Alberto de la Fuente CRS4 Bioinformatica The RAGNO Group c/o Parco Tecnologico POLARIS Ediﬁcio 1, Loc. Piscina Manna 09010 Pula Italy Sergio de la Fuente van Bentem University of Vienna Department of Plant Molecular Biology, Max F. Perutz Laboratories Dr. Bohr-Gasse 9 1030 Vienna Austria Miki Fujita RIKEN Plant Science Center Gene Discovery Research Group 3-1-1 Kouyadai Tsukuba Ibaraki 305-0074 Japan Yasunari Fujita Japan International Research Center for Agricultural Sciences Biological Resources Division 1-1 Ohwashi Tsukuba

Ibaraki 305-8686 Japan Jitender Giri University of Delhi South Campus Interdisciplinary Centre for Plant Genomics and Department of Plant Molecular Biology Benito Juarez Road New Delhi 110021 India Klaus Harter ¨bingen University of Tu Center of Plant Molecular Biology Auf der Morgenstelle 1 ¨bingen 72076 Tu Germany Heribert Hirt University of Vienna Department of Plant Molecular Biology, Max F. Perutz Laboratories Dr. Bohr-Gasse 9 1030 Vienna Austria Simone Jacob Bielefeld University Biochemistry and Physiology of Plants Faculty of Biology W5 ¨tsstrasse 25 Universita 33501 Bielefeld Germany Sanjay Kapoor University of Delhi South Campus Interdisciplinary Centre for Plant Genomics and Department of Plant Molecular Biology Benito Juarez Road New Delhi 110021 India

List of Contributors

Jitendra P. Khurana University of Delhi South Campus Interdisciplinary Centre for Plant Genomics and Department of Plant Molecular Biology Benito Juarez Road New Delhi 110021 India Joachim Kilian ¨bingen University of Tu Center of Plant Molecular Biology Auf der Morgenstelle 1 ¨bingen 72076 Tu Germany Priti Krishna University of Western Ontario Department of Biology London, Ontario N6A 5B7 Canada Chris W. Michiels K.U. Leuven Centre for Food and Microbial Technology Department of Microbial and Molecular Systems Faculty of Bioscience Engineering Kasteelpark Arenberg 22 3001 Leuven Belgium

Swatismita Ray University of Delhi South Campus Interdisciplinary Centre for Plant Genomics and Department of Plant Molecular Biology Benito Juarez Road New Delhi 110021 India

Kazuo Shinozaki RIKEN Plant Science Center Gene Discovery Research Group 3-1-1 Kouyadai Tsukuba Ibaraki 305-0074 Japan Karen Smeets Hasselt University Centre for Environmental Sciences Agoralaan Building D 3590 Diepenbeek Belgium Fuminori Takahashi RIKEN Plant Science Center Gene Discovery Research Group 3-1-1 Kouyadai Tsukuba Ibaraki 305-0074 Japan Narendra Tuteja International Centre for Genetic Engineering and Biotechnology Plant Molecular Biology Group Aruna Asaf Ali Marg New Delhi 110 067 India Akhilesh K. Tyagi University of Delhi South Campus Interdisciplinary Centre for Plant Genomics and Department of Plant Molecular Biology Benito Juarez Road New Delhi 110021 India

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| List of Contributors Jaco Vangronsveld Hasselt University Centre for Environmental Sciences Agoralaan Building D 3590 Diepenbeek Belgium Laurence Van Melderen Universite Libre de Bruxelles Genetique et Physiologie Bacterienne, IBMM-DBM 12 Rue des Professeurs Jeneer et Brachet 6041 Gosselies Belgium Dierk Wanke ¨bingen University of Tu Center of Plant Molecular Biology Auf der Morgenstelle 1 ¨bingen 72076 Tu Germany

Claus Wasternack Leibniz Institute of Plant Biochemistry Department of Natural Product Biotechnology Weinberg 3 06120 Halle (Saale) Germany Kazuko Yamaguchi-Shinozaki Japan International Research Center for Agricultural Sciences Biological Resources Division 1-1 Ohwashi Tsukuba Ibaraki 305-8686 Japan and University of Tokyo Laboratory of Plant Molecular Physiology, Graduate School of Agricultural and Life Sciences 1-1 Yayoi Bunkyo-ku Tokyo 113-8657 Japan

Part One From Model Systems to Crop Improvement

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1 General Stress Response of a Model Bacterium Abram Aertsen, Philipp De Spiegeleer, Laurence Van Melderen, and Chris W. Michiels

1.1 Introduction

Microorganisms have evolved to perform optimally in their normal habitat and they can attain very high growth rates under ideal conditions. Owing to their adaptational skills microorganisms set out the boundaries of the biosphere, and microbial habitats can include extreme environments such as hot water springs, cold water lakes, oceanic trenches, salt lakes, extreme acidic or alkaline locations, and so on [1]. Growth of most known microorganisms is, however, restricted to more moderate conditions and a shift to unfavorable surroundings inﬂicts a cellular stress that, depending on the severity, can kill them. In fact, the confrontation with stressful situations is quite common in nature and these nonextremophiles have acquired many different strategies to respond to a number of stresses [2, 3]. Dedicated stress responses exist that allow mesophilic bacteria such as Escherichia coli to cope with speciﬁc stress conditions for a particular period by repairing the stress-induced damage. A typical example of such a response is the induction of the SOS regulon triggered by DNA damage. This SOS response governs the expression of a variety of genes encoding repair functions, error-prone polymerases, and a cell division inhibitor, which all cooperate to repair the incurred DNA damage and restore growth after repair [4]. By contrast, the ‘‘general stress response,’’ which will be the focus of this chapter, is triggered by a wide variety of stresses and renders bacteria resistant to a broad variety of environmental insults. In fact, this response is rather preventive than reparative [5]. Over the last 20 years, the general stress response of the model bacterium E. coli has been the subject of intense and continuous study, and serves as a paradigm for the level of systemic complexity that can be reached in prokaryotic cells. 1.2 General Stress Response

In their natural environment bacteria are usually faced with a limited availability of nutrients and, as a consequence, starvation is one of the most prevailing stresses Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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| 1 General Stress Response of a Model Bacterium encountered [6, 7]. Under nutrient starvation, bacteria arrest growth and enter a stationary phase during which the cells reprogram their gene expression, change their metabolism, and start to exhibit a distinctive resistance toward a whole range of adverse environmental conditions, including low pH, high osmolarity, and low temperature [6, 8, 9]. At the molecular and genetic level, these physiological changes are established by an alternative sigma factor, sS or RpoS, which is the master regulator of the general stress response. Sigma factors are able to direct the speciﬁcity of the transcription machinery to a dedicated subset of promoters and changing the sigma factor associated with the RNA polymerase (RNAP) can correspondingly bring about a drastic reprogramming of the cell’s expression proﬁle. During the stationary phase, sS is able to hijack the RNAP from the regular housekeeping s70 factor that predominates during steady-state growth and to direct expression of about 500 genes, some of which indirectly [10–12]. 1.2.1 The sS Regulatory Network

Since the recent genome-wide expression analysis of Weber et al. [10] revealed that up to 10% of the E. coli genes are under direct or indirect control of sS, it is becoming clear that the general stress response constitutes a global regulatory network rather than a regulon [5, 13]. In fact, multiple connections exist between the sS network and other global regulons such as the cAMP/cAMP receptor protein (CRP) global regulon. Indeed, more than half of the sS-controlled genes contain a putative cAMP/CRP-binding site in their promoter regions and even rpoS expression itself is under the cAMP/CRP control (see Section 1.3.1). Moreover, a large number of sS-controlled genes in turn encode regulatory proteins that increase the possibility of interconnectivity and a hierarchical structure between various regulatory networks [10]. The sS positively regulated genes can be divided into a core set of genes that are controlled by most sS-inducing conditions and different subsets or modules that are controlled by more speciﬁc sS-inducing conditions [10]. The expression of the core set of genes is thought to change directly in parallel with the sS level, implying that their expression follows the induction of sS by multiple stresses. However, most of the sS positively controlled genes (>70%) fall in a ‘‘stress-speciﬁc’’ category, indicating that certain modules of the sS-dependent general stress response can be temporarily recruited by more stress-speciﬁc regulons (see Section 1.2.3). In general, the sS-controlled genes belong to various functional categories besides stress management, which actually accounts for only 11% of the total sS-controlled genes. A fair amount of genes coding for metabolic enzymes (19%), membrane transporters (14%), and regulatory proteins (8%) are under sS control, while a surprising 43% are of yet unknown function [10]. Several studies have highlighted the importance of sS in metabolic regulation during the stationary phase [10, 11]. The sS positively controls the expression of genes involved notably in glycogenesis, anaerobic respiration, and the pentose

1.2 General Stress Response

phosphate shunt, as well as membrane trafﬁcking [10]. The tricarboxylic acid (TCA) cycle and acetate-utilizing pathway are also affected [11]. Together, these metabolic changes might lead to an increase of the internalization of nutritional resources, and redirect the central metabolism to fermentation and anaerobic respiration. Another striking feature that was evidenced recently is the fairly large amount of genes that are actually negatively regulated by sS [10, 14]. This group includes genes required for ﬂagella synthesis, the TCA cycle, transport, and Rac prophageencoded genes [10, 11, 14]. This negative regulation might be the result of an indirect mechanism such as s factor competition for RNAP (see Section 1.3.4) or alternatively through direct repression by a sS-controlled repressor. sS also plays a role in the control of several pathways during logarithmic and early stationary phases in spite of its very low levels and activity at these growth stages [11, 14, 15]. Indeed, there are indications that sS is required during logarithmic growth for the protection against osmotic shock [16] and acid stress in certain culture media [17]. In fact, using an rpoS mutant of E. coli, Dong et al. [15] demonstrated that the modulation of gene expression by sS during the logarithmic phase is still quite extensive, with more than 250 genes found to be positively controlled by sS and 24 genes found to be negatively controlled. Genes coding for chaperones and for the utilization of iron and carbon sources appear to be part of the sS exponential regulatory network, and the Crl regulator is important for the transcription of some of these genes [15]. In what follows, we will discuss the role of the sS network in osmotic and acid shock resistance in more detail, thereby focusing on the function of sS-dependent genes. 1.2.2 E. coli Osmotic Shock Resistance

Microorganisms cope with osmotic challenges by controlling the level of intracellular osmolytes, thereby allowing the water content to be adjusted by osmosis. Osmolytes comprise notably amino acids (e.g., glutamate, proline), amino acid derivatives (e.g., ectoine, proline betaine), methylamines (glycine betaine), and sugars (trehalose). These solutes might accumulate through uptake or synthesis to high intracellular levels, without disturbing bacterial physiology [18]. Expression of enzymes and active channels involved in osmolyte production and uptake is tightly controlled at the transcriptional level, some of their genes being under the control of sS [16]. Here, we will focus on trehalose synthesis and proline and glycine betaine uptake in E. coli. The sugar trehalose is an important osmoprotectant in E. coli that is synthesized de novo since it cannot be taken up from the environment. The otsAB operon is responsible for trehalose production. The otsA gene encodes the trehalose-6-phosphate synthase that is responsible for the condensation of glucose-6-phosphate and UDP-glucose to generate trehalose-6-phosphate. This intermediate is then rapidly dephosphorylated by the trehalose-6-phosphate phosphatase enzyme encoded by the otsB gene [19]. The otsAB operon is under the control of sS and is strongly induced

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| 1 General Stress Response of a Model Bacterium upon osmotic shock, together with 420 other sS-dependent genes [10, 20]. Stationaryphase and carbon-starved E. coli cells are also highly osmotolerant [21, 22]. When trehalose is present in the extracellular medium, the TreA periplasmic trehalase hydrolyzes it into two glucose molecules that are taken up by glucose-speciﬁc phosphotransferase system (PTS) [23]. The treA gene is also under sS control and induced upon osmotic upshift [19]. Proline and glycine betaine play an important role in protecting cells from osmotic stress. The ProP transport system is responsible for the uptake of a wide variety of osmoprotectants, among them proline and glycine betaine. ProP is an H+ symporter located in the inner membrane. The proP gene transcription is driven by two different promoters P1 and P2. The P2 promoter is controlled by sS and the sS-dependent transcription is enhanced by the nucleoid-associated protein FIS [24]. 1.2.3 E. coli Acid Resistance: An Example of a Differentially Controlled sS Module

Acid resistance is the ability to sustain very low pH conditions. Due to its lifestyle in the mammalian digestive tract, E. coli has a remarkable ability to adapt to pH stress. This capacity enables E. coli to survive gastric acidity and volatile fatty acids produced by fermentation in the intestine. Numerous acid survival mechanisms have been identiﬁed, depending on the culture medium composition and the pH range examined [25]. Here, we will illustrate that depending on the stress conditions, the acid resistance genes will be governed by the sS regulatory control or not. The gadA and gadBC genes as well as their regulators gadE, gadX, and gadW are essential for acid resistance [26–29]. gadA and gadB encode glutamate decarboxylases, and gadC encodes a putative glutamate g-aminobutyric antiporter. Amino acid decarboxylase systems are thought to confer acid resistance by consuming intracellular protons. Under acid stress, glutamate is taken up by the cell using the GadC antiporter, and decarboxylation of glutamate by GadA and GadB produces g-aminobutyric acid that will expel through GadC. This results in alkalinization of the cytoplasm. Interestingly, these genes (except gadBC) and others involved in acid resistance are located in a cluster of sS-dependent genes called the ‘‘ﬁtness island for acid adaptation’’ [27]. Expression of these genes is strongly induced in the stationary phase in a sS-dependent manner giving a molecular explanation for the acid-resistant phenotype displayed by stationary-phase cells [30]. In addition, this cluster is under the control of another global regulator, the H-NS protein, which downregulates its expression [27]. Although sS expression is strongly induced upon acid stress and about 200 genes are expressed in a sS-dependent fashion, most of them appear to belong to the nonspeciﬁc core gene set [10]. Interestingly, however, the expression of the gad genes themselves upon an acid shift is mostly sS-independent, indicating a switch in the genetic control of these genes has occurred under such conditions. This underscores the existence of modules within the sS regulatory network that might

1.3 Regulation of sS

be controlled by multiple regulators depending of the environmental signal [10]. The GadE regulator has been proposed to control this switch, by integrating the stationary-phase signal through the GadX regulator and the ‘‘acid’’ signal most likely through the EvgSA two-component system and the YdeO pathway [10, 26]. Moreover, the GadW and GadY positive regulators might act as H-NS countersilencers by displacing H-NS off the promoter regions of the gad genes [31].

1.3 Regulation of sS

It is clear that given the profound physiological rearrangements caused by sS [32, 33], the expression and availability of this sigma factor must be tightly regulated and allowed only in times of stress. sS is barely detectable in rapidly growing cells in laboratory conditions and rpoS defective mutants show a growth rate comparable to that of wild-type cells [21, 34, 35]. Under stress or starvation conditions, however, the amount of sS rapidly rises up to 30% to that of s70, allowing for the formation of sS-associated RNAP that in turn activates sS-dependent genes. Therefore, the expression, stability, and activity of sS in the cell must be strongly regulated and controlled at the transcriptional, translational, and post-translational levels [36–38]. Moreover, all of these regulatory mechanisms allow the integration of different environmental cues and, consequently, the ﬁne-tuning of the response. The intricate regulation that is imposed on the general stress response counts as a true hallmark of bacterial complexity. 1.3.1 Transcriptional Regulation of sS

Although transcriptional regulation of rpoS has not been studied extensively and in depth, it is at least known to be controlled by several trans-acting factors [5, 13, 39]. The nlpD gene is located immediately upstream of the rpoS gene and harbors the main rpoS promoter, although some background expression stems from nlpD promoter itself [40]. The main rpoS promoter is s70-dependent and gives rise to a monocistronic mRNA transcript comprising a 567-bp untranslated region. Interestingly, the rpoS promoter contains two putative cAMP/CRP-binding sites, and several studies using mutants in cya (encoding adenylate cyclase) and crp have indicated that cAMP/CRP is a negative regulator of rpoS transcription in the exponential phase [21, 40]. It consequently follows that modulators of adenylate cyclase activity, like the Crr protein, in turn also affect rpoS transcription [41]. Recently, it has been established that not only rpoS itself is regulated by cAMP/CRP, but that also quite a number of sS-controlled genes contain putative cAMP/CRP-binding sites, indicating a strong overlap between the sS and cAMP/CRP regulons [10]. It was found that polyphosphate indirectly enhances rpoS expression, although the actual molecular mechanism still remains to be identiﬁed [42]. Inorganic polyphosphate is a linear polymer of hundreds of phosphate residues that can

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| 1 General Stress Response of a Model Bacterium accumulate in bacteria under stressful conditions [43]. The polymer is synthesized by polyphosphate kinase by polymerization of the terminal phosphate group of ATP to a phosphate chain [44], while degradation of polyphosphate is catalyzed by exopolyphosphatase [45]. Overexpression of exopolyphosphatase correspondingly inhibits the increase of sS levels upon entry into the stationary phase [42]. Interestingly, exopolyphosphatase activity is inhibited by the alarmone (p)ppGpp [46] – an effector of the stringent response that is produced when levels of amino acids, carbon, phosphate, or nitrogen become limited [47, 48]. This link between (p)ppGpp and polyphosphate is likely to explain earlier reports observing a positive effect of (p)ppGpp on rpoS transcription [49, 50]. In cells lacking (p)ppGpp, however, rpoS transcription was compromised at the level of elongation rather than the initiation of transcription [50]. Aside from the effects of polyphosphate or (p)ppGpp, it appears that both rpoS mRNA and RpoS protein levels are reduced in an E. coli barA mutant [51]. As BarA is a sensor kinase, its positive effect on rpoS transcription is probably mediated by a yet unknown cognate response regulator. 1.3.2 Translational Regulation of sS

E. coli produces a fair amount of rpoS mRNA even under conditions where sS protein is barely detectable [52]. It is assumed that the rate of translation is heavily controlled by the mRNA secondary structure, with base-pairing in the translational initiation region being responsible for the occlusion of the ribosome-binding site and the corresponding inhibition of translation under noninducing conditions. Several proteins and small regulatory RNAs (sRNAs) are involved in translational control, which makes the analysis of translational regulation a very complex endeavor [5, 13]. The Hfq protein is an RNA-binding protein [53] that is required for efﬁcient rpoS translation [54]. It has been suggested that binding of Hfq to rpoS mRNA occurs to U-rich sequences [55] and could either directly stabilize speciﬁc secondary structures in the rpoS transcript or facilitate its interactions with sRNAs. So far, three such sRNA species have been found to be involved in rpoS translation: DsrA and RprA promoting translation, and OxyS inhibiting it. DsrA has been described as an inhibitor of rpoS mRNA intramolecular basepairing using an anti-antisense mechanism in which DsrA pairs with the translational initiation region, thereby making the ribosome binding site fully accessible [56–59]. Hfq has also been reported to cooperate with DsrA [60]. Binding of Hfq to the noncoding DsrA sRNA accelerates the binding of DsrA to the rpoS mRNA [59]. DsrA further stimulates rpoS translation by binding to hns mRNA (see below) and inhibiting its translation [57, 58, 61]. DsrA itself is repressed by LeuO. The other sRNA that positively inﬂuences rpoS translation is RprA, but the rprA promoter is active only at temperatures below 30 1C [62]. Like DsrA, RprA stimulates rpoS translation by pairing with the rpoS mRNA, negatively regulates hns, and is repressed by LeuO [63].

1.3 Regulation of sS

The negative regulation of rpoS translation by OxyS sRNA is not yet understood [63], but may be due to binding of OxyS with Hfq, thereby inhibiting interaction between Hfq and rpoS mRNA [64]. OxyS is a member of the OxyR regulon and is induced by oxidative stress [65]. The repression of sS during oxidative stress makes sense, since certain overlaps exist between genes expressed by OxyR and sS. Repression would avoid the pointless drain on cellular resources [65]. Thus, sRNAs represent different signal transduction pathways that converge to regulate the amount of sS protein. In addition to Hfq, several other protein factors are involved in rpoS translation. HU, for example, is essentially a DNA-binding protein with binding preference for secondary structures such as bends or kinks [66]. However, it was shown to speciﬁcally bind rpoS mRNA and enhance its expression [67]. Another nucleoid structuring protein, H-NS, is a global regulator that preferentially binds to bended DNA and reduces the transcription of over 100 genes [68, 69]. However, it has been revealed that H-NS also negatively affects the translation of some gene transcripts, including rpoS [70]. This could explain why H-NS– mutants exhibit dramatically raised sS levels in the exponential phase, similar to those observed normally in stationary-phase cells [71]. Interestingly, the alarmone (p)ppGpp not only seems to play an important role in rpoS transcription, but also stimulates translational efﬁciency of rpoS mRNA. Brown et al. [72] found that rather than interacting directly with ribosomes, (p)ppGpp affects activity of the DksA protein, which was shown earlier to play a role in the translational regulation of rpoS. Other molecules that play a role in rpoS translation include DnaK, a heat shock chaperone, as well as the cold shock proteins CspC and CspE, EIIa(Glc), and UDP-glucose [5, 13]. All these regulatory factors contribute to a very complex and highly intertwined network that is characterized by positive and negative feedback mechanisms allowing a high degree of ﬁne-tuning. Therefore, the output of this network may be difﬁcult to predict under changing environmental conditions [5, 13]. 1.3.3 Post-Translational Regulation of sS

Although the rpoS gene is moderately expressed during the exponential phase of growth [7], cellular levels of the sS protein remain low. This is partly due to a high instability of this sigma factor, with a half-life of only 2 min. Interestingly, this half-life rises to more than 30 min on entry into the stationary phase or when a stress is inﬂicted upon the cell [73]. The identiﬁcation of cellular factors involved in this dramatic decrease in sS turnover, as well as how they are steered by environmental cues, has received much attention. The instability of sS in the exponential phase is caused by its rapid degradation by the ClpXP protease [74]. However, the increased stability of sS in the stationary phase could not be linked to a reduction in ClpXP concentration. In fact, Western analysis showed that the ClpXP concentration in stationary phase even increased by 50 % compared to that of exponentially growing cells [74]. Pratt and Silhavy [75]

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| 1 General Stress Response of a Model Bacterium showed that another important factor was involved in the regulation of sS turnover – the adaptor protein RssB (SprE) that binds directly to sS and targets it to the ClpXP protease [38]. Accordingly, a null mutation in rssB leads to stabilization of sS and elevated levels in the exponential phase [75]. Interestingly, RssB contains a conserved CheY response regulator domain and therefore it has been speculated that RssB activity is adjusted by phosphorylation [5, 13]. In the phosphorylated state it would bind to sS, thereby labeling the latter for degradation by the ClpXP complex. However, Peterson et al. [76] showed that an E. coli strain expressing a mutant RssB protein only missing the phosphorylation site resembled a wild-type strain rather than an rssB null mutant in its ability to control sS levels. They concluded that although phosphorylation might contribute to maximal RssB activity, it is not indispensable and other regulatory mechanisms, independent of (de)phosphorylation, must be involved. Recently, an antiadaptor protein has been discovered in E. coli, IraP (YaiB), that interferes with RssB functioning through direct protein–protein interactions and is independent of the phosphorylation status of the latter [77]. Interestingly, deletion of iraP only interferes with sS stabilization during phosphate starvation, but not during carbon starvation, and only partly during the stationary phase or nitrogen starvation. IraP synthesis itself is induced by phosphate starvation in a (p)ppGpp-dependent manner [78]. After the discovery of IraP, other proteins have been sought that could regulate RssB under the starvation conditions where IraP played no role. As such, two new antiadaptors were discovered, IraM and IraD, that can counteract RssB activity and stabilize sS. The IraM protein proved essential for stabilization of sS during magnesium starvation, while IraD proved important for its response to DNA damage [79]. Another part of the mechanism that can profoundly affect sS degradation by ClpXP is in fact the level of occupation of this protease by other proteins. It was shown [80] that inducing translational errors by speciﬁc mutations or drugs elevated sS stability. Indeed, the increase in erroneous and misfolded proteins that result from reduced ribosomal ﬁdelity saturate the ClpXP machinery and allow sS to accumulate. Correspondingly, artiﬁcially increasing translational ﬁdelity or ClpXP production attenuated sS stability. 1.3.4 Competition for RNAP and Promoters

When sS is ﬁnally formed and stabilized, it can only instigate the general stress response when it effectively associates with the RNAP core enzyme to reprogram gene expression. However, this association is by no means gratuitous, as it is believed that in vivo the availability of the RNAP core enzyme is limited so that different sigma factors are in ﬁerce competition for its acquisition. This phenomenon was nicely demonstrated by the fact that compromising sS function not only attenuated expression of sS-dependent genes, but also caused superinduction of several s70-dependent genes [81]. Therefore, sS needs to be able to compete

References

with the overabundant vegetative s factor to occupy the RNAP core enzyme [82]. However, as s70 naturally displays the highest afﬁnity for RNAP in vitro [83, 84], it can be expected that the mere availability of sS itself is not sufﬁcient. Interestingly, again a pivotal role is reserved for (p)ppGpp to bring about an effective shift in RNAP core sequestration in vivo. Although, as discussed earlier in this section, (p)ppGpp has a number of activities, it is well documented that it associates with the RNAP core enzyme [85, 86], where it seems to inﬂuence the differential binding abilities of sigma factors to core RNAP. As such, in the presence of (p)ppGpp, sS is able to sequester part of the available RNAP core enzyme and instigate the general stress response [47]. When sS is associated with the RNAP it recognizes promoters with a common sequence pattern and favors their expression. However, sS- and s70-dependent promoters bear similarity, so that sometimes additional factors will decide whether a promoter will be transcribed by RNAP-sS or RNAP-s70. The dps gene, for example, can be transcribed by RNAP-sS in the stationary phase, or by RNAP-s70 when it cooperates with OxyR that has been activated by H2O2 [87]. Another, more global, discriminator between RNAP-sS and RNAP-s70 at the same promoter seems to be the Lrp protein. Lrp is a nucleoid associated global regulator that can affect DNA structure [88] and such changes in DNA topology could shift sS/s70 selectivity [10]. 70

1.4 Conclusions

The sS network drives a systemic defense that integrates a great number of intraand extracellular cues, and that truly differentiates stationary phase from logarithmic-phase cells. In general, the competition between s70 and sS represents the bacterial tradeoff between growth and reproduction, on the one hand, and maintenance and repair, on the other. In this context, the massively imposed regulation serves to adequately synchronize the allocation of resources between these opposing states of proliferation and survival with the quality and demands of the surrounding environment [82]. Acknowledgment

A.A. is a Postdoctoral Fellow of the Research Foundation-Flanders (FWOVlaanderen).

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induction of RpoS at a translational level. J. Bacteriol., 184, 4455–4465. Zhou, Y. and Gottesman, S. (1998) Regulation of proteolysis of the stationary-phase sigma factor RpoS. J. Bacteriol., 180, 1154–1158. Schweder, T., Lee, K., Lomovskaya, O., and Matin, A. (1996) Regulation of Escherichia coli starvation sigma factor (sS) by ClpXP protease. J. Bacteriol., 178, 470–476. Pratt, L.A. and Silhavy, T.J. (1996) The response regulator SprE controls the stability of RpoS. Proc. Natl. Acad. Sci. USA, 93, 2488–2492. Peterson, C.N., Ruiz, N., and Silhavy, T.J. (2004) RpoS proteolysis is regulated by a mechanism that does not require the SprE (RssB) response regulator phosphorylation site. J. Bacteriol., 186, 7403–7410. Bougdour, A., Wickner, S., and Gottesman, S. (2006) Modulating RssB activity: IraP, a novel regulator of sigmaS stability in Escherichia coli. Genes Dev., 20, 884–897. Bougdour, A. and Gottesman, S. (2007) ppGpp regulation of RpoS degradation via anti-adaptor protein IraP. Proc. Natl. Acad. Sci. USA, 104, 12896–12901. Bougdour, A., Cunning, C., Baptiste, P.J., Elliott, T., and Gottesman, S. (2008) Multiple pathways for regulation of sigmaS (RpoS) stability in Escherichia coli via the action of multiple anti-adaptors. Mol. Microbiol., 68, 298–313. Fredriksson, A., Ballesteros, M., Peterson, C.N., Persson, O., Silhavy, ¨m, T. (2007) Decline in T.J., and Nystro ribosomal ﬁdelity contributes to the accumulation and stabilization of the master stress response regulator sigmaS upon carbon starvation. Genes Dev., 21, 862–874. ¨m, T. Farewell, A., Kvint, K., and Nystro (1998) Negative regulation by RpoS: a case of sigma factor competition. Mol. Microbiol., 29, 1039–1051. ¨m, T. (2004) Growth versus Nystro maintenance: a trade-off dictated by RNA polymerase availability and sigma factor competition? Mol. Microbiol., 54, 855–862.

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| 1 General Stress Response of a Model Bacterium 83 Jishage, M., Iwata, A., Ueda, S, and Ishihama, A. (1996) Regulation of RNA polymerase sigma subunit synthesis in Escherichia coli: intracellular levels of four species of sigma subunit under various growth conditions. J. Bacteriol., 178, 5447–5451. 84 Maeda, H., Fujita, N., and Ishihama, A. (2000) Competition among seven Escherichia coli sigma subunits: relative binding afﬁnities to the core RNA polymerase. Nucleic Acids Res., 28, 3497– 3503. 85 Toulokhonov, I.I., Shulgina, I., and Hernandez, V.J. (2001) Binding of the transcription effector ppGpp to Escherichia coli RNA polymerase is allosteric, modular, and occurs near the

N terminus of the betau-subunit. J. Biol. Chem., 276, 1220–1225. 86 Artsimovitch, I., Patlan, V., Sekine, S., Vassylyeva, M.N., Hosaka, T., Ochi, K., Yokoyama, S., and Vassylyev, D.G. (2004) Structural basis for transcription regulation by alarmone ppGpp. Cell, 117, 299–310. ´n, M., Huisman, G., 87 Altuvia, S., Almiro Kolter, R., and Storz, G. (1994) The dps promoter is activated by OxyR during growth and by IHF and S in stationary phase. Mol. Microbiol., 13, 265–272. 88 Wang, Q. and Calvo, J.M. (1993) Lrp, a major regulatory protein in Escherichia coli, bends DNA and can organize the assembly of a higher-order nucleoprotein structure. EMBO J., 12, 2495–2501.

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2 Moss as a Model System for Plant Stress Responses Andrew C. Cuming

2.1 Introduction

We live on a green planet, yet it was not always so. An observer from space sees the blue of the ocean and the green of the land, but this is a comparatively recent development in our planet’s history. Only in the last 450 million years has the planet’s land surface become colonized by plants. Until this time, the land was bare: rock, sand, and mud, devoid of organic matter and unable to support life. It is difﬁcult to determine precisely when the ﬁrst eukaryotes gained a secure foothold in the terrestrial environment. The most direct evidence derives from early fossil spores indicating colonization by plants, dating from the mid-Ordovician period (about 440–490 million years ago) [1, 2], although the earliest surviving megafossils indicating the anatomical features of early plants occur about 50 million years later in the fossil record [3]. What were these plants like and where did they come from? It is now generally accepted that today’s land plants evolved from aquatic green algae, and molecular systematic analysis has identiﬁed the charophytes [4] and more speciﬁcally the Charales [5] as the likely ancestral taxon to all modern land plants. The extant members of the Charales (e.g., Chara) remain aquatic and grow as branched ﬁlaments. They have a haplontic life cycle in which the product of sexual fusion immediately undergoes meiosis to generate haploid tissue. This is by contrast with the land plants, which exhibit a diplobiontic life cycle, with an alteration of haploid gametophyte and diploid sporophyte generations. Among today’s land plants, it is the bryophytes that represent the ﬁrst group to diverge in the land plant lineage. The bryophytes comprise three distinct subgroupings: the mosses, liverworts, and hornworts. All display characters that might be considered ‘‘primitive,’’ such as branched ﬁlamentous protonemal tissues (in the mosses) and cells containing a single, algal-like pyrenoid-containing chloroplast (in the hornworts). The earliest fossil sporangia have been suggested as most similar to those found in extant liverworts [2]. All have a dominant haploid gametophyte generation, in which the sporophyte is dependent on the gametophyte for its development and nourishment. While the relationship between these

Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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| 2 Moss as a Model System for Plant Stress Responses groups, in terms of their order of origin, remains disputed, it is agreed that, among extant plant species, it is the bryophytes that most closely resemble the likely ﬁrst common ancestor of the land plants. The ancestors of land plants were most likely found in the marginal areas of bodies of water and were exposed periodically as the water receded. The successful colonization of the land would have required a number of adaptations to permit the survival of such plants. Terrestrial environments are necessarily more variable in nature than aquatic environments. There are greater ﬂuctuations of temperature, over both short and long timescales. The availability of water – a necessity for life – is uncertain and there are greatly enhanced levels of radiation: a factor contributing to both temperature and the availability of water, and also directly damaging through the mutagenic effects of ultraviolet-induced DNA damage. Modern plants have acquired multiple anatomical and developmental adaptations to enable them to survive environmental extremes. The vascular plants that dominate today’s planetary surface have extensive, branching root systems that ramify throughout the soil, enabling them to scavenge water from the substratum. Evaporative water loss is reduced through the presence of protective surfaces: waxy cuticles, suberin, and lignin all act to retain water within the plant, whilst ligniﬁed vascular systems both mechanically support the development of massive structures, and enable the distribution of water scavenged from the soil to all parts of the plant. Evaporative water loss and gas exchange is facilitated through the presence of stomatal apertures on the leaf surfaces. The effects of incident radiation are ameliorated through the accumulation of pigments that serve as sunscreens (e.g., anthocyanins). Sexual reproduction no longer requires a layer of water for the dissemination of swimming gametes. The ﬁrst land plants lacked these adaptations. Their ability to survive and prosper therefore must necessarily have depended, at ﬁrst, on biochemical adaptations to withstand such environmental variability. Such adaptations would be expressed as metabolic responses, rather than as developmental in nature, and such adaptations can still be recognized in today’s land plants. Although such traits are commonly characterized as ‘‘primitive,’’ it should be emphasized that this term reﬂects their ancient origins, rather than their efﬁcacy. These ‘‘primitive’’ traits have been instrumental in the conquest of the land by plants and in their subsequent shaping of the terrestrial environment. Their importance is highlighted by the diversity of the bryophytes, speciﬁcally the mosses, in modern ecosystems. The terrestrial ﬂora is today dominated by the angiosperms, of which there are thought to be about 250 000 species. This diversity originated from a massive radiation in the mid-Cretaceous period (about 100 million years ago) and occurred in concert with a similarly extensive radiation among insect species that act as pollinators. Nevertheless, the bryophytes remain a large, diverse and successful group: it is estimated that there are approximately 10 000 moss species, which is second only in number to the angiosperms. Clearly, primitive traits retain their value, even after 450 million years. Mosses retain many of the properties we ascribe to the ﬁrst successful colonists of the land. Most signiﬁcantly, they are

2.2 Model Systems

habitat-forming organisms – they are capable of colonizing bare surfaces, such as bare rock and mud, and growing to envelop the surface. Their death and decay generates the organic matter that distinguishes a true soil from a simple mineral sludge, and provides the basis for colonization by other plants, more demanding in their substrate requirements. Whilst many mosses thrive best under conditions of shade and moisture, others are able to colonize and exploit exposed bare habitats apparently inimical to plant growth. Even in urban environments, the sight of mosses growing on bare roofs and walls is commonplace. The study of mosses, therefore, provides insights into the conquest of land by living organisms, and the origins of terrestrial life. Mosses provide a starting point for unraveling the evolution of plant gene function through comparative, ‘‘evodevo’’ genomic strategies and for the identiﬁcation of molecular strategies for adaptations to abiotic stress. Can a ‘‘systems biology’’ approach be applied to the study of these processes in mosses? Until recently, this might have appeared an unlikely prospect. However, in recent years, considerable progress has been made in developing genomic resources for one moss species, Physcomitrella patens, whose biology makes it particularly amenable for such an analysis. This species has now become a powerful model for deploying systems-level approaches for comparative analyses of plant stress responses.

2.2 Model Systems

Model systems provide us with tools to investigate processes common to entire classes of living organism. They are chosen for their experimental amenity, and vary according to their purpose and the particular expertise of the experimentalist, but certain features are more desirable than others. They should be representative of their class; thus, mice are good experimental models for the study of mammalian development, but zebraﬁsh may be more tractable for wider applications including all vertebrates. Arabidopsis thaliana is pre-eminent as a model plant, having a small sequenced genome, a genetic linkage map densely marked by molecular markers, a rapid life cycle, small size, and straightforward procedures for genetic manipulation by transgenesis (http://www.Arabidopsis.org). It is an outstanding workhorse for identifying how cellular organization and development are programmed within the ﬂowering plants, and also for how many fundamental plant processes are regulated, common to all taxa of green plants. However, in order to explore how these processes have evolved, we need to look beyond a single species. Other model plants include rice – as an example of the monocotyledonous plants and a representative cereal (the most important group of food crops on the planet) – and several other species of angiosperm, whose genomes have recently been deciphered (soybean, poplar, grape). Nevertheless, to undertake a wider comparative analysis, models are also required from outside the ﬂowering plants. To date, these are fewer in number, but include the diatom Ostreococcus [6], the

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| 2 Moss as a Model System for Plant Stress Responses unicellular green alga, Chlamydomonas rheinhardtii [7], the moss P. patens [8], and the lycophyte Selaginella moellendorﬁi (http://selaginella.genomics.purdue.edu/; http://genome.jgi-psf.org/Selmo1/Selmo1.home.html). What are the general features of mosses, how do they differ from the ﬂowering plants, and how can we use them experimentally? Anatomically, mosses are simpler than the ﬂowering plants [9]. Many of their structures are only a single cell in thickness (a particularly attractive feature for cell biologists, since the processes occurring within a single cell can be conveniently observed using modern imaging techniques). Genetically, the mosses (like all the bryophytes) are also distinctively different. All plants exhibit an alteration of generations, with a haploid gametophyte generation and a diploid sporophyte generation. In the so-called ‘‘higher’’ plants (pteridophytes, lycopods, gymnosperms, and angiosperms), the diploid sporophyte represents the dominant generation. The pollen grain and embryo sac (the microspore and megaspore) represent the gametophyte generation that derive from meiotic cell division and (in the case of the embryo sac) that depend on the sporophyte for their transitory existence. By contrast, the bryophytes have a dominant, haploid gametophyte generation, which produces gametes by mitotic division. Upon fusion of gametes, a diploid sporophyte develops that is entirely dependent on the gametophyte for its nourishment and growth. Within the sporophyte, meiocytes are formed that generate haploid spores by meiotic division. These spores are dispersed to initiate a new gametophyte. Stages in moss development are illustrated in Figure 2.1. When a moss spore germinates, it does so by extending a ﬁlamentous cell known as a protonema. This cell divides, generating a uniseriate protonemal ﬁlament in which the apical cell undergoes continuous mitotic divisions to extend the ﬁlament. Whilst the apical cell remains continuously active in cell division – in effect it is a unicellular meristem – the subapical cells are less active. Typically they may only undergo one more mitotic division to generate a side-branch initial. This initial can itself then divide to generate another uniseriate ﬁlament or it may initiate the formation of a three-dimensional ‘‘bud’’ containing a more-or-less tetrahedral meristematic apical cell that will proliferate the leafy shoots that are characteristic of mature moss colonies. There are thus a very limited number of cell types in a moss. The protonemal tissue may comprise either slower growing, chloroplast-rich chloronemal cells or rapidly growing caulonemal cells (the caulonemata enable the moss to spread over the substrate). The cells of the leafy shoot may include specialized midrib cells, in addition to those comprising the lamina, and in some mosses, such as Sphagnum spp. specialized water storage cells (hyaline cells). The leafy shoots represent the sexual organs of mosses, in that they bear the specialized male and female reproductive structures – gametangia – at their apices. The female gametangia are ﬂask-shaped archegonia, each containing a single egg cell, and the male gametangia – the antheridia – produce large numbers of motile ﬂagellate spermatozoids. Although there are some rudimentary conducting tissues in some mosses, they generally lack a vascular system and the analysis of the Physcomitrella genome

Figure 2.1 Stages in the development of the moss, P. patens. (a) The mature sporophyte is borne at the tip of the leafy shoot (the gametophore). (b) A mature spore. Each sporophyte contains between 2000 and 5000 haploid spores. The mature spores are enclosed in a sporopollenin wall. (c) Spores germinate to produce chloronemal ﬁlaments. This panel shows a germinated sporeling and an ungerminated spore (top left). (d) Protonemal ﬁlaments grow by repeated division and elongation of the apical cell. The subapical cells divide to generate side-branch initials or buds. (e) A bud initial at an early stage of development. Buds develop to form the leafy shoots. (f) Gametophores, with rhizoids just discernable at the base. (g) A mature gametophyte comprises many gametophores. This plant was initiated on agar medium inoculated with a ‘‘spot inoculum’’ of chloronemal tissue about 4 weeks earlier. (h) Following incubation at low temperature, the gametangia develop at the gametophore apex. The leaves have been stripped from the gametophore to reveal the ﬂask-shaped archegonium (the female structure containing the egg cell) and the small ovoid antheridia that produce ﬂagellate sperm. (i) Following fertilization of the egg cell by a sperm, the sporophyte develops within the archegonium. (j and k) Protoplasts are easily obtained by digestion of chloronemal ﬁlaments with the cell-wall degrading enzyme mixture ‘‘Driselase.’’ Each protoplast is totipotent and capable of regenerating to produce a new plant. Protoplasts are also readily transformed by uptake of exogenous DNA.

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| 2 Moss as a Model System for Plant Stress Responses sequence indicates that this moss, at least, lacks the genes necessary to synthesize lignin of the type found in the tracheophytes [8]. Neither do mosses produce extensive ramifying root systems that are able to scavenge water from the substrate, although the protonemal network may penetrate soils to a shallow depth. Gametophores are often characterized by a proliferation of empty cells – rhizoids – at their base and these have been postulated to have some root-like functions [10], possibly in nutrient assimilation or support, but the gametophyte in general lacks many of the adaptations used by the tracheophytes to restrict water loss, such as cuticular wax and somatal apertures. Nevertheless, these adaptations are not absent: they are restricted to the sporophyte phase of the life cycle, indicating their early evolution among the land plants.

2.3 Physcomitrella as a Model System

Very few mosses have been studied intensively at the molecular or biochemical level. However, one moss species, P. patens, has emerged as an excellent model system for undertaking genome-level analyses. Physcomitrella has been studied since the early years of the twentieth century and at the genetic level since 1968, when the ﬁrst mutants were isolated [11]. First, the cells of a fully differentiated plant retain their totipotency. Mosses are easily grown in axenic tissue culture by the simple expedient of fragmenting plants in water with a laboratory blender and dispersing the suspension over the surface of an agar plate [12]. The resultant suspension rapidly regenerates as a uniform mat of protonemal ﬁlaments, thus providing an excellent source of homogeneous tissue for biochemical or molecular analysis. Alternatively, small explants of ﬁlamentous tissue can be subcultured as ‘‘spot inocula’’ and they will recapitulate the entire developmental pathway to generate new, clonal plants. The haploid nature of the dominant gametophyte generation clearly facilitates the recognition of mutant phenotypes immediately following mutagenic treatment and this was exploited by Cove et al. in the isolation of auxotrophic mutants [13], as well as mutants affecting a number of processes relating to cellular differentiation [14], hormone responses [15], and the polar growth responses of single cells: the protonemal apical cell is the site of perception of, transduction and response to environmental stimuli such as light and gravity [16–19]. This provides the opportunity to elucidate how such processes operate within a single, easily observed cell, rather than in a multicellular organ containing a number of different cell types, as occurs in anatomically more complex plants. Furthermore, cellular differentiation processes are easily studied and manipulated: the transitions between chloronemal and caulonemal cells can be regulated by auxin [9, 20] and the carbohydrate nutritional status of the cell [21] whilst bud formation leading to gametophore development is triggered by the action of cytokinins [9, 22, 23, 24]. Owing to the simple anatomy of the moss, allowing accurate and quantitative analysis, and the predictable nature of the developmental transitions that occur, Physcomitrella

2.3 Physcomitrella as a Model System

developmental progression is highly amenable to a detailed computational analysis that allows predictions to be made and tested [25]. Consequently, Physcomitrella provides a highly suitable organism for systems-level analysis – especially when the genetic and genomic resources now available are considered. Whilst the utility of Physcomitrella as a subject for cellular level investigations was clear, its potential for comparative genomic analysis of the evolution of gene function was not immediately apparent. Only after the establishment of routine genetic transformation did one of the more remarkable properties of this organism emerge. Physcomitrella is routinely transformed by the polyethylene glycolmediated uptake of naked DNA by protoplasts [26]. This is not a particularly efﬁcient means of DNA delivery in any plant species, but development of the technique was aided by the ability of Physcomitrella protoplasts to regenerate with very high efﬁciency. During the early development of the protoplast transformation procedure, it was discovered that retransformation of a transgenic line with a second DNA construct containing vector sequences homologous with the ﬁrst transgene resulted (i) in an increased frequency of transformation by the second construct and (ii) genetic cosegregation of the two transgenes [27]. It was postulated that this arose from a propensity for the second transgene to become integrated at the ﬁrst transgenic locus by homologous recombination – a form of gene targeting. This was subsequently conﬁrmed by a landmark molecular analysis [28], which showed that transformation with a DNA construct containing sequence homology with an endogenous genomic sequence resulted in targeting of the transgene to the endogenous locus. This occurred at very high frequency (up to 100%) – an efﬁciency of gene targeting hitherto only observed in yeast and certainly very much higher than occurs in ﬂowering plants. Gene targeting technology thus allows ‘‘reverse genetic’’ analysis of gene function to be undertaken with great facility in Physcomitrella – a concept proven by the ﬁrst predetermined targeted knockout phenotype to be described in any plant, that of the Physcomitrella FTSZ gene. This gene encodes a plastid tubulin required for chloroplast division, the cells of ftsZ mutants generated by targeted disruption containing single giant chloroplasts [29]. Deployment of such a powerful tool for genetic manipulation can only be effective if the sequences of the genes to be targeted are known. This requirement stimulated a series of gene discovery programmes. Initially these comprised the accumulation of expressed sequence tag (EST) collections [30, 31] and culminated in the acceptance of the Physcomitrella genome for complete sequence determination by an international consortium based on the United States Department of Energy’s Joint Genome Institute Community Sequencing Program. The ﬁrst-draft sequence assembly of the Physcomitrella genome was released in 2007 [8]. Currently, the use of Physcomitrella as a model is supported by the genome sequence assembly – representing about 486 Mbp and encoding approximately 25 000 genes – complemented by a recently developed, molecular-marker-based linkage map which is anchored to the genome sequence. This will facilitate mutagenesis-based ‘‘forward genetic’’ screening to undertake the map-based cloning of genes responsible for selected phenotypic traits [32]. The EST resource

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| 2 Moss as a Model System for Plant Stress Responses comprises about 300 000 sequences, and microarray chips are available based on the EST resource and on the genome sequence. The facility with which gene targeting may be undertaken allows the ‘‘reverse genetic’’ analysis of gene function by gene disruption to generate knockout mutants. It is also possible to carry out more sophisticated manipulations that include the ability to construct ‘‘knock-in’’ fusions – for example, with reporter genes (b-glucuronidase, Green Fluorescent Protein, etc.) that allow gene expression to be visualized in lines where the reporter is introduced into the correct genetic locus, rather than at an ectopic site elsewhere in the genome, or with molecular tags to facilitate the isolation of molecular complexes (e.g., by generating epitope-tagged or afﬁnity-tagged proteins). Additionally, it is a relatively simple matter to undertake site-directed mutagenesis of speciﬁc genes, containing as little as a single base alteration. Such surgical precision is not available in any other plant model.

2.4 Water Stress and Abscisic Acid

As has been indicated above, the mosses retain many features that must have been characteristic of the ﬁrst land plants. These features include relatively high levels of tolerance to abiotic stresses. Most strikingly, many mosses exhibit a characteristically high degree of dehydration tolerance [33]. At its most extreme, this occurs in the form of desiccation tolerance – the ability to withstand dehydration to about 5–10% of the plant’s original water content. At this point, it is important to be clear about how this property is deﬁned. In plain English, the term ‘‘desiccation tolerance’’ simply refers to the ability to survive and recover from the desiccated state. However, this expression provides insufﬁcient precision, in that it does not specify the timescale over which a tolerant state is achieved. Consequently, a distinction must be made between plants that are able to achieve a viable, desiccated state only following a period of adaptation through relatively prolonged exposure to conditions that cause dehydration to occur over a period of time, and plants that can equilibrate rapidly with a dry atmosphere and reach the dehydrated state without a need for prior physiological adaptation [34]. The term ‘‘poikilohydric’’ is used to describe the latter class of plant [35]. Poikilohydry is a generally rare phenomenon that remains relatively common among the bryophytes, whereas the ability of plants to achieve a state of desiccation tolerance following a longer adaptive period is more generally widespread. However, poikilohydry is relatively rare among the tracheophytes, implying that it is a characteristic that has been lost during the evolution of anatomical complexity [35, 36]. Nevertheless, a small number of taxa among the tracheophytes still display desiccation tolerance. These so-called ‘‘resurrection plants’’ are able to undergo complete dehydration of the vegetative tissues and recover normal metabolic function rapidly following rehydration. From the distribution of this character within the land plant phylogeny, it is apparent that it is has independently re-evolved several times [36],

2.4 Water Stress and Abscisic Acid

implying that only a relatively small number of genes are required to mutate to result in the gain or loss of tolerance. Among poikilohydric mosses, the best-characterized is Tortula ruralis, the characteristic features of which will be discussed in Section 2.5. Many mosses that are not poikilohydric nevertheless still display a high tolerance of extreme dehydration, culminating in the ability to tolerate the desiccated state so long as they have undergone prior adaptation as a consequence of relatively slow drying [35]. Physcomitrella is in this second class of moss and in its responses to the application of water stress, it exhibits many features more commonly associated with the responses observed in ﬂowering plants. Most angiosperms are not thought of as desiccation-tolerant plants. However, this is misleading, because most angiosperms retain the property of surviving desiccation – but only during speciﬁc parts of their life cycle; in particular, during the development of reproductive propagules such as seeds and pollen grains. Owing to its economic importance, the acquisition of desiccation tolerance by seeds has been most extensively characterized. The ability to retain dry seeds in a viable form from one growing season to the next underpins all agricultural practice and the acquisition of desiccation tolerance is therefore intimately associated with the most signiﬁcant development in human history – the transition from hunter-gatherer populations with a limited resource base to agricultural societies capable of acquiring surpluses, undergoing population growth and initiating economic and cultural development. Desiccation tolerance in seeds is acquired as a result of controlled, progressive dehydration exerting biochemical and molecular changes within cells through the agency of the plant growth regulator, abscisic acid (ABA). ABA imposes growth inhibition in developing seeds (dormancy) to prevent precocious germination of embryos that would be otherwise unsupported by storage reserves and it also stimulates the accumulation of cellular components that are required for the survival of the seed tissues in the dry state. These components include sugars and protective proteins – the ‘‘late embryogenesis abundant’’ (LEA) proteins – whose requirement for the acquisition of desiccation tolerance is well established, but whose protective mechanisms remain enigmatic [37]. ABA also acts in the responses of vegetative tissues to water stress. The de novo synthesis and accumulation of ABA is an immediate response to water deﬁcit, and the processes stimulated by ABA include physiological changes, such as the closure of stomata, senescence, and abscission of leaves, as a further measure to restrict evaporative water loss, and the accumulation, in vegetative tissues of osmoregulatory components (compatible osmolytes that include amino acids (proline, glycine betaine), polyhydric alcohols, and disaccharides (principally sucrose) [38–41] as well as a subset of the LEA proteins that accumulate in dehydrating seeds. However, very few angiosperms have vegetative tissues that will survive desiccation, no matter how gentle or prolonged the prior period of adaptive stress. This is by contrast with nonpoikilohydric mosses, many of which undergo physiological adaptation to enable their vegetative tissues to survive desiccation. In Physcomitrella, the acquisition of vegetative stress tolerance is an ABAmediated process. Treatment with exogenous ABA, as well as slow dehydration

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| 2 Moss as a Model System for Plant Stress Responses imposed over a period of several days, results in the acquisition of desiccation tolerance [42, 43]. Figure 2.2 shows some of the structural and anatomical consequences of stress and ABA treatment. These experimental conditions likely reﬂect the ecological situation of P. patens; typically, this species occupies the

Figure 2.2 Effects of stress and ABA treatment of chloronemal tissue. (a) Chloronemal ﬁlaments incubated on normal growth medium. (b) Chloronemal ﬁlaments incubated in medium containing 10% mannitol as an osmotic stress treatment, for 2 h. Note the commencement of plasmolysis, as the cellular constituents shrink away from the crosswalls of each ﬁlament. (c) Chloronemal ﬁlaments subjected to drought by equilibration with an atmosphere of 75% relative humidity for 48 h. The tissue has undergone about 90% fresh weight loss and the cellular constituents have shrunk to form strands within each cell. (d) The same tissue shown in (c), seconds following rehydration. The cellular structure is rapidly becoming reorganized. The majority of the rehydrated cells will regain viability. (e) The structure of chloronemal cells grown on unsupplemented growth medium. (f) Chloronemal tissue incubated on medium supplemented with 105 M ABA for 2 weeks. Colonies grown on ABA appear smaller, as a consequence of the reduced length of the individual cells in each ﬁlament. The cells are generally shorter and fatter. (g) Cells in a colony grown on 104 M ABA for 14 days. The majority of the cells are smaller, rounded, densely cytoplasmic and thick-walled, and are differentiating into ‘‘brood cells.’’ A few empty cells (‘‘tmema,’’ arrowed and labeled ‘‘t’’) can be discerned.

2.4 Water Stress and Abscisic Acid

muddy margins of reservoirs, lakes, and ponds, and when exposed is probably subjected to relatively slow dehydration due to its close association with the underlying, generally water-retentive substratum (by contrast with species that colonize bare rock surfaces and that likely undergo much more rapid drying/ wetting cycles). The acquisition of desiccation tolerance is associated with membrane stabilization (cellular membranes do not undergo a phase transition that would result in leakiness) and the physical transition of the cytosol from a liquid to a glassy state [43]. Such changes are characteristic of organisms that exhibit anhydrobiotic survival. ABA induces a number of additional changes in Physcomitrella. Most striking is the growth arrest and differentiation of the chloronemal cells to form rounded, thick-walled ‘‘brood cells’’ (brachycytes). These cells become interspersed by empty, fragile cells (‘‘tmema cells’’) that are easily fractured, thus releasing the brachycytes that act, in effect, as vegetative spores, each able to initiate the formation of a new colony following dispersal and rehydration [44, 20]. Analysis of the biochemical and metabolic changes that occur following ABA or stress treatment highlights the similarities between the responses of Physcomitrella and of the developing seeds of angiosperm species. Transcriptional proﬁling of protonemal tissue subjected to such treatments has revealed a number of characteristic changes in gene expression that are initiated very rapidly. Within minutes of the application of ABA, transcripts encoding a number of LEA proteins can be observed to accumulate and these transcripts very rapidly become highly abundant [45]. Many of the genes expressed in response both to application of ABA and of the imposition of drought stress have homologs that are similarly regulated in angiosperms: they include a substantial component encoding LEA proteins, as well as a number of genes encoding proteins that have been identiﬁed as stressassociated in ﬂowering plants. Additionally, the underlying mechanisms by which these genes are regulated in response to ABA and to drought stress appear to have been conserved during the evolution of the land plants. In ﬂowering plants, drought-stress and ABA-induced gene expression is regulated through two principal classes of transcription factor. These are the abscisic-responsive element binding (AREB) factors [46, 47] and dehydration-responsive element binding (DREB) factors [48, 49] The AREB factors are members of the large and diverse basic domain/leucine zipper (bZIP) transcription factor family that interacts with cis-acting sequences that include a core ACGT motif. The DREB factors are members of the equally large APETALA2/ ethylene-responsive element binding (AP2/EREB) transcription factor family that recognizes cis-acting sequences containing the CCGAC motif. These two classes of factor interact in a number of the signal transduction pathways that lead to stress-induced gene expression in the vegetative tissues of ﬂowering plants [50, 51], and factors of both classes have also been identiﬁed as mediating both ABA- and osmotic stress-induced expression of LEA genes during seed development [52, 47, 48]. Many of the genes upregulated by ABA and drought stress in Physcomitrella contain promoter motifs characteristic of these recognition sites [45], and the

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| 2 Moss as a Model System for Plant Stress Responses function of the ACGT-core ABRE motif has been demonstrated to be required both for the expression of a cereal Group 1 LEA gene in moss cells [53] and for the homologous expression of its Physcomitrella ortholog [54]. Interestingly, the Group 1 LEA genes in ﬂowering plants are highly seed-speciﬁc in their pattern of expression. This is a consequence of their requirement for transcriptional activation by the ABI3 (ABA-INSENSITIVE3) transcription factor (encoded, in Arabidopsis, by the ABI3 gene) which is highly seed-speciﬁc in its pattern of expression, and which is required both for the onset of embryonic dormancy and for the acquisition of desiccation tolerance. Whereas in those ﬂowering plant genomes analyzed to date, there resides only a single copy of this key developmental regulatory gene, the Physcomitrella genome contains at least three ABI3 paralogs [8, 55], suggesting that the evolutionary origins of this gene were related to a primary role in mediating the drought stress-tolerance pathway, and that recruitment for a more specialized role in coordinating the seed developmental pathway was accompanied by loss of the additional members of this gene family.

2.5 T. ruralis: A Model for Poikilohydry

The moss T. ruralis is probably the best-characterized poikilohydric moss. It is able to survive rapid dehydration and recover full metabolic activity within minutes of rehydration [56]. Unlike Physcomitrella, which stands at the polar opposite end of the dehydration tolerance spectrum, Tortula does not show any striking alterations in the pattern of gene expression during the period of prior dehydration [57]. Instead, this species appears to be constitutively prepared for dehydration. This likely reﬂects its ecological distribution, being common in sand dunes and open grassland [35]. Signiﬁcant changes in gene expression associated with the desiccated state are apparent only following rehydration, when a number of novel transcripts appear in the polysomal mRNA pool. Designated ‘‘rehydrins’’ [58], these gene products were suggested to be involved in the repair of dehydrationassociated damage, early in the recovery phase of rehydration. However, it is interesting that an EST-based analysis of the rehydration-associated transcriptome of T. ruralis identiﬁed a signiﬁcant number of transcripts encoding LEA proteins among the most abundant class [59]. One rehydration associated gene product – a protein designated ‘‘Tr288’’ – is a member of the Group 2 LEA proteins [60]: a class of polypeptide initially deﬁned as ‘‘dehydrins’’ following their identiﬁcation as ABA- and dehydration-induced gene products in cereals [61]. In Physcomitrella – as in angiosperms – the orthologous gene is expressed during drought stress, salt stress, and in response to ABA treatment, [62] and the mutant strain derived by gene knockout exhibited hypersensitivity to osmotic and salt stress. Whilst the protective functions of LEA proteins are far from clearly understood, at the molecular level, it is generally agreed that they serve to protect macromolecular constituents of the cell from the consequences of dehydration, which include irreversible denaturation and the formation of inactive macromolecular

2.6 Cold Stress and Abscisic Acid

aggregates. Dehydrins, like most LEA proteins, are highly hydrophilic, and retain a high potential for sequestering water molecules [63]. It has been suggested that they might retain a minimal level of hydration within the cell, offer their hydrophilic side-chains as participants in hydrogen-bonding interactions with other macromolecules as ‘‘replacement water’’ [37, 64] or associate to form macromolecular ﬁbrillar structures that reinforce the structure of the cell as the components undergo the vitriﬁcation that is characteristic of dry cells [65, 66]. In considering why supposedly protective proteins might accumulate during the recovery from dehydration, an alternative possibility is that such highly hydrophilic proteins might act as moderators of dehydration/rehydration rates, restricting toorapid water loss during dehydration and acting to sequester incoming water upon rehydration. In this model, water would be retained in an osmotically unresponsive form: cells accumulating a signiﬁcant quantity of such proteins would become osmotically relatively stable – a property akin to that of camel erythrocytes (essential for survival in an animal that can undergo prolonged periods of exercise in a desert environment, punctuated by occasional, but substantial, intake of water), which has been ascribed to the increased water-binding capacity of the unusually hydrophilic form of hemoglobin found in this species [67]. A ﬁnal consideration is that the resilience of the desiccated state in poikilohydric mosses appears to be a feature only of the gametophyte generation. A study of the desert moss, Tortula inermis, revealed that whereas the gametophyte was highly tolerant of extreme desiccation, the sporophyte was far more sensitive [68]. Since sporophytes display a number of anatomical features that are more characteristic of the vascular plants (including the differentiation of conducting tissues, and the presence of stomata and cuticles) their desiccation sensitivity may be correlated with this additional anatomical complexity.

2.6 Cold Stress and Abscisic Acid

Many abiotic stresses elicit similar responses at the molecular and biochemical level. This reﬂects the extent of cross-talk that exists between the various stressassociated perception and signal transduction pathways (reviewed in Chapter 4), but also some of the common cellular consequences of individual stresses and the mechanisms that must subsequently be activated to bring about the amelioration of these stresses or the repair of stress-induced damage. Thus, water-deﬁcit stress responses have much in common with low-temperature and freezing stress. Both drought and freezing have the effect of reducing the available water in the cell. Both require the accumulation of solutes that can act as either compatible osmolytes or as antifreeze compounds, and both require an alteration of membrane lipids (typically the desaturation of fatty acids) to alter membrane ﬂuidity and reduce the likelihood of membrane phase transitions. As with the drought stress response, the freezing stress response and low-temperature acclimation have been studied in some detail in Physcomitrella.

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| 2 Moss as a Model System for Plant Stress Responses Physcomitrella is highly susceptible to temperatures below freezing. However, pretreatment with ABA for as little as 24 h substantially enhances freezing tolerance, with the temperature at which 50% of cells surviving freezing falling from about 2 to 8 1C as a consequence of this treatment [43, 69]. Analysis of gene expression by differential display, to identify novel genes, and of the expression of candidate genes selected by their similarity with genes known to mitigate freezing stress in ﬂowering plants indicated a signiﬁcant upregulation of these genes was required for the acquisition of tolerance. As well as genes encoding LEA proteins, these included genes for enzymes associated with sugar metabolism and the detoxiﬁcation of reactive oxygen species – the generation of which is a common consequence of abiotic stress treatments. ABA-treated tissue additionally exhibited a signiﬁcant degree of membrane stabilization following freezing [69] suggesting that modiﬁcation of membrane lipids comprised a substantial component of the mechanism of induced tolerance. Treatment with osmotically challenging concentrations of mannitol and NaCl also induces the expression of such genes [45, 69]. Oldenhof et al. [43] also demonstrated that ABA treatment increased tolerance to freezing, and that this was associated with an increase in endogenous sucrose levels. This is likely derived from the rapid breakdown of chloroplast-localized starch following the administration of ABA [70] and is accompanied by the accumulation of additional novel sugars such as the trisaccharide, theanderose [71]. However, it is an open question as to whether such treatments reﬂect the true mechanism by which freezing tolerance is mediated in Physcomitrella or simply highlight the extensive overlap in the responses of a stress-induced gene set to multiple agents. Thus, acclimation of Physcomitrella protonemal tissue by incubation at low temperatures for prolonged periods (up to 1 week) will also substantially enhance freezing tolerance, and induce the expression of many of the same genes upregulated by ABA treatments. Although these changes are essentially similar to those rapidly and massively induced by ABA treatment and although mutants that are insensitive to ABA exhibit a reduced level of freezing tolerance [72], the changes occurring during low-temperature acclimation do so in the apparent absence of any measurable increase in the endogenous concentrations of ABA present in the moss tissue [73], thus implicating multiple, parallel signal transduction pathways in the regulation of these stress responses.

2.7 Future Perspectives

The development of genomic resources for Physcomitrella has resulted in a concomitant burgeoning of interest among plant scientists for using this species for comparative studies of plant processes. In the near future, we can expect to see a more comprehensive accumulation of large transcriptomic, proteomic, and metabolomic datasets of the type that have been established for other model organisms. Proteomic approaches have already been applied to identify the polypeptide content of protonemal cells [74, 75] and to begin to decipher the

References

Physcomitrella phosphoproteome – an important prerequisite for the dissection of signal transduction pathways associated with cellular differentiation and stress responses [76, 77]. Whilst Physcomitrella provides the most easily manipulated species among the mosses, we should not focus on this species to the exclusion of others, whose study may return insights into processes not evident in Physcomitrella. Thus, T. ruralis will remain an exemplar for the analysis of poikilohydric desiccation tolerance and we can expect analysis of other mosses adapted to specialized environments to be of value in determining the basis of tolerance to these stresses. One such example is adaptation to heavy metal ions deposited at industrially polluted sites. Physcomitrella is generally susceptible to the toxic effects of heavy metal ions [78], unlike other mosses such as Fontinalis antipyretica [79], Scopelophila cataractae [80, 81], or Taxithelium nepalense [82]. As well as being of academic interest, an insight into the way in which nonmodel bryophytes adapt to such adverse conditions may provide novel approaches to engineering stress tolerance in crop species, through the identiﬁcation of novel genes, and for strategies for the phytoremediation of polluted environments [83]. The rapid advances in massively parallel DNA sequencing technology can be expected to make transcriptomic analysis possible for organisms that have previously not enjoyed the beneﬁts of extensive research funding. The use of technology such as the Roche 454 GS-FLX DNA sequencing procedure enables the acquisition of about 400 000 sequence traces of about 500 bases each, in a single day. This technology has already been used to sample the Physcomitrella epigenome [84]. When used to sequence ESTs derived from speciﬁc tissues or treatments, quantitative analysis of the clustered sequences generates mRNA abundance proﬁles over a wide dynamic range [85]. Whilst complete genome sequencing for many organisms remains a relatively distant prospect, transcriptome sampling by new-generation sequencing promises a much more immediate return, that can generate a sequence database that will interface with proteomic and metabolomic proﬁling for nonmodel species.

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| 2 Moss as a Model System for Plant Stress Responses 73 Minami, A., Nagao, M., Ikegama, K., Koshiba, T., Arakawa, K., Fujikawa, S., and Takezawa, D. (2005) Cold acclimation in bryophytes: lowtemperature-induced freezing tolerance in Physcomitrella patens is associated with increases in expression levels of stress-related genes but not with increase in level of endogenous abscisic acid. Planta, 220, 414–423. 74 Sarnighausen, E., Wurtz, V., Heintz, D., van Dorsselaer, A., and Reski, R. (2004) Mapping of the Physcomitrella patens proteome. Phytochemistry, b65, 1589–1607. 75 Cho, S.H., Hoang, Q.T., Kim, Y.Y., Shin, H.Y., Ok, S.H., Bae, J.M., and Shin, J.S. (2006) Proteome analysis of gametophores identiﬁed a metallothionein involved in various abiotic stress responses in Physcomitrella patens. Plant Cell Rep., 25, 475–488. 76 Heintz, D., Wurtz, V., High, A.A., Van Dorsselaer, A., Reski, R., and Sarnighausen, E. (2004) An efﬁcient protocol for the identiﬁcation of protein phosphorylation in a seedless plant, sensitive enough to detect members of signalling cascades. Electrophoresis, 25, 1149–1159. 77 Heintz, D., Erxleben, A., High, A.A., Wurtz, V., Reski, R., Van Dorsselaer, A., and Sarnighausen, E. (2006) Rapid alteration of the phosphoproteome in the moss Physcomitrella patens after cytokinin treatment. J. Proteome Res., 5, 2283–2293. 78 Rother, M., Krauss, G.-J., Grass, G., and Wesenberg, D. (2006) Sulphate accumulation under Cd2 þ stress in

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Physcomitrella patens – combined transcript, enzyme and metabolite proﬁling. Plant Cell Environ., 29, 1801–1811. Rau, S., Miersch, J., Neumann, D., Weber, E., and Krauss, J.-G. (2007) Biochemical responses of the aquatic moss Fontinalis antipyretica to Cd, Cu, Pb and Zn determined by chlorophyll ﬂuorescence and protein levels. Environ. Exp. Bot., 59, 299–306. Shaw, J.A. and Beer, S.C. (1989) Scopelophila cataractae (Mitt.) Broth. in Pennsylvania. Bryologist, 92, 112–115. Shaw, J.A. (1995) Genetic biogeography of the rare ‘‘copper moss’’, Scopelophila cataractae (Pottiaceae). Plant Systemat. Evol., 197, 43–58. Choudhury, S. and Panda, S.K. (2005) Toxic effects, oxidative stress and ultrastructural changes in moss Taxithelium nepalense (Schwaegr.) Broth. under chromium and lead toxicity. Water Air Soil Pollut., 167, 73–90. Prasad, M.N.V. and De Oliveira Freitas, H.M. (2003) Metal hyperaccumulation in plants – biodiversity prospecting for phytoremediation technology. Electron. J. Biotechnol., 6, 285–321. Axtell, M.J., Snyder, J.A., and Bartell, D.P. (2007) Common functions for diverse small RNAs of land plants. Plant Cell, 19, 1750–1769. Weber, A.P.M., Weber, K.L., Carr, K., Wilkerson, C., and Ohlrogge, J.B. (2007) Sampling the Arabidopsis transcriptome with massively parallel pyrosequencing. Plant Physiol., 144, 32–42.

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3 Emerging Trends in Functional Genomics for Stress Tolerance in Crop Plants Swatismita Ray, Prasant K. Dansana, Avantika Bhaskar, Jitender Giri, Sanjay Kapoor, Jitendra P. Khurana, and Akhilesh K. Tyagi

3.1 Introduction

The world’s population is growing at an alarming rate and it is likely to increase by 2.5 billion (i.e., from 6.7 to 9.2 billion) by 2050 (http://www.FAO.org). Presently, one-third of the world’s population is lacking food security. The reasons for the discord between the demand and supply of food include not only rapid population growth, but also higher population densities in traditional agricultural areas, fragmentation of small farmsteads, shortage of arable land, loss of agricultural land to urbanization, water shortages, pollution, poor land management, and inappropriate agricultural and economic policies. As a result, the capacity of the developing world to enhance food production may well be shrinking. Only 15 species of edible plants provide 90% of the world’s food energy intake and just three of them (rice, wheat, and corn) form the staple food for nearly two-thirds of the world’s population. The mantra to meet this demand in the near future is the application of molecular and breeding methods in tandem, leading to an increase in grain production. In the post-genome sequencing era, the new dimension of research is to study the function of genes on a genome-wide scale. Functional genomic study of plants for stress tolerance is one potential area to explore. Abiotic stresses are severe threat to agricultural production worldwide [1]. Plants are sessile multicellular organism, and thus endure environmental adversities such as soil salinity, drought, and cold temperature. Several plant developmental processes are adversely affected by various biotic and abiotic stress factors. Of the various abiotic stresses, cold, salt, and desiccation are the major stress factors that cause catastrophic crop failures [1–3]. This chapter aims to discuss recent advances in identifying potential genes for abiotic stress tolerance, functional characterization using the transgenic approach, and the signaling network involved in abiotic stress response.

Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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| 3 Emerging Trends in Functional Genomics for Stress Tolerance in Crop Plants 3.2 Abiotic Stresses Encountered by Plants

The stress signal is perceived by the plant, leading to a signal transduction cascade culminating in the stress response of the plant aimed at mitigating the perceived stress. Cold stress adversely affects the expression of the full genetic potential of plants owing to its direct effect on metabolic processes, and coldinduced osmotic, oxidative, and other stresses [4, 5]. Cold tolerance in plants is governed by membrane lipid composition. The presence of the more common cis double bond in unsaturated fatty acids helps maintain membrane ﬂuidity by introducing bends or kinks in the fatty acyl chains, thereby inhibiting tight packing of adjacent lipid molecules and reducing the membrane transition during cold stress. Another form of damage due to freezing stress is caused by the production of reactive oxygen species (ROS) [6]. Many important crop plants, such as rice, maize, soybean, cotton, and tomato, are sensitive to chilling stress and are incapable of cold acclimation; moreover, they cannot tolerate ice formation in their tissues [7]. Salinity disrupts the ionic equilibrium, reduces soil aeration, and alters water conductance [8]. It inhibits photosynthesis, causes photoinhibition and oxidative stress, and under prolonged exposure results in growth inhibition and accelerated senescence. Halophytes tolerate high salinity due to compartmentalization of Na þ in vacuoles [9], which is attributed to the membrane-localized Na þ /H þ antiporter. Water stresses, either ﬂooding or drought, affect multiple aspects of plant physiology and metabolism. Flooding results in reduced oxygen supply to roots, leading to the malfunctioning of critical root functions, including limited nutrient uptake and respiration. Drought causes disruption of the bilayer structure of the plasma membrane, which results in loss of cellular compartmentalization. Moreover, it leads to reduced activity of cytosolic and organelle proteins as well as disrupts cellular metabolism and enzyme function [10, 11]. Drought has also been reported to have adverse effects on reproductive development starting from initiation of ﬂowering to grain ﬁlling and greatly reduces grain yield [12]. In addition to the three main abiotic stress conditions encountered by plants, high temperature (heat stress), mineral deﬁciency, metal toxicity, mechanical wounding, and ultraviolet (UV) light can also pose abiotic stress challenge to plants. Heat stress causes cellular collapse by protein denaturation and aggregation, resulting in loss of membrane integrity, and limits growth and yield of crop plants when it occurs transiently or continually [13]. Very little is known about the plant response mechanism to a combination of abiotic stresses, which is of great importance, as crops are subjected to a combination of abiotic stresses in ﬁeld conditions [1].

3.3 Genome-Wide Investigations to Understand Components

3.3 Genome-Wide Investigations to Understand Components Involved in Abiotic Stress Responses

3.3.1 Transcriptome Analysis

The availability of genome-wide sequences from several plant species, such as Arabidopsis, rice, poplar, grape, papaya, Medicago, lotus, tomato, and maize, and advances in the high-throughput techniques involving global gene expression and interaction studies have revolutionized the gene discovery process [3]. In addition, for these model plants there has been a vast enrichment in a variety of genomic resources for rapid gene identiﬁcation, such as annotated cDNA/expressed sequence tags (ESTs), various gene prediction programs (FGENESH (monocot), GeneMark.HMM (Arabidopsis and rice) and GENSCAN (Arabidopsis and maize)), collection of insertional mutant lines, subtractive cDNA libraries, specialized population mapping, and an extensive breeding network to implement markerassisted breeding [3, 14–16]. cDNA and subtractive cDNA libraries from stresstreated samples have shown an increase in transcripts related to cell rescue, defense, transport, detoxiﬁcation, growth, and development [3]. Serial analysis of gene expression (SAGE) and massively parallel signature sequencing (MPSS) are two powerful sequencing-based techniques for genomewide transcriptional proﬁling. In SAGE, short sequence tags of 9–17 bp are obtained from unique regions of genes, and they are linked together and sequenced for transcript abundance [17]. SAGE of rice revealed that anaerobic conditions induce genes for expansin, prolamine, and glycine-rich cell wall protein [18]. In MPSS, unique signatures of 17–20 bp are generated from the mRNA population in the sample [19]. Series of MPSS libraries for four species (Arabidopsis, rice, grape and Magnaporthe grisea) have been created (http://mpss.udel.edu). The rice MPSS database includes 20 MPSS libraries with three abiotic stress-treated samples (cold, drought, and salt [20]) that are available for further analysis. The microarray approach has unraveled signiﬁcant information about the transcriptome proﬁle during the stress response. cDNA and oligonucleotide microarrays have been extensively used for global gene expression proﬁling of a variety of dicot and monocot crop plant species, such as Capsicum, chickpea, soybean, sunﬂower, barley, lotus, Medicago, rice, Populus, potato, tomato, sorghum, wheat, and maize, for abiotic stress response. The effect of cold stress at the early microspore stage (uninucleate) was studied using cDNA array in rice. Most of the chilling-responsive ESTs related to primary metabolism, signal transduction, defense, proteases, and secondary metabolism, including 12-oxo-phytodienoic acid reductases that showed downregulation, whereas genes related to translation showed upregulation [21]. In the IR29 salt-sensitive variety of rice, the stress-responsive genes showed a delayed response in terms of expression kinetics in comparison to salt-tolerant Pokkali variety [22]. Chao et al. [23] reported that the

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| 3 Emerging Trends in Functional Genomics for Stress Tolerance in Crop Plants early responsive genes (20 min) from a highly salt-tolerant rice variety, Nona Bokra, include transcription factors and kinases, whereas after 3 and 24 h of salt treatment, signal transduction, ion transport, cell defense, detoxiﬁcation, and cell senescence gene transcripts were prevalent. Enzymes involved in the ﬂavonoid biosynthetic pathway (i.e., phenylalanine ammonialyase 1 and chalcone synthase) were signiﬁcantly induced in the salt-sensitive IR29, but not in tolerant FL478 rice. Moreover, the diverse accumulation pattern of cell wall-related genes suggested that salinity triggers a lot of restructuring in the cell wall, which varies between the varieties [24]. Transcriptome proﬁling during the panicle initiation stage (P1) of rice also revealed that a large number of genes were induced by salinity stress in sensitive genotypes, IR29 (589 genes) and M103 (292 genes). Transcripts related to phase transition, organ identity, and size determination were differentially regulated in the sensitive variety as well as in the tolerant variety Agami, but not in another tolerant variety IR63731. However, increased transcript levels of ﬂoral transition-related genes, such as GIGANTEA, FT (FLOWERING LOCUST), TFL1 (TERMINAL FLOWER1), and EMF2 (EMBRYONIC FLOWER2), were found in IR63731 under controlled conditions [25]. Genes involved in detoxiﬁcation protection against oxidative stress and maintaining cell turgor showed high expression in dehydration-tolerant upland rice, whereas, in the sensitive lowland rice, genes functioning in the degradation of cellular components showed higher transcript abundance in response to polyethylene glycol treatment [26]. After 48 h of rehydration following dehydration stress, transporter genes and photosynthesisrelated genes were induced in the ﬂag leaf, shoot, and panicle [27]. Evidence for conserved pathways for different stresses has come from global transcriptome proﬁling studies. These studies indicated greater cross-talk between the signaling processes for drought stress and high salinity stress or for drought stress and abscisic acid (ABA) application than between the signaling processes for cold stress and high salinity stress or for cold stress and ABA application [28, 29]. In a microarray analysis employed to study the genes involved in pollination/fertilization in rice [30], it was found that most genes repressed during pollination/ fertilization were induced by dehydration, and they include the regulatory proteins involved in signal transduction and gene expression (transcription factors, protein kinases, protein phosphatases, and other signaling molecules, such as calmodulin (CAM) and EF-hand Ca2 þ -binding protein), and functional proteins (enzymes involved in osmoprotectant synthesis/degradation, protein degradation, protease inhibitor, ‘‘late embryogenesis abundant’’ (LEA) protein, heat shock protein, lipid transfer protein, and plant defense-related proteins). Transcript analysis of Arabidopsis under different abiotic stress conditions identiﬁed potential stressresponsive genes that could serve as useful input for crops. Transcription factors (basic helix–loop–helix (bHLH), basic domain/leucine zipper (bZIP), dehydrationresponsive element binding (DREB), ethylene-responsive factor (ERF), homeodomain, MYB, NAC, WRKY, and zinc ﬁnger proteins) involved in the ABA signal transduction pathways were found to be induced in response to drought and cold [31], whereas induction of transcription factors and signal transduction proteins, such as protein kinases, receptor protein kinases, and metabolic pathway proteins,

3.3 Genome-Wide Investigations to Understand Components

was reported in response to ABA treatment of Arabidopsis [32]. It should also be mentioned that certain studies have focused on transcriptional analysis related to gene families and identiﬁed stress-inducible component of such genes. In the SAP (STRESS-ASSOCIATED PROTEIN) gene family of rice, all members were found to be stress-inducible [33]. 3.3.2 Role of MicroRNAs in Stress

One of the mechanisms by which plants respond to environmental changes is through post-transcriptional regulation of gene expression by non-protein-coding small RNAs like microRNA (miRNA) and small interfering RNA [34]. miRNAs are approximately 22-nucleotide long regulatory RNAs that play important roles in plants by targeting mRNA degradation or translational repression [35]. About 178 miRNA families have been identiﬁed in 10 plant species, including Arabidopsis, rice, and Populus [35]. Apart from their role in growth and development, several of them have been found to be stress-responsive, playing important roles in nutrient starvation, drought, cold, mechanical, UV-B, and oxidative stress response. Under stress, stress-induced miRNAs target negative regulators of stress response, while stress-downregulated miRNAs may lead to the accumulation of positive regulators of stress response [36]. High-throughput sequencing of RNA libraries constructed from stressed plants has been used to identify stress-regulated miRNAs [37]. It was found that miR169g and miR393 levels were upregulated under drought stress in rice and analysis revealed the presence of two dehydration responsive elements in the promoter region of miR169g [38]. The abiotic stress-responsive miR393 leads to TIR1 mRNA degradation, which downregulates auxin signaling and growth under stress conditions [39]. ABA, a major regulator of dehydration stress and seed germination, was found to induce ABI3 (ABA-INSENSITIVE3)-dependent accumulation of miR159 mediating degradation of MYB101 and MYB33 transcripts, which are positive regulators of ABA response, thus acting as a negative feedback mechanism to resume growth when stress is relieved [40]. This shows that miRNAs function is a link between external environmental factors and endogenous hormone signaling. Expression of a large number of miRNAs was found to be altered in response to cold, heat, dehydration, and mechanical stress in Populus, suggesting their possible roles in growth of tree species [35]. Studies have shown that the oxidative stress-induced mRNA levels of Cu/Zn-superoxide dismutase (SOD; cytosolic CSD1 and plastidic CSD2) genes were found to be mediated by downregulation of miR398. The transgenics overexpressing a miR398-resistant form were more tolerant to light, heavy metal, and other forms of oxidative stress when compared to plants overexpressing regular CSD2 [41]. miRNAs have also been reported to play a role in coping with ﬂuctuations in mineral availability in the soil. Under phosphate stress, miR399 targets a phosphate transporter and a ubiquitin-conjugating enzyme (UBC24) [42], whereas miR395 regulates sulfate distribution in plants targeting sulfate transporter and ATP sulfurylases [43], thus maintaining nutrient homeostasis. Further

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| 3 Emerging Trends in Functional Genomics for Stress Tolerance in Crop Plants understanding of miRNA-mediated regulation of gene expression can help in developing novel strategies to improve plant yield, quality, and stress tolerance in crop plants. 3.3.3 Analysis of Abiotic Stress-Responsive Genes using Proteomic Approaches

Since the protein and not the gene is involved in determining the phenotype of an organism and post-translational regulatory mechanisms play a prominent role, it is essential to monitor the expression of proteins to capture the biological snapshot at any given time [44, 45]. A variety of high-throughput proteomic techniques have been developed for genome-wide proﬁling and identiﬁcation of proteins, such as twodimensional gel electrophoresis, differential in gel electrophoresis, mass spectrometry, multidimensional protein identiﬁcation technology, yeast two-hybrid screens, isotope-coded afﬁnity tagging, and stable labeling by amino acid in cell culture. The effect of progressive low-temperature stress (15, 10, and 5 1C) on rice seedlings revealed the identity of 60 upregulated proteins, using mass spectrometry, which included molecular chaperones, proteases, components involved in cell wall biosynthesis, and antioxidants [46]. In another study, differentially regulated proteins in response to chilling treatment (6 1C) were found to be involved in signal transduction, RNA processing, translation, protein processing, photosynthesis, redox homeostasis, and photorespiration [47]. Proteins related to energy metabolism were found to be upregulated, while the defense-related proteins were down-regulated in the leaf blades under long-term (48 h) exposure to cold stress (4 1C) [48]. In an attempt to identify the low-abundance proteins that differentially accumulate under chilling stress in rice leaves, cysteine proteinase, thioredoxin peroxidase, RING zinc ﬁnger like-protein, and a ﬁbrillin-like protein were identiﬁed to be upregulated [49]. Partial protein degradation of rice anther has been observed at the trinucleate stage under low-temperature treatment [50]. Protein proﬁling under salt stress shows that upregulated proteins in rice leaf sheath include fructose bisphosphate aldolases, OEE2 (OXYGEN-EVOLVING ENHANCER PROTEIN2), and SOD. Expression of SOD is a common response to cold, drought, salt, and ABA stresses in leaf sheath, thus suggesting its protective role against stress conditions [51]. The proteome of the root plasma membrane fraction of a salt-tolerant variety, IR651, was analyzed under salinity and normal conditions, and 24 differentially expressed proteins were identiﬁed [52]. In another study, 32 proteins were found to be differentially regulated by long- and short-term salt stress in rice leaf lamina, where an increase in ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) activase and ferritin occurred by 24 h of exposure to sodium chloride (50 mM) and continued to increase during the following 6 days, whereas accumulation of SOD increased and accumulation of Sadenosyl-L-methionine synthetase decreased after 7-day salt treatment, indicating a time-dependent accumulation of speciﬁc proteins during salinization [53]. The effect of salinity stress at three stages of development of rice panicle was studied using two-dimensional gel electrophoresis. Out of 29 differentially accumulating

3.3 Genome-Wide Investigations to Understand Components

proteins, a few were known to be highly abundant proteins (e.g., ascorbate peroxidase 1, triosephosphate isomerase, and glutathione-dependent dehydro-ascorbate reductase) [54]. Proteomic studies of other plant species under salinity stress have also been reported and several salt-inducible proteins identiﬁed [55–58]. The proteome of a drought-tolerant IR62266 (lowland indica) and a susceptible CT9993 (upland indica) cultivars were compared under drought stress. The proteins that increased most in abundance, in both the cultivars, in response to drought were the S-like RNase homolog, actin depolymerizing factor and RuBisCO activase, whereas the protein that decreased most was isoﬂavone reductase-like protein [59]. The protein levels of an actin depolymerizing factor, a light harvesting complex chain II, a SOD, and a salt-induced protein were changed by drought and osmotic stresses, but not by cold or salt stress, or ABA treatment. Moreover, an actindepolymerizing factor and a light-harvesting complex chain II were found to be present at high level in a drought tolerant variety, suggesting them to be important target proteins under drought stress [60]. The protein proﬁle of three genotypes (Khazar-1, Afghani, and Arvand) of wheat was studied under drought stress. Of the 121 proteins showing signiﬁcant change under drought, 57 were identiﬁed using matrix-assisted laser desorption/ionization time-of-ﬂight mass spectrometry belonging to categories of stress/defense, protein synthesis and assembly, metabolism, and storage protein. Among the 57 identiﬁed proteins, 38 proteins were thioredoxin targets, indicating a close link between drought and oxidative stress [61]. Proteins like small heat shock protein, cytosolic Cu/Zn-SOD, 20 cysteine peroxidase, cyclophilin, and the large subunit of RuBisCO were identiﬁed from drought-treated sugar-beet under ﬁeld conditions [62]. Proteomic studies have also been performed for many other plant species (maize, pea, chickpea, lupinus, holm oak, Elymus elongatum, Norway spruce, Medicago, and Vitis vinifera) under drought stress conditions. Most of these investigations identify a number of proteins as being differentially expressed during stress, but their functional signiﬁcance in the life of a plant needs further investigation. In the post-genomic era, the yeast two-hybrid system has proved to be indispensable in the ﬁeld of interaction proteomics. A large-scale yeast two-hybrid analysis has been performed to identify rice proteins involved in stress and development [63]. Conserved domains were used as bait, and one set of prey involved cold stress, drought stress, and ABA-treated samples, while another set of prey included different developmental stages of rice seed, callus and panicle. Proteins like phosphatase-type 2A regulatory B subunit were found to interact with itself, and with inositol phosphatase-like protein, 14-3-3 protein, an ortholog of a wheat translation initiation factor, and carboxypeptidase, indicating their involvement in the abiotic stress response network [63]. To unravel the interactome of proteins associated with the abiotic stress response in wheat, the yeast two-hybrid GAL4 system was used. Transcription factors and signal transduction components (73 proteins) associated with the stress response and also vernalization and/or ﬂower development were used as bait. Cold acclimated, dehydration, and development libraries were screened, and interaction networks were prepared from the data involving 97 interaction pairs. On average, proteins had 2.4 interaction partners and

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| 3 Emerging Trends in Functional Genomics for Stress Tolerance in Crop Plants overlapping three- to four-interaction loops composed of signal transduction components (phospholipase C, receptor-like protein kinase, GTP-binding protein, a-tubulin and TaTIL (true lipocalin)) were found to be involved in abiotic stress [64].

3.4 Quantitative Trait Loci for Abiotic Stress Tolerance

Most of the useful agronomic traits of crop plants, including stress tolerance, are quantitatively inherited and controlled by multiple quantitative trait loci quantitative trait locus (QTLs). QTLs have been identiﬁed for tolerance against submergence, salinity, and drought. The SUB (SUBMERGENCE TOLERANCE) gene was mapped as a major QTL using the submergence-tolerant variety FR13A [65]. The QTL region codes for three ERF-like transcription factors. The SKC1 QTL conferring salt tolerance is located on chromosome 1 and has been found to be a sodium transporter (OsHKT8 [66]). Bernier et al. [67] have reported a QTL on chromosome 12 for dehydration stress tolerance, and found it to increase grain yield and harvest index under stress conditions. Another QTL on chromosome 1 confers drought tolerance in rain-fed lowland rice [68]. It is obvious that identiﬁcation and introgression of useful QTLs can contribute signiﬁcantly to natural variation in the abiotic stress response, and it is of great importance to improve crop yield [16]. Apart from the utility of natural allelic variation, induced mutant populations of crops also have great potential to uncover stress tolerance-related variability by employing TILLING (Targeting Induced Local Lesions in Genomes) [69–73].

3.5 Networking the Stress Response Gene Function

Various abiotic stresses like salt, drought, and cold stress, although distinct in their physical nature, elicit speciﬁc plant responses as well as activate some common reactions in plants like osmotic stress and associated oxidative stress [74]. This entails an understanding of the cross-talk and networking during plant stress response. 3.5.1 Sensing Systems

Few stress sensors have been identiﬁed, but the speciﬁcity or cross-talk at the level of sensors is yet to be deciphered. In yeast, an osmosensory histidine kinase, SLN1, senses osmotic stress and activates the high osmolarity glycerol response 1– mitogen-activated protein kinase (MAPK/MPK) cascade [75]. An SLN1 homolog, AtHK1, a histidine kinase, was identiﬁed in Arabidopsis whose expression is induced by salt [76]. The NtC7 transcript coding for a putative receptor like kinase

3.5 Networking the Stress Response Gene Function

accumulates rapidly under wounding, salt and osmotic stress. Its overexpression induces osmotic stress tolerance [77]. CREI, a cytokinin receptor gene in Arabidopsis, has been implicated in osmotic stress sensing in plants [78]. CREI and SLN1 have similar organization of the cytoplasmic histidine kinase and receiver domain. 3.5.2 Calcium and Calcium-Sensing Proteins

Calcium (Ca2 þ ) is considered as the second messenger in abiotic stress signaling [79]. Numerous physiological processes, including stomatal movement, are regulated by speciﬁc calcium signatures. It was found that in wild-type Arabidopsis guard cells, different abiotic stresses and ABA elicited calcium oscillations inducing stomatal closure. In det3 mutant (V-ATPase mutant), only cold stress and ABA elicited calcium oscillations resulting in stomatal closure but not oxidative stress, indicating that there are speciﬁc calcium-dependent pathways for different stresses [80]. The calcium signature changes depending on particular stress and cell type [81]. The response to stress alters markedly after previous stress experiences. It was seen that drought pretreatment increased the level of droughtinducible calcium-regulated gene expression and tolerance to stress [82], indicating cross-talk among the abiotic stress signal transduction pathways. Calcium channels have also been found to be involved in abiotic stress signaling. As found in tomato, cADP-ribose-gated calcium channels are involved in ABAinduced expression of cold stress-regulated genes [83]. Inositol 1,4,5-triphosphate (IP3)-gated calcium channels have been found to be involved in dehydration- and salt stress-induced cytosolic calcium elevation [84, 85]. The phosphatase-deﬁcient Arabidopsis fry1 mutant exhibits impaired ABA-induced IP3 transient expression and leads to accumulation of IP3 and hypersensitivity to ABA, salt and cold stress [86]. When ABA synthesis is induced under salinity, the downstream G-proteinassociated receptor may elicit a speciﬁc calcium signature during salt stress. These various stress-induced calcium signatures are then sensed and transduced by the calcium signature sensors, CAM, calcium-dependent protein kinase (CDPKs), calcineurin B-like proteins (CBLs), and calcium-regulated phosphatases. Calmodulin proteins consist of calcium-binding EF-hands (a helix-loop-helix, HLH structure). Snedden and Fromm [87] reviewed the involvement of CAMs in a range of cellular processes. The Arabidopsis genome harbors seven CAM and 50 CAMlike protein-encoding genes, and their expression is associated with every plant organ [88]. Three CAM-encoding genes are identiﬁed in rice (OsCAM1, 2, and 3). Among these, OsCAM1 and 3 are regulated by abiotic stress [89]. Three Arabidopsis CAM genes show abiotic stress-induced temporal differential accumulation. CML37 displayed around 100-fold increase in response to wounding, osmotic stress, and drought. CML24, also known as TCH2, is induced by heat, cold, H2O2, ABA and indole acetic acid, indicating its involvement in multiple stress and hormone signaling cascades [90]. The involvement of CAMs in various abiotic stress signaling pathways has also been shown via its interacting proteins, as

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| 3 Emerging Trends in Functional Genomics for Stress Tolerance in Crop Plants reported in tobacco and rice where MAPK phosphatases, NtMKP1 and OsMKP1, are found to be involved in wounding response cascade [91, 92]. CDPKs have been shown to be involved in both biotic and abiotic stress signaling [93]. Arabidopsis CDPK genes AtCPK10 (AtCDPK1) and AtCPK11 (AtCDPK2) are induced by drought and high salt stress, but not by low temperature and heat [94]. In recent years, AtCPK3, AtCPK6, AtCPK4, AtCPK11, and AtCPK32 have been shown to regulate ABA signal transduction pathways, guard cell ion regulation, and salt stress responses [95–97]. In rice, OsCPK13 (OsCDPK7) and OsCPK7 (OsCDPK13) have been implicated in stress signaling, which are induced under cold and salt stress [98, 99]. However, transgenic lines overexpressing OsCDPK7 showed enhanced expression of salt- and drought stress-induced genes, but not those induced by cold stress [98], while the proteomic analysis of OsCPK7 overexpressing lines revealed the upregulation of proteins like fructokinase and a-tubulin, which are involved in repairing damage caused by cold stress [100]. It has been proposed that OsCPK13 (OsCDPK7) acts at one of the branch points of cold- and salt/drought-responsive pathways, where it acts upstream of several late induced genes like Rab16A, SalT, and WSI18 [98]. In a recent study, of the 29 CDPKs of rice transcripts, 17 genes were found to be regulated by either cold, drought, salt, and/or heat stress [101]. We have identiﬁed two more members of the CDPK gene family in rice and out of 31 CDPKs, seven were differentially regulated under cold and/or desiccation stress [102]. Similarly, 12 wheat CDPK genes have also been implicated in stress responses [103]. The presence of multiple isoforms with very speciﬁc inducibility in specialized cell types makes it difﬁcult to decipher the networking of CDPKs. It is, however, obvious that CDPKs perceive a wide range of signals and integrate at different levels of signaling cascades, modulating the appropriate downstream responses. Two gene families, encoding nonenzymatic CBLs and its kinase partner CBLinteracting protein kinase (calcineurin B-like protein-interacting protein kinaseCIPK), form a complex network of interactions between CBLs and CIPKs [104]. This suggested an array of cross-talk between different signal transduction pathways and highly regulated signaling events based on CBL–CIPK interaction. The most studied members of this family are SOS3 and SOS2. SOS3 belongs to a novel subfamily of EF-hand type calcium-binding proteins, which when myristoylated help targeting it to membranes where SOS1 is located [74]. SOS1 encodes a plasma membrane Na þ /H þ exchanger (antiporter) [105]. SOS2 represents a novel family of proteins that contain an SNF1-like catalytic domain and a unique regulatory domain that interacts with SOS3 [106]. In the SOS pathway, the salt stress is sensed by some unknown sensor that elicits cytoplasmic calcium perturbation. High sodium stress initiates a calcium signal that activates the SOS3– SOS2 protein kinase complex, which in turn stimulates the Na þ /H þ exchange activity of SOS1 and regulates, both transcriptionally and post-transcriptionally, the expression of genes. This complex may also stimulate or suppress the activities of other transporters involved in ion homeostasis under salt stress, such as vacuolar H þ -ATPases and pyrophosphatases, vacuolar Na þ /H þ exchanger,

3.5 Networking the Stress Response Gene Function 2þ

þ

vacuolar transporters like the CAX1 Ca /H antiporter, and plasma membrane K þ and Na þ transporters [74, 106]. Expression of the constitutively active SOS2 in sos2 or sos3 mutant plants can rescue the salt-sensitive phenotype in the mutant shoot, suggesting that SOS2 kinase activity is sufﬁcient for the SOS pathway in salt tolerance [74]. Additional targets of SOS-regulatory pathway have been identiﬁed where Na þ ion homeostasis is maintained by interacting with other regulatory proteins. AtHKT1 has low afﬁnity for Na þ as a transporter and seems to mediate Na þ entry into the root cells during salt stress [107]. The athkt1 mutation suppresses the sos3 mutation [108], indicating that the SOS3–SOS2 kinase complex may be a negative regulator of AtHKT1 gene and may be involved in preventing Na þ ion inﬂux during salt stress [74]. Among the 30 OsCIPK genes identiﬁed from rice, 20 genes are induced by either/or drought, salt, ABA, polyethylene glycol, and cold stress conditions. Two OsCIPK genes, OsCIPK01 and OsCIPK09, are induced by multiple stress conditions, while OsCIPK03, 12, and 15 are involved in tolerance to cold, drought and salt stress, respectively, indicating speciﬁc response, as well as multiple stress response among isoforms of OsCIPKs in abiotic stress signaling pathways [109]. The expression of CIPK3 from Arabidopsis was modulated by ABA and stress conditions, but the cipk3 mutant did not show altered expression pattern of the drought-induced genes, indicating the CIPK3 gene to be at a nodal position of the ABA-dependent and ABA-independent pathways in the stress response [110]. CBLs are characterized by four HLH calcium-binding domains termed EF-hands. In Arabidopsis, 10 isoforms of CBL have been discovered [111]. AtCBL1 functions as positive regulator of drought and salt response, but as a negative regulator of cold response, and was shown to interact with 10 CIPKs among the 25 CIPKs identiﬁed from Arabidopsis [112]. Only four CIPKs (AtCIPK1, 8, 18, and 24) exhibited similar afﬁnity towards both the CBL1 and CBL9 proteins, indicating that these isoforms of kinases could be involved in speciﬁc stress pathways that are independent or could be at nodes of diverse pathways that converge at different levels of interaction [113]. In contrast to CBL1, the expression of CBL9 is not only induced by salt, cold, and drought stress, but it is also highly and rapidly induced by ABA. Another CBL gene product, AtCBL10, physically interacts with CIPK24 (SOS2), and is responsible for salt storage and detoxiﬁcation [114]. These ﬁndings indicate the CBL–CIPK interacting network is an integral part of abiotic stress response pathways. 3.5.3 MAPK Proteins: At the Crossroads of Signaling Pathways

The MAPK signaling cascade shows strong evidence for cross-talk in abiotic stress signaling. The Arabidopsis genome encodes 60 MAPK kinase kinases (mitogenactivated protein kinase kinase kinaseMAPKKK/MEKKs), 10 MAPK kinases (mitogen-activated protein kinase kinase, MAPKK/MKKs), and 20 MAPKs. In Arabidopsis, signals perceived by the 60 MAPKKKs are transduced through 10

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| 3 Emerging Trends in Functional Genomics for Stress Tolerance in Crop Plants MAPKKs followed by 20 MAPKs [115, 116]. In Arabidopsis, MPK4 and MPK6 are activated by cold, salt, drought, wounding, and touch [117], and MPK3 is activated by osmotic stress [118]. Functional and interaction analysis showed that MEKK1 functions upstream of signaling cascade with MKK1, MKK2, and MPK4 in response to cold and salt stress [119]. The MEKK1–MKK2–MPK4/MPK6 cascade functions as part of cold and salt stress signaling [117, 120]; in contrast, the MEKK1–MKK4/MKK5–MPK3/MPK6 cascade regulates the pathogen defense response pathway via the WRKY22 and WRKY29 genes [121, 122]. This shows the convergence and divergence between abiotic stress signaling pathways, as well as between biotic and abiotic stress signaling cascades. Rice MAPKs, OsBWMK1 (OsMPK12), OsWJUMK1 (OsMPK8), OsMAPK5, and OsMAPK4, are differentially upregulated under high salinity, drought, cold, and a range of environmental cues [123, 124]. The kinase activity of OsMAPK5 is found to be inducible by ABA, biotic, and abiotic stresses, and it positively regulates drought, salt, and cold tolerance, and negatively modulates disease resistance [125]. High NaCl or hyperosmotic condition activates salt-induced MAPK (saltinduced mitogen-activated protein kinase, SIMK) in alfalfa cells and salicylic acidinduced protein kinase (SIPK) in tobacco cells [126, 127]. SIMK kinase, a close homolog of AtMKK4, AtMKK5, and NtMEK2, was found to activate SIMK under salt stress and, further, SIMKK activates MMK3 [127]. Hence, different MAPKs might interact with speciﬁc MAPKKs and multiple MAPK cascades can be triggered by same signal. Abiotic and biotic stress signals trigger MAPK (SIPK/ wounding-induced protein kinase (WIPK)) and calcium-dependent protein kinase (NtCDPK2) signal transduction pathways. These two pathways converge partially and induce a subset of early stress-responsive genes, and control the induction of various stress/defense responses. Moreover, NtCDPK2 causes inhibition of SIPK and WIPK activation through ethylene as a part of a resetting system, suggesting a balanced interplay between parallel stress signaling pathways [128]. These ﬁndings indicate that such MAPK genes are activated by multiple stress signals that are transduced through signaling cascades. These cascades show convergence of diverse stress signals in transducing pathways.

3.5.4 Other Pathways

Few other prominent pathways involved in abiotic stress response are being deciphered. Glyoxalase I and II are zinc-binding enzymes of the glyoxalase pathway that carry out catabolism of methyl glyoxal (MG), which accumulates during salt stress [129]. The overexpression of glyoxalase genes, glyI from Brassica juncea and glyII from Oryza sativa, individually or together in tobacco, conferred tolerance against high salt and MG [130], and their yield remained unaffected. The transgenic plants showed increased tolerance to zinc stress by maintenance of glutathione homeostasis and increase of phytochelatins, which are involved in quenching of ROS generated under stress conditions [131, 132].

3.5 Networking the Stress Response Gene Function

The study of the sfr (sensitivity to freezing) mutant in Arabidopsis showed a change in expression of cold-inducible genes. Tolerance to osmotic stress was also found to be reduced in the sfr6 mutant, consistent with its role in osmotic stress tolerance for the cold-, ABA-, and drought-inducible genes whose expression is affected by the sfr6 mutation [133, 134]. Thus, SFR6 clearly is involved in both cold and ABA signaling pathways, and could play a cross-talk component in both of the signaling pathways. Analysis of A20/AN1 zinc ﬁnger domain-encoding genes in rice (18 genes), Arabidopsis (14 genes), Zea mays, and Populus revealed that all the members of this family showed stress-responsive expression [135, 136]. The well-characterized gene belonging to this class from rice, OsiSAP1, is responsive to multiple environmental stresses and confers abiotic stress tolerance on overexpression in tobacco [137]. The SAP family proteins may be involved in downregulating pathways associated with abiotic stress-associated injuries such as cell death by ubiquitinylating the key proteins in cytosol and hence targeting them for degradation [33, 135, 137]. The SnRKs constitute a family of protein kinases that play an important role in plant responses to nutritional and environmental stresses. SnRK2 (type 2 SNF1related protein kinase) that is activated by ABA (OST1 (OPEN STOMATA1)/ SnRK2E) has been shown to function upstream of ABA-responsive expression of RD22 and RD29B, and of stomatal closure in an ABA signal transduction pathway [138]. The C-terminal regulatory domain of SRK2E/OST1 is required for its activation by both ABA and osmotic stress in Arabidopsis. This domain is functionally divided into two: Domain II is required only for the ABA-dependent activation of SRK2E/OST1 and Domain I is responsible for the ABA-independent activation. It was found that the regulatory domain of SRK2E/OST1/SnRK2.6 interacts with ABI1, and integrates ABA and osmotic stress signals controlling stomatal closure in Arabidopsis [139]. In rice, 10 SnRK2 protein kinases are reported and all of them were shown to be activated by salt stress in protoplasts, while three of them are activated by ABA [140]. Rice ABA-activated SnRK2 can phosphorylate an ABREbinding factor TRAB1 [141]. 3.5.5 Transcription Factors at the Junction

Abiotic stress response pathways can be broadly divided in two categories: ABAdependent and ABA-independent pathways [142]. The ABA-independent pathway includes dehydration-responsive element binding (DREB)/C-repeat binding (CBF) transcription factors that bind to drought response element/C-repeat cis-regulatory elements. DREBs belong to the ERF family of transcription factors consisting of two subclasses, DREB1/CBF and DREB2, that are induced by cold and dehydration, respectively. In Arabidopsis, DREB1A/CBF3, DREB1B/CBF1, and DREB1C/CBF2 are induced early and transiently by cold, but not by dehydration and high salinity [143, 144]. On the other hand, DREB2A and DREB2B are induced by dehydration and not by cold stress [143]. In rice, DREB1A, DREB1B,

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| 3 Emerging Trends in Functional Genomics for Stress Tolerance in Crop Plants DREB1C, DREB1D, and DREB2A have been isolated. OsDREB1A and OsDREB1B on overexpression impart improved tolerance to drought, high salt and cold stress [145]. The MYC-type bHLH transcription factors were found to have redundant involvement in regulation of cold-responsive expression of DREB1/CBF genes. SIZ1 (a SUMO E3 ligase)-dependent sumoylation of ICE1 (INDUCER OF CBF EXPRESSION1) activates and stabilizes the protein, and facilitates the expression of CBF3/DREB1A and represses MYB15, which is a negative regulator [146], and this in turn leads to low-temperature tolerance [147]. It was reported that both CBF2 and ESK1 (ESKIMO1) are negative regulators of CBF genes, but they act through independent pathways [148, 149]. MYC-like sequence-binding proteins, ANAC019, ANAC055, and ANAC072, bind to novel cis-acting element of ERD1 promoter [150]. Zat12 is another abiotic stress-responsive transcription factor, which is induced under cold conditions and downregulates the expression of CBF genes, indicating the negative regulatory circuit that affects CBF cold response pathway [151]. HOS1 encodes a novel protein with a RING-ﬁnger motif. The hos1 mutation results in sustained and superinduction of CBF2, CBF3 and their target regulon genes speciﬁcally during cold stress [152]. Therefore, HOS1 acts as a negative regulator of COR genes by modulating the expression level of the CBFs [153]. ABA is an important component in adaptation to abiotic stresses, drought, and high salinity, as well as in seed maturation and dormancy [138, 154]. ABAinducible genes contain a conserved, ABA-responsive cis-regulatory element named abscisic acid-responsive element ABRE (PyACGTGGC) and the transcription factors that bind to them are known as ABRE-binding protein/factors (abscisic acid-responsive element binding protein/factorAREB/ABFs). AREB1/ ABF2, AREB2/ABF4, and ABF3 are upregulated by ABA, dehydration, and high salinity stresses, and their overexpression results in ABA-hypersensitive phenotypes and improvement of drought stress tolerance and induced expression of some ABA-responsive genes, such as LEA class genes (RD29B, Rab18) [155]. MYC-1F, AtMYC2 (RD22BP1), and AtMYB2 were shown to bind cis elements in the RD22 promoter, resulting in ABA-dependent expression of the droughtinducible RD22 gene [156, 157]. A NAC transcription factor known as RD26 is induced by drought, salt, ABA, and jasmonic acid treatment, and mediates an important role in the cross-talk between ABA signaling and jasmonic acid signaling [158]. SNAC1 and SNAC2 transcription factors from rice have similarity in both stress-induced expression and ABA sensitivity [159, 160]. SNAC1 is not induced by wounding and its overexpression improves drought resistance, but not cold tolerance, whereas SNAC2 is induced by wounding and its overexpression improves cold stress tolerance [159]. Another NAC transcription factor from rice, OsNAC6, induced by cold, drought, and salinity, on overexpression improved tolerance to dehydration and high salt, but the transgenic rice plants exhibited growth retardation and low reproductive yields [161]. This suggests that different stress-responsive NAC transcription factors might activate transcription of different sets of target genes, which have diverse functions but ﬁnally lead to stress tolerance. Heat stress transcription factors also form a network controlling

3.6 Functional Characterization of Stress Response Genes by the Transgenic Approach

expression of genes involved in the heat stress response and also seem to have a role in other abiotic stress responses [162].

3.6 Functional Characterization of Stress Response Genes by the Transgenic Approach

A large number of successful efforts have been made to characterize abiotic stressresponsive genes using the transgenic approach [3]. Plant genes encoding either effector proteins, like osmolyte (e.g., proline, trehalose)-producing proteins, channel proteins, and detoxiﬁcation enzymes, or regulatory molecules (e.g., kinases and transcription factors) that can regulate a whole downstream cascade of genes and eventually increase tolerance, have been widely studied in crop plants (see [3] and references therein, [109, 159, 161, 163–170]). Transgenic systems also have great potential to serve as a way to tag genes and promoters related to plant stress response [171–175]. A good yield from the transgenic crop plants under stress conditions is the ultimate aim to be achieved. However, limited reports have shown the effect on yield due to the presence of transgenes and these could be categorized in two distinct types; either the yield is penalized in the transgenics under normal conditions, but they perform better than the wild-type under stress conditions, or else the yield in transgenics is comparable to the wild-type without stress and better under stress condition. Recently, examples of both types of effect on yield have been seen with certain genes. When the OsLEA3-1 gene was overexpressed in a drought-sensitive japonica rice Zhonghua 11, transgenics showed signiﬁcant higher relative yield (yield under drought stress treatment/yield under normal growth conditions) than the wild-type under drought stress conditions although a yield penalty in the transgenic lines compared to the wild-type was noticed under normal growth conditions [176]. Similarly, rice transgenics expressing a SAP family protein gene (OsSAP8) showed 50% reduction in yield during unstressed conditions, although they performed better than wild-type under stress [168]. Use of a stress-inducible promoter might solve the problem of yield penalty in the future. Solanum tuberosum cv. Umatilla, expressing three Arabidopsis CBF genes constitutively, showed compromised plant growth and tuber formation, which could be overcome using a stress-inducible promoter for transgene expression [177]. In the later category of effect on yield, transgenic Brassica napus expressing Arabidopsis Na þ /H þ antiporter gene (AtNHX1) showed no compromise of yield in normal conditions and a higher yield when under salt stress [178]. Interestingly, AtNHX1 on overexpression in tomato plants conferred salt tolerance with no penalty on fruit per plant either in stressed or unstressed conditions [179]. Field trials with transgenic wheat carrying the same gene have also shown improved grain yield in saline soil [180]. Tobacco plants expressing PDH45 (PEA DNA HELICASE45) constitutively did not develop any sign of stress and set normal viable seeds under continuous salinity stress without any reduction in plant yield in terms of seed weight per pod [181]. Two Brassica genes involved in the

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| 3 Emerging Trends in Functional Genomics for Stress Tolerance in Crop Plants glyoxalase pathway, glyI and glyII, were overexpressed in tobacco and even double transformants showed hardly any yield penalty under normal and stress conditions [130, 132]. Rab16A, a gene from salt tolerant indica rice Pokkali, in transgenic tobacco produced normal seed set in unstressed condition. However, under salinity, transgenics produced 79–86% of the total seeds compared with untreated wild-type plants, while the wild-type plants failed to sustain growth within 1 week after salt treatment (200 mM NaCl) and could not ﬂower or set viable seeds [182]. In maize, overexpression of the endogenous phosphatidylinositol-speciﬁc phospholipase C gene ZmPLC1 did not produce any pleiotropic effect in transgenic plants. However, ZmPLC1 lines have higher yield after desiccation stress, although the yield is not comparable to wild-type under normal conditions [183]. Similar results were found during ﬁeld trials of maize expressing a transcription factor, ZmNF-YB2, wherein the best-performing transgenic maize line had around 50% more yield relative to controls under drought stress conditions [184]. Thus, incremental beneﬁts have been imparted by various transgenes used to improve abiotic stress tolerance in terms of plant growth and crop yield. 3.7 Conclusions

The availability of genome sequences for various monocot and dicot species has thrown open a challenge for unraveling the coded language for its function. Determining the functional aspect of the sequences needs a combination of various genomic and proteomic approaches. Networks of genes involved in abiotic stress responses have already been proposed. With recent developments, it has become clear that cross-talk among the various abiotic pathways is prominent and extends even further [185]. Certain genes are found to be working at nodal positions of diverging pathways and even distinct pathways converge at various phases of networking. The potential genes and QTLs that could confer tolerance against abiotic stresses are being functionally characterized using transgenic approach (overexpression and RNA interference), where the effects of the transgene(s) under various stress conditions are studied. For this, use of stress-inducible promoters is becoming important. Genome-wide studies have accelerated the discovery of new genes involved in the complex stress-responsive pathways, which will facilitate the identiﬁcation of key players of the network worth manipulation through either breeding or genetic engineering for the development of stresstolerant plants without jeopardizing their yield.

Acknowledgments

Our research is supported by the Department of Biotechnology, Government of India. P.K.D., A.B., and J.G. acknowledge research fellowships from the University Grants Commission and Council of Scientiﬁc and Industrial Research.

References

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Part Two Stress Responses and Newly Involved Plant Hormones

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4 Stress Physiology of Higher Plants: Cross-Talk between Abiotic and Biotic Stress Signaling Miki Fujita, Yasunari Fujita, Fuminori Takahashi, Kazuko Yamaguchi-Shinozaki, and Kazuo Shinozaki

Abstract

Plants have developed a number of unique mechanisms to cope with diverse biotic and abiotic stresses during the course of evolution. Under natural circumstances, plants may be exposed to multiple environmental stresses simultaneously. However, the molecular mechanism involved in dealing with each stressor has been studied relatively independently; thus, to produce a comprehensive picture of the plant response to multiple environmental stresses under natural conditions it is necessary to identify the convergence points between these biotic and abiotic stress signaling pathways. Several key players involved in the cross-talk between stress signaling pathways have been identiﬁed, including transcription factors and kinases. Accumulating evidence also suggests that the pathways regulated by abscisic acid, salicylic acid, jasmonic acid, and ethylene, as well as reactive oxygen species, play crucial roles in the cross-talk between biotic and abiotic stress signaling. Moreover, recent observations suggest that epidermal tissues, including the cuticle and stomata, are pivotal points of convergence between multiple stress signaling pathways and function as a ﬁrst line of defense against environmental stress.

4.1 Introduction

Unlike animals, plants are immobile, which means they cannot select their own environment; thus, they have evolved a wide range of unique and sophisticated survival strategies to cope with biotic and abiotic stresses. Signiﬁcant effort has been focused on understanding the signal transduction pathways activated by these stimuli and our knowledge in this area is considerable. In several cases, we Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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| 4 Stress Physiology of Higher Plants: Cross-Talk between Abiotic and Biotic Stress Signaling have constructed linear models of individual signal transduction cascades. However, considering that stress may occur at diverse stages of plant development and that multiple stressors may affect plants simultaneously, these signal transduction pathways must interconnect to allow plants to prioritize their responses and ensure their survival using limited resources. The plant hormones ethylene (ET), jasmonic acid (JA), salicylic acid (SA), and abscisic acid (ABA) are endogenously produced, low-molecular-weight molecules that primarily regulate protective responses against biotic and abiotic stresses through synergistic and antagonistic actions, referred to collectively as signaling cross-talk [1–3]. In addition, the production of reactive oxygen species (ROS) has been proposed as a key link between biotic and abiotic stress signaling [4–6]. Moreover, epidermal tissues such as the cuticular layer and stomata appear to play crucial roles not only as barriers to protect the aerial organs from damage caused by biotic and abiotic stresses, but also in biotic and abiotic stress signaling. Accumulating data derived from largescale transcriptome analyses based on DNA microarray technology strongly support the existence of such cross-talk [7–11]. Biotic and abiotic stresses regulate the expression of different but overlapping sets of genes. Thus, an expanding body of evidence supports the view that plant signaling pathways consist of intricate networks with frequent cross-talk, which allow plants to regulate both abiotic stress tolerance and disease resistance. In this chapter, we focus on several key molecules that comprise the convergence points between biotic and abiotic stress signaling pathways, including those found in the cuticle and stomata, which serve as the ﬁrst line of defense against environmental stimuli, with special emphasis on the role of transcription factors and protein kinases, particularly those involved in hormone and/or ROS signaling (Figure 4.1).

4.2 Cuticles and Stomata

As the plant surface is continually exposed to external stimuli, it can be considered the ﬁrst site of convergence between biotic and abiotic stresses. The cuticle, which consists of cutin and waxes, represents a primary barrier that minimizes water and solute loss and protects the aerial organs in plants from damage caused by various abiotic and biotic stresses [12, 13]. In Arabidopsis, WIN1/SHN1 (WAX INDUCER1/SHINE1), an AP2/ERF (APETALA2/ethylene-responsive element binding factor)-type transcription factor, activates the expression of genes associated with wax and cutin biosynthesis [14, 15]. Transgenic Arabidopsis plants overexpressing WIN1/SHN1 display increased cuticular permeability, reduced stomatal density, and enhanced drought tolerance [16]. Additionally, the disruption of one of the direct targets of WIN1/SHN1, LACS2 (LONG-CHAIN ACYL-COA SYNTHETASE2), which is required for the biosynthesis of cutin polyester and has been implicated in the alteration of cuticular permeability [17], results in increased resistance to Botrytis cinerea and Sclerotinia sclerotiorum [18, 19]. Bessire et al. [18] demonstrated that the increased permeability of the cuticle in the lacs2 mutant

4.2 Cuticles and Stomata

Figure 4.1 Convergence points in abiotic and biotic stress signaling networks.

enabled the induction of antifungal compounds leading to the inhibition of infection by speciﬁc necrotrophic pathogens, suggesting that a permeable cuticle, rather than functioning as a direct barrier against pathogen invasion, alters the perception of elicitors. This view has been corroborated by recent studies of chitinase-expressing plants and the bdg (bodyguard) mutant, which exhibits defects in cuticle structure and integrity [20, 21]. Taken together, these ﬁndings suggest that the composition of the cuticle is modulated by speciﬁc environmental cues. Stomatal pores located in the plant epidermis control transpirational water loss. The guard cells surrounding each pore integrate a variety of environmental and endogenous signals to tightly regulate stomatal aperture [22, 23]. Stomatal closure during water stress requires ABA and several downstream signal transduction components, including the guard cell-speciﬁc kinase OST1 (OPEN STOMATA1), hydrogen peroxide (H2O2), and nitric oxide (NO) [22, 24–26]. These signals increase the cytosolic level of Ca2 þ , which induces stomatal closure via the efﬂux of potassium and anions from guard cells and the removal of organic osmolytes [26, 27] (Figure 4.2). Interestingly, the stomatal regulatory pathway shares a number of its signaling components with the disease response pathway. For instance, the NADPH-dependent respiratory burst oxidase homologs AtrbohD and AtrbohF, which regulate the production of ROS during ABA-induced stomatal closure, also mediate hypersensitive cell death in response to an avirulent pathogen attack [6, 28, 29]. NO is also a multifunctional signaling molecule. A loss of AtNOA1 (NO-associated) function, which is involved in NO production, impairs

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Figure 4.2 Signaling cross-talk in stomatal guard cells.

NO-mediated stomatal closure in response to ABA [30] and diminishes the elicitor response, resulting in compromised basal resistance to Pseudomonas syringae [31]. Furthermore, the Atnoa1 mutant exhibits enhanced sensitivity to salt and oxidative stress, suggesting that NO functions as a ROS scavenger [32]. Recent work has revealed that stomata also play an important role in host defense (Figure 4.2). It has generally been assumed that stomatal pores are passive sites of entry for pathogens. However, Melotto et al. [33] revealed that stomata close in response to microbes to prevent their invasion. Arabidopsis guard cells are able to perceive fungal elicitors [34] and bacterial cell-surface molecules known as microbe-associated molecular patterns (MAMPs), such as lipopolysaccharide (LPS) and ﬂagellin, which trigger NO synthesis and stomatal closure [33, 35]. The stomata of the ABA-insensitive stomata mutant ost1-2 and the ABA-deﬁcient mutant aba3-1 fail to close in response to LPS and ﬂagellin, indicating that MAMP-triggered stomatal closure is dependent on ABA and NO signaling. Interestingly, virulent pathogenic bacteria are able to overcome this barrier and re-open the stomata. Melotto et al. [33] revealed that the phytotoxin coronatine (COR), made by pathogenic P. syringae, promotes stomatal re-opening through the E3 ligase subunit COI1, a key component of JA signaling (Figure 4.2). COR did not prevent ABA-induced NO synthesis in wild-type plants, suggesting that COR acts downstream of ABA and NO to prevent stomatal closure [33]. A comparable response to the phytopathogenic fungus S. sclerotiorum has also been reported [36]. Oxalic acid, a virulence factor for several phytopathogenic fungi, interferes with ABA-induced stomatal closure causing foliar dehydration and lesion expansion. The open stomata are also exploited for hyphal emergence, resulting in secondary colonization and sclerotia formation [36]. Thus, the manipulation of stomatal

4.3 Hormone Signaling Governs Biotic and Abiotic Stress Responses

conductance could be a common strategy for suppressing plant resistance by both biotrophic and necrotrophic microorganisms. Taken together, these data support the view that epidermal tissues, including the cuticular layer and stomata, are a crucial point of convergence between biotic and abiotic stress response signaling pathways and function as the ﬁrst line of defense against environmental stresses. 4.3 Hormone Signaling Governs Biotic and Abiotic Stress Responses

Plant hormones are key regulators of stress signaling pathways as well as plant growth and development. In general, SA, JA, and ET are thought to play central roles in biotic stress signaling upon pathogen infection, whereas ABA is thought to mediate abiotic stress signaling. Recently, however, the involvement of ABA in defense signaling via synergistic or antagonistic interactions with other hormone signaling pathways was reported. In many cases, ABA acts as a negative regulator of disease resistance [3, 37]. For example, suppression of the SA-dependent defense response by ABA has been shown in the ABA-deﬁcient sitiens tomato mutant, which shows increased resistance to PstDC3000 and B. cinerea [38, 39]. More recently, Yasuda et al. [40] demonstrated that ABA suppresses the induction of systemic acquired resistance (SAR) meditated by SA in Arabidopsis. ABA or NaCl treatment inhibits induction of the SAR pathway both upstream and downstream of SA, which is activated by the SAR-inducing chemicals 1,2-benzisothiazol-3(2H)-one1,1-dioxide and benzo(1,2,3)-thiadiazole-7-carbothioic acid S-methyl ester, respectively [40]. Antagonistic interactions between the ABA and JA/ET pathways have also been intensively studied. The ET-insensitive ein2 mutant, which shows increased susceptibility to necrotrophic pathogens [41], is allelic to era3 (enhanced response to aba3), which produces enhanced ABA sensitivity during seed germination [42, 43]. Furthermore, the jar1/jin4 (ja-resistant1/ja-insensitive4) mutants, which are hypersensitive to the ABA-mediated inhibition of germination, exhibit antagonistic responses to ABA and JA [44, 45]. Additionally, the exogenous application of ABA downregulates JA- and ET-responsive defense gene expression in wild-type plants, whereas increased expression of these genes was observed in untreated ABA-deﬁcient mutants [46]. Taken together with the ﬁnding that exogenous methyl jasmonate and ET do not affect the suppression of defense gene expression induced by exogenous ABA, these data suggest that the ABA-mediated abiotic stress response is a dominant process [46]. Recently, de Torres-Zabala et al. [158] demonstrated that PstDC3000 activates the ABA biosynthetic pathway in order to promote its virulence. Considering the fact that pathogenic bacteria must maintain a high water potential in order to establish disease inside a plant [47], the well-established role of ABA in water stress may also explain the effect of this hormone on plant susceptibility. ABA is also associated with disease resistance. ABA-deﬁcient mutants are insensitive to -aminobutyric acid (BABA)-induced resistance. BABA, a nonprotein amino acid, is a potent inducer of resistance to an exceptionally wide range of

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| 4 Stress Physiology of Higher Plants: Cross-Talk between Abiotic and Biotic Stress Signaling pathogenic organisms [3]. In the case of BABA-induced resistance to oomycetes and fungi, ABA acts as a positive regulator of disease resistance through the potentiation of callose deposition [48, 49]. Recent studies have shown that the basic helix–loop–helix transcription factor AtMYC2 plays a role in multiple hormone signaling pathways, suggesting that AtMYC2 is a key regulator of the cross-talk between the biotic and abiotic stress responses via hormone signaling. Genetic analysis of the jasmonateinsensitive jin1 mutant revealed that JIN1 is allelic to AtMYC2 [50], which was initially identiﬁed as a transcriptional activator involved in ABA-mediated drought stress signaling [51]. Mutations in this gene result in the increased expression of JA/ET-mediated pathogen defense genes such as PR4/HEL and PDF1.2, presumably through the suppression of ERF1, a transcriptional activator of PDF1.2 [52], causing increased resistance to necrotrophic fungal pathogens [46, 50, 53]. However, even in atmyc2/jin1 plants, exogenous ABA has an inhibitory effect on the JA-regulated expression of defense genes, suggesting that AtMYC2 is not involved in the antagonistic effect of ABA on the JA/ET defense pathway, and that it is not the only point of convergence [46]. Indeed, genetic analyses using JA and ABA signaling mutants support the view that ABA regulates AtMYC2 expression by activating COI1-dependent JA signaling [50]. Thus, the antagonistic interactions between ABA and the JA/ET signaling pathway appear to regulate defense- and stress-responsive gene expression in response to biotic and abiotic stresses [46, 50]. On the other hand, JIN1/AtMYC2 positively regulates the JA-mediated wound response, which involves VSP (VEGETATIVE STORAGE PROTEIN) and LOX (LIPOXYGENASE) expression, causing resistance to insect pests and tolerance to oxidative stress [46, 50, 52]. Furthermore, SA signaling is relevant to AtMYC2 [54] in that atmyc2/jin1 mutants show increased resistance to the biotrophic bacterial pathogen P. syringae and necrotrophic fungal pathogens [54, 55]. This enhanced resistance is correlated with increased PR1 (PATHOGENESISRELATED1) expression and depends on the accumulation of SA [54]. Moreover, JIN1/AtMYC2 is implicated in normal symptom development in response to P. syringae via an SA-independent mechanism [54]. Thus, AtMYC2 is a key player in the defense response to biotrophic pathogens via cross-talk between the JA and SA hormone-signaling pathways. AtMYC2 and the R2R3MYB-type transcription factor AtMYB2 bind cis elements in the dehydration-inducible RD22 gene to cooperatively activate its expression [51]. Interestingly, transgenic plants overexpressing AtMYC2 and AtMYB2 display increased sensitivity to ABA and enhanced osmotic stress tolerance [51]. In addition, Botrytis infection induces the expression of BOS1 (BOTRYTIS-SUSCEPTIBLE1), which shares high sequence similarity with AtMYB2, through a JA-mediated defense signaling pathway [56]. Disruption of BOS1 results in increased sensitivity to necrotrophic pathogens and impaired drought, salinity, and oxidative stress tolerance. In addition, BOS1 mediates biotic and abiotic stress signaling via ROS. Therefore, R2R3MYB transcription factors may serve as important mediators of multiple stress signaling pathways.

4.4 Roles of ROS at Points of Convergence between Biotic and Abiotic Stress Response Pathways

4.4 Roles of ROS at Points of Convergence between Biotic and Abiotic Stress Response Pathways

ROS, including H2O2, superoxide (O2–), singlet oxygen (1O2), and hydroxyl radicals, are involved in multiple cellular processes in plants [4]. ROS are thought to play a dual role as toxic byproducts of aerobic metabolism, and as signaling molecules in developmental, growth, disease resistance, and stress response pathways [4, 5, 57]. As mentioned above, rapid ROS production plays a pivotal role in ABA signaling and disease resistance responses [6, 28, 29, 58, 59]. To prevent cell injury or death due to excess ROS, ROS-scavenging enzymes (e.g., superoxide dismutase, glutathione peroxidase, and ascorbate peroxidase (APX)) function to regulate the steady-state level of ROS [4, 5]. Large-scale transcriptome analyses of plants subjected to various abiotic and biotic stress treatments have revealed the induction of a large set of genes encoding ROS-scavenging enzymes under these conditions [5, 10, 11]. Several scavenging enzymes have been used to engineer abiotic stress-tolerant plants (reviewed in [60, 61]). Microarray analyses using cultured Arabidopsis cells have revealed that many ABA-inducible genes are responsive to oxidative stress [62]. The absence of the cytosolic ROS-scavenging enzyme APX1 results in growth retardation, increased levels of H2O2, and protein oxidation in Arabidopsis, indicating that APX1 is a central component of the reactive oxygen gene network [8, 63]. In the apx1 mutant, transcripts encoding signal transduction proteins such as HSF21 (HEAT SHOCK FACTOR21), C2H2-type zinc ﬁnger transcription factors (e.g., RHL41/Zat12, ZAT10/STZ, and ZAT7), and WRKY25 are upregulated [63]. Interestingly, compared to wild-type plants, apx1 mutants show increased salt tolerance [64]. Furthermore, the transcription of a large number of pathogen-responsive genes, including disease resistance genes, is elevated in apx1 plants in response to light stress treatment, demonstrating the high degree of overlap that exists between biotic and abiotic stresses [8]. Taken together with the fact that the expression of APX1 is enhanced in response to oxidative stress, as well as pathogen attack [65, 66], the ROS-mediated signaling cascade including APX1 may play a key role in the cross-talk between biotic and abiotic stress signaling. Zinc ﬁnger proteins, including RHL41/ZAT12, ZAT10/STZ, and ZAT7, whose transcripts are upregulated in the apx1 mutant, are thought to play a key role in regulating the defense response of Arabidopsis to abiotic stresses [64, 67–69]. Each of these proteins contains an EAR (ethylene-responsive element binding factor-associated amphiphilic repression) transcriptional repressor domain, and some suppress the transcription of endogenous and/or reporter genes [70]. ZAT expression is elevated in response to stressors such as abiotic stress and pathogen infection [8, 11, 71, 72]. Deﬁciencies in RHL41/ZAT12, which was originally identiﬁed as an acclimatization response protein [73], suppress the expression of APX1 leading to an elevated level of H2O2-induced protein oxidation [68]. Overexpression of ZAT12 results in the upregulation of

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| 4 Stress Physiology of Higher Plants: Cross-Talk between Abiotic and Biotic Stress Signaling oxidative and light stress-responsive genes, and in enhanced tolerance to light, freezing, and oxidative stress [8, 68, 71, 73]. Interestingly, ZAT12 expression is regulated by a redox-sensitive transcription factor, HSF21, which is likely to be an initial sensor of H2O2 accumulation in response to various stresses [8]. Constitutive expression of ZAT10/STZ, which is thought to be a downstream target of DREB1A [74], or ZAT7 also results in growth retardation and enhanced abiotic stress tolerance [64, 67–69]. A reduced-functional analysis demonstrated that ZAT7 negatively regulates a repressor of the salt defense response. Additional mutational analyses have shown that the EAR motif is directly involved in tolerance but not in growth retardation [64]. The EAR motif in ZAT7 physically interacts with WRKY70, whose expression is upregulated in apx1 knockout mutants, similar to ZAT7. Interestingly, WRKY70 plays an important role at the point of convergence between JA and SA signaling to promote plant defense, and it modulates abiotic stress and senescence [75–78]. The expression of WRKY70 and ZAT7 is strongly induced by B. cinerea; however, that induction is severely reduced in nahG, but not coi1, plants compared to wild-type plants [75], indicating that ZAT7, as well as WRKY70, is regulated by JA and SA. These ﬁndings imply that ZAT7 plays a role in biotic and abiotic stress tolerance.

4.5 Transcription Factors Involved in the Cross-talk between Abiotic and Biotic Stress Signaling

Transcription factors, master proteins that control gene expression by binding to promoter elements upstream of target genes, have been implicated in many biological processes. Recent studies have identiﬁed plant-speciﬁc transcription factors such as NAC, AP2/ERF, and WRKY as promising candidates involved directly or indirectly in the cross-talk between biotic and abiotic stress-responsive gene expression networks in plants. Plant-speciﬁc NAC family transcription factors containing an N-terminal NAC DNA-binding domain are associated with diverse functions [79], including the regulation of biotic and abiotic signaling. For example, the ATAF subfamily of NAC genes, which includes ATAF1 and 2, RD26/ANAC072, ANAC019, ANAC055, and AtNAP in Arabidopsis, and OsNAC6 in rice, is responsive to abiotic stress and ABA treatment [80–82]; moreover, most ATAF genes are induced by biotic stresses such as JA or SA treatment, wounding, or pathogen infection [10, 81, 83, 84]. OsNAC6 is an NAC transcriptional activator involved in both the response and tolerance to biotic and abiotic stresses [81]. The constitutive expression of OsNAC6 in transgenic rice activates a subset of stressrelated genes, resulting in enhanced drought and salt tolerance and moderately increased resistance to blast diseases [81]. Recently, Hu et al. [85] reported that the overexpression of SNAC1, a member of the closest subfamily to OsNAC6, signiﬁcantly enhanced drought resistance at the reproductive stage in transgenic rice in the ﬁeld under conditions of severe drought while producing no

4.5 Transcription Factors Involved in the Cross-talk between Abiotic and Biotic Stress Signaling

phenotypic changes or yield penalty. In Arabidopsis, ATAF2 functions as a repressor PR genes [84]. ATAF2 overexpression represses PR gene expression and increases plant susceptibility to soil-borne fungal pathogens. More recently, Lu et al. [86] reported that ATAF1, a homolog of ATAF2, is a transcriptional repressor of dehydration-responsive genes. T-DNA insertion lines of this gene show increased dehydration-responsive gene expression and drought tolerance [86]. Another ATAF subfamily member, RD26, which is involved in ABAdependent stress signaling, regulates genes involved in the detoxiﬁcation of ROS, defense, and senescence, suggesting that it may function at a point of convergence among the ABA, pathogen defense, and senescence signaling pathways [80]. Leaf senescence is induced by biotic and abiotic stresses such as pathogen infection, drought, extreme temperature, and oxidative stress [87, 88]. Recently, AtNAP was shown to be involved in senescence [89]. In addition, all ATAF subfamily NAC genes are upregulated by senescence in Arabidopsis leaves [90] and by abiotic stress [80]. Taken together with the recent observation that the suppression of drought-induced senescence enhances drought tolerance in transgenic tobacco plants expressing the IPT (ISOPENTENYLTRANSFERASE) gene driven by a stress- and maturation-induced promoter [91], these ﬁndings suggest that leaf senescence is closely related to NAC-mediated stress responses. DREBs/CBFs (dehydration-responsive element binding protein/C-repeat binding factor) and ERFs are two major subfamilies within the plant-speciﬁc AP2/ERF family of transcription factors that play multiple roles in biotic and abiotic stress signaling [92, 93]. DREB/CBF proteins bind speciﬁcally to dehydration-responsive elementDREs/C-repeatCRT, present in the promoter region of many abiotic stressinducible genes [94]. In Arabidopsis, DREB1/CBFs, DREB1A/CBF3, DREB1B/CBF1, and DREB1C/CBF2 activate gene expression in response to cold stress, whereas DREB2A regulates gene expression in response to drought, salt, and high temperatures [94, 95]. The transgenic expression of each of these genes confers tolerance to freezing, drought, salt, and heat [95–100]. Interestingly, DREB2A was upregulated in an activation-tagged allele of ADR1 (ACTIVATED DISEASE RESISTANCE1), which confers broad-spectrum disease resistance [101] and enhanced drought tolerance [102]. DREB2A transcript accumulation in the adr1 mutant was found to be regulated by an SA-dependent NPR1 (NONEXPRESSOR OF PATHOGENESIS-RELATED GENES1)-independent signaling pathway [102]. The inhibition of SA signaling in the adr1 mutant by narG or eds1 mutation resulted in a signiﬁcant reduction in drought tolerance, suggesting that SA is a positive regulator of drought stress signaling. On the other hand, the adr1 mutant showed increased sensitivity to heat and salt, suggesting that SA functions as a negative regulator of some abiotic-responsive signaling pathways [102]. The members of the ERF subfamily, which was ﬁrst identiﬁed in tobacco, interact with a GCC-box sequence, also known as an ERE (ethylene-responsive element), in the promoter region of a large number of ET-inducible genes, including those encoding PR proteins [103]. In tobacco, the ERF transcription factor TSI1 (TOBACCO STRESS-INDUCED1) plays an important role in both biotic and abiotic stress signaling [104]. TSI1 expression is induced by salt,

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| 4 Stress Physiology of Higher Plants: Cross-Talk between Abiotic and Biotic Stress Signaling ethephon, SA, methyl jasmonate, and wounding, but not by drought stress or ABA [104]. The overexpression of TSI1 in transgenic tobacco or hot pepper activates the expression of multiple PR genes and increases both resistance to pathogen invasion and tolerance to osmotic stress [104, 105]. The coexpression of TSI1 and TSIP1 (TSI1-INTERACTING PROTEIN1), a DNAJ-type zinc ﬁnger protein that interacts with TSI1, conferred greater salt tolerance and pathogen resistance than the overexpression of TSI1 or TSIP1 alone [106]. Transgenic tobacco and Arabidopsis overexpressing the pepper ERFs CaERFLP1 and CaPF1, respectively, also showed enhanced tolerance to abiotic and biotic stresses [107, 108]. In each case, the ERFs exhibited afﬁnity for GCC-boxes and DRE/CRT cis elements, and transgenic plants overexpressing these ERFs showed upregulated expression of genes containing a GCC-box or DRE/CRT in their promoters [104, 106–108]. Fairly recently, TINY, a DREB-type transcription factor, was reported to function in both DRE- and ERE-dependent signaling pathways [109]. These ﬁndings imply that several AP2/ERF genes play a role in the cross-talk between abiotic and biotic stress-responsive signaling by connecting the DRE- and ERE-mediated signaling pathways.

4.6 Mitogen-Activated Protein Kinase Cascade

Reversible phosphorylation is a major type of post-translational modiﬁcation that regulates a wide variety of cellular processes, ranging from stress responses to developmental processes. In eukaryotes, mitogen-activated protein kinase (MAPK/ MPK) cascades are highly conserved central modulators of diverse cellular processes, including differentiation, proliferation, growth, death, and stress responses [110]. In plants, a MAPK cascade is thought to play a pivotal role in a wide range of biotic and abiotic stress responses, as well as in hormone and ROS signaling [111]. An increasing body of evidence suggests that MPK3, MPK4, and MPK6 in Arabidopsis, and their homologs in other species, are key kinases activated in response to biotic and abiotic stresses [110]. MPK3, MPK4, and MPK6 are activated by the MAMP ﬂg22, a 22-amino acid long peptide derived from ﬂagellin that induces a range of pathogen-related responses [112]. In other species, including tobacco, tomato, and parsley, the functional orthologs of these genes are activated by MAMPs [107, 113–115]. MPK3, MPK4, and MPK6 are also activated by hyperand hypo-osmotic stress in suspension-cultured Arabidopsis cells [116, 117]. In addition, MPK4 is activated by cold and salt stress [118], whereas MPK3 and MPK6 are activated by ozone within 30 min of exposure [119]. Moreover, MPK3 and MPK6 are activated by exogenous H2O2 [120]. Furthermore, rice OsMPK5, an Arabidopsis MPK3 ortholog, is positively involved in abiotic stress tolerance but negatively involved in disease resistance [121], whereas the tobacco salicylic acidinduced protein kinase kinase–NtMPK4 cascade, which is an ortholog of the Arabidopsis MKK1/MKK2–MPK4 pathway (where MKK indicates a MAPK kinase), is involved in wound, ozone, and JA signaling [122]. Thus, MPK3, MPK4, and

4.6 Mitogen-Activated Protein Kinase Cascade

MPK6 appear to play important roles in the cross-talk between biotic and abiotic stress signaling. However, since these MPKs are also involved in a wide range of developmental processes, such as stomatal patterning and embryo development, these data suggest that MPK3, MPK4, and MPK6 modulate not only stress signaling but also multiple important developmental signaling pathways (Figure 4.3). MEKK1 (a MAPK kinase kinase) expression is induced by cold, salt, drought, touch, and wounding in Arabidopsis [123]. Moreover, the MEKK1–MKK2–MPK4/ MPK6 cascade is involved in cold and salt stress signaling [118, 124]. In contrast, the MEKK1–MKK1/MKK2–MPK4 and MEKK1–MKK4/MKK5–MPK3/MPK6 cascades modulate pathogen defense response pathways. The MEKK1–MKK1/MKK2– MPK4 cascade is thought to play an important role in ﬂg22-induced signaling, which is involved in the negative regulation of MAMP- and pathogen-induced cell death [125–130], MEKK1 and MPK4 are thought to play an important role in ﬂg22induced signaling, which is involved in the negative regulation of MAMP- and pathogen-induced cell death. MPK4 speciﬁcally phosphorylates MKS1 (MPK4 SUBSTRATE1) as well as WRK25 and WRK33, two WRKY-type transcription factors [131, 132]. In the MEKK1–MKK4/MKK5–MPK3/MPK6 cascade [112, 127, 130], MPK3/MPK6 regulate camalexin biosynthesis [133]. In addition, MPK3 speciﬁcally phosphorylates VIP1, a basic leucine zipper transcription factor [134], whereas MPK6 speciﬁcally phosphorylates and stabilizes ACS2 and ACS6, which are ratelimiting ET biosynthetic enzymes [135, 136]. Likewise, MPK3, MPK4, and MPK6 are not only activated by abiotic and biotic stresses, but are also involved in hormone signaling. MPK3 functions in ABA

Figure 4.3 MAPK pathways and the cross-talk between biotic and abiotic stress signaling.

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| 4 Stress Physiology of Higher Plants: Cross-Talk between Abiotic and Biotic Stress Signaling signaling at the postgermination stage [137], whereas MPK6 is thought to be involved in JA-dependent root growth and AtMYC2 expression [138]. Recently, it was reported that a novel MAPK pathway, MKK9–MPK3/MPK6, positively regulates both ET biosynthesis and ET signaling by stabilizing EIN3 (ET-INSENSITIVE3) phosphorylation [139, 140]. The MKK9–MPK3/MPK6 pathway is negatively regulated by CTR1 [140], a negative regulator of ET signal transduction [141]. Taken together with the fact that CTR1 also phosphorylates EIN3 to reduce its stability, ET signaling is mediated by a ﬁnely tuned balance of positive and negative MAPK signaling. MPK3 and MPK6 are also activated by oxidative stress in cultured Arabidopsis cells [120, 142]. In Arabidopsis, the activity of MPK3 and MPK6 is modulated by the serine/threonine kinase OXI1, whose activity is in turn induced by H2O2 [143]. In addition, ANP1, an Arabidopsis MAPK kinase kinase, or its paralogs ANP2 and ANP3, activate MPK3 and MPK6 during oxidative stress signaling. Tobacco plants overexpressing a constitutively active form of tobacco NPK1 (an ANP1 homolog) exhibit improved tolerance to freezing, heat, drought, and salt [120]. Similarly, transgenic maize overexpressing NPK1 displays enhanced tolerance to drought, cold, and freezing, in addition to enhanced sugar metabolism and the expression of various stress-related genes [144]. Thus, MAPK signaling is involved in ROS signaling and it affects plant tolerance to environmental stress by increasing the ROS scavenging capacity.

4.7 Effects of Humidity and Temperature on Biotic Stress Responses

Several environmental conditions such as temperature and humidity affect plant disease development by attenuating plant disease resistance while promoting pathogen growth. For instance, high temperatures prevent the biosynthesis of SA and the induction of PR gene expression, resulting in reduced disease resistance [145]. Several lesion-mimic mutants, which exhibit precocious cell death and tend to show high constitutive levels of PR gene expression and SAR, are sensitive to temperature and humidity [146–152]. For example, the cell death phenotypes seen in overexpressors of RPW8 [150] and the slh1 mutant [148] in Arabidopsis are suppressed by both high temperatures and high humidity. In the ssi4 (supressor of SA insensitive 4) mutant, high levels of humidity inhibit the constitutive activation of MPK3 and MPK6 as well as H2O2 production, suggesting that a humidity sensor acts early in the signaling cascade [152]. The phenotypes of the mekk1 mutant, including cell death, H2O2 accumulation, callose deposition, and the MAMPresponsive hyper-activation of MPK3 and MPK6, are also completely suppressed by high temperatures [127]. It is possible that MEKK1 negatively regulates cell death pathways activated by speciﬁc R proteins that are unstable at high temperatures. Recently, it was shown that the abundance of the barley R proteins MLA1 and MLA6 decreased dramatically in response to a temperature shift from 18 to 37 1C without a reduction in transcription, suggesting that environmental conditions

References

may inﬂuence R protein stability [153]. High humidity and high temperatures also affect receptor-like kinase (RLK) type R gene-mediated disease resistance [154]; thus, abiotic stress may also destabilize RLK-type R proteins and/or common signaling components between cytosolic- and membrane-anchored R proteins.

4.8 Conclusions

Current evidence suggests that plant responses to various biotic and abiotic stresses under natural conditions are orchestrated by a complex network of regulatory genes and signaling molecules [1–3, 111, 155]. Abundant data suggest that a number of regulatory components such as transcription factors and protein kinases play key roles in the response to abiotic and biotic stresses through ROS and multiple hormone signaling pathways. However, our current understanding of the cross-talk between these pathways is limited. In natural environments, plants are exposed to a wide range of stimuli simultaneously. Hence, further study is required to comprehend the full range of cross-talk between plant signaling pathways. Combining the existing data derived from large-scale transcriptome, hormonome, phenome, ionome, lipidome, and metabolome analyses in plants will further our understanding of the regulatory networks mediating the response to biotic and abiotic stresses. Much work is needed to clarify the biological signiﬁcance of and mechanisms underlying the cross-talk between the signaling pathways that operate under conditions of stress. In the near future, our understanding of the molecular mechanisms involved in the cross-talk between biotic and abiotic stress-responsive pathways will enable us to create new crop varieties by combining desirable traits related to abiotic stress tolerance and enhanced disease resistance.

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5 Jasmonates in Stress, Growth, and Development Claus Wasternack

5.1 Introduction

In contrast to animals, plants are under continuous pressure to adapt to ﬂuctuations in the environment. Apart from the essential factors of light, water, and nutrients, they have to monitor other abiotic factors such as oxygen, salt, gravity, touch, osmotic pressure, temperature, and chemicals. For unfavorable levels of these factors, plants have developed a reprogramming of gene expression leading to adaption to such stress conditions. Similar dramatic reprogramming of plant gene expression takes place upon biotic stress by pathogenic microorganisms, by herbivorous insects, or symbiotic interactions such as arbuscular mycorrhiza. Each of these interactions and adaptations is based on a complex signaling network of convergent and divergent signaling pathways. In addition to other plant hormones, such as ethylene (ET), abscisic acid (ABA), cytokinins, and auxins, jasmonic acid (JA) and its derivatives are important signals in plant stress responses. JA and its metabolites such as JA methyl ester (jasmonic acid methyl ester JAME) and amino acid conjugates, commonly named jasmonates, are lipid-derived signals. In the last two decades, most of them including the JA precursor 12-oxo-phytodienoic acid (OPDA) were recognized as being components of an at least partially occurring intracellular, intercellular, systemic as well as interorganismal signal transduction in response to biotic and abiotic stress. However, many developmental processes are also known in which jasmonates function as an essential signal. Among them are germination and root growth, senescence, tuberization, and some stages of ﬂower development. Over the last two decades, there has been a steady increase in publications on jasmonates. Consequently, reviews have appeared continuously covering different aspects of JA biosynthesis, action and signal transduction (e.g., since 2005: [1–12]). This chapter will discuss recent results on some aspects of the biosynthesis, metabolism and action of jasmonates in stress responses and development. I will exclude roles of JA in the local and systemic wound response, of JA in mycorrhization, senescence, as well as cross-talk in JA, ET, salicylate, and ABA signaling.

Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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| 5 Jasmonates in Stress, Growth, and Development These aspects have been reviewed in 2008 [9–12] and will also be covered by a special issue of Phytochemistry appearing at the end of 2009.

5.2 JA Biosynthesis

JA and its derivatives are cyclopentanone compounds and are structurally similar to prostaglandins – their counterparts in animals. Elucidated by Vick and Zimmermann [13], biosynthesis of JA takes place by reactions similar to prostaglandin biosynthesis. All of the enzymes of JA biosynthesis have now been cloned from several plant species, leading to molecular and genetic tools for analyzing the mode of action of jasmonates. The initial reaction is a release of a-linolenic acid (a-LeA), the substrate for JA formation, from galactolipids of chloroplast membranes. Although phospholipase A2 (PLA2) was thought to function in JA biosynthesis [14], today there is strong evidence that PLA1 and a galactolipase are active in JA biosynthesis. First, the Arabidopsis mutant dad1 (defective in anther dehiscence1)was shown to be affected in a PLA1 accompanied by reduced JA levels in ﬂowers, diminished ﬁlament elongation, and, consequently, male sterility [15]. However, the corresponding lipase in the leaves was still missing. Recently, a homolog of DAD1, DGL (DONGLE; galactolipase), was characterized as a galactolipase with weak PLA1 activity. DGL releases a-LeA from galactolipids of chloroplast membranes, thereby providing the substrate for the basal level of JA in vegetative growth and for the JA burst in early phases upon wounding [16]. a-LeA is the substrate of the lipoxygenases 9-LOXs and 13-LOXs, which insert molecular oxygen at C-9 and C-13, respectively, leading to hydroperoxy derivatives, which are further converted in at least seven different branches of the LOX pathway [17]. JA originates from (13S)-hydroperoxyoctadecatrienoic acid, which is converted by a 13-allene oxide synthase (13-AOS) to an unstable allene oxide (Figure 5.1). Nonenzymatic cleavage leads to a- and g-ketols, whereas an allene oxide cyclase (AOC) catalyzes the formation of the cyclopentenone structure in OPDA. An interesting exception is the tomato 9-AOS which acts in vitro as a multifunctional protein catalyzing with the linoleic acid (9S)-hydroperoxide as substrate the formation of the corresponding allene oxide, but also hydrolysis and cyclization of the allene oxide [18]. All of the enzymes of OPDA formation (DGL, 13-LOX, 13-AOS, AOC) are located within the chloroplast. Most of them carry an active target sequence, and chloroplast location was proven by immunocytochemical analysis as well as import studies as for AOS and AOC [19–24]. Conversion of OPDA into JA takes place in peroxisomes, which requires transport of OPDA. So far an OPDA transporter is unknown, but the partial JA deﬁciency of the CTS (COMATOSE) mutant upon wounding suggests that CTS is involved [25]. CTS is an ATP-binding cassette (ABC) transporter also known as PXA1/PED3 [26]. Within peroxisomes, OPDA is speciﬁcally reduced to the corresponding cyclopentanone OPC-8 by the OPDA reductase OPR3 [27–29], whereas OPR1 seems to reduce the nonenzymatically formed phytoprostanes [30]. Hypothesized for a long time, participation of the peroxisomal fatty acid b-oxidation

5.2 JA Biosynthesis

Figure 5.1 Biosynthesis of JA in the chloroplast and peroxisome. Reactions are described for conversion of a-LeA (18 : 3) via the intermediate OPDA. In a parallel pathway leading to dnOPDA a 16 : 3 polyunsaturated fatty acid is the substrate. Enzymes/proteins: At5g63380 and At1g20510, 4-CLlike acyl-CoA synthetases; TE, thiolase; see text for other abbreviations.

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| 5 Jasmonates in Stress, Growth, and Development in JA biosynthesis could be demonstrated recently. Acyl-CoA oxidase (ACX), multifunctional protein (MFP), and 3-ketoacyl-CoA thiolase (KAT) were shown to be involved in wound-induced JA formation by mutant analysis and transgenic approaches [31–34]. Additional evidence was given by JA deﬁciency upon wounding in pex6, an Arabidopsis mutant affected in an essential protein of peroxisome biogenesis [34]. Although functional peroxisomes are essential for wound-induced JA formation, the proliferation of peroxisomes is uncoupled from JA formation [35]. The b-oxidative shortening of the carboxylic acid side-chain of OPDA needs two additional reactions: (i) activation of the corresponding precursor to the CoA ester and (ii) release of JA from its activated form jasmonoyl-CoA by a thioesterase. An OPC-8 CoA ligase (OPCL1) contributes at least partially to wound-induced JA formation [36]. Other activating enzymes were found among a clade of the superfamily of adenylate-forming enzymes with similarity to 4-coumarate: CoA ligases (4-CLs) [37, 38]. These 4-CL-like enzymes can activate OPDA [37], OPC-8, and OPC-6 [38] (Figure 5.1). The regulation of JA biosynthesis seems to be deﬁned by at least ﬁve different aspects: (i) substrate availability, since only induced substrate generation (e.g., by wounding) leads to JA formation in AOS- or AOC-overexpressing lines [39, 40]; (ii) feed-forward regulation, since all genes encoding enzymes in JA biosynthesis are JA-inducible [8]; (iii) speciﬁc location of the enzymes in distinct cell types or even subcompartments of the chloroplast (e.g., of AOC in vascular bundles of tomato, which differs from AOS and LOX) [41, 42]; (iv) different size of gene families; and (v) activity control by protein–protein interactions in subcompartments. The latter two aspects are less clear. The number of gene family members for JA biosynthesis genes is strikingly different. LOX family members are ﬁrst distinguished by their speciﬁcity for oxygen insertion at carbon C-9 or C-13, thus leading to many different oxylipins. Furthermore, additional speciﬁcity might be given in the case of 13-LOXs by different location within the chloroplast subcompartments, thereby attributing to metabolic ﬂux through the alternative branches of the LOX pathway (e.g., the AOS branch and the hydroperoxide lyase branch) [43]. Furthermore, interaction between AOS and AOCs is discussed based on import studies for members of the A. thaliana AOC gene family [24] and a proteome analysis of the inner envelope [44, 45]. Among the plant species, the number of gene family members of the AOS branch exhibits interesting differences. In A. thaliana, there are one AOS and four AOC genes, whereas in tomato, three genes encode AOSs, while AOC is encoded by a single copy gene [22, 40, 46–48]. Consequently, in A. thaliana, JA biosynthesis might be regulated downstream of the AOS spatially and temporally by individual AOC isoforms. Initially, AOC was puriﬁed to homogeneity as a dimer [49]. The crystal structure of the AOC2 of A. thaliana suggests that this protein occurs as a trimer and is a member of the lipocalin family known to function in the transport of small, hydrophobic molecules [50]. Therefore, hetero-homodimerization may occur as an additional regulatory principle as shown for the nine isoforms of 1-amino-cyclopropane-1-carboxylic acid (ACC) synthase [51]. In summary, the four isoforms of AOCs of A. thaliana represent a regulatory potential for the

5.3 JA Metabolism

ﬁne-tuning of JA formation. This is supported by promoter activities of AOC1– AOC4 recorded with promoter GUS (b-GLUCURONIDASE) lines during development and in response to various stimuli [6], and by expression data in the public databases. In contrast to A. thaliana, in tomato speciﬁcity for JA generation might be given by the one AOC downstream of AOSs (three genes) and upstream of OPRs (three genes). This is reﬂected in tomato AOC promoter activities appearing in response to developmental and environmental stimuli [52]. Furthermore, speciﬁcity for JA formation is strengthened by a cell-speciﬁc location of AOC in parenchymal cells of minor veins of tomato and within vascular bundles of main veins and even in sieve elements [41, 42], supporting the idea that JA is active in systemic signaling [4, 53]. The full scenario of regulation of JA formation and action depends on two additional aspects, the metabolism of JA and COI1 (CORONATINE-INSENSITIVE PROTEIN1)/proteasome-mediated signaling, both of them described in the following sections.

5.3 JA Metabolism

As well as a basal level, JA accumulates transiently within the ﬁrst hour in response to external stimuli such as wounding and is frequently used as a marker of stress responses linked to JA-induced alteration of gene expression. The initial product of JA biosynthesis is ( þ )-7-iso-JA, which equilibrates to the more stable ()-JA. However, numerous metabolites have been known for a long time and were neglected in respect of JA responses due to their putative minor accumulation. More recently, these metabolites have attracted great attention by hints for speciﬁc functions as well as much more abundant occurrence than previously detected. The following metabolites have been found so far (Figure 5.2): JAME, the methyl ester of JA formed by a JA methyltransferase (JMT) [54], the JA amino acid conjugates such as JA-Ile formed by JA : amino acid synthetase (JAR1) which adenylates JA followed by exchange of the AMP moiety by an amino acid [55, 56], 11-hydroxy-JA formed possibly nonenzymatically and 12-hydroxy-JA (12-OH-JA) formed by a so far unknown hydroxylase; 12-O-glucosyl-JA (12-O-Glc-JA) formed by a so far unknown glucosyl transferase; sulfated 12-OH-JA (12-HSO4-JA) formed in A. thaliana by the 12-OH-JA sulfotransferase AtST2a [57] and in tomato by a SlST2a (Neumerkel and Wasternack, unpublished); several jasmonoyl-1-bglucosyl esters [58], 12-OH-JA-Ile conjugate, possibly formed by JAR1 [59, 60], 12carboxyjasmonoyl-L-isoleucine [60], cis-jasmone formed by an unknown decarboxylase [61] or possibly formed in a parallel pathway via iso-OPDA [62]; and ﬁnally, the conjugate of JA with ACC [56]. Presumably, we can expect further JAderived compounds from various plant species (e.g., a 5u-(hydroxysulfonyloxy)-JA has been recently isolated from a mangrove [63]). The question on separate or overlapping signaling properties of JA and its metabolites is of special interest. Transgenic approaches revealed that JAME is only active upon its cleavage to JA [64]. In contrast, JA and JA-Ile showed

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Figure 5.2 Metabolism of JA. Reduction of the keto group within the cyclopentanone ring leads to cucurbic acid. The pentenyl side-chain can be hydroxylated to 11-OH-JA and 12OH-JA, which is further converted to its sulfated or Oglucosylated derivative. Also, isoleucine conjugates of 12-OHJA and a 12-carboxy-JA were found. The carboxylic acid sidechain can be conjugated to the ET precursor ACC or to amino acids such as Ile by the JA amino acid conjugate synthase (Arabidopsis: JAR1; tobacco: JAR4), can be methylated by a JMT, decarboxylated to cis-jasmone, or glucosylated to jasmonoyl glucose ester. Enzymes cloned so far are framed.

independent gene expression responses [65]. For some of these metabolites, speciﬁc functions were identiﬁed. A prominent example is 12-OH-JA, also called tuberonic acid due to its tuber-inducing properties in potato [66, 67]. Among other compounds, a speciﬁc enantiomer of 12-O-Glc-JA is active as a leaf-closing factor in Albizzia species [68]. These compounds bind in speciﬁc motor cells responsible for nyctinastic movements [69], which require aquaporins [70]. It will be interesting to see whether the enhanced JA levels observed in mechano-stimulated Medicago truncatula plants [71] are accompanied by altered amount of 12-O-Glc-JA. The volatile cis-jasmone is active in plant defense reactions [72]. A recent transcriptome analysis revealed a speciﬁc set of genes induced by cis-jasmone, thereby attributing it to speciﬁc behavioral responses of specialist and generalist insects [73]. Signaling properties independent from JA were repeatedly described for OPDA. Initially, tendril coiling of Bryonia was explained in terms of increased OPDA levels [74]. Meanwhile several microarray analyses showed different sets of genes expressed in response to OPDA or JA [30, 75, 76]. A clear answer on separate signaling properties of OPDA and JA was already shown by plant defense reactions in the JA-deﬁcient mutant opr3 [77]. Most of these data led to the concept of biological activity of compounds carrying a reactive a,b-unsaturated carbonyl structure – the reactive electrophilic species. Among them there are OPDA and phytoprostanes, but not JA [30, 78].

5.4 Bound OPDA – Arabidopsides

Until now it has been an open question how JA signaling can be switched off. Recent data revealed that JA-induced inhibition of seed germination and root growth are not caused by 12-OH-JA or 12-HSO4-JA [79]. Furthermore, both compounds are unable to switch on expression of JA biosynthesis genes or woundinducible genes. This suggests that, as known for other plant hormones, hydroxylation of JA and subsequent sulfation inactivates JA [79]. In this respect, it is of interest that both compounds and 12-O-Glc-JA occur abundantly in various organs and tissues of different plant species at up to three orders of magnitude higher levels than JA [79]. Finally, upon wounding of tomato leaves, 12-OH-JA, 12-HSO4JA, and 12-O-Glc-JA accumulate after JA to much higher levels than JA [79]. A JA-dependent formation of 12-OH-JA was evidenced by mutants and transgenic lines affected in JA biosynthesis [79]. A similar situation in respect of 12-OH-JA accumulation was recently detected for A. thaliana. Here, a rapid burst in JA accumulation upon wounding occurs, and is followed by accumulation of 12OH-JA, 12-OH-JA-Ile, and 12-HOOC-JA-Ile [60]. Obviously, hydroxylation inactivates JA, thereby counteracting the worse regulation of JA biosynthesis given by the positive feed-forward loop and substrate generation upon wounding by ‘‘removal’’ of excess of the signaling compound JA.

5.4 Bound OPDA – Arabidopsides

The oxylipins derived from LOX activities occur either as nonesteriﬁed fatty acid derivatives or are esteriﬁed to chloroplast membrane constituents [22]. In the case of OPDA, the ﬁrst substance was found in galactolipids (monogalacto diacylglycerol (MGDG)) in the sn-1 position in untreated A. thaliana leaves [80]. These compounds were called arabidopsides according to their nearly exclusive occurrence in Arabidopsis species [81]. Meanwhile, numerous types were found (Table 5.1). They are derivatives of MGDG and digalacto diacylglycerol (DGDG), respectively, and contain up to three OPDA and/or dinor (dn) OPDA residues. Up to 17 species of oxylipin-containing phosphatidyl-glycerols, MGDGs, and DGDGs were identiﬁed, including complex lipids with 18 : 3, 18-carbon ketol acids and 16-carbon ketol acids beside the OPDA or dnOPDA moiety [82–84]. Presumably, some of the arabidopsides occur in thylakoid membranes as shown by common localization with the photosystem I/II supercomplex, light-harvesting complex II, and photosystem I [81]. Upon wounding, the amount of arabidopsides (e.g., A, B, E, and G; Table 5.1) increase dramatically up to 1000-fold. Furthermore, recognition of the phytopathogenic bacterial avirulence peptides AvrRpm1 and AvrRPt2 led to a dramatic increase in arabidopside E up to 7–8% of total lipid content [83], and arabidopside E inhibits growth of a bacterial pathogen in vitro. Later, arabidopside G was identiﬁed and found to accumulate also in response to the two bacterial effectors mentioned above as well as upon wounding [84]. The formation of arabidopsides E and G was dependent on an interaction of RPS2 (RESISTANCE TO PSEUDOMONAS SYRINGAE2) and

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| 5 Jasmonates in Stress, Growth, and Development Table 5.1 Arabidopsides and their constituents.

Arabidopside A B C D E F G

sn-1-OPDA, sn-2-dnOPDA-MGDG sn-1-OPDA, sn-2-OPDA-MGDG sn-2-dnOPDA-DGDG sn-1-OPDA, sn-2-OPDA-DGDG sn-1-OPDA, sn-2-dnOPDA, Gal C-6-OPDA-MGDG sn-2-dnOPDA-MGDG (Ipomea) sn-1-OPDA, sn-2-OPDA, Gal-C-6-MGDG

NDR1 (NON-RACE-SPECIFIC DISEASE RESISTANCE1). Disease resistance and salicylate (SA) signaling mutants such as rps2, ndr1, pad4sag101 (phytoalexin-deﬁcient4 and senescence-associated gene101) double mutant, sid2 (salicylic acid induction-deﬁcient2), and npr1 (nonexpresser of pathogenesis-related (PR) genes1) were unaffected in accumulation of these two arabidopsides. As expected by the wound-inducible accumulation, both arabidopsides did not accumulate in the coi1 and jar1 mutants (Table 5.2). Summarizing, these and additional data suggest that the pathogen- and wound-induced accumulation of arabidopsides E and G is triggered by two signaling pathways that converge in jasmonate signaling downstream of NDR1 and SA [84]. It seems to be another example of cross-talk between SA- and JA-dependent signaling, which can be antagonistic and synergistic [85]. The arabidopsides E and G may have dual functions: (i) antipathogenic activity, and (ii) a role as storage compounds from which OPDA and dnOPDA, respectively, can be released [80, 86]. In respect of the different accumulation of the various arabidopsides upon various stimuli, we have to consider more putative scenarios for the function of arabidopsides: (iii) JA-mediated ampliﬁcation in arabidopside formation and compensation of rapid ﬂux in OPDA synthesis by its esteriﬁcation, and (iv) storage of newly formed OPDA and/or possible exchange of the oxylipin residues between various arabidopsides. Whereas enzymes of JA biosynthesis, such as LOX, AOS, and AOC, are inactive in vitro with their esteriﬁed substrates [80], an adduct between LTP1b (LIPID TRANSFER PROTEIN1b) and a toxic allene oxide 9-hydroxy-10-oxo-12(Z)-octadecanoic acid was found in barley seeds [116]. It was generated by a 9-LOX and an AOS. Lipid transfer proteins are ubiquitous suggested to be involved in diverse functions of plant development and stress responses. However, their precise role is still unknown. Such adduct formation between toxic oxylipins and lipid transfer proteins combined with the activity of enzymes of JA biosynthesis is an interesting new facet of the functions of these enzymes.

5.5 Mutants of JA Biosynthesis and Signaling

Since about 1995, several mutant screens have been initiated to pick up mutants affected in JA biosynthesis and JA signaling (Table 5.2). Most of the mutants in JA biosynthesis, at least in A. thaliana, are male-sterile and/or exhibit JA deﬁciency. As

5.5 Mutants of JA Biosynthesis and Signaling Table 5.2 Mutants and genes functioning in JA biosynthesis

and JA signaling in Arabidopsis and tomato. Mutants

Gene product

Phenotype

Reference(s)

JA biosynthesis dgl dad1

galactolipase A1 phospholipase A1

Reduced JA level in leaves reduced ﬁlament elongation, malesterile, delayed anther dehiscence, JA-deﬁcient in ﬂowers male-sterile, delayed anther development, altered a-LeA level deﬁcient in a-LeA and JA levels, no wound response, suppressed prosystemin expression JA-deﬁcient, decreased resistance to pathogens male-sterile, delayed anther development JA-deﬁcient, decreased resistance to pathogens, reduced ﬁlament elongation, male-sterile JA-deﬁcient, reduced ﬁlament elongation, delayed dehiscence JA-deﬁcient, reduced wound response abnormal ﬂower meristem, reduced fertility Reduced JA content

[16] [15]

fad3-2fad7-2fad8 spr2a

fatty acid desaturases o-3 fatty acid desaturase

aos

AOS

dde2-2

AOS

opr3

OPR3

dde1

OPR3

acx1a

acyl-CoA oxidase

aim1

multifunctional protein 1 CTS/PXA1 ABC transporter

comatose Constitutive JA response cev1 cet1-9

cellulose synthase CES3 ?

cex1

?

cas1 joe1

? ?

hy1-101

heme oxygenase HY1

joe2

?

Others ore9, max2

F-box protein

constitutive expression of vegetative storage proteins constitutive expression of thionins, increased JA levels constitutive root growth inhibition, constitutive expression of JAresponsive genes constitutive expression of AOS increased expression of LOX2, increased accumulation of anthocyans increased JA level, stunted growth, phytochrome chromophore deﬁciency reduced inhibition of root growth, increased expression of LOX2

[87] [88]

[89] [90] [28]

[91] [92] [93] [94]

[95, 96] [97] [98]

[99] [100]

[101]

[100]

[102, 103]

(continued )

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| 5 Jasmonates in Stress, Growth, and Development cos1

Reduced sensitivity to JA coi1

coi1-16

jai1a)

jar1/jin4/jai2

jin1/jai1

lumazine synthase

F-box protein COI1

F-box COI1 þ PEN2, a glycoside hydrolase tomato homolog of COI1

JA amino acid conjugate synthase AtMYC2 (basic helix–loop–helix zip transcription factor)

jai3 jue1-3 oji

? ?

mpk4

AtMPK4

rcd1

radical-induced cell death 1 RUB (related to ubiquitin)activating enzyme AtSGT1b

axr1

jai4/sgt1b

delayed leaf senescence, more axillary branches suppressors of JA-dependent defects in coi1 (root growth, senescence, defense)

[104]

reduced root growth inhibition, male-sterile, reduced ﬁlament elongation, enhanced sensitivity to necrotrophic pathogens COI1 phenotype þ loss of penetration resistance of pathogens

[105, 106]

female-sterile, altered trichome development, increased sensitivity to pathogens, decreased wound response reduced root growth inhibition by JA, increased sensitivity to necrotrophic pathogens reduced root growth inhibition

[109]

reduced root growth inhibition in ein3 background reduced expression of LOX2 enhanced sensitivity to ozone, reduced root growth inhibition dwarf phenotype, altered expression of JA- and SA-response genes, reduced sensitivity to JA, ET and ABA, impaired in ozone signaling

[107, 108]

[55, 56, 110] [111]

[111] [100] [112] [113]

[114] reduced root growth inhibition by JA

[115]

reduced root growth inhibition in the ein3 background

[111]

a

Tomato mutants. Modiﬁed after [1] and [7].

discussed below, male sterility is preferentially caused by the role of JA for proper ﬁlament elongation and/or an essential role of a-LeA content of the tapetum for proper anther development and pollen dehiscence. Another common phenotype of JA deﬁciency is a decreased resistance to pathogens and diminished wound response. In the case of signaling mutants, altered sensitivity to JA (e.g., in root

5.6 COI1–JAZ–JA-Ile-Mediated JA Signaling

growth inhibition) and screens with JA-responsive promoter-reporter lines (e.g., promoters of LOX2, VSP (VEGETATIVE STORAGE PROTEIN), or Thi2.1 (THIONIN2.1)) have been used. These screens led to the isolation of mutants with a constitutive JA response or with reduced sensitivity to JA. Cloning of the affected genes led to identiﬁcation of exciting new JA signaling components. One of the ﬁrst identiﬁed mutants was coi1 (coronatine-insensitive1), which is affected in the F-box protein COI1, the key player in JA signaling [105] (see Section 5.6). Another example is the jar1 (jasmonate-resistant1) mutant, which was ﬁrst identiﬁed in 1992 by the root growth inhibition assay [110]. Identiﬁcation of the affected gene took more than a decade due to the minor sequence homology to known A. thaliana genes. The corresponding gene is a member of a superfamily that codes for enzymes adenylating a carboxylic acid with subsequent transfer to a second substrate. In the case of JAR1, the substrates are JA and amino acids such as Ile leading to the JA-Ile conjugate [55, 56]. The fact that lack of JA-Ile, but not JA, affects JA signaling shed exciting new light on the mechanism of JA signaling. Now, we can understand these aspects at least partially by the COI1–jasmonate ZIM domain (JAZ)–JA-Ile interaction (see Section 5.6). Finally, identiﬁcation of cev1 (constitutive expression of vsp1) as a mutant affected in the subunit 3 of the cellulose synthase was important, since a link between cellulose synthesis/cell elongation and JA signaling was found, including elevated levels of jasmonates [95, 96]. 5.6 COI1–JAZ–JA-Ile-Mediated JA Signaling

In 1998, COI1 was identiﬁed as an F-box protein [105]. The coi1 mutant exhibits defects in numerous JA-dependent processes such as biosynthesis of secondary metabolites, pathogen and insect resistance, fertility, and wound responses. Since 2000, gene expression data have accumulated showing how many genes are expressed in a COI1-dependent manner [117–120]. Another key player in JA signaling was identiﬁed by analysis of the mutant jin1( jasmonate-insensitive1)/myc2. JIN1/MYC2 (henceforth referred to as MYC2) encodes a basic helix–loop–helix transcription factor [111], which is involved in positive and negative regulation of JA-dependent transcriptional activation [121]. Since the identiﬁcation of COI1, JA-induced gene expression was explained in terms of a repressor model, where a negative regulator is degraded via the SCFCOI1 complex (SKP1 (S-PHASE KINASE-ASSOCIATED PROTEIN1), Cullin, F-box protein E3, ubiquitin ligase) and the 26S proteasome. Among the components of the SCFCOI1 complex, COI1 is the speciﬁcity-determining subunit that selectively recruits target proteins which were unknown until recently. Three independent approaches led to the identiﬁcation of a gene family of previously unknown function, coding for ZIM domain proteins [122]: 1. A subgroup of 12 members, the JAZ proteins, was found by rapid expression of their corresponding genes in ﬂower ﬁlaments upon JA treatment of the opr3 mutant [123]. In addition to the ZIM domain, JAZ proteins carry the highly conserved Jas motif near the C-terminus.

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| 5 Jasmonates in Stress, Growth, and Development 2. The dominant jai3 (ja-insensitive3) mutant was characterized as a splicing defect in JAZ3 leading to lack of the Jas motif in JAZ3 [124]. Consequently, interaction of JAZ3 with MYC2, a central regulator in JA-induced gene expression, is lost [124]. 3. Finally, overexpression of an isoform of JAZ10 that carries an incomplete Jas motif led to diminished repression of JA-regulated growth retardation [125]. All three approaches together with the previously identiﬁed JA-Ile-forming activity of JAR1 suggested a model of JA signaling via the SCF complex, where COI1, JAZ proteins, MYC2, and JA-Ile are key players [10, 12] (Figure 5.3). Under uninduced conditions JA-responsive genes are repressed by the negative regulators JAZs, which inhibit at least MYC2. Upon an environmental stimulus such as wounding, an endogenous rise of JA occurs rapidly as discussed above. It is conjugated at least partially to JA-Ile by JAR1 [56]. Most interestingly, JA-Ile but not or much less other JA amino acid conjugates increases in vitro interaction of

Figure 5.3 The JAZ–COI1-directed proteasome. JA-induced gene expression is switched on by the transcription factor JIN1/ MYC2. However, without an external signal JIN1/MYC2 is repressed by JAZ protein(s). In the presence of JA-Ile generated by JAR1 upon endogenous rise of JA in response to environmental factors, JAZ protein(s) can interact with the F-box protein COI1. COI1 is a member of the SCF complex consisting of the SKP1/ASK proteins, a Cullin, and the F-box protein. Upon JA-Ile-mediated interaction of JAZ and COI1, JAZ is ubiquitinated and degraded by the 26S proteasome, thereby liberating JIN1/ MYC2 from its repressor and allowing JA-induced gene expression (see text for details) (designed by B. Hause).

5.6 COI1–JAZ–JA-Ile-Mediated JA Signaling

COI1 and JAZ1 within the SCF complex [123]. Recently, ( þ )-7-iso-JA-L-lle was shown to be the most bioactive jasmonate compound in COI1-JAZ-interactions suggesting that the active ligand of the JA receptor is formed by epimerization in a narrow time window [158]. Subsequently, the repression of MYC2 by JAZ proteins is lost, since JAZ proteins are directed to degradation via the 26S proteasome. Many details are in agreement with this repressor model in JA signaling: COI1

1. Overexpression of JAZ genes did not affect JA signaling. 2. JAZ1, JAZ3, JAZ10, and MYC2 were found to be located in the nucleus [123– 125]. 3. JAZ1 and JAZ3 interact with COI1, if the Jas motif is present and if JA-Ile is available. 4. JA signaling is lost in the mutants myc2, coi1, and jar1, and the jaz mutants lacking the Jas motif. 5. JAZ proteins have overlapping functions since jaz knockout mutants do not show any JA phenotype such as growth retardation. 6. JAZs, MYC2, and some JA biosynthesis genes are primary response genes, which are rapidly upregulated if the COI1-dependent turnover of a labile repressor (JAZ) is blocked by cycloheximide [126]. The COI1–JAZ–JA-Ile interaction and JAR1 as well as MYC2 activity seems to be conserved among species (e.g., all components and analogous interactions have been found in tomato) [123, 126, 127]. JAZ genes are rapidly induced by JA, several of them in a MYC2-dependent manner. Consequently, JAZ degradation will facilitate expression of themselves, representing a classical negative loop [12]. On the other hand, MYC2 expression is negatively regulated by its own expression [121]. Obviously, the regulatory interplay among the key components in JA signaling is sustained by positive and negative regulation, allowing a proper time window and strength of interaction. Moreover, the complete scenario described in Figure 5.3 seems to be not essential for wound-induced expression of JAZ genes, of several JA biosynthesis genes, and of wound-responsive genes such as PDF1.2 (a plant defensin) or VSP2 [126, 128]. Although they are expressed COI1-dependently, their expression does not require JAR1. Different speciﬁcity of JAZ proteins with respect to the activation by the various JA compounds may occur. However, most of them interact in vitro with COI1 preferentially in the presence of JA-Ile [12]. So far, there is no clear mechanistic explanation for JA- and OPDA-speciﬁc gene expression, which has been repeatedly observed [30, 75–77]. Meanwhile, mechanistic details of interaction of JA-Ile with the tomato COI1–JAZ complex could be found [12]. Binding and competition assays with coronatine and the COI1–JAZ3 complex showed 100-fold higher activity of coronatine compared to JA-Ile [12]. Coronatine, a virulence factor of P. syringae, is regarded as a molecular mimic of JA-Ile [10] and was used in the initial screens on JA-insensitive mutants such as coi1. The much stronger binding of coronatine than of JA-Ile indicates that the stabilized ring structure in coronatine is important. The stronger binding of coronatine highlights another important aspect of interaction of

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| 5 Jasmonates in Stress, Growth, and Development the coronatine-producing P. syringae with the host. By exploiting the host’s hormone signaling pathways (e.g., JA), the pathogen promotes infection [12]. In tomato, the homolog of COI1 is JAI1 (Table 5.2). The jai1-3 tomato mutant carries a point mutation in Leu418 of the leucine-rich repeat. Interaction of coronatine with the COI1–JAZ3 complex was strongly reduced in leaf extracts of jai1-3 plants, suggesting that the complex is an active JA receptor [12]. These data ﬁt the initial suggestion that COI1 might be the JA receptor [129]. This idea came up upon identiﬁcation of TIR1 (TRANSPORT INHIBITOR RESPONSE1) as the auxin receptor and its homology to COI1 [130, 131]. Surprisingly, the mutated Leu residue in jai1-3 is in a similar position as Ile406 in TIR1, where the auxin/indole acetic acid substrates are recognized in the auxin-binding pocket [134]. The critical role of COI1 for a JA receptor function is strengthened by the fact that its complex with JAZ3 requires the C-terminal region, where the highly conserved Jas motif is located [12]. In a most recent work on this Jas motif, two positively charged amino acids of the Jas domain in JAZ1 were identiﬁed to be essential for JA-Ile-(coronatine)-dependent JAZ–COI1 interaction, but not for JAZ–MYC2 interaction [133]. This was supported by a very strong JA-insensitive phenotype of mutants lacking these amino acids. The ongoing work on the JA receptor will cover its crystallization and further structure–activity tests. However, a striking similarity is already obvious in the type and sequences of components of the auxin receptor TIR1 and the JA receptor COI1. These aspects of the conserved mechanism of hormone sensing are discussed in the most recent review on COI1–JA-Ile–JAZ interaction [12].

5.7 Transcription Factors Involved in JA Signaling

MYC2 is a central regulator in positive and negative regulation in JA signaling as shown by expression analyses [9, 111, 121]. Its important role is also indicated by physical interaction of MYC2 with JAZ proteins (Figure 5.3) [123, 124]. These data place MYC2 downstream of COI1 (Figure 5.4). Among genes expressed upon herbivory or pathogen attack via COI1 and MYC2 are those encoding enzymes in jasmonate and ﬂavonoid biosynthesis and metabolism, encoding proteinase inhibitors (PINs) and other wound-/herbivore-induced proteins such as VSP, LOX, Thi2.1, or proteins active in oxidative stress tolerance [119, 121]. Genes repressed by MYC are active in pathogen defense such as PR1, PDF1.2, b-CHI (BASIC CHITINASE), or HEL (HEVEIN), and in tryptophan biosynthesis and tryptophan-dependent glucosinolate metabolism [9, 121]. Furthermore, MYC2 is known to mediate cross-talk in both JA/ABA as well as JA/SA signaling [9, 111]; for example, the ABA-dependent drought response is downregulated by MYC2 [134] and JA biosynthesis is upregulated by ABA [135]. An interesting facet of MYC2 activity is its position downstream of the mitogen-activated protein kinase (MAPK) pathway regulated by the MAPK kinase MKK3 and the MAPK MPK6 [136] being active in parallel to an MKK3/MPK6independent branch (Figure 5.4). The diverse functions of MYC2 suggest that additional transcription factors are required to deﬁne the positive and negative

5.7 Transcription Factors Involved in JA Signaling

Figure 5.4 Different regulation of JA-dependent processes by JIN1/MYC2. Positive regulation is indicated by arrows, negative regulation is indicated by blunt arrows. TFs, transcription factors. Adapted from [9].

regulation by MYC2. Indeed, transcription factors such as ERF2 (ETHYLENERESPONSIVE FACTOR2), ERF6, ERF11, WRKY26, WRKY33, MYB51, and MYB109 seem to be involved in MYC2-regulated gene expression [121]. Important counterparts of MYC2 are in the ERF family – a subgroup of the large AP2 (APETALA2)/ERF superfamily [137]. ERF1 acts as a COI1-dependent transcription factor, which upregulates antagonistically to MYC2 defense genes such as PDF1.2 or b-CHI, thus being an integrator of JA and ET signaling [111]. Other members of the ERF family are the ORA (octadecanoid-responsive Arabidopsis AP2/ERF) transcription factors. One of them, ORA59, was recently identiﬁed as being partially redundant to ERF1 [139]. ERF1 and ORA59 are expressed in response to JA and ET, act downstream of COI1, and lead to expression of defense genes such as PDF1.2; however, ORA59, even partially redundant to ERF1, is essential [138]. Other members of the ORA family are ORA33, ORA47, and ORA37 [139]. ORA47 has been characterized as a key transcription factor in the positive-feedback loop of JA biosynthesis with LOX2, AOC2, and OPR3 as primary targets. ORA33 positively regulates tryptophan biosynthesis and secondary metabolism, whereas ORA37 acts antagonistically to ORA59 and ERF1, thus repressing PDF1.2 expression, but inducing VSP, LOX, and Thi2.1 expression [141]. ERF1, ORA59, ORA33, ORA37,

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| 5 Jasmonates in Stress, Growth, and Development and ORA47 are COI1-dependently expressed, but an interaction with JAZ proteins is yet unknown. Due to the diverse regulatory roles of MYC2 in transcriptional activation an intriguing question is which signaling component functions downstream of MYC2. Recently, members of the NAC (NAM/ATAF1,2/CUC2) protein family of plant speciﬁc transcription factors were identiﬁed as targets of MYC2. AtNAP was found to be involved in leaf senescence [140]. NAC019 and NAC055 function COI1-dependently downstream of MYC2 as transcriptional activators of JAresponsive genes [141]. NAC019 interacts with the CATGT and CACG motifs in the promoter of the AtVSP1 gene. This scenario is consistent with the abovementioned regulation of transcription factors such as ERF2, ERF6, ERF11, WRKY33, and MYB109 by MYC2. Interestingly, genes encoding these factors carry enrichments of strong MYC2-binding sites such as CACNGT, a core motif, as well as CACGTG and CACATG in their upstream region [121]. Although orchestrated with respect to the regulatory output, the key players in JA signaling seem to function in a hierarchical order: there are MYC2, other transcriptions factors such as NAC019, NAC055, and ERF2, and any JA-responsive gene, each of them functioning downstream of the former component. Another important class of plant-speciﬁc transcription factors is the WRKY family characterized by the presence of a DNA-binding domain containing the conserved WRKYGQK sequence and a zinc ﬁnger motif [142]. Several of the 74 WRKYs such as WRKY53, WRKY33, WRKY62, and preferentially WRKY70 mediate the cross-talk between SA and JA downstream of NPR1 and COI1. These aspects were recently reviewed in detail [9, 11, 120, 121]. Apart from transcription factors, Ca2 þ signaling was found as a regulatory element. It will be interesting to see whether the altered oxylipin formation in the ﬂou2 mutant is caused by an affected putative Ca2 þ permeant nonselective cation channel [143, 144, 145].

5.8 Jasmonates and Oxylipins in Development

Mainly by identiﬁcation of mutants in JA biosynthesis and JA signaling, the roles of JA in various developmental processes became obvious (Table 5.2). Among them there are root growth, seed germination, tuber formation, tendril coiling, nyctinasty, trichome formation, senescence, and different aspects of ﬂower development [8]. Apart from the already mentioned tuber formation, tendril coiling, and nyctinasty (see Section 5.3), only few recent data on JA in root growth and ﬂower development will be discussed here. Root growth inhibition by JA was one of the two ﬁrst JA responses observed [110, 146], and several screens to isolate mutants in JA biosynthesis and signaling or cross-talk to other hormones were based on this assay [2]. There is, however, no ﬁnal mechanistic explanation for root growth inhibition by JA. Involvement of components in JA signaling such as COI1, MYC2, JAI4/SGT1b, and AXR1

5.8 Jasmonates and Oxylipins in Development

(AUXIN-RESISTANT1) was suggested by mutant phenotypes and gene expression data (e.g., mutants with constitutively elevated levels of JA such as cev1 (Table 5.2) have reduced root length and a stunted growth phenotype as occurring upon JA treatment). Many of the genes encoding enzymes in JA biosynthesis are expressed in the elongation zone, which corresponds to upregulation of JA-responsive genes in these tissues [147]. Root elongation is preferentially deﬁned by auxin homeostasis [148]. However, there are several examples on antagonistic cross-talk in auxin/JA signaling. Interestingly, increased AOC promoter activity, which correlates with an increase in JA formation, was found in the elongation zone of roots of 10-day-old tomato seedlings [52]. This AOC promoter activity was inhibited by IAA and inhibition was compromised by the antiauxin p-chlorophenoxyisobutyric acid. It is tempting to speculate on an antagonistic interaction of auxin and JA based on gene expression data [120]. Furthermore, a cross-talk between auxin and JA signalling was explained mechanistically by auxin-dependent JAZ1 expression [159] as well as by jasmonate–induced auxin biosynthesis via expression of ANTHRANILATE SYNTHASE a1 [160]. The most signiﬁcant proof for a role of JA in ﬂower development was given by mutants affected in JA biosynthesis and signaling (Table 5.2). In the case of Arabidopsis mutants, male sterility was caused among other aspects by insufﬁcient ﬁlament elongation as shown for dad1 and opr3 [15, 28]. Reduced ﬁlament length correlated with a decrease in JA levels at the corresponding ﬂowering stage (dad1). Interestingly, the arf6/arf8 (auxin response factor6/auxin response factor8) double mutant impaired in two auxin response factors showed reduced JA levels in the mutant ﬁlaments [149], suggesting auxin-dependent JA formation in ﬂowers. A ﬁrst explanation was given by identiﬁcation of the JA-inducible transcription factors MYB21 and MYB24 as key regulators of proper stamen development [150]. This was strengthened by the observation that AGAMOUS controls late stamen development via the expression of JA biosynthesis genes [151]. AGAMOUS is a homeotic C-class gene encoding a MADS-box transcription factor. Genetic and biochemical evidences were found that AGAMOUS directly regulates DAD1 expression, thus affecting JA generation in stamens. This scenario on JA action in stamen development is supported by a similar pattern of promoter activities of AOC and that of JA-responsive genes such as Thi2.1 and aquaporin AthH2 [6, 152, 153]. Another interesting aspect is the variable pattern of jasmonates in different ﬂower organs [41]. The total amount and ratio of jasmonates can vary remarkable, which is called the ‘‘oxylipin signature.’’ Since the amount is elevated by constitutive overexpression of tomato AOC, regulation of JA biosynthesis seems to differ between ﬂowers and leaves, where additional substrate generation is necessary [154]. The role of JA in ﬂowers is also indicated by the sequential action of the following events, all of them proven experimentally: accumulation of glucose in the nonphotosynthetic ovules which represents a sink-tissue - glucoseinduced AOC promoter activity; AOC expression and AOC protein accumulation preferentially occurring in ovules - abundant accumulation of jasmonates in ovules - expression of JA-inducible plant defense genes such as those for PIN2,

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| 5 Jasmonates in Stress, Growth, and Development threonine deaminase, or leucine amino peptidase - increased defense status of ovules as shown by lower infestation by insects [41, 52, 155–157]. A surprising result was the female sterility of the tomato mutant jai1, which is affected in a gene homologous to COI1 of Arabidopsis [109], where the corresponding Arabidopsis mutant coi1 is male-sterile (Table 5.2). Both genes encode the F-box protein COI1 essential for JA signaling (Figure 5.3). The fact that functionally identical proteins lead to different signaling outputs in different genetic backgrounds gives rise to interesting future questions for resolving signaling pathways and their evolution. 5.9 Conclusions

The central role of jasmonates in plant stress responses and development has been established in the last decade. Future work will give insights into the mechanism of activity of signaling components by analysis of their crystal structure, by deeper analysis of the regulatory network of jasmonate-induced gene expression, including its cross-talk to other plant hormones. New aspects will be the similarities and divergences in the jasmonate-dependent regulatory network in response to different biotic stressors, but also natural variegation will come into the focus of the jasmonate research. Finally, new techniques in the analyses of jasmonates will help to ﬁnd new active and inactive jasmonate compounds, thus helping us to answer the question, ‘‘How is jasmonate signaling switched on and switched off?’’. Acknowledgments

I apologize for references not cited due to space limitations. The research in the author’s own laboratory was funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) within the SFB 363 project C5; the SFB 106 project C2; the SPP 1067, WA 875/3-1/2/3, WA 875/6-1; and the graduate program (TP13) of the excellence initiative ‘‘Biosciences’’ of Sachsen-Anhalt. I thank C. Dietel for typing the manuscript, C. Kaufmann for help in the design of the ﬁgures, B. Hause, M. Quint, and I. Feussner for critical reading, and all members of the laboratory for fruitful scientiﬁc activities. References 1 Lorenzo, O. and Solano, R. (2005) Molecular players regulating the jasmonate signalling network. Curr. Opin. Plant Biol., 8, 532–540. 2 Browse, J. (2005) Jasmonate: an oxylipin signal with many roles in plants. Vit. Horm., 72, 431–456.

3 Rosahl, S. and Feussner, I. (2005) Oxylipins, in Plant Lipids: Biology, Utilization and Manipulation (ed. D.J. Murphy), Blackwell, Oxford, pp. 329–354. 4 Schilmiller, A.L. and Howe, G.A. (2005) Systemic signaling in the wound

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6 Brassinosteroids Confer Stress Tolerance Uday K. Divi and Priti Krishna

6.1 Introduction

Brassinosteroids (BRs) are a group of plant steroidal hormones that are structurally related to animal and insect steroid hormones. BRs regulate a wide range of physiological responses in plants, including cell elongation, photomorphogenesis, xylem differentiation, seed germination [1], and stress responses [2, 3]. Although the growth-promoting properties of BRs were recognized in the early 1970s, the ﬁrst genetic evidence to suggest that BRs are essential for proper plant development came with the isolation of the BR-deﬁcient mutants det2 (de-etiolated2) and cpd (constitutive photomorphogenic dwarf ) [4, 5]. Isolation and sequence analysis of DET2 and CPD genes revealed that the encoded proteins share sequence similarities with steroid 5a-reductases and steroid hydroxylases, respectively, indicating a role for these proteins in steroid metabolism. Indeed, feeding det2 and cpd mutant seedlings with BRs rescued their mutant phenotypes to wild-type in a dose-dependent manner, clearly establishing the roles of DET2 and CPD in BR biosynthesis. Numerous other Arabidopsis BR-deﬁcient and BR-insensitive mutants, displaying phenotypic alterations such as dwarﬁsm, small dark-green leaves, a compact rosette structure, delayed ﬂowering and senescence, and reduced fertility [1], were instrumental in the identiﬁcation of BR signaling components and in understanding to some extent how BR regulates gene expression [6, 7]. Numerous reviews detailing BR effects on plant growth and development and BR signaling mechanisms have surfaced in the recent literature [6–10]; these aspects have therefore been discussed only brieﬂy here. The present chapter is focused on the relatively less explored topic of BR-mediated stress responses in plants [3] and highlights the progress made towards understanding the molecular basis of BRmediated plant stress tolerance.

Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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| 6 Brassinosteroids Confer Stress Tolerance 6.2 BR Signaling

BR is perceived at the cell surface by BRI1 (BR-INSENSITIVE1), a plasma membrane-localized leucine-rich repeat (LRR) receptor-like kinase (RLK) [11, 12]. BR binding to BRI1 induces a series of biochemical events, such as autophosphorylation of BRI1 in its C-terminal domain, dissociation of an inhibitory protein, BKI1 (BRI1 KINASE INHIBITOR1), and association of BRI1 with another LRR RLK, BAK1 (BRI1-ASSOCIATED RECEPTOR KINASE1) [13, 14]. The recent demonstration that BR-dependent activation of BRI1 precedes association with BAK1 has led to a model in which BAK1 enhances signaling output through reciprocal BRI1 transphosphorylation [15]. However, whether the main function of BAK1 is to enhance signaling output of BRI1, promote endocytosis of BRI1, or to help link BRI1 with a downstream signaling protein remains to be determined [7]. Other known components of the BR signaling pathway include GSK3 (GLYCOGEN SYNTHASE KINASE3), BIN2 (BR-INSENSITIVE2), which negatively regulates the transcription factors BZR1 (BRASSINAZOLE-RESISTANT1) and BES1 (BRI1-EMS SUPPRESSOR1) by phosphorylating them [16, 17], while the phosphatase BSU1 (BRI1 SUPPRESSOR1) positively regulates BR signaling possibly by dephosphorylating BZR1 and BES1 [18]. BIN2-catalyzed phosphorylation likely inhibits BZR1 and BES1 functions through targeted degradation, reduced DNA binding, and cytoplasmic retention through interaction with 14-3-3 proteins [19, 20]. In summary then, BRI1 binding to BR inactivates BIN2 and activates BSU1, resulting in the activation and nuclear accumulation of BZR1 and BES1. Activated BZR1 and BES1 directly bind the promoters of BR-regulated genes to affect their expression [21, 22]. Neither BIN2 nor BSU1 has been shown to interact with BRI1; thus, how BR signal is transmitted to these downstream proteins is currently not known. Recently, quantitative proteomic studies of BR-responsive proteins have led to the identiﬁcation of three BR signaling kinases (BSK1, BSK2, and BSK3), which can be phosphorylated by BRI1 in vitro and that interact with BRI1 in vivo [23]. The BSK proteins contain a kinase domain at the N-terminal side and tetratricopeptide repeat (TPR) domains at the C-terminus; the TPR domain is involved in mediating protein–protein interactions. Although genetic studies point to BSK3 functioning downstream of BRI1 [23], it remains to be seen if BIN2 is a direct downstream target of a BSK protein(s). Numerous BR-regulated genes have been identiﬁed by genome-wide microarray analyses [6, 24–26]. The majority of the known BR-regulated genes are associated with plant growth and development processes, such as cell wall modiﬁcation, cytoskeleton formation, and hormone synthesis [27]. The modes of action of BZR1 and BES1 are currently known for only a limited set of BR-responsive genes. BZR1 binds to the CGTG(T/C)G motif found in the promoters of BR biosynthetic genes, CPD and DWF4 (DWARF4), to suppress their expression [21], while BES1 binds to the CANNTG motif (E-box) in the SAUR-AC1 (SMALL AUXIN UP RNA 1 FROM ARABIDOPSIS THALIANA ECOTYPE COLUMBIA) promoter to activate

6.3 BR Increases Stress Tolerance

gene expression [22]. In view of the number of physiological processes that BRs regulate, it was hypothesized that BZR1 and BES1 heterodimerize with other transcriptional factors to regulate transcriptional processes. Indeed, BES1 has been demonstrated to interact with BES1-interacting MYC-like proteins (BIMs), leading to enhanced binding of BES1 to the SAUR-AC1 promoter [22], and to the JMJ (JUMONJI) domain-containing proteins ELF6 (EARLY FLOWERING6) and REF6 (RELATIVE OF EARLY FLOWERING6) that are involved in regulating ﬂowering time [28]. ELF6 and REF6 are recruited to BR target gene promoters by BES1 and possibly modify gene expression through histone modiﬁcations. Since JMJ domain-containing histone demethylases are involved in many developmental processes and diseases, it is probable that recruitment of these proteins by BES1 is one of the ways by which BR affects diverse biological processes.

6.3 BR Increases Stress Tolerance

Similar to the growth-promoting effects of BR, the stress-protective properties of BRs had also been noticed prior to the time BRs caught the interest of the larger plant community. However, these preliminary studies manifested considerable variability in the efﬁcacy of BRs to promote stress tolerance, which likely contributed to the relatively slower pace of progress made in this direction. It is our belief that the mode of BR application (seed soak, root soak, or foliar spray), as well as the developmental stages at which BR was applied, were the primary reasons for the inconsistency in the results of earlier studies. Recent attempts to study BR effects on plant stress responses under standardized conditions have indeed yielded reproducible effects. These, together with studies of BR signaling mutants and genome-wide expression data, have provided convincing evidence for a role of BR in plant stress responses. Here we have attempted to summarize the progress made towards understanding BR effects on diverse processes that converge into producing enhanced tolerance to a broad range of stresses (Figure 6.1). 6.3.1 Temperature Stress

The effects of BR on a plant’s ability to cope with high and low temperatures have been evaluated in several studies. Positive consequences of BRs in combating chilling stress were reported in maize, cucumber, tomato and rice [29–31]. A general conclusion that can be derived from these studies is that the BR effects on growth and yield are more pronounced during cold stress conditions than under normal conditions. More recently, the regulatory relationship between BR and chilling was investigated in a proteomic study using mung bean epicotyls. Treatment with 24-epibrassinolde (EBR), a BR, could partly recover the elongation of mung bean epicotyls after initial suppression of growth by chilling conditions [32]. Concomitantly, 17 proteins observed to be downregulated by chilling stress

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Figure 6.1 BR potentiates diverse cellular process involved in stress tolerance. The level of activation in each case is higher in BR-treated seedlings as compared to untreated seedlings.

were upregulated by EBR. These upregulated proteins were functionally linked with methionine assimilation, ATP synthesis, cell wall construction, and stress response [32]. The archetypical response to high temperature stress is the accumulation of heat shock proteins (HSPs) accompanied by reduced synthesis of other cellular proteins. EBR-treatment of Brassica napus, tomato, and Arabidopsis seedlings enhanced not only the basic thermotolerance of seedlings, as assessed by higher survival rates in comparison to untreated seedlings, but also led to higher accumulation of HSPs in B. napus during and after stress while maintaining cellular protein synthesis [33–35]. The maintenance of protein synthesis and higher HSP synthesis in EBR-treated B. napus seedlings versus untreated seedlings was largely due to the protection and upregulation of some of the translational machinery components in treated seedlings [34]. The results of an earlier study showing the maintenance of protein synthesis and accumulation of heat shock granules in BR-treated wheat leaf cells in response to high-temperature stress [36] are also explained by the observations made in B. napus [34]. In tomato, positive effects of EBR under high-temperature conditions were noted as better photosynthetic efﬁciency, in vitro pollen germination and pollen tube growth, and reduction in pollen bursting as compared to untreated plants. These effects correlated with higher accumulation of mitochondrial small heat shock proteins [37], reduction in total hydrogen peroxide and malonaldehyde

6.3 BR Increases Stress Tolerance

contents, and increases in the activities of antioxidant enzymes such as superoxide dismutase (SOD), ascorbate peroxidase (APX), guaiacol peroxidase, and catalase in EBR-treated tomato plants [38]. This BR-mediated increase in carboxylation efﬁciency and antioxidant enzyme activities must contribute to mitigating the detrimental effects of high temperatures on plant growth. 6.3.2 Salt Stress

Salinity stress inhibits seed germination and plant growth. BR reduced the inhibitory effects of salt on seed germination in Eucalyptus camaldulensis [39], rice [40], and B. napus [35]. In salt-sensitive IR28 rice, EBR reduced the extent of oxidative damage incurred by salt stress, which correlated with less lipid peroxidation, signiﬁcant increase in the activity of APX, higher soluble protein content, and higher accumulation of the protective osmolyte proline [41]. 6.3.3 Drought Stress

Measurements of both growth and physiological parameters suggest that BRs diminish the negative effects of water stress. Arabidopsis and B. napus seedlings grown for 3 and 2 weeks, respectively, on a nutrient medium containing 1 mM EBR were transplanted in coarse sand and subjected to drought stress by withholding water for different time periods. In both cases the EBR-treated seedlings survived the drought conditions that proved lethal to untreated seedlings [35]. Similarly, soaking the roots of Robinia pseudoacacia L. seedlings in brassinolide (BL) prior to planting increased the survival and growth of seedlings under simulated drought conditions [42]. BL-treated seedlings accumulated higher levels of osmolytes like proline and soluble sugars, had higher leaf water content, and showed greater increases in the activities of antioxidant enzymes SOD, peroxidase and catalase as compared to untreated seedlings. Also, the stress-induced reduction in the transpiration rate, stomatal conductance, and malondialdehyde content was less severe in BL-treated seedlings as compared to untreated seedlings [42]. A drought-susceptible variety of French bean was less affected in nodule number, nodulated root mass, and root length under drought stress in response to EBR treatment [43]. An indirect correlation between BR and drought stress was recently revealed through the study of Arabidopsis SIZ1 (a SUMO E3 ligase). The sumoylation pathway resembles the better-studied ubiquitination pathway and it has been implicated in many aspects of plant developmental processes, as well as plant stress responses [44, 45]. The null mutant siz1-3 has reduced expression of genes involved in BR biosynthesis and jasmonate responses, as well as signiﬁcantly lower tolerance to drought stress as compared to wild-type [46]. Future studies to determine whether BR levels are compromised in the siz1-3 mutant, and if altered BR levels are the prime reason for drought sensitivity, can be very useful in establishing a direct correlation between BR and drought tolerance.

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| 6 Brassinosteroids Confer Stress Tolerance 6.3.4 Pathogen Attack

Despite some good leads towards the possible involvement of BR in plant disease resistance [2, 47], reports on molecular analysis of this property of BR have not been forthcoming. In earlier studies it was noted that the reduced incidence of infection by fungal pathogens in BR-treated potato plants was associated with the presence of phenolic and terpenoid substances, and enhancement of abscisic acid (ABA) and ethylene levels, and in BR-treated cucumber, with increased activities of peroxidase and polyphenoloxidase enzymes, which are involved in the metabolism of polyphenols [48, 49]. These biochemical changes were suggested as factors contributing to BR-induced disease resistance. While the general conclusion from these observations is that BRs have potential as fungicides, it should be noted that certain concentrations of BR, as well as application of BR at certain developmental stages of the plant, can also stimulate fungal growth and disease progression. Thus, BR concentration, and timing and method of BR application, are important considerations when using BRs as fungicides [2, 3, 47]. The length of BR treatment can also determine the outcome. For example, a long-term treatment (14 days) of tomato plants with EBR considerably reduced the disease symptoms caused by Verticillium dahliae infection, but a short-term EBR treatment (24 h) prior to inoculation with V. dahliae had no affect [3]. The potential of BR to induce resistance to a broad range of pathogens, including fungal, bacterial, and viral, was demonstrated in tobacco and rice [50]. Tobacco plants treated with BL exhibited enhanced resistance to the viral pathogen tobacco mosaic virus (TMV), the bacterial pathogen Pseudomonas syringae, and the fungal pathogen Oidium sp. Similarly, BL-treated rice plants were resistant against rice blast and bacterial blight caused by Magnaporthe grisea and Xanthomonas oryzae, respectively. Application of brassinazole, a BR biosynthesis inhibitor, had a suppressive effect in a dose-dependent manner on the defense response of tobacco against TMV, indicating that endogenous BR is involved in disease resistance [50]. Interestingly, in this study BL treatment had no affect on endogenous salicylic acid (SA) levels or the expression of PR (PATHOGENESISRELATED) genes, suggesting that BR-mediated disease resistance is distinct from systemic acquired resistance and wound-inducible resistance. In contrast to these ﬁndings, higher PR gene expression in response to BR treatment has been noted in a few studies. The BR-deﬁcient mutant cpd showed remarkably lower expression of PR1, PR2, and PR5 as compared to wild-type, while overexpression of the CPD cDNA resulted in a signiﬁcant induction of these genes in the complemented lines [5]. Short-term treatment (1 and 6 h) of Arabidopsis seedlings with BL also induced the expression of PR1 and PR2 genes [51]. Furthermore, we have found that EBR can induce the expression of PR1 in SA-deﬁcient and SA-insensitive mutants, suggesting that BR affects PR1 gene expression in an SA-independent manner (Krishna et al., unpublished data). These results together make a case for PR genes to be primary response genes of BR. Surely

6.3 BR Increases Stress Tolerance

then, the mechanism by which BR induces their expression warrants exploration in the future. If BR has a major role in disease resistance, it is possible that some pathogens may have developed mechanisms to downregulate the BR pathway or BR activity. The possibility that such a mechanism is employed by a trichothecene toxinproducing fungi, came from the results of Arabidopsis seedlings treated with the T-2 toxin synthesized by the Fusarium species [52]. The T-2 toxin-treated seedlings exhibited dwarﬁsm and showed upregulation of, amongst others, the AtST1 (RaR047) gene, which is homologous to the B. napus BnST3 (BR SULFOTRANSFERASE3) gene that is involved in inactivation of BR biological activity [53]. Analysis of BR composition and content in T-2 toxin-treated seedlings, and of the impact of T2 toxin on BR signaling mutants such as bes1-D [54] and CPD or DWF4 overexpressors, should help clarify the role of BR in disease resistance. Other circumstantial evidence linking BR and plant viral infection came from the study of geminiviruses, the beet curly top virus (BCTV) and the tomato golden mosaic virus. BCTV infection causes hyperplasia in the phloem, which has been attributed to the BCTV C4 protein. Functional analysis of this protein showed that it interacts with AtSKZ/BIN2, the negative regulator of the BR signaling pathway (Section 2). AtSKZ/BIN2 phosphorylated BCTV C4 at threonine and serine residues, suggesting a host defense mechanism whereby the viral protein is phosphorylated by BIN2 and targeted for degradation, similar to the fate of BES1 and BZR1 upon phosphorylation by BIN2 [55]. Studies in the future detailing the fate of BCTV C4 in different BR mutant backgrounds may distinguish whether the BCTV C4–BIN2 interaction amounts to a host defense mechanism or to a counterdefense measure by the virus. An interesting observation made during a study involving downregulation of the 26S proteasome subunit RPN9 that led to the inhibition of broad-spectrum viral systemic transport and altered plant vascular development, was the upregulation of the tobacco ortholog of the vascular-localized BRL1 in RPN9-silenced plants [56]. The signiﬁcance of these results can be explained in the context of the role of BR in xylem development. Increased endogenous BR levels or increased BR perception promotes xylem and suppresses phloem formation [57]. The BR-like proteins BRL1 and BRL3 are BR receptors speciﬁcally expressed in vascular cells that affect xylem formation [57]. The upregulation of tobacco BRL1, possibly leading to enhanced BR perception in vascular tissues of RPN9-silenced leaves, resulted in more xylem and less phloem in the vascular bundles of these plants, and since many plant viruses rely primarily on phloem for their systemic transport the alternation of xylem and phloem ratio in silenced plants resulted in inhibition of viral transport. The RPN9-silenced plants also accumulated higher levels of BZR1 as compared to the controls, suggesting that RPN9 is involved in regulating vascular development by targeted degradation of speciﬁc proteins, such as BZR1. This report [56] is the ﬁrst to connect structural changes induced by BR with disease resistance. We hypothesize that BR-mediated cell wall modiﬁcations and other such alterations also have a role in BR-mediated stress tolerance.

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The involvement of BR in signaling events during ultraviolet (UV)-B stress (280– 315 nm) was investigated in Arabidopsis BR-deﬁcient (det2, dim1 (diminuto1), cpd) and BR-insensitive bri1 (br-insensitive1) mutants [58]. These mutants showed reduced expression of a set of UV-B-responsive defense genes [CHS (CHALCONE SYNTHASE), PYROA, PR5, and a gene regulated by very low levels of UV-B, MEB5.2)] as compared to wild-type and a small size control mutant irx (irregular xylem). Interestingly, the expression of PR5 transcripts was most affected by BR deﬁciency. Overall, these results indicate that a complete BR pathway is required for proper UV-B-dependent gene expression in Arabidopsis [58]. A few studies have demonstrated that BR treatment can protect against the toxic effects of heavy metals [59], as well as block the uptake and accumulation of heavy metals [60]. While earlier studies suggested that BRs can protect plants from chemical damage caused by pesticides and insecticides [61], these effects have not been substantiated in recent studies. 6.4 Anticancer and Antiviral Effects

Several reports sourced mainly from the Laboratory of Virology at the University of Buenos Aires, Argentina, have concluded that synthetic analogs of BR have potent antiviral activities against animal viruses such as herpes simplex virus type 1, vesicular stomatitis virus, junin virus, and measles virus [62–65]. Time-related experiments have shown that synthetic BRs can inhibit, depending on the virus, either an early event or a late event of virus growth. A synthetic BR was also demonstrated to exert immunomodulatory activities, such as enhanced production of interleukin-6 that acts as both a proinﬂammatory and anti-inﬂammatory cytokine, and blockage of virus-induced activation of nuclear factor-kB in virus infected cells [62]. A recent report from the Palacky University in the Czech Republic makes the intriguing observation that natural BRs, such as EBR and 28-homocastasterone, have anticancer and antiproliferative activities [66]. Both EBR and 28-homocastasterone inhibited the growth of several human cancer cell lines in a dose-dependent manner without affecting the growth of normal cells. Clearly these observations highlighting the potential of BRs as anticancer and antiviral drugs necessitate further investigation of this feature of BR. Time will tell whether enough interest can be generated within scientists of appropriate expertise to further these observations and for the drug companies to build on those leads. 6.5 Genetic Evidence for a Role of BR in Plant Stress Responses

Although numerous studies described herein have suggested a role for BR in plant stress responses, only a few genetic studies have been conducted to conﬁrm this

6.6 BR-Independent Role of BAK1 in Innate Immunity and Cell Death

property. The most convincing evidence comes from the analysis of knockout mutants of OsGSK1, the rice ortholog of BIN2 [67]. The OsGSK1 knockout mutants obtained by T-DNA insertion showed enhanced tolerance to cold, heat, salt, and drought stresses when compared with nontransgenic segregants. For example, the wilting ratios for knockout mutants were about 20, 26, and 36% lower as compared with nontransgenic plants after cold, heat, and salt stress, respectively. Furthermore, three abiotic stress-responsive genes, SalT, lip5, and OsDhn1, had higher expression in the knockout plants under different abiotic stress conditions. Such physiological and gene expression changes in the OsGSK1 knockout plants would be expected if BR signaling inﬂuences stress responses and if the negative regulator of BR signaling, OsGSK1, had been disrupted. However, since the increase in tolerance to various abiotic stresses was observed in only a single OsGSK1 allele, there remains the possibility that some of the stress tolerance phenotypes were caused by mutation at a locus other than OsGSK1. Studies of additional allelic lines of OsGSK1, and functional complementation of OsGSK1 to knockout plants, will strengthen the currently reported observations of Koh et al. [67], but the strong likeness of the current results with those obtained in BR-treated B. napus and Arabidopsis [33, 35], strongly suggest that BR has roles in plant stress responses. It is now of great interest to investigate how BR-signaling leads to the induction of genes participating in various stress signal transduction pathways. One question that arises with such observations is whether changes in endogenous BR levels are normally involved in mediating the plant’s response to stress, as is the case with ABA. Studies in pea have shown that while water stress causes a signiﬁcant increase in ABA levels, it does not result in altered BR levels in either apical, internode or leaf tissue. Also, the plant’s ability to increase ABA levels in response to water stress is not affected by BR deﬁciency in lkb (BR-deﬁcient) and lka (BR-perception) mutant plants [68]. On the basis of these results it is argued that in pea changes in endogenous BR levels are not part of the plant’s response to water stress. While these are important observations, there are other possibilities that cannot as yet be ruled out (e.g., subtle changes in BR levels in speciﬁc cell types, changes in BR composition, and intermediates with greater biological activity).

6.6 BR-Independent Role of BAK1 in Innate Immunity and Cell Death

Arabidopsis contains more than 600 RLKs, which possess different extracellular domains providing ligand speciﬁcity, and cytoplasmic kinase domains that initiate downstream signaling [69, 70]. Of the functionally characterized RLKs, some are implicated in plant growth and development, such as BRI1 in perceiving BR, and others in plant defense by functioning as pattern-recognition receptors (PRRs) [71, 72]. The PRRs detect pathogens by recognizing pathogen-associated molecular patterns (PAMPs) or microbe-associated molecular patterns and trigger signaling events leading to immune responses. Recently, two well-characterized PPRs in

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| 6 Brassinosteroids Confer Stress Tolerance Arabidopsis, the ﬂagellin receptor FLS2 (FLAGELLIN-SENSITIVE2), and the elongation factor EF-Tu receptor (EFR) for bacterial elicitors ﬂagellin (ﬂg22) and EF-Tu, respectively, were demonstrated to associate with BAK1 in a stimulusspeciﬁc manner [73, 74]. bak1 mutants had abnormal ﬂg22 and EF-Tu-triggered responses, and they displayed altered disease susceptibility to several pathogens. Thus, in addition to being a signaling partner with BRI1, BAK1 is a positive regulator of PAMP receptors and inﬂuences innate immunity. It is noteworthy that although BAK1 is a partner of both BRI1 and FLS2, BR and ﬂg22 do not produce overlapping responses. Also, since bak1 mutants have normal binding of BR to BRI1 and ﬂg22 to FLS2, but reduced BR- and ﬂg22-dependent responses, it is concluded that BAK1 is not involved in signal perception, but rather functions as an adaptor or partner of different receptor complexes to regulate different responses. The importance of BAK1 in host immune responses also makes it a target for manipulation by successful pathogens as a means to interfere with host immune responses. It is indeed found that the P. syringae virulence effectors AvrPto and AvrPtoB, which suppress host defense responses and promote bacterial proliferation, interact with BAK1 and block its interaction with FLS2, suppressing signaling through the BAK1–FLS2 complex to mount an immune response in host cells [75]. The AvrPto interferes also with the formation of the BRI1–BAK1 complex and impedes BR signaling. Another BR independent and plant immunity-associated role for BAK1 lies in controlling programmed cell death (PCD) [76]. PCD is a common host response to pathogen invasion, leading to both resistance against biotrophic pathogens and susceptibility to necrotrophs [77]. The link between BAK1 and PCD was suggested in the phenotypes of bak1 mutants, which showed increased lesion formation and disease indices upon infection as compared to wild-type. Complementation assays rescued the susceptible phenotypes, while overexpression of BAK1 resulted in reduced necrosis and susceptibility to fungal infection. BAK1 functions redundantly with BKK1 (BAK1-LIKE1). The double-null mutants bak1/bkk1 exhibited a seedling-lethality phenotype due to a constitutive defense response, spontaneous cell death, callose deposition, and reactive oxygen species accumulation [78]. These ﬁndings together suggest that BAK1 functions as a negative regulator of PCD in a BR-independent manner. Recapitulating the roles of BAK1 as a positive regulator of BR signaling and as a negative regulator of BR-independent cell death pathway [78], it would appear that because of a high degree of cross-talk between the two pathways when the BR-signaling pathway is enhanced, the BR-independent pathway is automatically reduced, leading to upregulation of defense-related genes and cell death (Figure 6.2). This would explain why treatment with exogenous BR or overexpression of BR biosynthesis leads to upregulation of defense-related genes [3, 5]. The conglomerate of genes that may be under negative control of BAK1 but become induced during BR signaling could be responsible for the increase in abiotic stress tolerance, as well as resistance to some, if not all, biotrophic pathogens. Conversely, if the immune response pathway is activated with concomitant reduction

6.6 BR-Independent Role of BAK1 in Innate Immunity and Cell Death

in the BR signaling pathway, the plants would display a BR-deﬁcient phenotype (Figure 6.2). It has indeed been reported that Arabidopsis plants treated with bacterial, viral, or fungal elicitors mimic a BR-deﬁcient phenotype, such as dwarﬁsm [79, 80]. With the above-described observations and speculations, there is little doubt that it is of utmost interest to laboratories studying BR-mediated stress tolerance to determine if BR-mediated upregulation of stress-responsive genes is a direct effect of BR signaling or it is simply the result of cross-talk via BAK1 with other signaling pathways. Either way, BAK1 would turn out to be an intriguing example of a signaling component that links developmental programs with stress responses of plants (Figure 6.2).

Figure 6.2 A tentative model for integration of BR developmental pathway with stress-responsive pathways. BAK1 interacts independently with BRI1 to promote BRmediated growth responses, and with FLS2/EFR to promote BR-independent immune responses. BAK1 also acts as a negative regulator of the cell death pathway. It is hypothesized that when BR-mediated signaling is on, repression of the cell death pathway by BAK1 is released, resulting in the expression of defense genes (grey arrow) and increase in stress tolerance. BR-mediated increase in stress tolerance could also occur either through the known BRI1 and BAK1 interaction or via interaction of BRI1 with an unknown stress receptor, with either pathway leading to activation of stress responses. In the presence of pathogen elicitors, interaction of BAK1 with FLS2/EFR may repress BR signaling, leading to BR-deﬁcient phenotypes.

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| 6 Brassinosteroids Confer Stress Tolerance 6.7 Systematic Study to Dissect the Role of BR in Abiotic Stress Tolerance

To systematically dissect the mechanisms involved in BR-mediated tolerance to abiotic stresses, we ﬁrst established that BR treatment could consistently increase the freezing, heat and drought tolerance of plants [3, 33, 35, 81]. We next embarked on understanding if BR affects the expression in B. napus and Arabidopsis of prototype genes upregulated in response to heat, cold, and drought stress. Interestingly, while BR-treated B. napus seedlings accumulated signiﬁcantly higher levels of HSPs in response to heat stress [33], there was little to no difference in HSP levels in BR-treated Arabidopsis seedlings as compared to untreated seedlings [35]. In response to cold stress, transcript levels of structural genes such as rd29A, an ortholog of BN115, and COR47 were observed at much higher levels in BRtreated Arabidopsis seedlings as compared to untreated seedlings. By contrast, there were no noticeable differences between untreated and treated B. napus seedlings in the expression of regulatory genes BNCBF5 and BNDREB, and structural genes BN115, BN28, and HSP90 [35]. While these results indicate that BR-mediated stress-responsive gene expression proﬁles are somewhat different in B. napus and Arabidopsis, it is also clear that, in general, there is higher induction of stress-responsive genes in BR-treated seedlings versus untreated seedlings. Further investigation of why BR-treated B. napus seedlings accumulate higher levels of HSPs indicated that BR treatment both limits the loss of the components of the translational apparatus during heat stress and increases their levels during recovery, which correlate with higher HSP synthesis during stress, more rapid resumption of cellular protein synthesis following heat stress, and a higher survival rate [34]. To identify additional BR-induced gene expression changes in B. napus seedlings, the differential display/reverse transcription polymerase chanin reaction technique was used. Substantial changes were identiﬁed in the expression levels of genes encoding a mitochondrial transcription termination factor (mTERF)-related protein, GRP22 (GLYCINE-RICH PROTEIN22), myrosinase, and 3-ketoacyl-CoA thiolase [82]. Transcripts of mTERF-related protein, GRP22, and myrosinase were present at approximately two-, four-, and sixfold higher levels, respectively, in treated seedlings before heat stress, whereas those of 3-ketoacyl-CoA thiolase rose to higher levels in treated seedlings during exposure to heat stress. These results indicate that BR treatment in B. napus leads to substantial changes in the expression levels of genes involved in a variety of physiologic responses, either before or during stress exposure. Due to the many resources available for the Arabidopsis experimental system, more recently we have focused our BR research on this model plant. To determine if BR is vital for HSP synthesis, we examined HSP expression in Arabidopsis wild-type and BR-deﬁcient mutant (det2-1 and dwf4) seedlings. Contrary to the expectation that BR-deﬁcient mutants may be compromised in HSP expression, transcripts of all four major classes of HSPs (HSP100, HSP90, HSP70, and small HSP) were present at high levels in untreated det2-1 and dwf4 mutant seedlings [35]. Notably, untreated det2-1 seedlings accumulated HSP transcripts,

6.8 Future Directions

including the heat-induced HSP101 and small HSP, even in the absence of any heat stress. HSP101 protein was also present in det2-1 seedlings in the absence of heat stress. Two conclusions that can be derived from these results are (i) BR is not essential for HSP induction and (ii) BR deﬁciency is either a developmental anomaly or a form of cellular stress that triggers, via unknown pathways, the synthesis of stress-related genes. Whether cross-talk with other signaling pathways via BAK1 or a related protein is involved remains to be seen. Analysis of global gene expression in BR-treated and untreated Arabidopsis before, during, and after heat stress indicated that majority of the upregulated genes have been associated previously with abiotic and biotic stress tolerance (Krishna et al., unpublished data). These results have shed light on a macro scale on how BR may promote stress tolerance in plants, as well as how BR-mediated regulation of stress responses may be integrated with its effects on plant growth and development. Such an understanding is important when contemplating changes of plant architecture, productivity, or sustainability through manipulation of BR levels. Morphological and physiological characterization of knockout mutants of genes identiﬁed in the microarray screen has revealed new stressrelated genes in Arabidopsis (Krishna et al., unpublished data). Since a subset of genes identiﬁed in the microarray screen is related to biosynthesis and signaling of other plant hormones, it is possible that some of the newly identiﬁed stress-related genes are primary response genes of other phytohormones. Nevertheless, their identiﬁcation adds to the larger question of how long-term treatment with BR makes a plant more tolerant to a broad range of abiotic stresses. BR interacts with other hormones. The collaboration between BR and auxin in regulating plant growth has been known and widely studied for many years [83]. Thus, it is also possible that BR collaborates with other stress hormones to modulate stress responses in plants. Such collaboration can either be at the gene expression level or at the level of hormone biosynthesis. Consistent with a previous observation [84], we have found that genes involved in jasmonic acid biosynthesis are upregulated by BR (Krishna et al., unpublished data). To address the involvement of other hormones in BR-mediated increase in thermotolerance, we used a collection of Arabidopsis mutants that were either deﬁcient in or insensitive to SA, ethylene, jasmonic acid, and ABA. BR treatment enhanced thermotolerance of all but one mutant included in the study (Krishna et al., unpublished data). While from these results it would appear that BR exerts its antistress effects independently, the involvement of the aforementioned hormones in enhancing these effects remains a possibility.

6.8 Future Directions

From the current literature review it is clear that BRs do have the ability to enhance stress tolerance in plants; however, while the molecular mechanisms underlying this property of BR have begun to be explored, there is much that remains to be learnt. The coming years will see the identiﬁcation of metabolic pathways and

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| 6 Brassinosteroids Confer Stress Tolerance novel stress-related genes, directly or indirectly regulated by BR, which contribute to an increase in stress tolerance in response to BR. Detailed functional analysis of genes encoding regulatory proteins like transcription factors, protein kinases, phosphatases, and other signaling components will help to integrate different signaling pathways and diverse physiological processes (Figure 6.1) that together constitute the BR-directed increase in stress tolerance. The intriguing example of BAK1 as a candidate for possible cross-talk of the BR pathway with signaling pathways of plant defense responses (Figure 6.2) should provide great impetus for identifying additional partners of both BRI1 and BAK1. Changes in gene transcription have been thought to be the major mode of action by phytohormones, but post-transcriptional changes such as changes in transcript and protein stability, and protein synthesis are also possibilities that need to be studied in this context. The effect of BR on protein synthesis during heat stress was clearly demonstrated in B. napus [34]. A recent proteomic study for understanding BR responses identiﬁed 42 BR-regulated proteins in Arabidopsis, the majority of which were not identiﬁed by earlier microarray screens [85]. Since BR-induced changes in transcript levels are modest, it is possible that BR produces more substantial changes at the protein level. Thus, proteomics analysis of BR-mediated stress responses will certainly expand our understanding of how BR affects stress tolerance. Alongside unraveling the mode of action of BR, other aspects such as uptake, transport, and stability of BRs should continue to be explored. It is only with this combined knowledge that unique mechanisms of stress resistance can lead to implementations, with predictable effects of BR application in the ﬁeld, allowing for the full potential of BRs to be harnessed in the future.

Acknowledgment

This work is supported by a grant to P.K. by the Natural Sciences and Engineering Research Council of Canada.

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7 Cold, Salinity, and Drought Stress Narendra Tuteja

Abstract

Genetically modiﬁed crops are emerging as a key weapon to ﬁght the negative impact of abiotic stresses on agricultural production. Among abiotic stresses, cold mainly causes mechanical constraint to the membrane, whereas salinity and drought exert their negative impact essentially by disrupting the ionic and osmotic equilibrium of the cell. Cytosolic free Ca2 þ concentration ([Ca2 þ ]cyt) has been found to increased in response to the abiotic stress. The stress signal is ﬁrst perceived at the membrane level by the receptors and then transduced in the cell to switch on various stressresponsive genes for mediating tolerance. The products of stress-inducible genes function both in the initial stress response and in establishing plant stress tolerance. Some genes have been reported to be upregulated in response to more than one stress, indicating the presence of cross-talk between the different stress signaling pathways. The generation of reactive oxygen species represents a universal mechanism in cellular responses to environmental stresses. Plants also accumulate a variety of osmoprotectants that improve their ability to combat abiotic stresses. Understanding the mechanism of abiotic stress tolerance is important for crop improvement. In this chapter various aspects of cold, salinity, and drought stresses along with the role of calcium are discussed. 7.1 Introduction

The world population is increasing at an alarming rate and is expected to reach more than 9 billion by the end of 2050 (http://www.unfpa.org/swp/2007/presskit). However, food productivity is decreasing due to the negative effect of various stress factors. Minimizing these losses is a major area of concern for all nations. Among these, abiotic stress is the principal cause of decreasing the average yield of major Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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| 7 Cold, Salinity, and Drought Stress crops by more than 50%, which causes losses worth hundreds of millions of dollars each year. In 2000, the United Nations Secretary-General, Koﬁ Annan, called for a ‘‘Blue Revolution’’ and said that there was an urgent need for more crops out of dry land. In 2007, the United Nations Food and Agriculture Organization warned of food shortages in new climates (‘‘food security is in danger’’). Recently, in 2008, Neena Fedoroff, Science and Technology adviser to the United States Secretary of State, emphasized the acute need for a ‘‘Second Green Revolution.’’ Climate change and the decreased availability of fertile land will create a problem for future crop production. In fact, these stresses threaten the sustainability of agricultural industry. The challenge now is to produce additional food under stress conditions and in less soil. Therefore, it is now necessary to obtain stress-tolerant varieties to cope with this upcoming problem of food security. It is important, ﬁrst of all, to understand the notion of stress. Stress in physical terms is deﬁned as a mechanical force per unit area applied to an object. In biological systems stress can be deﬁned as an adverse force, effect, or inﬂuence that tends to inhibit normal systems from functioning [1, 2]. Various stress signals, both abiotic as well as biotic, serve as elicitors for the plant cell. Abiotic stresses include heat, cold, drought, salinity, wounding, heavy metals toxicity, excess light, excess water (ﬂooding), high speed wind, nutrient loss, anaerobic conditions, and radiation. Biotic stresses include pathogens (bacteria, fungus, virus), herbivores, weeds, insects, nematodes, and mycoplasma. Plants respond to stress as individual cells and synergistically as a whole organism. In general, the stress signal is ﬁrst perceived by receptors of the plant cells. Following this the signal information is transduced, resulting in the activation of various stress-responsive genes. The products of these stress genes ultimately lead to a stress tolerance response or plant adaptation, and help the plant to survive and surpass unfavorable conditions [1, 2]. The response could also result in growth inhibition or cell death, which will depend upon how many and which kinds of genes are up- or downregulated in response to the stress. The various stress-responsive genes can be broadly categorized as early- and late-induced genes. Early genes are induced within minutes of stress signal perception, which include various transcription factors. Late genes include the major stress-responsive genes such as RD (RESPONSIVE TO DEHYDRATION)/KIN (COLD INDUCED)/COR (COLD RESPONSIVE), which encode and modulate the proteins needed for synthesis, for example, late embryogenesis abundant (LEA)-like proteins, antioxidants, membrane-stabilizing proteins, and osmolytes [2]. Overall, the stress response is a coordinated action of many genes encoding signaling proteins/factors, including protein modiﬁers (methylation, ubiquitination, glycosylation, etc.), adaptors, and scaffolds [2, 3]. In this chapter I have emphasized the general response to abiotic stress followed by cold, salt, and drought stresses, and the reason for these stresses being injurious for plants. Various genes involved in cold acclimation and their role towards membrane stabilization are discussed. The role of calcium in relation to stress is covered. Furthermore, the role of the salt overly sensitive (SOS) pathway in salt tolerance and the role of glycine betaine (GB, N,N,N-trimethylglycine-betaine) as a major osmolyte in response to salt stress are also described.

7.2 Abiotic Stress Response and Stress-Induced Genes

7.2 Abiotic Stress Response and Stress-Induced Genes

A generic pathway in response to salinity, drought, and cold stresses is depicted in Figure 7.1. To sense these environmental signals, higher plants have evolved a complex signaling network, which may also cross-talk. Stress signal transduction pathways start with signal perception by receptors (phytochromes, histidine kinases, receptor-like kinases, G-protein-coupled receptors (GPCR), hormone receptors, etc.). Heterotrimeric G-proteins mediate the coupling of signal transduction from activated GPCRs to generate secondary signaling molecules (inositol phosphatase, reactive oxygen species (ROS), abscisic acid (ABA), etc.). These secondary molecules can modulate the intracellular Ca2 þ levels by receptor-mediated Ca2 þ

Figure 7.1 Generic pathway under salinity, drought, and cold stresses. Salinity and drought stresses mainly disrupt the ionic and osmotic equilibrium of the cell. These stresses can also cause injury to the cellular physiology, which leads to metabolic dysfunctions followed by growth inhibition. Cold stress mainly exerts its negative effect by disruption of membrane integrity and solute leakage. Finally, in response to all these stresses several stress-responsive genes are upregulated whose products can directly or indirectly help the plant in stress tolerance.

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release or can bypass Ca2 þ in early signaling steps and initiate protein phosphorylation cascades (protein phosphatase, mitogen-activated protein kinase (MAPK), calcium-dependent protein kinase (CDPK), SOS3/protein kinase S, etc.), which activate speciﬁc stress-responsive genes for cellular protection through transcription control (MYC/MTB, C-repeat binding (CBF)/dehydration-responsive element binding (DREB) factors) [2, 3, 5, 6]. Salinity and drought exert their inﬂuence on a cell mainly by disrupting the ionic and osmotic equilibrium [2]. Thus, excess of Na þ ions and osmotic changes in the form of turgor pressure are the initial triggers, leading to a cascade of events, which can be grouped under ionic and osmotic signaling pathways, the outcome of which is ionic and osmotic homeostasis, leading to stress tolerance. These stresses are marked by symptoms of stress injury, including chlorosis and necrosis, and may also exert negative inﬂuences on cell division resulting in growth retardation of plants [2]. Reduction in shoot growth, especially leaves, is beneﬁcial for plants as it reduces the surface area exposed for transpiration, hence minimizing water loss. Plants may also sacriﬁce or shed older leaves, which is another adaptation in response to drought. Stress injury may occur through denaturation of cellular proteins/enzymes or through the production of ROS, Na þ toxicity, and disruption of membrane integrity. In response to injury stress plants trigger a detoxiﬁcation process, which may include change in the expression of LEA/dehydrin-type gene synthesis of molecular chaperones, proteinases, enzymes for scavenging ROS, and other detoxiﬁcation proteins. This process functions in the control and repair of stressinduced damage, and results in stress tolerance. Cold stress mainly results in disruption of membrane integrity, leading to severe cellular dehydration and osmotic imbalances. Cold acclimation results in the triggering of various genes, which result in a restructuring of the cellular membranes by changes in the lipid composition and the generation of osmolytes, which prevent cellular dehydration and enhances stress tolerance (Figure 7.1). Plants suffer from dehydration or osmotic stress under drought, salinity, and also under low-temperature conditions that cause reduced availability of water for cellular function and maintenance of cellular turgor pressure. Prolonged periods of dehydration lead to high production of ROS in the chloroplasts, causing irreversible cellular damage and photoinhibition. Overall, in response to all these stresses several stress-responsive genes are upregulated whose products can directly or indirectly help the plant through stress tolerance. Understanding the molecular mechanism for abiotic stress tolerance is still a major challenge in biology. Many chemicals are also critical for plant growth and development, and play an important role in integrating various stress signals and controlling downstream stress responses by modulating gene expression machinery and regulating various transporters/pumps and biochemical reactions. Some of the chemicals include calcium (Ca2 þ ), cyclic nucleotides, polyphosphoinositides, nitric oxide, sugars, ABA, jasmonates, salicylic acid, and polyamines [7]. Microarrays employing cDNAs or oligonucleotides are a powerful tool for analyzing the gene expression proﬁles of plants exposed to abiotic stresses. A 7000 full-length cDNA microarray was utilized to identify 299 drought-inducible genes,

7.3 Cold Stress

54 cold-inducible genes, 213 high salinity-inducible genes, and 245 ABA-inducible genes in Arabidopsis [8, 9]. More than half of these drought-inducible genes were also induced by high salinity and/or ABA treatments, implicating signiﬁcant cross-talk between the drought, high salinity, and ABA response pathways. Recently, Shinozaki and Yamaguchi-Shinozaki [10] summarized the gene networks involved in drought stress response and tolerance. By using transgenic technology, Bhatnagar-Mathur et al. [11] have also described the recent progress in the improvement of abiotic stress tolerance in plants, which includes a discussion on the evaluation of abiotic stress responses and protocols for testing transgenic plants for their tolerance under close-to-ﬁeld conditions. Emerging evidence indicates CDPKs sense the Ca2 þ concentration changes in plant cells, and play important roles in signaling pathways for disease resistance and various stress responses. Among the 20 wheat CDPK genes studied, 10 were found to respond to drought, salinity, and ABA treatment [12].

7.3 Cold Stress

Each plant has its own set of temperature requirements, which are optimum for its proper growth and development. Deviation from optimum temperature may lead to plant growth inhibition and yield loss. The cold stress experienced by plants can be classiﬁed into two types: those occurring at (i) temperatures below freezing and (ii) low temperatures above freezing (nonfreezing temperatures).This section covers various aspects of cold stress. 7.3.1 Effect of Low-Temperature Stress on Plant Physiology

Many plants such as maize, soybean, cotton, tomato, and banana are sensitive to nonfreezing temperatures (10–15 1C) and exhibit signs of injury [13–15]. Various phenotypic symptoms in response to chilling stress include reduced leaf expansion, wilting, and chlorosis, which may lead to necrosis. Low temperature can also severely hamper the reproductive development of plants, as reported in rice [16]. Freezing temperatures can induce severe membrane damage, which is largely due to the acute dehydration associated with freezing [14, 17]. The temperature at which a membrane changes from a semiﬂuid state to a semicrystalline state is known as the transition temperature. Chilling-sensitive plants usually have a higher transition temperature as compared to the chilling-resistant plants, which have a lower transition temperature. An understanding of how freezing induces plant injuries is essential for the development of frost-tolerant crops. The real cause of freeze-induced injury to plants is ice formation rather than the low temperatures. Ice formation in plants begins in the apoplastic space having relatively low solute concentrations. This causes a mechanical strain on the cell wall and plasma membrane leading to cell rupture [18]. Freezing temperatures exert

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| 7 Cold, Salinity, and Drought Stress their effects largely by membrane damage due to severe cellular dehydration, but certain additional factors including ROS also contribute to damage induced by freezing. Overall, chilling ultimately results in loss of membrane integrity, which leads to solute leakage. The integrity of intracellular organelles is also disrupted, leading to the loss of compartmentalization and impairment of photosynthesis, protein assembly, and general metabolic processes. The primary environmental factor responsible for triggering increased tolerance against freezing is the phenomenon known as ‘‘cold acclimation.’’ It is the process where certain plants increase their freezing tolerance upon prior exposure to low nonfreezing temperatures [2]. 7.3.2 Cold Acclimation

Cold temperatures induce a number of alterations in cellular components, including the extent of unsaturated fatty acids, the composition of glycerolipids, changes in protein and carbohydrate composition, and the activation of ion channels [2, 19]. For cold acclimation, membranes have to be stabilized against freeze injury, which can be achieved through changes in the lipid composition and induction of other nonenzymatic proteins that alter the freezing point of water. Accumulation of sucrose and other simple sugars also contributes to the stabilization of membranes as these molecules can protect membranes against freeze-damage. Low temperatures activate a number of cold-inducible genes, such as those encoding dehydrins, lipid transfer proteins, translation elongation factors, and the LEA proteins [2, 14, 19]. Overall, cold acclimation results in protection and stabilization of the integrity of cellular membranes, enhancement of the antioxidative mechanisms, increased intercellular sugar levels as well as accumulation of other cryoprotectants including polyamines that protect intracellular proteins by inducing the genes encoding molecular chaperones. All these modiﬁcations help plants to withstand and surpass severe dehydration associated with freezing stress [2, 19]. 7.3.3 Function of Cold-Regulated Genes in Freezing Tolerance

The Arabidopsis FAD8 gene [20] encodes a fatty acid desaturase that contributes to freezing tolerance by altering the lipid composition. Cold-responsive genes encoding molecular chaperones include heat shock protein genes spinach hsp70 [21] and Brassica napus hsp90 [22], and contribute to freezing tolerance by stabilizing proteins against freeze-induced denaturation. Many cold-responsive genes encoding various signal transduction and regulatory proteins have been identiﬁed and this list includes those for a MAPK [23], MAPK kinase kinase [24] and genes for calmodulin-related proteins [25]. The largest class of cold-induced genes encodes polypeptides that are homologs of LEA proteins – polypeptides that are synthesized during late embryogenesis, just prior to seed desiccation and also in seedlings in response to dehydration [26]. Other examples of

7.3 Cold Stress

cold-responsive genes include COR15a, alfalfa Cas15, and wheat WCS120 [2]. The expression of COR genes has been shown to be critical for both chilling tolerance and cold acclimation in plants [27]. Arabidopsis COR genes include COR78/RD29, COR47, COR15a, COR6.6, and the genes encoding LEA-like proteins [27]. These genes are induced by cold, dehydration, or ABA. The analysis of the promoter elements of COR genes revealed that they contain dehydration-responsive elementDREs or C-repeats (CRTs) and some of them contain ABA-responsive element-responsive elements (abscisic acid-responsive element-responsive elementABREs) as well [28, 29]. Induction of the COR genes was accomplished by overexpression of the transcription factor CBF [29]. CBF binds to the CRT/DRE elements that are present in the promoters of the COR and other cold-regulated genes. The overexpression of these regulatory elements resulted in increased freezing and drought tolerance [30]. Lee et al., in 2001 [31], genetically analyzed the HOS1 (HIGH EXPRESSION OF OSMOTICALLY RESPONSIVE1) locus of Arabidopsis. HOS1 encodes a ring ﬁnger protein and is constitutively expressed but is drastically downregulated within 10 min of cold stress. Genetic analysis led to the identiﬁcation of ICE1 (INDUCER OF CBF EXPRESSION1) as an activator of CBF3 [32]. ICE1 encodes a transcription factor that speciﬁcally recognized MYC sequence on the CBF3 promoter. Transgenic lines overexpressing ICE1 did not express CBF3 at warm temperatures but showed a higher level of expression for CBF3 as well as RD29 and COR15a at low temperatures. This study suggests that cold-induced modiﬁcation of ICE1 is necessary for it to act as an activator of CBF3. Two CBF1-like cDNAs, CaCBFIA and CaCBFIB, have been cloned and characterized from hot pepper [2]. These were induced in response to low-temperature stress (4 1C), but not in response to wounding or ABA. The gene expression as well as protein accumulation of Oryza sativa OsCDPK13 were upregulated in response to cold. Cold-tolerant rice varieties exhibited higher expression of OsCDPK13 than cold-sensitive varieties [33]. Proline has been shown to be an effective cryoprotectant and this is also one of the major factors imparting freezing tolerance. The esk1 (eskimo1) gene is known to play an important role in freezing tolerance. The concentration of free proline [34] in esk1 mutants was found to be 30-fold higher than in the wild-type plants. Sui et al. [35] have reported that overexpression of glycerol-3-phosphate acyltransferase improves chilling tolerance in tomato. Recently, soybean GmbZIP44, GmbZIP62, and GmbZIP78 genes have been shown to function as negative regulators of ABA signaling, and their overexpression confers salt and freezing tolerance in transgenic Arabidopsis [36]. Recently, O. sativa cold shock domain protein OsCSP transcripts are reported to be transiently upregulated in response to low-temperature stress and rapidly return to a basal levels of gene expression [37]. OsCSP1 and OsCSP2 (O. sativa CSD protein) encode putative proteins consisting of an N-terminal CSD and glycine-rich regions that are interspersed by four and two CX2CX4HX4C (CCHC) retroviral-like zinc ﬁngers, respectively. In vivo functional analysis conﬁrmed that OsCSPs could complement a cold-sensitive bacterial strain, which lacks four endogenous cold shock proteins.

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| 7 Cold, Salinity, and Drought Stress 7.3.4 Calcium Signaling in Cold Stress Response

The generic pathway for the plant response to cold stress is shown in Figure 7.2. The extracellular cold stress signal is ﬁrst perceived by the membrane receptors/ sensors and then activates a complex intracellular signaling cascade including the generation of secondary signals. Increases in cytosolic free Ca2 þ ([Ca2 þ ]cyt) are common to many stress-activated signaling pathways, including the response to cold environments. In Arabidopsis [25] and alfalfa [38], cytoplasmic Ca2 þ levels increase rapidly in response to low temperature, largely due to an inﬂux of Ca2 þ from extracellular stores. Through the use of pharmacological and chemical reagents, it has been demonstrated that Ca2 þ is required for the full expression of some of the cold induced genes including the CRT/DRE controlled COR6 and KIN1 genes of Arabidopsis [38]. Ca2 þ release can occur primarily from extracellular sources (apoplastic space) as addition of the calcium chelators EGTA (ethyleneglycol bis(b-aminoethyl ether)-N,N,Nu,Nu -tetraacetic acid) or BAPTA (O,Oubis(2-aminophenyl)ethyleneglycol-N,N,Nu,Nu -tetraacetic acid) was shown in many cases to block Ca2 þ effects (Figure 7.2). Ca2 þ release may also result from activation of phospholipase C (PLC), leading to hydrolysis of phosphatidylinositol bisphosphate (PIP2) to inositol triphosphate (IP3) and diacylglycerol (DAG), which trigger the subsequent release of Ca2 þ from intracellular Ca2 þ stores [2, 3]. Furthermore, Ca2 þ -binding proteins (Ca2 þ sensors) can provide an additional level of regulation in Ca2 þ signaling. These Ca2 þ sensor proteins recognize and decode the information provided in the Ca2 þ signatures, and relay the information downstream to initiate a phosphorylation cascades. These cascades regulate the expression of genes, like SNOW and ICE1, which in turn regulate cold binding factors to induce the DRE/CRT and ABRE regulatory elements to upregulate the level of cold-responsive genes like COR, KIN, LT1, and RD [2]. The product of these cold stress-responsive genes can provide cold stress tolerance directly or indirectly (Figure 7.2). Overall, the cold stress response could be a coordinated action of many genes, which may also cross-talk with each other. 7.4 Salinity Stress

In general, the term salinity means the presence of salts in the soil. These soils can be categorized into two types: (i) sodic (or alkali) and (ii) saline (a third type can also be referred to as saline/sodic soils). Sodic (or alkaline) soils contain high concentrations of free carbonate and bicarbonate and excess of sodium. The pH of this soil is greater than 8.5 and sometimes up to 10.7. Saline soils are dominated by sodium cations, and usually soluble chloride and sulfate anions, and the pH values of these soils are much lower than in sodic soils. Generally, soil salinity arises due to many factors such as (i) use of poor-quality irrigation water, (ii) unsustainable irrigation practices (heavy irrigation), (iii) high evaporation, and (iv) previous exposure of the land to seawater. Seawater contains approximately 3% of NaCl and in terms of

7.4 Salinity Stress

Figure 7.2 Generic pathway for plant responses to cold stress. The extracellular cold stress signal is ﬁrst perceived by membrane receptors/sensors, which activate PLC to hydrolyze PIP2 to generate IP3 and DAG. These compounds increase the level of Ca2 þ ions in the cytosol, which is sensed by calcium sensors to activate phosphorylation cascades. This pathway then induces cis-regulatory elements, like SNOW and ICE1, which in turn regulate cold binding factors, which in turn induce DRE/CRT and ABRE regulatory elements to upregulate cold-stress responsive genes like COR, KIN, LT1, and RD. The product of these cold stress-responsive genes can provide cold stress tolerance directly or indirectly.

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| 7 Cold, Salinity, and Drought Stress

molarity of different ions, Na þ is about 460 mM, Mg2 þ is 50 mM and Cl around 540 mM, along with smaller quantities of other ions [2, 3]. Many crop species are very sensitive to soil salinity and are glycophytes, whereas salt-tolerant plants are known as halophytes. In general, glycophytes cannot grow at 100 mM NaCl, whereas halophytes can grow at salinities over 250 mM NaCl. The salinity-sensitive plants restrict the uptake of salt and strive to maintain an osmotic equilibrium by the synthesis of compatible solutes, such as proline, GB, and sugars. The salinity-tolerant plants have the capacity to sequester and accumulate salt in the cell vacuoles, thus preventing the build up of salt in the cytosol and maintaining a high cytosolic K þ /Na þ ratio in their cells. Generally, salinity tolerance is inversely related to the extent of Na þ accumulation in the shoot [3]. The basic physiology of high-salinity stress and drought stress plants overlaps with each other. High salinity also leads to increased cytosolic Ca2 þ , which initiates the salinity stress signal transduction pathways for stress tolerance as described above in Sections 7.2 and 7.3.4. In Arabidopsis, the transcript proﬁles of various genes under salinity and other stresses have been made openly available through several databases, such as TAIR, NASC, and Genevestigator [39]. The various genes that have been reported to be upregulated in response to salinity stress are listed in Ma et al. [39]. Earlier, we have shown a novel role of a DNA helicase and G-proteins in salinity stress tolerance [40, 41]. Recently, it has been shown that overexpression of the trehalose-6-phosphate phosphatase gene OsTPP1 confers salt, osmotic, and ABA tolerance in rice, and results in the activation of stress-responsive genes [42]. The conservation in the mechanisms of salt responses and stress tolerance has been observed between bryophytes and higher plants [43]. Some of the web sites for further study on salinity stress include: i. Affymetrix microarray data (http://www.arabidopsis.org/ portals/expression/microarray/ATGenExpress.jsp). ii. The abiotic transcript proﬁle data (Affymetrix microarray data) (http//www.weigelworld.org/resources/microarray/ ATGenExpress/). iii. Glass microarray data (http://ag.arizona.edu/microarray/), the resulting GPR ﬁles can be analyzed by TIGR-TM4 (http://www.tm4.org/). 7.4.1 Negative Impact of Salinity Stress

The adverse effects observed in response to high salinity stress are: 1. Salinity stress interferes with plant growth and development as it can also lead to physiological drought conditions and ion toxicity, and therefore causes both hyperionic and hyperosmotic stresses that lead to plant demise [44, 45]. 2. High salt deposition in the soil leads to a low water potential in the soil. This makes it increasingly difﬁcult for the plant to acquire water as well as nutrients.

7.4 Salinity Stress

3. High salt also decreases the soil porosity and thereby reduces soil aeration. 4. Salinity causes ion-speciﬁc stresses resulting in altered K þ /Na þ ratios. External Na þ can negatively impact intracellular K þ inﬂux. 5. K þ ions are one of the essential elements required for growth. Alterations in K þ ions (due to the impact of high salinity stress) can disturb the osmotic balance, function of stomata, and function of some enzymes. 6. Salinity leads to the accumulation of Na þ and Cl in the cytosol, which can be ultimately detrimental for the cell. The Na þ can dissipate the membrane potential and therefore facilitate the uptake of Cl– down the gradient. 7. Higher concentrations of sodium ions (above 100 mM) are toxic to cell metabolism, and can inhibit the activity of many essential enzymes, cell division and expansion, membrane disorganization, and osmotic imbalance [44, 46]. 8. Higher concentrations of sodium ions can also lead to a reduction in photosynthesis, and increase in the production of ROS and polyamines [47]. 9. High salinity can also injure cells in transpiring leaves, which leads to growth inhibition. This is the salt-speciﬁc or ion-excess effect of salinity, which causes the toxic effects of salt inside the plant. Salt can concentrate in old leaves that subsequently die – a process that can be crucial for the survival of a plant [48]. 10. High salinity affects the cortical microtubule organization and helical growth in Arabidopsis [49].

7.4.2 Calcium Signaling and SOS Pathways in Relation to Salinity Stress

High salinity results in increased cytosolic Ca2 þ that is transported from the apoplast as well as the intracellular compartments [50]. This transient increase in cytosolic Ca2 þ initiates the stress signal transduction leading to salt adaptation. As described in Section 7.3.4, this Ca2 þ release occurs primarily from extracellular sources (apoplastic space), but also from the activation of PLC, leading to hydrolysis of PIP2 to IP3 and subsequent release of Ca2 þ from intracellular Ca2 þ stores [2, 3]. The Ca2 þ -binding proteins sense and relay the information downstream to initiate phosphorylation cascades, leading to gene expression [5]. Wu et al. [51] commenced a mutant screen for Arabidopsis plants, which were over-sensitive to salt stress. As a result of this screen, three genes SOS1, SOS2, and SOS3 were identiﬁed. SOS3 (also known as AtCBL4) encodes a calcineurin B-like protein (CBL, Ca2 þ sensor), which is a Ca2 þ -binding protein, and senses the change in cytosolic Ca2 þ concentration and transduces the signal downstream. The SOS pathway results in the exclusion of excess Na þ ions out of the cell via the plasma membrane Na þ /H þ antiporter and helps in reinstating cellular ion homeostasis. The discovery of SOS genes paved the way for the elucidation of a novel pathway linking Ca2 þ signaling in response to salt stress [52, 53]. SOS genes (SOS1, SOS2, and SOS3) were genetically conﬁrmed to function in a common pathway of salt tolerance [54].

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| 7 Cold, Salinity, and Drought Stress In the SOS pathway, the salinity stress signal is perceived by an unknown hypothetical plasma membrane sensor. The resulting cytoplasmic Ca2 þ perturbation is sensed by SOS3 followed by transduction of the signal to the downstream components. The myristoylation motif of SOS3 results in the recruitment of the SOS3–SOS2 complex to the plasma membrane, where SOS2 phosphorylates and activates SOS1 [55]. SOS1 is a Na þ /H þ antiporter and sos1 mutants are hypersensitive to salt and show an impaired osmotic/ionic balance. The SOS pathway also seems to have other branches, which help to remove the excess of Na þ ions out of the cell and thereby maintain the cellular ion homeostasis. In Arabidopsis, Na þ entry into root cells during salt stress appears to be mediated by AtHKTI, a lowafﬁnity Na þ transporter, which blocks entry of Na þ [2, 45]. SOS2 also interacts and activates the vacuolar NHX (Na þ /H þ exchanger), resulting in sequestration of excess Na þ ions and pushing it into vacuoles, and thereby further contributes to Na þ ion homeostasis. Some other Ca2 þ -binding proteins like calnexin and calmodulin also sense the increased level of Ca2 þ and can interact and activate NHX. Overexpression of AtNHX1 substantially enhanced salt tolerance of Arabidopsis [56]. The H þ /Ca2 þ antiporter CAX1 has been identiﬁed as an additional target for SOS2 activity reinstating cytosolic Ca2 þ homeostasis. This reﬂects that the components of the SOS pathway may cross-talk and interact with other branching components to maintain cellular ion homeostasis, which helps in salinity tolerance. So far, the main avenue in breeding crops for salt tolerance has been to reduce Na þ uptake and transport from roots to shoots. It has been demonstrated that retention of cytosolic K þ could also be considered as another key factor in conferring salt tolerance in plants. Recently, Zepeda-Jazo et al. [57] have shown that the expression of NORC was signiﬁcantly lower in salt-tolerant genotypes. NORC is capable of mediating K þ efﬂux coupled to Na þ inﬂux, suggesting that the restriction of its activity could be beneﬁcial for plants under salt stress. 7.4.3 ABA and Transcription Factors in Salinity Stress Tolerance

ABA is a phytohormone that regulates plant growth and development, and also plays an important role in the plant’s response to abiotic stresses including salinity stress (reviewed in [2, 3, 45, 58]). The role of ABA in salinity stress was conﬁrmed by a study of Zhu’s group where it was shown that ABA-deﬁcient mutants performed poorly under salinity stress [59]. ABA levels are known to be induced under stress conditions, which is mainly due to the induction of the enzymes responsible for ABA biosynthesis. The induction of osmotic stress-responsive genes imposed by salinity is transmitted through either ABA-dependent or ABA-independent pathways, although some others are only partially ABA-dependent [60]. However, the components involved in these pathways often cross-talk through Ca2 þ with other stress signaling pathways. The transcript accumulation of RD29A gene is reported to be regulated in both an ABA-dependent and ABA-independent manner [61]. Proline accumulation in plants can be mediated by both ABA-dependent and ABA-independent signaling pathways [45]. The salinity stress-induced upregulation of transcripts of PDH45 (PEA

7.4 Salinity Stress

DNA HELICASE45) follows an ABA-dependent pathway [40] while CBL and CBLinteracting protein kinase from pea followed the ABA-independent pathway [62]. The role of Ca2 þ in ABA-dependent induction of P5CS (PYRROLINE-5-CARBOXYLATE SYNTHASE) during salinity stress has been reported [63]. Overall, the ABA-dependent pathways are involved essentially in osmotic stress gene expression. The transcriptional regulatory network of cis-acting elements and transcription factors involved in ABA and salinity stress-responsive gene expression has been described [3]. The ABA-dependent salinity stress signaling activates basic leucine zipper transcription factors called ABRE-binding proteins, which binds to ABRE elements to induce the stress responsive gene RD29A. Transcription factors like DREB2A and DREB2B activate the DRE cis elements of osmotic stress genes, and thereby are involved in maintaining the osmotic equilibrium of the cell. Some genes such as RD22 lack the typical CRT/DRE elements in their promoter, suggesting their regulation by some other mechanism. The MYC/MYB transcription factors, RD22BP1 and AtMYB2, could bind MYCRS and MYBRS elements, respectively, and help in the activation of RD22 [2, 3]. Overall, these transcription factors may also cross-talk with each other for their maximal response to stress tolerance. 7.4.4 Water Stress due to Salinity

One of the consequences of salinity stress is the loss of intracellular water. To prevent water loss from the cell and protect the cellular proteins, plants accumulate many metabolites that are also known as ‘‘compatible solutes.’’ These solutes do not inhibit the normal metabolic reactions [64]. Frequently observed metabolites with an osmolyte function are sugars, mainly fructose and sucrose, alcohols, and complex sugars like trehalose and fructans. In addition, charged metabolites such as GB, proline, and ectoine also accumulate. The accumulation of these osmolytes facilitates the osmotic adjustment [65]. Water moves from sites of high water potential to low water potential, and accumulation of osmolytes decreases the water potential inside the cell and therefore prevents intracellular water loss. 7.4.5 Proline and GB in Salinity Stress

Proline and GB are two major osmoprotectant osmolytes that are synthesized by many plants (but not all) in response to stress including salinity stress [66]. In higher plants the amino acid proline is synthesized by glutamic acid by the actions of two enzymes P5CS and P5CR (PYRROLINE-5-CARBOXYLATE REDUCTASE). Overexpression of the P5CS gene in transgenic tobacco resulted in increased production of proline and salinity/drought tolerance [67]. The exogenous application of proline also provided osmoprotection and facilitated growth of salinitystressed plants. Proline can also protect cell membranes from salinity-induced oxidative stress by upregulating activities of various antioxidants [68]. It is reported

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| 7 Cold, Salinity, and Drought Stress that salt stress enhances proline utilization in the apical region of barley roots [69]. The function of proline is thought to be an osmotic regulator under water stress and its transport into cells is mediated by a proline transporter. However, recently, Ueda et al. [70] have reported that altered expression of barley HvProT (Hordeum vulgare proline transporter) causes different growth responses in Arabidopsis, as it leads to the reduction in biomass production and decreased proline accumulation in leaves. Impaired growth of HvProT transformed plants was restored by exogenously adding proline, which suggested that growth reduction was caused by a deﬁciency of endogenous proline. In plants where GB is not produced, transgenic plants overexpressing GB-synthesizing genes showed production of sufﬁcient GB to tolerate stresses including salinity stress. GB is synthesized from choline by the action of choline monooxygenase and betaine aldehyde dehydrogenase enzymes. The overexpression of the genes encoding betaine aldehyde decarboxylase from the halophyte Suaeda liaotungensis improved salinity tolerance in tobacco plants. The codA (choline dehydrogenase) gene from Arthrobacter globiformis helped salinity tolerance in rice (see [66] and references therein). Overexpression of N-methyl transferase in cyanobacteria and Arabidopsis resulted in accumulation of GB and improved salinity tolerance [71]. It is also reported that foliar application of GB to low- or nonaccumulating plants helped in improving the growth of plants under salinity stress conditions as reported in Zea mays [72]. In plants, betaine is synthesized upon abiotic stress via choline oxidation, in which choline monooxygenase is a key enzyme. Although it had been thought that betaine synthesis is well regulated to protect abiotic stress, recently it has been shown that exogenous supply of precursors such as choline, serine, and glycine in the betaine-accumulating plant Amaranthus further enhances the accumulation of betaine under salt stress, but not under normal conditions [73]. Recently, Waditee et al. [74] have shown that expression of Aphanothece 3-phosphoglycerate dehydrogenase in Arabidopsis plants enhances levels of betaine by providing serine as precursor for both choline oxidation and glycine methylation pathways. 7.4.6 ROS in Salinity Stress

ROS typically result from the excitation of O2 to form singlet oxygen (1O2) or transfer of one, two, or three electrons to O2 to form superoxide radical (O21), hydrogen peroxide (H2O2), or a hydroxyl radical (OH) respectively. The enhanced production of ROS during stresses can pose a threat to plants because they are unable to detoxify effectively by the ROS scavenging machinery. Unquenched ROS spontaneously react with organic molecules and cause membrane lipid peroxidation, protein oxidation, enzyme inhibition, DNA and RNA damage, and so on [3, 66]. Oxidative stress arising under environmental stresses including salinity may exceed the scavenging capacity of the natural defense system of plants. The major ROS-scavenging mechanisms of plants include superoxide dismutase, ascorbate peroxidase, catalase, and glutathione reductase, which help in

7.5 Drought Stress

deactivation of active oxygen species in multiple redox reactions and thereby contribute to the protective system against oxidative stress. ROS scavengers can increase plant resistance to salinity stress. Overexpression of aldehyde dehydrogenase in Arabidopsis has been reported to confer salinity tolerance. Aldehyde dehydrogenase catalyzes the oxidation of toxic aldehydes, which accumulate as a result of side-reactions of ROS with lipids and proteins. The enhancement of stress tolerance in transgenic tobacco plants has been shown by overexpressing Chlamydomonas glutathione peroxidase in the chloroplast or cytosol (see [66] and references therein).

7.5 Drought Stress

Water-deﬁcit stress is known as drought stress, which reduces agricultural production mainly by disrupting the osmotic equilibrium and membrane structure of the cell. Climate models have indicated that drought stress will become more frequent because of the long-term effects of global warming, which indicate the urgent need to develop adaptive agricultural strategies for a changing environment. Actually, the water stress within the lipid bilayer results in displacement of membrane proteins, which contributes to loss of membrane integrity, selectivity, disruption of cellular compartmentalization, and loss of membrane-based enzyme activity. The high concentration of cellular electrolytes due to the dehydration of the protoplasm may also cause disruption of the cellular metabolism. To avoid drought stress, plants close their stomata, repress cell growth and photosynthesis, activate respiration, reduce leaf expansion, and start shedding older leaves to reduce the transpiration area [10]. Relative root growth may be enhanced, which facilitates the capacity of the root system to extract more water from deeper soil layers. The components of drought and salt stress cross-talk as both these stresses ultimately result in dehydration of the cell and an osmotic imbalance. Overall, drought stress signaling encompasses three important parameters [75]: 1. Reinstating the osmotic as well as the ionic equilibrium of the cell to maintain cellular homeostasis under the conditions of stress. 2. Control as well as repair of stress damage by detoxiﬁcation. 3. Signaling to coordinate cell division to meet the requirements of the plant under stress. As a consequence of drought stress many changes occur in the cell, which include changes in the expression level of LEA/dehydrin-type genes, and synthesis of molecular chaperones that help in protecting the partner protein from degradation and proteinases that function to remove denatured/damaged proteins. Drought stress also leads to the activation of enzymes involved in the production and removal of ROS [53, 76]. Overexpression of some genes has been now reported to help plants in drought stress tolerance [10]. Some examples are mentioned below.

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| 7 Cold, Salinity, and Drought Stress Overexpression of barley group 3 LEA gene HVA1 in leaves and roots of rice and wheat led to improved tolerance against osmotic stress as well as improved recovery after drought and salinity stress [77]. Dehydrins are also known to accumulate in response to both dehydration as well as low temperature stresses [78]. Overexpression of the vacuolar Na þ /H þ antiporter and H þ -pyrophosphatase pump has resulted in enhanced tolerance to both salinity [79, 80] and drought stress [81, 82]. These results suggest that the enhanced vacuolar H þ -pumping in the transgenic plants provide an additional driving force for vacuolar sodium accumulation via the vacuolar Na þ /H þ antiporter. Brini et al. [83] reported that overexpression of wheat Na þ /H þ antiporter TNHX1 and H þ -pyrophosphatase TVP1 improve salt- and drought-stress tolerance in Arabidopsis thaliana plants. Recently, Jung et al. [84] showed that overexpression of AtMYB44 enhanced stomatal closure and confers dehydration stress tolerance in transgenic Arabidopsis. Recently, Jia et al. [85] have shown that a Ca2 þ -binding protein calreticulin from wheat is involved in the plant response to drought stress; TaCRT-overexpressing tobacco (Nicotiana benthamiana) plants grew better and exhibited less wilting under drought stress. Plants produce compounds in roots that are transported to shoots via the xylem sap. Some of these compounds are vital for signaling and adaptation to drought stress. Recently, Alvarez et al. [86] observed metabolomic and proteomic changes in the xylem sap of maize under drought stress. The application of these new techniques provides insight into the range of compounds in sap, and how alterations in their composition may lead to changes in development and signaling during adaptation to drought. 7.5.1 Effect of Drought on Stomata and Photosynthesis

The ﬁrst response of plants to drought stress is the closure of stomata to prevent transpirational water loss [87]. The closure of stomata may result from direct evaporation of water from the guard cells with no metabolic involvement and is referred to as hydropassive closure. Stomatal closure may also be metabolically dependent and involve processes that result in reversal of the ion ﬂuxes that cause stomatal opening. This process of stomatal closure, which requires ions and metabolites, is known as hydroactive closure. Plant growth and response to a stress condition is largely under the control of hormones. ABA promotes the efﬂux of K þ ions from the guard cells, which results in the loss of turgor pressure leading to stomata closure. The closure of stomata does not always depend upon the perception of water-deﬁcit signals arising from leaves. In fact, stomata closure also responds directly to soil desiccation even before there is any signiﬁcant reduction in leaf mesophyll turgor pressure. The fact that ABA can act as a long distance communication signal between water-deﬁcient roots and leaves, inducing the closure of stomata, has been known for decades [88]. Stomatal closure in response to drought stress primarily results in a decline in the rate of photosynthesis. Severe drought was reported to decrease ribulose-1,5bisphosphate carboxylase/oxygenase (RuBisCO) activity, which leads to limited

7.5 Drought Stress

photosynthesis [89]. The photosystem II has been reported to decline under drought conditions [90] and the decline in the rate of photosynthesis in drought stress is primarily due to CO2deﬁciency [91]. Decreasing intracellular CO2 levels also result in the over-reduction of components within the electron transport chain and electrons get transferred to oxygen at photosystem I. This process generates ROS including superoxide, H2O2 and hydroxyl radicals. These ROS need to be scavenged by the plant as they may lead to photo-oxidation. Plantdetoxifying systems, which include ascorbate and glutathione pools, control the intracellular concentration of ROS. Under longer drought situations, plant cells can undergo shrinkage, leading to mechanical constraints on cellular membranes, which impairs the functioning of ions and transporters as well as membrane-associated enzymes. 7.5.2 Sugars and other Osmolytes in Response to Drought Stress

To cope with drought stress plants need to perform osmotic adjustments whereby they decrease their cellular osmotic potential by the synthesis/accumulation of solutes including proline, glutamate, GB, carnitine, mannitol, sorbitol, fructans, polyols, trehalose, sucrose, oligosaccharides, and inorganic ions like K þ . These compounds help plant cells to maintain their hydrated state, and therefore function to provide resistance against drought and cellular dehydration [92]. The hydroxyl groups of sugar alcohols substitutes the OH group of water to maintain the hydrophilic interactions with membrane lipids and proteins, and therefore help to maintain the structural integrity of membranes. These stress-accumulated solutes do not intervene with normal cellular metabolic processes. The accumulation of simple sugars such as glucose and fructose increases invertase activity in leaves of drought-challenged plants [93]. ABA has been implicated in enhancing the activity and expression of vacuolar invertase [93]. ABA biosynthesis is also directly controlled by glucose, as transcripts of several genes responsible for ABA synthesis increase by glucose in Arabidopsis seedlings [94]. Cross-talk may exist between the sugars and plant hormones such as ABA and ethylene. Glucose and ABA signaling act in coordination for regulating plant growth and development. A high concentration of ABA and sugars can inhibit growth under severe drought stress, while low concentrations can promote growth. Osmolytes function at low concentrations to protect macromolecules by stabilizing tertiary structures or by scavenging ROS [95]. However, high accumulation of osmolytes in transgenic plants can impair the growth in the absence of any stress probably due to plant adaptation strategies to conserve water in acute stress [2]. Therefore, a controlled synthesis of osmolytes is the main concern in designing transgenic strategies for crop improvement. Oligosaccharides such as rafﬁnose and galactinol are among the sugars synthesized in response to drought. These compounds seem to function as osmoprotectants rather than providing osmotic adjustment. Mannitol is one of the most widely distributed sugar alcohols in nature, and functions to scavenge ROS and hydroxyl radicals, and also

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| 7 Cold, Salinity, and Drought Stress stabilizes the macromolecular structure of enzymes [96]. Trehalose is a nonreducing disaccharide of glucose, and has been shown to exert its positive inﬂuence during drought by stabilizing membranes and macromolecules. Trehalose overexpression helps in the maintenance of an elevated capacity for photosynthesis primarily due to increased protection of photosystem II against photooxidation [97]. Overexpression of P5CS from Vigna aconitifolia in tobacco, leads to increased levels of proline and consequently improved growth under drought stress [98]. 7.5.3 Phospholipid Signaling in Drought Stress

Lipids are important membrane components and are also major targets of environmental stresses including drought stress. The changes in the lipid composition may help to maintain membrane integrity and preserve cell compartmentalization under water stress conditions. Gigon et al. [99] have shown that in response to drought, total leaf lipid contents decrease progressively. However, for leaves with a relative water content as low as 47.5%, total fatty acids still represented 61% of control contents. The lipid content of extremely dehydrated leaves rapidly increased after rehydration. In general, phospholipids from plant cell membranes constitute a dynamic system that generates a multitude of signaling molecules like IP3, DAG, and phosphatidic acid [53]. In response to stress, PLC is activated, which catalyzes the hydrolysis of PIP2 into IP3 and DAG. IP3 releases Ca2 þ from internal stores, as described in Figure 7.2. Several studies have shown that in various plant systems IP3 levels rapidly increase in response to hyperosmotic stress [2, 100]. IP3 levels also increase upon treatment with exogenous ABA in Vicia faba guard cell protoplasts [101] and in Arabidopsis seedlings [102]. Arabidopsis AtPLC is also induced by salt and drought stress [103]. In guard cells, IP3 induced a Ca2 þ increase in the cytoplasm, and leads to stomatal closure and thus retention of water in the cells [104]. PLD was shown to be rapidly activated in response to drought stress in two plant species – Craterostigma plantagineum and Arabidopsis [105, 106]. When drought stress-induced PLD activity was compared between drought-resistant and -sensitive cultivars of cowpea, it was found that PLD activities were higher in the drought sensitive cultivars [107].

7.6 Conclusions and Future Prospects

Plant adaptation to different stresses is dependent upon the activation of cascades of molecular networks involved in stress perception, signal transduction, and expression of speciﬁc stress-responsive genes. The maintenance of intracellular ionic homeostasis is fundamental to the physiology of a living cell. It is vital for the cell to keep the concentration of toxic ions below a threshold level and accumulate essential ions. As stress imposes a major environmental threat to agriculture,

7.6 Conclusions and Future Prospects

understanding the basic physiology and genetics of cells under stress is crucial for developing any transgenic strategies. Plants have also evolved mechanisms to respond at the morphological, anatomical, cellular, and molecular levels for avoidance of and/or tolerance to various abiotic stresses. In response to stress, plants respond by gene expression leading to cellular homeostasis and detoxiﬁcation of toxins, ultimately aiming to recovery of growth. These adaptive mechanisms can be investigated by molecular, biochemical, and physiological studies. Transgenic research has opened up a new opportunity in crop improvement allowing the transfer of desirable gene(s) across species and genera for developing transgenic plants with novel traits, such as built-in protection, improved nutritional qualities, and so on. Physiological, biochemical, and molecular studies have revealed that a number of genes are induced by abiotic stresses, and various transcription factors are involved in the regulation of stress-inducible genes. Functional genomic studies may provide tools for dissecting abiotic stress responses in plants through which networks of stress perception, signal transduction, and defense responses can be examined from transcriptomic through proteomic to metabolomic proﬁles of stressed tissues. The major attempt to enhance plant tolerance is the manipulation of genes that are either directly involved in protection of cells against water loss or the genes that are involved in regulating signal transduction pathways in response to water stress. A deeper understanding of the transcription factors regulating these genes, the products of the major stress responsive genes, and the cross-talk between different signaling components should remain an area of intense research activity in the future. The knowledge generated through these studies should be utilized in producing transgenic plants that are able to tolerate stress conditions without showing any growth and yield penalty. In the improvement of crops it is very important to perturb the natural machinery as little as possible and activate the stress genes at a correct time. Therefore, it is desirable that appropriate stressinducible promoters drive the stress genes as well as transcription factors, which will minimize their expression in nonstressed conditions and thereby reduce yield penalty. Attempts should be made to design suitable vectors for stacking relevant genes of one pathway or complementary pathways to develop durable tolerance. These genes should preferably be driven by a stress-inducible promoter to have maximum beneﬁcial effects. Additionally, due importance be given to the physiological parameters such as the relative content of different ions present in the soil as well as the water status of the crop in designing transgenic plants for the future. A better understanding of the speciﬁc roles of various metabolites in stresstolerant plants will give rise to strategies for metabolic engineering of plant tolerance to abiotic stress. Much effort is still required to uncover in detail each product of a gene induced by cold, salinity, and drought stress, and their interacting partners, to understand the complexity of the stress signal transduction pathways. The role of endogenous small interfering RNAs in regulating these stresses is also an important area and will further help to better understand the mechanisms of stress tolerance. The

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| 7 Cold, Salinity, and Drought Stress determination of the upstream receptors or sensors that monitor the stress stimuli as well as the downstream effectors that regulate the responses is essential and will also expedite our understanding of the stress signaling mechanisms in plants. Interconnected signal transduction pathways leading to multiple responses to abiotic stresses have been difﬁcult to study due to their complexity and the large number of genes involved in the various defense responses. Understanding how cells coordinate the activity of multiple signaling pathways to prevent unwanted cross-talk remains a challenge. Transcriptome analyses based on microarrays have also provided powerful tools for the discovery of stress-responsive genes. The stress tolerance could also be enhanced by pyramiding various genes. This can be done by either combining multiple genes of a single protective pathway or by combining key regulatory genes of different protective pathways. Overall, a combination of a good genetic background with multiple transgenes/allele mining and promising performance in ﬁeld conditions will reveal the success of the development of abiotic stress-tolerant plants.

Acknowledgments

I thank Dr. Renu Tuteja for critical reading and scientiﬁc corrections, Mrs. Suzanne Karvacic for English corrections, and Mr. Hung Dang Quang for his help in the preparation of Figure 7.2. I also thank the Department of Biotechnology, and the Department of Science and Technology, Government of India grants for partial support. I apologize if some references could not be cited due to space constraint.

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8 Heavy Metal Stress in Plants Ann Cuypers, Karen Smeets, and Jaco Vangronsveld

8.1 Introduction

Over the past two centuries, anthropogenic and industrial activities have led to high emissions of toxic metals into the environment at concentrations signiﬁcantly exceeding those originating from natural sources (Nriagu, 1988). Mining and industrial processing are the main sources of heavy metal contamination in soil [1, 2]. However, heavy metal pollution of soils resulting from the application of phosphate fertilizers and sewage sludge, and irrigation with sewage efﬂuents or wastewater also causes major concern due to the potential risks involved [3]. Metals can be subdivided in essential micronutrients (e.g., iron, copper, zinc, cobalt, and nickel) that are critical for normal development and growth of organisms [4, 5] and other elements (e.g., such as cadmium, lead, and mercury) that are generally considered nonessential. Whereas deﬁciencies of micronutrients can seriously disturb normal development, excess of metals in general adversely affects biochemical reactions and physiological processes in organisms, causing a major risk for the environment and human health. Uptake and accumulation by food and feed crop plants represents a main entry pathway for potentially health-threatening toxic metals into food chains. Population-based studies as well as cellular studies investigating the mechanisms of metal toxicity have shown that elevated metal concentrations in the environment pose a tremendous risk for human health [3, 6–9]. Improving our knowledge concerning metal acquisition and homeostasis together with defense and tolerance mechanisms in plants has numerous (biotechnological) applications with regard to alleviating micronutrient deﬁciency as well as soil metal contamination. This chapter covers general aspects related to metal toxicity ranging from metal uptake, distribution, and homeostasis, to cellular stress responses. It is our intent to focus particularly on the mechanisms of metal-induced oxidative stress-related responses and to point out the relation with signal transduction under metal stress. This is becoming a scientiﬁc subject area of intense investigation and will provide essential information to comprehend cellular responses to metal toxicity.

Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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| 8 Heavy Metal Stress in Plants 8.2 Uptake and Distribution of Metals in Plants

To minimize damage caused by nutrient deﬁciencies or metal toxicity, plants possess a complex network of processes controlling metal uptake and transport as well as metal homeostasis. Over the last decade, multiple excellent reviews have been published describing our knowledge on metal uptake, transport, and homeostasis in plants [10–17]. Plant metal uptake occurs mainly through roots; direct plant metal uptake through the leaves is rather limited. Therefore, soil and water metal contents, and more speciﬁcally metal bioavailability, are relevant for plant metal acquisition. The bioavailability of metals to plants depends upon metal speciation [18, 19], several soil characteristics, such as pH, texture, and organic matter [2, 5, 18], the occurrence of plant-associated microorganisms (i.e., mycorrhiza and bacteria) and the plant species. As iron deﬁciency is one of the most widespread nutrient imbalances in agriculture, different plant species developed new strategies to solubilize and acquire Fe3 þ . Some plants acidify the soil through exudates and reduce Fe3 þ before transport, while others synthesize and transport Fe3 þ -chelating agents named phytosiderophores [14, 15, 17]. Apart from the bioavailable fraction of metals in the soil solution, uptake activity and translocation efﬁciency also determine the plant’s metal uptake [11]. The cell wall exerts binding places for metals [20], but with low selectivity and low afﬁnity. Transport of cationic metals across the plasma membrane is forced by the negative membrane potential, and the presence of intracellular binding and storage sites [12]. Several cation transporters have been identiﬁed, for example, transporters belonging to the zinc- and iron-regulated protein (ZIP) family (zinc-regulated transporter/iron-regulated transporter-like protein) are essential for iron and zinc uptake, transporters of the natural resistance-associated macrophage protein (Nramp) family are involved in iron acquisition, and the copper transporter is highly speciﬁc for copper uptake [17]. Some of these transporters show a broad substrate range enabling nonessential metals to enter the root cells [12]. Passage of the plasma membrane by metals is enhanced by intracellular binding and sequestration. Once metal ions enter the cell, they are bound to chelators and chaperones. Chelators contribute to metal detoxiﬁcation by buffering cytosolic free metal concentrations. Chaperones speciﬁcally deliver metal ions to organelles and metal-requiring proteins [21]. Metal chelators include phytochelatins, metallothioneins [22], organic acids, and amino acids [23, 24]. Generally, it is assumed that the major sites of metal sequestration in the roots are the vacuoles. Extensive research, performed on vacuolar sequestration, has revealed a range of gene families involved in intracellular metal transport. These include heavy metal ATPases (HMAs), the multidrug-resistance-associated protein subfamily belonging to ABC transporters, Nramps, the cation diffusion facilitator family, the ZIP family, and cation antiporters that are excellently described in the multiple reviews mentioned above. The activities of metal-sequestering pathways in root cells are crucial in determining the rate of metal translocation to the aerial parts. The latter is an essential

8.3 Metal Stress Affects the Plant’s Physiology

prerequisite when phytoextraction is utilized for the clean-up of metal-polluted soils. Until recently, little was known about these transport mechanisms, but rapid progress has been made in the identiﬁcation and characterization of some of these transporters, and in the role of metal ion ligands in metal homeostasis. Haydon and Cobbett [16] recently reviewed the role of metal ion ligands for iron, zinc, copper, manganese, and nickel in plants. In particular, they described the contribution of mugineic acid, nicotianamine, organic acids, histidine, and phytate to metal homeostasis and the proteins implicated in their transport. Xylem loading of metal ions/ligands is a tightly controlled process mediated by membrane transport proteins that are currently under intense study. Recently, FRD3 (FERRIC REDUCTASE-DEFECTIVE3), a member of the multidrug and toxin extrusion family of transporters, was shown to be a citrate transporter involved in iron loading into the xylem [25]. Current ﬁndings in the research focusing on the membrane transporter HMA4 in cadmium/zinc tolerance indicate a role in the root-to-shoot translocation of cadmium and zinc [26–28]. In general, metal acquisition and sequestration into the leaf cells are more or less similar as compared to the roots, with the exception of sequestration of metals to the trichomes, for which scientiﬁc information is rather scarce.

8.3 Metal Stress Affects the Plant’s Physiology

Decreased biomass production has commonly been observed in plants subjected to elevated metal concentrations. From a general biological as well as from plant physiological point of view, essential and nonessential metals can be distinguished. Essential micronutrients play a role as components of metalloproteins, as cofactors in enzymatic catalysis, and in manifold other cellular processes. At supraoptimal concentrations, however, micronutrients become phytotoxic and affect plant physiology. Although slight growth stimulation might be observed at low concentrations of some non-essential metals (e.g., cadmium, lead, etc.) they distinctly interfere at higher concentrations, demonstrating similar effects as phytotoxic amounts of micronutrients. Several metals show a high afﬁnity for sulfur and nitrogen donors, and potentially bind to functional groups of macromolecules, causing metabolic disruption [29, 30]. In general, almost all metals strongly bind to thiol groups and thus to cysteine-rich proteins. This also makes up one of the most important detoxiﬁcation mechanisms against free metal ions in the cytosol – chelation of metal ions to phytochelatins and/or metallothioneins. Both these groups of molecules are cysteine-rich and are involved in metal homeostasis (for a review, see [22]). Phytotoxic concentrations of metal ions can also lead to substitution of essential micronutrients in functional and structural proteins, resulting in a disturbance of the cellular metabolism [31–33].

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| 8 Heavy Metal Stress in Plants Since metal phytotoxicity results in leaf chlorosis and growth inhibition, its interference with photosynthesis, mineral nutrition, and the water balance has been intensively studied (Table 8.1). Table 8.1 Physiological processes affected in plants exposed to elevated metal concentrations.

Physiological effects on

Metal Plant

References

Photosynthesis

Cd

Hordeum vulgare Oryza sativa Thlaspi caerulescens Cucumis sativus Populus

[34]

H. vulgare

[39]

Pinus sylvestris Brassica oleracea Lolium perenne L. perenne

[40]

H. vulgare, Avena sativa O. sativa Nicotiana tabacum A. thaliana

[44]

Zea mays

[48]

Rumex japonicus L. perenne Brassica rapa L. perenne Phaseolus vulgaris C. sativus C. sativus

[49]

L. perenne Lupinus albu

[43] [53]

chloroplast and thylakoid membrane, PSI/II activity pigment content and thylakoids chlorophyll ﬂuorescence parameters net photosynthetic rate, calvin cycle enzymes chlorophyll content and photosynthetic parameters CO2 ﬁxation carbohydrate metabolism chlorophyll content and chloroplast ultrastructure photosystem II activity, CO2 ﬁxation

chlorophyll content and photosystem II activity chlorophyll content and photosynthetic rate Mineral nutrition Fe, Zn, Mn, Cu, Mg contents Fe translocation Na, Mg, P, S, K, Ca, Mn, Fe, Cu, Zn contents Mn, Mg, Cu, Zn, Fe, Ca contents

Water balance

Cd Cd Cd/ Cu Cu Cu/ Mn Cu/ Ni Ni Zn Cr Pb Cd Cd Cd

Ca, Mg, Fe contents

Cd and Ni Cu

Mg, Mn contents Fe, Mn contents Fe, P, Ca, Mn, Mg contents Ca, Fe, Mn, Zn, Cu contents

Zn Zn Cr Pb

K, Ca, Mg, Fe, Cu, Zn contents stomatal conductance

Mn Cd/ Cu Cr Hg

transpiration rate water content

[35] [36] [37] [38]

[41] [42] [43]

[45] [46] [47]

[42] [50] [43] [51] [52] [37]

8.4 Unraveling the Cellular Responses of Metal Stress

8.4 Unraveling the Cellular Responses of Metal Stress

The search for primary targets of metal injury and thus the complete understanding of the mechanisms underlying metal toxicity has become an important research area in many studies, but many aspects still remain elusive. Examining global gene and protein expression under metal stress will help us to explore the functioning and regulation of cellular metabolism under these circumstances. The gathered data provide a basis for further research in order to unravel the dynamics of metal-induced biological responses. Transcriptional and/or proteomic proﬁling has recently been performed for different trace elements: cadmium [32, 54, 55], aluminum [56], selenium [57], arsenic [58, 59], and cesium [60]. Most of the affected genes and/or proteins can be categorized into the following six groups. The ﬁrst group consists of genes related to metal transport and homeostasis that can be speciﬁcally affected by metal exposure (see Section 8.2). A second group is composed of genes related to cell wall metabolism that are affected by metal stress. Jones et al. [61] demonstrated a rigidiﬁcation of the cell wall in roots of aluminum-exposed maize seedlings. Also, ligniﬁcation seems to be important under copper and zinc stress [62], where in this case more extracellular binding sites for metals are hypothesized. A third group comprises enzymes and proteins involved in energy and cell metabolism. It is clear that a large number of enzymes involved in energy metabolism are induced under metal stress as well as enzymes involved in the biosynthesis of amino acids. A speciﬁc feature is the effect on sulfur metabolism that is strongly inﬂuenced by most metal stresses. These data are linked with glutathione metabolism. Glutathione plays a crucial role in the defense against metal stress either through complexation – glutathione is the substrate for phytochelatin production – or as a constituent of the antioxidant defense system and as such is important in the cellular redox state [63]. A fourth group includes genes and proteins involved in proper protein synthesis, folding, and modiﬁcation as well as proteolysis. Interaction of metals with biomolecules, through binding to functional groups or replacement of essential elements, is known to affect protein turnover. Plant cells often produce heat shock proteins (HSPs), after being subjected to metal stress [64]. These molecular chaperones induced during metal stress could prevent irreversible protein denaturation or help to channel their proteolytic degradation [55]. An important group of genes or proteins coming to the fore in research on metal-exposed plants are involved in general defense responses. Oxidative stress-related genes/proteins and glutathione-S-transferases are categorized in this group and are strongly affected by metal stress [65]. Finally, it is clear that metal toxicity also activates components of signal transduction pathways that make up another group of metal-affected genes/proteins. Both metalinduced oxidative stress responses and signal transduction will be discussed in the remainder of this chapter.

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| 8 Heavy Metal Stress in Plants 8.4.1 Metal-Induced Oxidative Stress

In general, growth reduction is observed in plants exposed to elevated metal concentrations. Nevertheless, it is difﬁcult to detect a common (path)way of action at the cellular level, due to complex interactions between metal ions and metabolism. Metal-induced oxidative stress has been demonstrated in multiple studies (Table 8.2). This oxidative stress can lead to disruption of cellular macromolecules (e.g., degradation of proteins, cross-links in DNA, and membrane fatty acid peroxidation). However, the elevated reactive oxygen species (ROS) concentrations can also act in signal transduction [80, 81]. Owing to the toxic effects of ROS, it is key to keep their production and detoxiﬁcation under tight control. Mittler et al. [82] described a large gene network consisting of at least 152 genes in Arabidopsis controlling the delicate balance between ROS toxicity and ROS signaling. Redox sensing and signaling associated with both chloroplasts and mitochondria are integrated networks that are highly important in the regulation of cellular processes in both control and stress conditions [83]. Multiple studies have indicated that exposure of plants to a diverse array of metals elicits oxidative stress in plant cells (for a review, see [84]; Table 8.2). Whereas the majority of published articles have focused on metal-induced antioxidant defense mechanisms, it is clear that the sources of ROS production are currently under investigation (Table 8.2). Under natural conditions ROS are produced in organelles with a highly oxidizing metabolic rate or that possessing electron transport chains, such as chloroplasts, mitochondria, and peroxisomes [81]. Under metal stress, the chemical behavior of the metal studied is relevant in terms of ROS production. Free redox-active metals, such as copper and iron, directly enhance the production of hydroxyl radicals through the Fenton reaction. Reduction of the oxidized metal ion can be achieved by the Haber–Weiss reaction with superoxide radicals (O2 ) as a substrate [81]. In addition to direct metalinduced ROS production, plant cell NADPH oxidases come into play. They have an important role in the cellular responses against both biotic and abiotic stresses, among which is metal stress [70, 74, 85]. NADPH oxidase transfers electrons from cytoplasmic NADPH to extracellular O2 to form O2 , followed by its dismutation to H2O2. As such, they can function as intercellular responders to create local ROS transients, possibly via the generation of a secondary messenger hydrogen peroxide (H2O2). From the results obtained over the last few years, it is clear that elevated ROS production is a general response of plants exposed to metal stress that either leads to cellular damage, but can also act in signal transduction [80]. The contribution of ROS in metal-induced signal transduction will be described in the ﬁnal section that discusses the ﬁndings on different signaling pathways during metal stress.

8.5 Signaling Under Metal Stress Table 8.2 Oxidative stress-related parameters affected in

plants exposed to elevated metal concentrations. Oxidative stress-related parameters ROS production

H2O2 NADPH oxidases

O2 , H2O2, NO , NADPH oxidases H2O2, NADPH oxidases NADPH oxidases, mitochondrial electron transfer lipoxygenase ROS, H2O2

O2 , H2O2, NADPH oxidases

Antioxidant defense mechanism

H2O2 H2O2 O2 , H2O2, NADPH oxidases antioxidant enzymes ascorbate, glutathione antioxidant enzymes antioxidant enzymes, glutathione ascorbate, glutathione, antioxidant enzymes ascorbate, glutathione, a-tocopherol, thiol-based reductases and peroxidases antioxidant enzymes glutathione, redox state ascorbate, glutathione, antioxidant enzymes antioxidant enzymes ascorbate, glutathione, antioxidant enzymes antioxidant enzymes antioxidant enzymes

Metal Plant

References

Cd Cd

[66] [67]

Cd Cd Cd Cd Cd/ Hg Cd/ Cu Mn Cr Ni Cd Cd Cd Cd/ Hg Cd/ Hg Cd/ Cu Cd/ Cu Cu Cu

A. thaliana BY-2 tobacco cells Pisum sativum A. thaliana several plants

[68] [69] [70]

A. thaliana [47] Medicago sativa [71] A. thaliana

[72]

C. sativus Brassica juncea Triticum durum A. thaliana A. thaliana A. thaliana Z. mays

[52] [73] [74] [66] [69] [47] [75]

M. sativa

[71]

A. thaliana

[76]

A. thaliana

[72]

Brassica napus P. vulgaris

[77] [78]

Cu/ Zn Mn

P. vulgaris

[79]

C. sativus

[52]

Cr Pb

B. juncea P. vulgaris

[73] [51]

8.5 Signaling Under Metal Stress

Genes induced by metal stress can be classiﬁed into two groups: genes encoding for proteins (i) giving direct protection against metal stress, such as detoxiﬁcation enzymes and other functional proteins, and (ii) regulating gene expression and

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| 8 Heavy Metal Stress in Plants signal transduction, that is, transcription factors and kinases [86]. Despite our growing knowledge regarding toxic responses towards heavy metals and detoxiﬁcation mechanisms, information on metal sensing, regulation, and signal transduction is rather limited. Kacperska [87] described possible (path)ways in signal sensing engaged in plant responses to various abiotic stresses, suggesting a complex network depending on the intensity of the stressor. As many oxidative processes are inﬂuenced by exposure of plants to metal stress (Table 8.2), formation of ROS might be the basis for interconnecting different signaling pathways. It has been proposed that the metal-induced formation of ROS, together with changes in the cellular redox state, provides signals leading to changes in gene expression. H2O2 production is an immediate response to increased metal concentrations [69] and probably is a key molecule involved in signal transduction events after plant metal exposure [88–90]. In fact, ROS, such as H2O2, are ideal signaling molecules as they are small and able to diffuse over short distances. They can inﬂuence the expression of a number of genes involved in signal transduction, metabolism, cellular organization, cell rescue, and so on [80, 82, 91, 125]. ROS are able to induce antioxidant defense mechanisms directly, such as via the ‘‘antioxidant-responsive element’’ (ARE) commonly found in the promoter region of such genes. One of these ARE-induced genes is from CAT1 (CATALASE1), whose gene expression was signiﬁcantly upregulated during cadmium toxicity [47, 92]. Cytosolic ascorbate peroxidases such as APX1 and APX2, and the plastidic iron superoxide dismutase FSD1 are also known to be activated by H2O2 under various stress situations [93] and are clearly affected by metal stress [47, 92]. Secondly, ROS can activate scavenging mechanisms via redox-sensitive transcription factors or via the activation of kinase cascades, which in turn activate transcription factors that trigger target gene transcription [82, 94]. Taking the above data into consideration, the sites at which stress-induced alterations in the redox status of a cell and the generation of H2O2 molecules take place are likely sensors of the stressful situation [87]. As such, apoplastic H2O2 formation by NADPH oxidases and intracellular production from chloroplasts and mitochondria are candidates for sensing and consecutively activating gene regulation under metal stress [70]. Metals affect the gene transcription, expression, and activation of numerous signaling proteins including mitogen-activated protein kinase (MAPK) proteins and nuclear transcription factors, proteins involved in calcium and lipid signaling as well as hormone signaling pathways [90]. The effects of metal stress on intracellular signal transduction may be direct through the interaction of metals with proteins or indirect through the formation of metal-induced ROS. ROS can be detected by several cellular components such as ROS receptors and redox-sensitive transcription factors [82]. Detection of ROS by receptors can result in the release of Ca2 þ from intracellular stores or in the activation of phospholipases [89]. The generation of ROS, Ca2 þ signals, and the activation of speciﬁc phospholipases are thought to activate Ca2 þ -dependent kinases as well as other signal transduction cascades including the MAPK pathway [82, 95]. Previous

8.5 Signaling Under Metal Stress

studies already demonstrated a coordinated link between cadmium exposure and Ca2 þ signaling, whether or not via H2O2 as an intermediate signaling molecule or as a second messenger produced via a Ca2 þ -induced oxidative burst [67]. In the case of cadmium toxicity, Garnier et al. [67] demonstrated the accumulation of H2O2 was preceded by an increase in cytosolic Ca2 þ , essential to activate NADPH oxidases in BY-2 cells. Yakimova et al. [96] also demonstrated the involvement of calcium in cadmium-induced programmed cell death in tomato suspension cells. Furthermore, they showed that lipid signaling takes part in this cadmium-induced programmed cell death. Whereas information on metal-induced lipid signaling is limited, increasing evidence indicates that lipids also function as mediators in signal transduction [97]. When toxic metals are taken up from soil solution into roots cells, the plasma membranes of these cells can be considered as a primary target for metal action [98]. Moreover, membrane lipid peroxidation is a very sensitive response caused by metal stress [78, 99]. Various components involved in the phosphatidic acid signaling, such as phospholipase D, are important elements in the responses of Arabidopsis thaliana roots to cadmium exposure (Smeets, unpublished results). MAPKs are one of the largest families of serine-threonine kinases in higher plants that transduce extracellular signals to regulate cellular processes such as cell division, hormone production, and defense mechanisms [100]. MAPK can be activated in a matter of minutes and de novo translation is not required. The MAPK pathway has been mentioned as a rapid activation mechanism after exposure to cadmium [101], copper [85, 102], and iron (Tsai et al., 2006). Transcription factors such as HSF, ZAT, WRKY, and MYB can be activated via multiple components of the MAPK cascade [82, 103], and are also affected by metal stress (unpublished results). Plant stress hormones are involved in signal transduction during metal stress. The ﬁrst indications suggesting the inﬂuence of stress hormones, such as ethylene and jasmonates, appeared decades ago and evidence is still being produced [90, 104, 105]. Jasmonates and ethylene are considered as stress-responsive hormones that can act as long-distance signaling compounds, but also can upregulate a defense network by regulating the expression of transcription factors [57, 90, 106, 107]. The involvement of ethylene in plant responses to a variety of biotic and abiotic stresses is well known [90, 104]. Ethylene signaling is negatively regulated by the ethylene receptors; downstream signaling is only activated when ethylene is present [108]. EIN2 (ETHYLENE-INSENSITIVE2) is one of the components involved in the ethylene response and has strong similarities to members of the Nramp metal-ion transporter family. Based on this similarity EIN2 might regulate ethylene responses by altering ion concentrations of for instance calcium [108]. Exposure to metal stress is correlated to the formation of ethylene. Copper stimulates the production of this phytohormone in intact bean seedlings [109, 110]. Formation of its immediate precursor 1-aminocyclopropane-1-carboxylic acid (ACC) in the roots could be a result of copper treatment in this organ. ACC may quickly migrate to the aerial plant organs and there be converted into ethylene, as was described by Jackson [111]. Cadmium-induced cell death was accompanied by a small but signiﬁcant rise in ethylene production in tomato suspension

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| 8 Heavy Metal Stress in Plants cultures [96]. It is clear that ethylene production is stimulated by severe heavy metal stress that in turn may be triggered by intracellular oxidative stress [104]. Oxylipins are biologically active signaling molecules derived from oxygenated polyunsaturated fatty acids [112]. Many oxylipins, in particular those belonging to the jasmonate family, are discussed as general inter- and intracellular signaling compounds involved in multiple defense reactions. Jasmonates are oxylipins originating from linolenic acid that is oxygenated by lipoxygenases to hydroperoxide derivatives [88, 107]. Lipid peroxidation initiated by cytoplasmic lipoxygenases seems to play a prominent role under both cadmium and copper exposure [92, 113]. According to previous research, jasmonates and their derivatives are important signaling routes under cadmium and copper stress [32, 72, 90,114–116]. WRKY transcription factors were reported to be involved as a downstream component of jasmonate signaling [107] and are also induced at transcriptional level after metal exposure (Smeets, personnal communication). Extensive cross-talk between signalization pathways is common within various types of (a)biotic stress situations [86]. Therefore, it is important to emphasize the activation of multiple signal transduction pathways and their coordinated regulation during metal stress. The strong effect of metals on oxidative processes can form the basis for other connections with signaling responses. Current research is demonstrating cross-talk between multiple signalization pathways, such as crosstalk between calcium-dependent protein kinases and MAPKs under cadmium stress as well as NADPH oxidase involvement in cadmium-induced MAPK activation was demonstrated [85]. Recent evidence suggests that H2O2 increases calcium inﬂux in Arabidopsis root cells [117] and similar genes involved in calcium signaling were upregulated in response to selenate and activated a defense response [57]. Despite observations of extensive interactions between distinct hormonal signaling pathways, our knowledge on the molecular mechanisms involved in these interactions is still rudimentary and restricted to the case of heavy metal stress. Metal-induced gene expression occurs primarily at the level of transcription, and regulation of the temporal and spatial expression patterns of speciﬁc stress genes is an important part of the plant metal stress response. Numerous studies have shown that transcription factors are important in regulating the plant responses to environmental stresses [118, 119]. Although our knowledge concerning post-transcriptional regulation of metal-induced gene expression is still in its infancy, it has been shown that microRNAs are involved in mRNA degradation or translational repression [120]. Sunkar et al. [121] already showed a role of miRNA398 in the regulation of Cu/Znsuperoxide dismutases during iron and copper stress, but clearly further research is needed to elucidate and understand these mechanisms in metal stress responses.

8.6 Conclusions

Anthropogenic metal contamination is a worldwide problem. It is of great importance to better understand the underlying molecular mechanisms of

References

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96 Yakimova, E.T., Kapchina-Toteva, V.M., Laarhoven, L.-J., Harren, F.M., and Wortering, E.J. (2006) Involvement of ethylene and lipid signalling in cadmium-induced programmed cell death in tomato suspension cells. Plant Physiol. Biochem., 44, 581–589. 97 Wang, X. (2004) Lipid signaling. Curr. Opin. Plant Biol., 7, 329–336. 98 Vangronsveld, J. and Clijsters, H. (1994) Toxic effects of metals, in Plants and the Chemical Elements (ed. M.E. Farago), VCH, Weinheim, pp. 149–177. 99 Smeets, K., Cuypers, A., Lambrechts, A., Semane, B., Hoet, P., Van Laere, A., and Vangronsveld, J. (2005) Induction of oxidative stress and antioxidative mechanisms in Phaseolus vulgaris after Cd application. Plant Physiol. Biochem., 43, 437–444. 100 Pitzschke, A. and Hirt, H. (2006) Mitogen-activated protein kinases and reactive oxygen species signaling in plants. Plant Physiol., 141, 351–356. 101 Jonak, C., Nakagami, H., and Hirt, H. (2004) Heavy metal stress. Activation of distinct mitogen-activated protein kinase pathways by copper and cadmium. Plant Physiol., 136, 3276–3283. 102 Yeh, C.M., Hung, W.C., and Huang, H.J. (2003) Copper treatment activates mitogen-activated protein kinase signalling in rice. Physiol. Plant., 119, 392–399. 103 Kovtun, Y., Chiu, W.L., Tena, G., and Sheen, J. (2000) Functional analysis of oxidative stress-activated mitogenactivated protein kinase cascade in plants. Proc. Nat. Acad. Sci. USA, 97, 2940–2945. 104 Lynch, J. and Brown, K.M. (1997) Ethylene and plant responses to nutritional stress. Physiol. Plant., 100, 613–619. 105 Franchin, C., Fossati, T., Pasquini, E., Lingua, G., Castiglione, S., Torrigiani, P., and Biondi, S. (2007) High concentrations of zinc and copper induce differential polyamine responses in micropropagated white poplar (Populus alba). Physiol. Plant., 130, 77–90.

References 106 Waters, B.M., Lucena, C., Romera, F.J., Jester, G.G., Wynn, A.N., Rojas, C.L., ´ntra, E., and Pe ´rez-Vicente, R. Alca (2007) Ethylene involvement in the regulation of the H þ -ATPase CsHA1 gene and of the new isolated ferric reductase CsFRO1 and iron transporter CsIRT1 genes in cucumber plants. Plant Physiol. Biochem., 45, 293–301. 107 Balbi, V. and Devoto, A. (2008) Jasmonate signalling network in Arabidopsis thaliana: crucial regulatory nodes and new physiological scenarios. New Phytol., 177, 301–318. 108 Chen, Y.-F., Etheridge, N., and Schaller, G.E. (2005) Ethylene signal transduction. Ann. Bot., 95, 901–915. 109 Weckx, J., Vangronsveld, J., and Clijsters, H. (1993) Heavy metal induction of ethylene production and stress enzymes: I. Kinetics of the responses, in Cellular and Molecular Aspects of the Plant Hormone Ethylene ´, and (eds. J.C. Pech, A. Latche ´), Kluwer, Dordrecht, C. Balague pp 238–239. 110 Vangronsveld, J., Weckx, J., KubackaZebalska, M., and Clijsters, H. (1993) Heavy metal induction of ethylene production and stress enzymes: II. Is ethylene involved in the signal transduction from stress perception to stress responses? in Cellular and Molecular Aspects of the Plant Hormone ´, and Ethylene (eds. J.C. Pech, A. Latche ´), Kluwer, Dordrecht, pp. C. Balague 240–246. 111 Jackson, M. (1997) Hormones from roots as signals for the shoots of stressed plants. Trends Plant Sci., 2, 22–28. 112 Kazan, K. and Manners, J.M. (2008) Jasmonate signalling: toward an integrated view. Plant Physiol., 146, 1459–1468. ´rzyn ´ska-Polit, E., Pawlikowska113 Sko Pawl˛ega, B., Szczuka, E., Dr˛az˙kiewicz, M., and Krupa, Z. (2006) The activity and localization of lipoxygenases in Arabidopsis thaliana under cadmium and coppers stresses. Plant Growth Regul., 48, 29–39.

114 Koeduka, T., Matsui, K., Hasegawa, M., Akakabe, Y., and Kajiwara, T. (2005) Rice fatty acid a-dioxygenase is induced by pathogen attack and heavy metal stress: activation through jasmonate signaling. J. Plant Physiol., 162, 912–920. 115 Kumari, G.J., Reddy, A.M., Naik, S.T., Kumar, S.G., Prasanth, J., Srirangangayakulu, G., Reddy, P.C., and Sudhakar, C. (2006) Jasmonic acid changes protein pattern, antioxidative enzyme activities and peroxidase isozymes in peanut seedlings. Biol. Plant., 50, 219–226. 116 Maksymiec, W., Wojcik, M., and Krupa, Z. (2007) Variation in oxidative stress and photochemical activity in Arabidopsis thaliana leaves subjected to cadmium and excess copper in the presence or absence of jasmonate and ascorbate. Chemosphere, 66, 421–427. 117 Demidchik, V., Shabala, S.N., and Davies, J.M. (2007) Spatial variation in H2O2 response of Arabidopsis thaliana root epidermal Ca2 þ ﬂux and plasma membrane Ca2 þ channels. Plant J., 49, 377–386. 118 Singh, K.B., Foley, R.C., and OnateSanchez, L. (2002) Transcription factors in plant defence and stress responses. Curr. Opin. Plant Biol., 5, 430–436. 119 Chen, W., Provart, N.J., Glazebrook, J., Katagiri, F., Chang, H.-S., Eulgem, T., Mauch, F., Luan, S., Zou, G., Whitham, S.A., Budworth, P.R., Tao, Y., Xie, Z., Chen, X., Lam, S., Kreps, J.A., Harper, J.F., Si-Ammour, A., Mauch-Mani, B., Heinlein, M., Kobayashi, K., Hohn, T., Dangl, J.L., Wang, X., and Zhu, T. (2002) Expression proﬁle matrix of Arabidopsis transcription factor genes suggest their putative functions in response to environmental stresses. Plant Cell, 14, 559–574. 120 Sunkar, R. and Zhu, J.K. (2007) Micro RNAs and short-interfering RNAs in plants. J. Integr. Plant Biol., 49, 817–826. 121 Sunkar, R., Kapoor, A., and Zhu, J.K. (2006) Posttranscriptional induction of

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9 Systematic Analysis of Superoxide-Dependent Signaling in Plant Cells: Usefulness and Speciﬁcity of Methyl Viologen Application Simone Jacob and Karl-Josef Dietz

9.1 Reactive Oxygen Species and Antioxidant Defense 9.1.1 Reactive Oxygen Species – Generation and Biological Relevance

In their natural environment plants are exposed to various biotic and abiotic factors that can affect their growth, development, and productivity. Intrinsic disturbances that result from these external inﬂuences lead to imbalances in metabolism, including the cellular redox state, and thereby favor electron transfer reactions (e.g., with molecular oxygen) [1]. The ground-state oxygen (O2) is converted into different reactive intermediates either by physical activation via energy transfer generating singlet oxygen (1O2) or by chemical activation via electron transfer (Table 9.1). The latter mechanism successively produces the superoxide anion (O2 ), hydrogen peroxide (H2O2), or the hydroxyl radical (OH ), depending on the number of electrons transferred [2]. These partially reduced forms are referred to as reactive oxygen species (ROS), and display a much higher reactivity towards many cellular compounds than O2 and facilitate oxidative damage if produced in excess [3]. In such oxidative stress situations, ROS abstract electrons from target molecules and show detrimental effects on the different macromolecules that are found within the cell. Damage to DNA, proteins, and unsaturated lipids results in modiﬁcation and eventually inactivation of these molecules, and subsequently to cellular dysfunction that can ultimately lead to cell death [4, 5]. On the other hand, another established property of ROS is their ability to function as secondary messengers. Such a function depends on preferential and highly afﬁne interactions with cell sensors in order to speciﬁcally regulate different biological processes such as transcription, translation, and post-translational states, which in turn modulate growth and hormone signaling, abiotic and biotic stress responses, development, and programmed cell death [6–9]. Consequently, their dual function as mediators of oxidative stress and signaling molecules Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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| 9 Systematic Analysis of Superoxide-Dependent Signaling in Plant Cells Table 9.1 Compilation of important ROS and RNS of plant cells with their basic biochemical properties and the antioxidants leading to their detoxiﬁcation.

Reactive species

Half-lifea

Distance traveleda

Redox potentialb

Concentration rangeb

Detoxiﬁcationa

1

1 ms

30 nm

–

–

1 ms 1 ms

30 nm 1 mm

þ 0.94 V þ 0.54 V

o1 nM 1–100 mM

1 ns o0.1 sd

1 nm ?

þ 2.20 V ?

negligible ?

ascorbate, carotenes, tocopherol ascorbatec, SOD CAT, PRX, GRX, thioredoxin, peroxidase, ascorbate–glutathione cycle ascorbatec PRXe

O2

O2 H2O2

OH ONOO a

Extracted from [10], distance travelled in one half-life. Extracted from [11]. c [12]. d [14]. e [13]. b

necessitates a tight control of the generation and accumulation of ROS and reactive nitrogen species (RNS) that can only be provided by an elaborated network of antioxidants, which in fact act as chemical quenchers. The generation of ROS in plant cells is a continuous process, being in balance with detoxiﬁcation reactions under resting conditions [11, 15]. Their numerous sites of production are found throughout the plant cell, as observed in the cytosol, chloroplast, mitochondrion, peroxisome, plasma membrane, and apoplast [16–18]. They are generated as byproducts of normal metabolism through electrons ‘‘leaking’’ from electron transport chains, where primarily O2 is produced, as in the case of photosynthesis in chloroplasts and respiration in mitochondria [19]. During photosynthesis, 1O2 also evolves as excited chloroplast pigments transfer energy to O2 [16]. In peroxisomes, ROS generation is also associated with normal metabolism [18], mostly due to enzymatic activities, generating H2O2 as a byproduct of the photorespiratory glycolate oxidase reaction, fatty acid b-oxidation, and the enzymatic reaction of ﬂavin oxidases [20]. Furthermore, O2 is produced by xanthine oxidase and by a short electron transport chain in the peroxisomal membrane [20]. Apart from their generation during normal metabolism, ROS can also be produced in a directed and regulated manner, by NADPH oxidases that are associated with the plasma membrane of plant cells and cell wall-bound perox idases, and generate O2 and H2O2, respectively; both playing a crucial role in the context of hypersensitive response and pathogen defense [21]. Various reactions interconvert particular ROS. In addition to a rapid turnover of O2 to H2O2 by the mechanism of disproportion [22], Fenton or Haber–Weiss reactions produce OH 2þ catalyzed by Fe at the expense of H2O2 or lead to the turnover from O2 and H2O2 to form OH [19, 23].

9.1 Reactive Oxygen Species and Antioxidant Defense

As well as ROS, there is another kind of reactive species involved in redox-state regulation, even extending the network of oxidative stress and signal transduction pathways: the RNS with their major compound nitric oxide (NO) [24]. This gaseous and reactive radical has a very short half-life [25]. NO itself is an important signaling molecule in the plant cell, to which many different regulatory functions have been assigned during recent years. In addition to modulating disease resistance via triggering hypersensitive cell death and expression of defense genes [26], its various physiological roles include regulation of development by inﬂuencing leaf extension and root growth as well as the delay of leaf senescence and fruit maturation and regulation of stomatal closure [27, 28]. A direct involvement of NO in signal transduction pathways as it is known from animal systems could also be shown for plants; here, for instance, an activation of mitogen-activated protein kinase (MAPK) cascades was observed [29] as well as transcript regulation of genes important for synthesis and response to jasmonic acid [30]. In animal cells, NO is mainly produced by NO synthase (NOS), catalyzing the conversion of L-arginine to L-citrulline and NO; in plant cells, NO generation has not yet been ultimately clariﬁed. Up to now, two enzymatic pathways could be identiﬁed either utilizing L-arginine or nitrate as substrate [31]. There appear to exist various sites of NO production, such as the cytosol, chloroplast, mitochondrion, and peroxisome, and these coincide with the sites of ROS generation. As a consequence, the production of further reactive species by reaction of ROS with NO is unavoidable. O2 , for instance, reacts with NO to yield peroxynitrite (ONOO ) when their generation takes place in near proximity [32]. Additionally, the nitrosylation of thiol groups generates S-nitrosothiols and if O2 is available the formation of NO2 takes place. On the other hand, NO can be a longdistance signal under hypoxic conditions [33]. Under abiotic stress situations as shown for salt stress, an increase in NO as well as total S-nitrosothiols indicate the occurrence of nitrosative stress often in parallel with ROS-mediated oxidative stress situations [34]. 9.1.2 Detoxiﬁcation of ROS – Antioxidative Network in Plants

Plant cells synthesize and accumulate low-molecular-mass metabolites both in the lipid and polar phases that exhibit high reactivity with ROS and RNS. In addition to tocopherols, ﬂavonoids, alkaloids, and carotinoids [9], as well as proline and amines [35], ascorbate and glutathione constitute the two main redox buffers of the plasmatic compartments of the cell [36]. They accumulate to millimolar concentrations [37, 38], with variations depending on tissue and physiological state [36]. Furthermore, the ratio of reduced to oxidized glutathione allows for a tentative description of the redox status of a cell, being reduced at least by about 95% under nonstress conditions [39]. Live cell imaging with the redox-sensitive Green Fluorescent Protein (GFP) even suggests a reduction state in the range of 99% [40]. The ratio of reduced to oxidized ascorbate can act as an important factor in apoplast metabolism and plant defense responses [41].

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| 9 Systematic Analysis of Superoxide-Dependent Signaling in Plant Cells Reduced glutathione is the tripeptide g-glutamylcysteinyl glycine (g-Glu–Cys– Gly) that, on the one hand, can be oxidized by direct reaction with ROS, subsequently forming homodimers that are linked by disulﬁde bridges (GSSG), or, on the other hand, can act as an electron donor to glutaredoxins (GRX), which among other functions are linked to type II peroxiredoxin (PRX) activity [42]. Furthermore, it is an important molecule providing the post-transcriptional modiﬁcation of glutathionylation that regulates protein function and protects from irreversible oxidation [42], as for instance glutathionylation modulates glyceraldehyde-3phosphate dehydrogenase and thioredoxin activities [43, 44]. Ascorbate acts as primary antioxidant by quenching 1O2, O2 , and HO [12]. As an electron donor, ascorbate is involved in the regeneration of oxidized a-tocopherol and as a cofactor of violaxanthine deepoxidase [45]. Moreover, ascorbate and glutathione work as cosubstrates in the ascorbate–glutathione cycle during detoxiﬁcation of H2O2 [46]. During this cycle ascorbate is oxidized to monodehydroascorbate (MDHA) by the enzyme ascorbate peroxidase (APX), which reduces H2O2 to yield H2O. Further oxidation of MDHA leads to formation of dehydroascorbate (DHA). Regeneration is provided by the NADPH-dependent MDHA reductase and DHA reductase, respectively. The latter utilizes glutathione, which itself is re-reduced by NADPH-dependent glutathione reductase [47]. Superoxide dismutase (SOD) is in the ﬁrst line of antioxidant defense, and functions by catalyzing the dismutation of O2 to H2O2 via the cyclic reduction and oxidation of the transition metal ion in its active site, which may be copper/zinc, iron, or manganese, at a rate very near to that of diffusion [48]. Unlike many other organisms, plants have multiple isoforms of this enzyme [1]. In Arabidopsis thaliana, eight genes code for SODs, being present in all subcellular localizations that are generation sites for O2 [49]. This is crucial for O2 detoxiﬁcation, as this charged molecule is not able to cross phospholipid membranes and has to be removed directly at its site of generation [50]. The subdivision of SODs is based on the kind of metal cofactor being used by the enzyme as well as on their localization [51]. In the plant cell, several enzymes efﬁciently remove H2O2. As part of the ascorbate–glutathione cycle, APXs are among the primary H2O2-scavenging enzymes of the chloroplast and the cytosol [46]. In A. thaliana there are nine different genes coding for isoforms that remain in the cytosol or that are targeted to almost every compartment of the cell [49]. The chloroplast contains three isoenzymes: the thylakoid-bound ascorbate peroxidase tAPX, the stromal APX and another isoenzyme found in the lumen [52]. Catalases (CATs), ubiquitous enzymes among aerobic organisms, also convert H2O2 to oxygen and water [53]. These are tetrameric, heme-containing enzymes with three main isoforms in plants. In A. thaliana three genes were identiﬁed encoding for individual subunits, forming at least six different homo- and heteromeric isoforms of the enzyme [54]. They are present in peroxisomes but differ in their tissue speciﬁc distribution [54]. PRXs are ubiquitous non-heme-containing peroxidases also known as thiol-speciﬁc antioxidants or thioredoxin peroxidases [55, 56]. The catalytic activity of this peroxidase family depends on cysteine residues and leads to detoxiﬁcation of H2O2, alkyl

9.2 Methyl Viologen

peroxides as well as ONOO [57–59]. Depending on the number and localization of conserved cysteine residues in their primary structure, four different subgroups can be deﬁned: 1-Cys PRX, 2-Cys PRX, PRX Q, and type II PRX [60]. Recently, the plant glutathione peroxidase (GPX) family was assigned to be a ﬁfth group of PRXs, as they also display a thioredoxin-dependent function and show similarities to PRXs [61]. The coordinated function of all these components of the antioxidative defense system is particularly important to prevent generation of OH by maintaining a balance of low steady-state levels of O2 and H2O2 as the latter reacts with transition metals and therefore potentially produces highly toxic OH in a cellular environment [62]. Deviations from low norm levels of ROS indicate metabolic imbalances that should be sensed and reacted upon. In this context the elaborated antioxidant network is necessary to also control signaling pathways that utilize O2 , H2O2, and 1O2, as these are known to play a critical role in various signal transduction processes involved in stress response, growth, development, and many more functional ﬁelds [9]. The redox homeostasis and antioxidant system is particularly complex in plant cells that are exposed to fast and pronounced changes in environmental parameters [63]. The complexity and intrinsic redundancy of the system allow for efﬁcient compensation if the activity of single system components fails. As an example, the knockout of the mitochondrial PRXIIF was fully compensated by upregulation of mitochondrial APX and GPX [64], which, as discussed above, is a PRX-like thioredoxin-dependent peroxide reductase [61]. Only under stress imposed for instance by cadmium was the compensation insufﬁcient and root growth was strongly inhibited in the PRXIIF knockout plants [64].

9.2 Methyl Viologen: From Redox Indicator and Herbicide to Application as Effector in Oxidative Stress Investigation 9.2.1 General Considerations to Methyl Viologen as Herbicide and Toxin

Methyl viologen (1,1’-dimethyl-4,4’-bipyridylium dichloride), also known as paraquat, is a quaternary nitrogen compound that is routinely used as an effector in oxidative stress research and during characterization of the antioxidative network in plants [65–67]. Chemically, this substance belongs to the class of viologens or bipyridylium compounds and was ﬁrst described by Weidel and Russo in 1882 [68]. Its redox properties were investigated by Michaelis and Hill in 1933 [69], and methyl viologen was initially used as a redox indicator due to its intensely colored radical form (see Section 9.2.2) [70]. Only in the middle of the twentieth century was it discovered that methyl viologen acts as a nonspeciﬁc contact herbicide [71] and it was subsequently used in agriculture as rapid desiccant of green plant tissue [72]. Its ability to adsorb to soil particles and organic soil matter [73, 74] limits the bioavailability for microorganisms and plants [75], and fosters a rapid biological

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| 9 Systematic Analysis of Superoxide-Dependent Signaling in Plant Cells deactivation. Additionally to this advantage of methyl viologen as an herbicide, its limited mobility leads to the avoidance of ground water contamination [76]. Furthermore degradation of the unbound fraction to less-toxic compounds is realized by external factors such as ultraviolet light [77], sunlight [78], and a variety of soil microorganisms [79], leading to chemical or biological catabolism, respectively (see Figure 9.1) [73, 78]. However, methyl viologen is also a highly toxic compound if ingested or absorbed by humans and animals, with various cases of poisoning and casualties being reported [80, 81]. Irrespective of the form of its application, methyl viologen has severely damaging effects on most tissues and leads to a high rate of mortality resulting from multiorgan failure of, for example, liver, kidney, lung, and brain [70, 82]. The most characteristic feature of methyl viologen poisoning occurs via damage of the lung, where this compound has been shown to be accumulated by an energy-requiring process in a time-dependent manner [83]. As a consequence of these health risks for humans and animals, nowadays, the methyl viologen application as herbicide is considered controversial.

Figure 9.1 Degradation of methyl viologen. Depicted are the metabolic and photochemical pathways of methyl viologen degradation, realized by microorganisms and catalyzed by irradiation with sunlight or ultraviolet radiation, respectively. Source: Scheme modiﬁed according to Roberts et al. [73].

9.2 Methyl Viologen

9.2.2 Mechanism of Methyl Viologen Toxicity in Plants and Animals

The primary mechanism of methyl viologen toxicity is its ability to undergo redoxcycling reactions in the presence of oxygen and upon continuous electron supply [70]. A one-electron reduction leads to the transformation of the cation to the intensely colored free radical form that is stable only under anaerobic conditions [69]. In an aerobic environment, the radical immediately reacts with O2 to regenerate the cation with concomitant production of O2 (Figure 9.2). The reactivity of both partners results in a very fast reoxidation of the cation with a rate constant of 7.7 108 M1 s1 [84]. The redox potential of MV2 þ /MV þ is very negative (E 0 ¼ 0.45 V) while that of molecular oxygen (O2/O2 ) is considerably higher (E 0 ¼ 0.16 V) [84], facilitating the electron ﬂow from methyl viologen to molecular oxygen. In the animal system, the electrons for reduction of the cation are provided by NADH or NADPH, being enzymatically transferred, for example, by NADPH cytochrome P450 reductases, diaphorases, or NOS (for review, see [85]). This reaction leads to the depletion of NADPH from cell metabolism, and renders the cell unable to perform essential physiological and biochemical functions [70]. Therefore, the deﬁciency of reducing equivalents is a second critical event in methyl viologen toxicity in addition to the generation of ROS. In planta the electrons for reduction of methyl viologen cations are transferred from the chlorophyll molecules of photosystem I, leading to the generation of O2 in the chloroplast [86, 87]. This process inhibits reduction of þ NADP by competing for electrons at photosystem I and consequently the free radical in the cell is generated at the expense of NADPH. Additional evidence for this mode of action is provided by the fact that the damaging effects require light as well as oxygen and are restricted to the green parts of the plants at low concentration [88].

Figure 9.2 Schematic depiction of ROS generation upon methyl viologen treatment in plant and animal systems, and the involvement of enzymatic antioxidants.

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| 9 Systematic Analysis of Superoxide-Dependent Signaling in Plant Cells 9.2.3 Lipid Peroxidation as a Consequence of Oxidative Stress upon Methyl Viologen Application

Oxidative stress as a very important mechanism of methyl viologen toxicity has been investigated in different in vivo and in vitro systems [89], and was shown not only for animals and plants but also for bacteria [90]. As generation of O2 by continuous redox cycling of methyl viologen is the primary mechanism of toxicity, this can lead to the subsequent generation of other harmful ROS such as H2O2 and OH (see Figure 9.2) [76]. This potential of O2 as an effective oxidant causes the oxidative denaturation of proteins and nucleic acids, and ultimately leads to lipid peroxidation of polyunsaturated fatty acids not only in the mammalian system [91]. Also in planta the damage of membranes and the concomitant loss of membrane integrity is a welldescribed effect of methyl viologen treatment [92]. Nowadays, this particular consequence of methyl viologen is often used as an indicator for antioxidative potential upon progressive oxidative damage in plants, since membrane rupture leads to leakage of the cell [93, 94]. Ion loss from the cell is measured as the increasing conductance of the surrounding medium. The maintenance of low conductance in the presence of methyl viologen indicates the antioxidative potential of a plant under certain conditions [94]. 9.2.4 Requirement of the Antioxidative Network upon Methyl Viologen Application

In order to counteract oxidative damage, cells have evolved a complex network of mechanisms in order to enable the quenching of ROS (Figure 9.2) [15]. Since methyl viologen exerts its toxic effect via oxidative stress-mediated mechanisms, an increase of antioxidative capacity can lead to an elevated resistance [76, 95]. Especially in the context of the high toxicity of this compound towards humans and animals [70, 82], different therapies of methyl viologen intoxication are necessary (for review, see [85]). Additional to various treatments aiming at the removal of methyl viologen from the organism (e.g., by treatment with charcoal [96]), supportive therapies are mostly directed at preventing the generation and distribution of ROS as well as scavenging of reactive metabolites, including the maintenance of effective levels of antioxidants [76]. Therefore, both enzymatic and nonenzymatic antioxidants were employed, although with limited success as an exogenous application was shown to be ineffective due to the inability of these molecules to cross cell membrane barriers or as consequence of rapid hydrolysis [97, 98]. The avoidance of this limitation by using either liposome-encapsulated molecules as shown for SOD, glutathione, and a-tocopherol [99, 100], or by application of lower-molecular mimetics of SOD [97], improved protection against methyl viologen-induced injury. In planta sublethal levels of methyl viologengenerated oxidative stress induce an elevation of antioxidative potential that includes upregulation of diverse enzymatic antioxidants [101]. Also, transgenic plants overexpressing isoforms of CAT, SOD, and APX in different subcellular

9.3 Gaining Insights into Superoxide Anion-Mediated Signaling in Plants

compartments either as a single or a double mutant showed an enhanced tolerance upon the oxidative challenge by methyl viologen treatment as compared to the wild-type [66, 67, 102–104]. Furthermore, antisense and knockout mutants that were deﬁcient in different APX isoforms showed a higher susceptibility towards methyl viologen stress [105, 106]. However, stimulation of the already existing antioxidative capacity of the cell has a stronger effect in plants than in the animal system, where the emerging picture of damage upon methyl viologen application is considerably more complex.

9.3 Gaining Insights into Superoxide Anion-Mediated Signaling in Plants – Goals and Limitations of Methyl Viologen Application 9.3.1 Superoxide Anion and Hydrogen Peroxide Signaling: A Problem of Differentiation?

Apart from their destructive potential when excessively formed during severe oxidative stress, ROS such as 1O2, O2 , and H2O2 also constitute important signaling molecules that trigger or affect various signal transduction pathways (for comparison, see Section 9.1.1) [9]. However, studies aimed at clarifying O2 functions within signal transduction in plants remain comparably scarce as their investigation proves difﬁcult. One reason is its short lifetime. Within a few milliseconds only, it rapidly disproportionates to H2O2 and O2 with a rate constant of about 2 105 M1 s1 at neutral pH [107, 108]. This delimits the mean radius of O2 diffusion inside the cell to approximately 0.3 mm [109]. Furthermore, with the enzyme SOD, the cell contains a powerful converter of this reactive metabolite, enhancing the rate constant to about 2 109 M1 s1 at pH 7.4 [108]. Consequently, it is difﬁcult to discriminate between signaling processes induced by either O2 or H2O2 species. Therefore, authors often refer to O2 /H2O2dependent signaling when methyl viologen is administered [110, 111]. Never theless, knowledge of O2 involvement in signaling events is increasing due to new approaches utilizing transgenic plants in combination with different O2 generating agents [67, 112, 113]. 9.3.2 Transgenic Plants as a Powerful Tool towards Understanding the Participation of Superoxide Anion in Signal Transduction Processes

Many biotic and abiotic strains generate metabolic imbalances between oxidants and antioxidants, and may cause oxidative stress [19, 114]. For several reasons it is extremely difﬁcult to link speciﬁc responses exclusively to speciﬁc reactive intermediates [115]: (i) different types of ROS are evolved in various subcellular and also extracellular sites, (ii) numerous metabolic sources release ROS, (iii) a combinatorial set of ROS is usually released rather than a single ROS species, and

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| 9 Systematic Analysis of Superoxide-Dependent Signaling in Plant Cells (iv) an elaborated and redundant antioxidative network counteracts the development of oxidative stress in a site speciﬁc and regulated manner [19, 116]. Consequently, selective and novel approaches are needed to provide a more detailed insight into the role of the different antioxidative components during oxidative insult [117]. The employment of transgenic plants, being altered in deﬁned components of the antioxidative system, tentatively promises to discriminate between the ROS functions [115, 117]. Convincing evidence from transgenic plants overexpressing enzymes of the antioxidant defense system revealed enhanced oxidative stress tolerance under certain environmental conditions. The established treatment of leaves involves the application of methyl viologen at a low concentration of about 1 mM [102–104]. To address the question of signaling functions of single ROS, the generation and analysis of mutants deﬁcient in certain enzymes of the antioxidant defense system often appears more straightforward than utilization of overexpressing plants. The rationale is that upon additional application of exogenous effectors that enhance production of particular ROS usually being rapidly quenched, the monitoring of their impacts on signaling processes becomes possible. Also, by the use of such approaches H2O2 was assigned a function in different signaling pathways [118, 119]. Likewise, these approaches help to clarify the role of O2 in signal trans duction processes (Figure 9.3). To investigate the role of O2 in pathogen attackrelated cell death, Jabs et al. [112] used the mutant lsd1 (lesion-simulating disease resistance response), which in the presence of O2 develops lesions otherwise known from the hypersensitive response during pathogen attack. In these experiments extracellular O2 was generated with the xanthine–xanthine oxidase system by exogenous application to the leaves in vivo. The xanthine–xanthine oxidase system is widely used to mimic extracellular O2 production during pathogen attack responses [120–122]. It cannot be used to elicit intracellular O2 signaling. Therefore methyl viologen was used, as it provides the option of a rather site speciﬁc generation of O2 in the chloroplast [86, 87]. In the light of the rapid turnover of O2 to H2O2, the application of this bipyridylium compound alone is not sufﬁcient to characterize the signaling function of O2 . In combination with transgenic plants expressing altered H2O2 turnover capacities, the application of methyl viologen is a powerful tool to investigate the effects of O2 generation [113, 123]. An alternative approach aims at identifying changes in gene expression patterns after methyl viologen treatment and to subsequently exclude those responses triggered by H2O2 [124, 125]. This is realized by comparison with datasets derived from H2O2 favoring conditions as it is found in CAT- or APXdeﬁcient plants or upon exogenous H2O2 application [124, 125]. To differentiate between 1O2 and O2 /H2O2-dependent signaling pathways, Laloi et al. [110, 111] employed the conditional ﬂu ( ﬂuorescent) mutant that accumulates protochlorophyllide in the dark and generates 1O2 when shifted into the light, whereas it shows a normal growth if kept under continuous light [110, 126]. To discriminate between the ROS speciﬁc signaling pathways, the gene expression patterns of the ﬂu mutant upon dark-to-light shift (generating 1O2) were compared to methyl viologen treatment upon continuous light (generating O2 and

9.3 Gaining Insights into Superoxide Anion-Mediated Signaling in Plants

Figure 9.3 Exemplary compilation of different approaches towards the understanding of superoxide anion mediated responses. Source: Jabs et al. [112], Kim et al. [113], Laloi et al. [110], Gadjev et al. [124], and Rizhsky et al. [123].

subsequently H2O2) [110]. The crossing of the ﬂu mutant with an overexpressing line of tAPX further allowed for a more detailed investigation of the speciﬁc signaling responses especially in the context of cross-talk between different ROS signaling pathways [111]. As concluded from these studies, methyl viologen is an appropriate inductor for the cellular generation of O2 . Its production mainly takes place in the chloroplast, while methyl viologen reactivity in the plant mitochondrion is considered to be low [86, 127]. The site-speciﬁcity of O2 generation in the chloroplast is of use for a high number of studies employing methyl viologen as an efﬁcient mediator of oxidative stress [65–67, 101–104]. However, thorough discrimination between signaling events remains a critical issue to enable the precise assignment of signaling processes to speciﬁc ROS. Furthermore, it should be kept in mind that the alteration in a single component of the antioxidative network elicits cell responses such as the differential and compensatory regulation of other enzymatic antioxidants, on the one hand, and affects the kind of reactive species generated in the system, on the other hand. New imaging methods with high spatial and time resolution now allow for quantitative monitoring as to how speciﬁc ROS are generated in subcellular compartments, and how these processes affect the redox state of the cell. Technologies such as redox-sensitive GFPs [40] or ﬂuorescent

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| 9 Systematic Analysis of Superoxide-Dependent Signaling in Plant Cells markers speciﬁc for particular ROS [128] are expected to give the urgently needed insights into the dynamics of ROS generation and accumulation – a prerequisite for understanding the signaling processes. 9.3.3 Towards Understanding of Superoxide Anion Signaling in Plants

Following their generation, ROS trigger and affect plant signal transduction pathways, although the components of these networks are only slowly being identiﬁed [116, 129]. While signaling mediated by H2O2 has been analyzed in some detail [130, 131], data on signaling triggered by other reactive intermediates like O2 are just beginning to be collected. The deciphering of O2 -induced transcript regulation was mostly attempted by comparative approaches, excluding the transcripts regulated by other reactive species. Deﬁned gene clusters have been shown to respond to each type of ROS [124]. In plants with decreased Cu/Zn-SOD activity, O2 accumulates. Of the 77 probes for chloroplast-encoded genes on the ATH1 Affymetrix Genome array, for instance, 61 were represented exclusively in one of the O2 responding clusters. A highly speciﬁc O2 sensor mechanism is suggested to reside in the thylakoids, which upon O2 -dependent activation speciﬁcally upregulates plastid transcript levels [123, 124]. Even though photosynthesis was also impaired under conditions of preferential H2O2 production a similar transcript regulation as in the SOD knockdown plants was not observed [123]. In part, even an antagonistic response pattern was observed between conditions preferentially releasing O2 and H2O2. Transcripts related to anthocyanin biosynthesis were considerably upregulated in response to O2 , but downregulated in CAT-deﬁcient plants [124]. This differential transcript regulation was mirrored in anthocyanin amount of the corresponding transgenic plants [123, 131] and clearly demonstrates speciﬁc signaling pathways triggered by different ROS [124]. The utilization of the conditional ﬂu mutant enabled a further distinction of ROS signaling pathways that either spe ciﬁcally regulate transcripts in response to 1O2 (42 genes) or in an O2 /H2O2mediated manner (48 genes) [110, 126]. The additional noninvasive modulation of the H2O2 level in transgenic lines overexpressing tAPX strongly suggests a crosstalk between H2O2 and 1O2 in an antagonizing manner [111]. Indications for the upregulation of special transcription factors and genes related to signal transduction pathways were obtained in a study aiming at characterizing the early response of the chloroplast to O2 generation induced by methyl viologen under conditions when the levels and activities of chloroplastidic and antioxidant proteins were yet unaffected [125]. The approach allowed for the identiﬁcation of a group of 22 genes, being selectively responsive to methyl viologen treatment in comparison to any other investigated stress condition. H2O2 is generally considered to act as signaling molecule in pathogen defense responses and programmed cell death [132, 133]. A similar role in apoptosis could also be attributed to O2 . The ﬁrst evidence for its involvement was obtained as O2 , but not H2O2, led to an induction of lesion formation and accumulation of

9.4 Conclusions

PR1 (pathogenesis-related protein1) mRNA in Arabidopsis lsd1 mutant. When grown under long days, these mutants displayed a spreading cell death with O2 being the critical signal as it was necessary and sufﬁcient to induce lesion formation [112]. For parsley cells it was shown that O2 rather than H2O2 is an essential element of the signaling cascade that stimulates phytoalexin production. Phytoalexins play an important role in hypersensitive cell death [120]. In tomato one member of the extensin family, which comprises structural proteins with a presumed role in primary cell wall organization, responded to O2 in contrast to H2O2 with its transcript being accumulated [121]. In Arabidopsis rcd1 (radical-induced cell death) mutant the cellular accumulation of O2 that causes cell death is induced by exogenous O2 and ozone application rather than by H2O2. O2 -dependent cell death is modulated by ethylene signaling pathways as exogenously applied ethylene was shown to increase the ion leakage resulting from cell death events [122]. In general, ROS also inﬂuence the regulation of development and growth for instance through the interaction of H2O2 with hormone function [134, 135]. Knowledge on the role of O2 in this context is just slowly emerging. Results from sense and antisense transgenic potato plants with modiﬁed chloroplastidic Cu/Zn-SOD activities indicate that O2 displays a regulatory role in plant growth and tuber development by interference with gibberellins [113]. O2 affects the expression of genes within their biosynthetic pathways. As overexpression and suppression of chloroplastidic SOD affected the production of ROS and allowed for their modulation, this approach is unique for studying the physiological role of ROS in relation to various concentrations of O2 and H2O2 [113]. Another aspect that has to be taken into consideration is the reaction of O2 with NO. NO is often produced at the same sites as O2 and both molecules react in a stoichiometric manner to generate ONOO with a diffusion controlled rate [136]. Interestingly, ONOO does not act as a signal for cell death in plants, as is described for the animal system [26]. Nevertheless, ONOO is hypothesized to have intrinsic signaling functions in plants as well [136]. In particular, its ability to cause protein nitration at tyrosine residues may modulate protein activities [13, 137], and is expected to play an important physiological and signaling role under biotic stress.

9.4 Conclusions

Albeit still imprecise at some point, the circumstantial experimental evidence is overwhelming that O2 fulﬁls signaling functions in plant cells. Also, the involvement in pathogen attack-related responses is a well-described feature of O2 signaling. However, the signiﬁcance of O2 signaling on physiological and morphological properties of the whole tissue and plant, and in addition the nature of sensors and signal transmitters, are still poorly understood. Knowledge on O2 -dependent regulations on transcript levels is well advanced; however, data concerning the proteomic or metabolic level of the O2 response are missing. Such data are urgently needed to approach a better understanding of the

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O2 -mediated responses and their signiﬁcance on a systems biology level. The new technologies of live cell imaging in combination with sensitive and speciﬁc molecular probes will allow for linking the biochemical responses to ROS production with high time and space resolution of the cells. It is likely that methyl viologen will keep its unique role in experimentally eliciting O2 -dependent processes. In parallel to the experimental approach, modeling and simulation of ROS signaling based on estimated rates of generation, measured enzyme activities, properties of the different ROS species and assumed characteristics of sensors are needed to dissect and predict the signaling pathways and assess response behaviour. A ﬁrst model was established by Polle [138] describing limiting steps in H2O2 metabolism in the chloroplast. Extending such modeling and simulation approaches to other ROS might pave the way for a better understanding of ROS metabolism and signaling networks in the cell.

Acknowledgments

This survey was part of our research efforts within the Deutsche Forschungsgemeinschaft Research Unit 804 on Retrograde Signaling in Plant Cells.

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Part Three From Transcriptomics and Proteomics to Signaling Networks

| 199

10 Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress Expression Proﬁle Dataset Dierk Wanke, Kenneth W. Berendzen, Joachim Kilian, and Klaus Harter

10.1 Introduction

Sessile organisms like plants cannot migrate away from nonpermissive abiotic environmental conditions to more favorable sites. As a consequence, these organisms display a broad range of physiological and morphological plasticity, enabling them to cope and ﬂourish even under different conditions. Conditions off the individual’s optimum can be deﬁned as stresses. The range of physiological responses in an organism after an environmental stimulus is termed phenotypic plasticity. Fluctuations in abiotic environmental conditions that remain close to the individual optimum result in only slight physiological responses. Information on more drastic changes or on permanent damage needs to be transduced and integrated into a complex process of concerted cellular reprogramming. This might possibly result in stress acclimation – a reversible physiological process essential for retaining the organism’s function. Enduring nonpermissive environmental conditions can cause irreversible morphological changes and developmental retardation. In plants, the transduction of abiotic stress information is mediated by small signaling molecules and involves changes in cytoplasmic calcium, a transient increase in reactive oxygen species (ROS), and the synthesis of phytohormones that leads to a more systemic response [1, 2]. In recent years, recognizable progress has been made in understanding how abiotic stress responses are orchestrated in higher plants. Expression proﬁle studies, using microarray technology, which allows us to monitor expression changes of thousands of genes simultaneously, have been especially informative [3–6]. The analysis of differentially expressed genes under various stress conditions revealed that the early responses of different abiotic stresses overlap and engage the identical set of genes [7–9]. These ﬁndings not only suggest a general stress response common to many if not all stresses, but also hint at a common set of signal transduction components that trigger these responses. Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

200

| 10 Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress The early steps of a rather unspeciﬁc stress response can be divided into three general functions [9]: (i) the activation of general stress-responsive that will subsequently lead to the onset of a more speciﬁc stress response, (ii) genes related to growth and developmental processes are repressed, and (iii) a number of genes are induced whose functions are associated with chromatin remodeling. Although various stresses have intensively been studied with the help of microarray technology, experiments have been conducted either on one or on only a few stresses simultaneously. As there are no common standards on how to conduct expression proﬁle experiments and to evaluate gene expression data, a comparison of independent expression experiments is complicated and meaningful information difﬁcult to extract.

10.2 The AtGenExpress Abiotic Stress Experiment

To overcome the problem of incomparability between independent microarray experiments, a large-scale gene expression experiment with nine different stress conditions has been conducted bench-to-bench [9]. Coordinated by the Deutsche Forschungsgemeinschaft-funded Arabidopsis Functional Genomics Network (http://www.uni-tuebingen.de/plantphys/AFGN/atgenex.htm), this abiotic stress experiment is part of a large scale-project for genome-wide expression proﬁling of the Arabidopsis thaliana accession Col-0 (AtGenExpress) on the platform of the Affymetrix ATH1 gene chip microarray [9–11]. At the end of 2005, there were 41 different experimental core conditions completed and made publicly available, which constitute 1295 independent microarray hybridization events. The AtGenExpress abiotic stress experiment contains samples of nine abiotic environmental conditions comprising heat, cold, salt, high osmolarity, drought, ultraviolet (UV)-B light, and wounding as well as genotoxic and oxidative stress [9]. Root and shoot tissue samples were taken at 0 min, 30 min, 1 h, 3 h, 6 h, 12 h, and 24 h after the onset of the stress treatment (e.g., high osmolarity) or after stress application (e.g., wounding). Although the experiment appears simple in its biological setup, the AtGenExpress abiotic stress experiment constitutes the ﬁrst fourdimensional expression proﬁle dataset in plant science [12]. Here, the four dimensions are composed of a gene condition time tissue matrix. However, only preliminary steps have been taken in the analysis of expression datasets with three dimensions and suitable approaches for datasets of four or more dimensions are lacking [13–15]. While this experiment was performed in 2003–2004 and many publications have used it as a valuable source of information [6], a global survey taking all stress conditions into account has not yet been made. In this chapter we provide a full evaluation of all the nine stresses and give suggestions for suitable marker genes. Moreover, we have performed bioinformatic analyses to gain insights into the principal components underlying and governing the different responses of condition dependent expression changes. An

10.3 General Findings

overview of signaling components is completed by an analysis of cis-regulatory elements (CREs) that are enriched in the promoters of stress-responsive genes. Finally, some preliminary developments are presented that will allow the direct comparison of two or more expression proﬁle datasets.

10.3 General Findings

Generally, one can classify the stresses according to how the treatments were applied. Some of them were applied in a transient way, with stress treatment stopped after a certain time and plants returned to standard conditions for recovery. Such transient stresses were performed for drought, heat, and UV-B light stress. In contrast, other plants were left to endure stress conditions after the initial onset. These conditions did not allow recovery, but led to stress acclimation or to lethality, if the enduring conditions were nonpermissive and exceeded over the organisms pessimum. From the nine conditions, a permanent stress application for the entire experimental time (24 h) was conducted for cold, osmotic, salt, genotoxic, and oxidative stresses. The wounding stress was neither transient nor permanent. Here, the treatment was performed at one precise point in time and the experiment monitors the plant’s recovery process over 24 h. Before discussing the stresses independently, we would like to present some general ﬁndings on the nine stresses. A comparison of the overall number of genes whose expression responded to one or more of the treatments showed that the number of transcripts that increased in abundance exceeded the number of genes downregulated under the respective condition. The number of stress-responsive genes differs signiﬁcantly between the stresses (Table 10.1). While in some stresses thousands of genes are scored responsive, in others only a few hundred genes are found differentially expressed compared to the controls. Surprisingly, the number of responsive genes does not correlate with the duration of the treatment. For example, the cold and osmotic treatments evoke many transcriptional changes involving a lot of genes, while the genotoxic and oxidative stressed comprise only a few responsive genes in total – all of the stresses were applied permanently. Vice versa, drought, heat, and UV-B light stresses were conducted transiently. Their total number of genes differs in a condition and tissue dependent manner from a few in drought stress to several hundred in UV-B light and heat stress. A comparison of the genes expressed in the root and shoot tissue revealed different kinetics in their expression patterns (Figure 10.1). For cold, heat, and osmotic stress a constant increase in the number of expressed genes was found during stress application. This differs from the expression patterns observed during salt, UV-B light, wounding, and drought stress treatments, which exhibited transient alterations in the number of genes expressed over the 24-h experimental period. The expression kinetics for drought and wounding displayed biphasic

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| 10 Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress Table 10.1 Number of regulated genes.

Total

Up

Down

shoot root

3795 2376

2054 1387

2002 1034

shoot root

1750 4147

1006 2261

764 1919

shoot root

1299 603

1050 413

258 255

shoot root

4382 2751

2138 1437

2257 1335

shoot root

2242 2684

934 1156

1429 1596

shoot root

398 936

319 619

83 366

shoot root

213 315

198 196

17 144

shoot root

817 957

625 697

224 284

shoot root

3077 1184

1928 794

1191 515

Cold

Sum 4972

Salt

5148

Wound

1771

Osmotic

5842

Heat

3990

Genotoxic

1219

Oxidative

511

Drought

1562

UV-B

3792

expression trajectories, indicating that two consecutive physiological processes were monitored. As we could show, there is no correlation between the total number of regulated genes and the way the stress conditions are applied. However, it seems that the responses to some stresses are more similar to each other. For example, cold, drought, and osmotic stress responses share several responsive genes, which implies that coping with these stresses utilizes overlapping signaling cascades to integrate similar kinds of information. Interestingly, gene expression changes occur very rapidly after stress onset. Signiﬁcant changes in gene expression are already observed as early as 15 min after the start of the experiments, which can be explained by preformed signal transduction cascades (Figure 10.1). Some of the stresses were applied to only one of the tissues. For example, UV-B light irradiation affected only the shoot tissue, while salt and osmotic stresses were

203

Figure 10.1 Total number of differentially regulated genes. Number of up- (black bars) and downregulated (white bars) genes in the root or shoot tissue in response to the nine indicated stresses. The RNA samples for the experiment were taken at the indicated time points. Data processing was conducted as described earlier in Kilian et al. [9].

204

| 10 Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress applied exclusively to the roots. These stresses induced a fast response in gene expression in the stressed tissue, but also in the untreated organ. Functional categorization, using Gene Ontology (GO) terms [16], showed that for most stresses (with the exception of genotoxic and oxidative stress) the responding genes comprise signiﬁcantly more transcription factor genes than would be expected on average. This is indicative of the huge cellular reprogramming necessary to carry out the complex physiological acclimation processes.

10.4 The Nine Stresses

This section provides a closer insight into the individual stresses. As general descriptions and complete protocols associated with the AtGenExpress abiotic stress experiment are available elsewhere [9], we only provide a brief description of the treatments followed by a report on the most characteristic ﬁndings. 10.4.1 UV-B Light Stress

The plants were irradiated for 15 min with UV-B light that has a biological effective quantity of 1.18 W m2 using Philips TL40W/12 ﬂuorescent tubes. Previous expression studies have revealed that these conditions evoke both the photomorphogenic long-wavelength as well as the damaging high-energy shortwavelength UV-B light responses in Arabidopsis [17, 18]. After stress treatment, the plants were returned to standard conditions for recovery. As the UV-B light irradiation affected the shoots, it is not surprising that many more genes respond in the shoot tissue (3077) than in the roots (1184). In total, the transcript abundance of 2722 genes increased and that of 1706 decreased after stress application (Table 10.1). A comparison of UV-B light-responsive genes with the genes responding to the other stresses disclosed a substantial overlap, which is indicative of shared components involved in the integration of the signaling information transduced during and after stress treatment (Figure 10.2). Functional categorization, using GO terms [16], revealed that the responding genes comprise signiﬁcantly more transcription factor genes than could be expected on average (p r 109). Additionally, many UV-B light-responsive genes can be associated with responses to heat and kinase activity (both p r 109). Several genes could be identiﬁed as molecular markers for the UV-B light response. On the basis of scored signal intensities and on fold-difference in transcript abundance, we recommend a subtilase family protein (At1g32960), an ankyrin repeat family protein (At4g03450), a protein kinase (At4g04500), and WAK3 (WALL-ASSOCIATED KINASE3, At1g21240) as suitable UV-B light-speciﬁc marker genes (Table 10.2).

Figure 10.2 Analysis of overlap within the stresses. The number of regulated genes responding in more than only one stress treatment has been performed on the basis of gene list comparison. The genes that were found responsive in the initial stress (highlighted with a grey background) are compared for their common involvement in the other eight stresses. The number of up- (red bars) and downregulated (blue bars) genes is given. White bars represent the number of genes that are of alternating responsiveness and are up/ downregulated in a time-dependent manner. The total number of regulated genes for each of the other eight stresses is shown in gray. Accordingly, the proportional overlap with each list of responsive genes has been color coded.

206

| 10 Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress

Figure 10.2 (Continued)

10.4.2 Osmotic Stress

The plant roots were exposed to high osmolarity stress by incubation in MS (Murashige and Skoog) liquid media supplemented with mannitol to a ﬁnal concentration of 300 mM [4, 19]. The hyperosmotic environmental conditions were applied to the roots. Surprisingly, a total of 4382 genes are found differentially expressed in the untreated shoot tissue, whereas only 2751 responded in the treated roots (Table 10.1). About the same number of genes were shown to increase or decrease in their transcript abundance. The high number of genes upregulated in the shoot might be the consequence of the mannitol taken up by the roots and transported to the photosynthetic shoot, where it might interfere with sugar related primary metabolism [19–21]. A comparison of osmotic stress-responsive genes with the genes responding to the other stresses disclosed a considerable overlap (Figure 10.2). About 50% of the genes up- or downregulated during salt, cold or UV-B light stress were also up- or downregulated under high osmotic conditions. Moreover, the majority of drought, genotoxic, wounding, or oxidative stress-responsive genes overlap with the genes that respond to the osmotic stress treatment. Functional categorization, using GO terms [16], revealed that the high osmolarity-responsive genes comprise signiﬁcantly more transcription factor genes and

10.4 The Nine Stresses Table 10.2 Suitable stress-speciﬁc marker genes.

Stress

Affy ID

AGI

Name

Cold

254066_AT 254075_AT 247061_AT 248428_AT 248750_AT 257540_AT 264612_AT 265084_AT 253060_AT 245033_AT

At4g25480 At4g25470 At5g66780 At5g51760 At5g47530 At3g21520 At1g04560 At1g03790 At4g37710 At2g26380

255879_AT 245531_AT 265984_AT 249732_AT

At1g67000 At4g15100 At2g24210 At5g24420

245422_AT 245275_AT 254443_AT

At4g17470 At4g15210 At4g21070

246132_AT 257670_AT 266841_AT 256245_AT 248657_AT 248332_AT 261242_AT 259559_AT 255406_AT 255341_AT 263754_AT

At5g20850 At3g20340 At2g26150 At3g12580 At5g48570 At5g52640 At1g32960 At1g21240 At4g03450 At4g04500 At2g21510

261101_AT

At1g63030

DREB1a DREB1c expressed protein PP2C auxin-responsive dopamine b-monooxygenase unknown protein AWPM-19-like membrane family protein zinc ﬁnger (CCCH-type) family protein VQ motif-containing protein disease resistance protein-related/LRR proteinrelated protein kinase family protein serine carboxypeptidase S10 family protein TPS10 glucosamine/galactosamine-6-phosphate isomerase-related palmitoyl protein thioesterase family protein ATBETA-AMY C3HC4-type RING ﬁnger family protein/BRCT domain-containing protein DNA repair protein RAD51 expressed protein HSFA2 HSP70 ROF1 HSP81-1 subtilase family protein WAK3 ankyrin repeat family protein protein kinase family protein DNAJ heat shock N-terminal domaincontaining protein DDF2

Osmotic

Salt

Drought

Genotoxic

Oxidative Heat

UV-B

Wounding

genes involved in protein synthesis than could be expected on average (both p r 109). Additionally, many osmotic stress-responsive genes can be associated with processes in the chloroplast and with metabolism and membrane bound processes (all p r 109). Due to the huge overlap with genes responsive during the other stress treatments, the identiﬁcation of suitable molecular markers speciﬁc to the osmotic stress response was difﬁcult. Many of the well-expressed genes also responded under cold, drought, and salt stresses. None of the genes exhibited a speciﬁc response exclusively restricted to osmotic stress. The six genes we suggest as

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| 10 Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress suitable marker genes were also found to be responsive under salt stress treatment, but with delayed response kinetics. The genes are an auxin-responsive dopamine b-monooxygenase (At5g47530), a putative PP2C (PROTEIN PHOSPHATASE2C, At5g51760), an AWPM-19-like membrane family protein (At1g04560), an expressed protein (At5g66780), an unknown protein (At3g21520), and a zinc ﬁnger (CCCH-type) family protein (At1g03790) (Table 10.2). 10.4.3 Salt Stress

As in the osmotic stress treatment, the plant roots were exposed to high salinity by incubation in MS liquid media supplemented with NaCl to a ﬁnal concentration of 150 mM. The salt stress treatment is believed to comprise several overlapping stress response inducing agents, such as high osmolarity and perturbed ion homeostasis as well as sodium or chloride ion effects [4, 20, 22, 23]. High salinity conditions were applied to the roots, in which 4147 of the 5148 responsive genes were identiﬁed (Table 10.1). However, a similar number of genes were found increased and decreased in transcript abundance. A simple comparison of the total number of genes found to be differentially regulated in the shoots and roots during osmotic and salt stress revealed that both treatments evoke overlapping, but different stress-speciﬁc responses (Figure 10.1) [4, 20]. Both are high osmolarity stresses. Nevertheless, during osmotic stress 4382 genes were upregulated in the shoots – twice as many as found during salt stress in the same tissue (Table 10.1). Vice versa, 4147 genes were salt-stress induced in the roots compared to 2751 genes under osmotic stress. This might be explained by the effects of a perturbed ion homeostasis, which is not present during the mannitol treatment [4, 19]. Many of the genes found responsive under salt stress conditions also responded under all the other stresses (Figure 10.2). Interestingly, the overlap of responsive genes is higher for induced than for repressed genes. This implies unique signaling information under salt stress that results in the speciﬁc repression of genes, whereas genes with increased transcript abundance are of a more general function in the other stress as well. Remarkably, most of the genes responsive under oxidative stress conditions are also regulated under high salinity conditions. Functional categorization, using GO terms [16], revealed that the high salinityresponsive genes comprise signiﬁcantly more transcription factor, peroxidase and kinase activity genes than could be expected on average (all p r 109). Additionally, salt stress-responsive genes can be associated with defense responses, membrane bound processes, and auxin or abscisic acid (ABA) hormone response (all p r 109). The identiﬁcation of suitable molecular markers speciﬁc to the salt stress response was tricky as many of the well expressed genes also responded under cold, osmotic and drought stress. Due to this overlap, the genes we propose as suitable marker genes were also found responsive under drought stress. However,

10.4 The Nine Stresses

the transcript abundance of the genes we suggest as marker genes was 10-fold higher under salt stress than during drought treatment. We propose a VQ motifcontaining protein (At4g37710), a leucine-rich repeat (LRR) disease resistance protein (At2g26380), a protein kinase family protein (At1g67000), and a serine carboxypeptidase S10 family protein (At4g15100) as suitable high-salinity marker genes (Table 10.2). 10.4.4 Cold Stress

The plants were transferred to the 4 1C cool room and placed on ice to rapidly chill down the media. This procedure was found to be best to apply the cold stress treatment to both root and shoot tissues with similar cooling kinetics [9]. Additionally, the cool room that was used to perform the cold stress treatment decreased the ambient light regime to 60 mmol m2 s1. The cold stress response is characterized by a continuous increase in the number of regulated genes over time (Figure 10.1). The longer the plants were stressed the more genes were found responsive. In total, the transcript abundance of about 3441 genes increased, while 3036 decreased during cold stress treatment (Table 10.1). In general, the cold response has been found to be delayed in comparison to the other stresses, which is likely due to decelerated physiological and biophysical processes in the cell [4, 5, 9, 24]. A comparison of cold stress-responsive genes with the genes responding under other stresses revealed that all the other stresses share a considerable number of genes with the cold temperature treatment (Figure 10.2). For the heat stress treatment the overlap in genes is proportional to the number of responsive genes for up- or downregulated transcripts in cold stress. This is an exception, as the majority of upregulated genes were also upregulated under most of the other conditions. The same was found for genes that decreased in transcript abundance. Again, this is indicative of shared components involved in the integration of the signaling information transduced during and after the stresses. Functional categorization, using GO terms [16], revealed that the cold stressresponsive genes comprise signiﬁcantly more transcription factor genes and genes involved in protein phosphorylation than could be expected on average (both p r 109). Additionally, many cold stress-responsive genes can be associated with processes in the chloroplast and with membrane-bound processes (all p r 109). The identiﬁcation of suitable molecular markers speciﬁc for the cold stress response was difﬁcult, due to the considerable overlap with the genes responding to the other stresses [4, 6]. Most of the well expressed genes also responded under salt, osmotic and drought stress. On the basis of a very early response after the onset of the cold stress and on a 100-fold-difference in transcript abundance, we recommend the dehydration response element-binding proteins DREB1a (C-REPEAT/DEHYDRATION-RESPONSIVE ELEMENT BINDING FACTOR3, At4g25480) and DREB1c (At4g25470) as marker genes (Table 10.2). Additional

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| 10 Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress reasons for our decision in favor of these paralogous genes were that they have already been used as suitable marker genes and that physiological data placed both genes within the signaling cascades leading to cold temperature acclimation [25– 28]. DREB1a and DREB1c expression patterns might appear of only intermediate responsiveness under cold stress, when compared with their expression trajectories under the other stress conditions. However, keeping in mind that cold treatment slows down the physiological processes in the cell, both are rapidly upregulated marker genes.

10.4.5 Drought Stress

The plants were transferred to a clean bench, and both the shoot and roots were exposed to the stream of air. During a 15-min incubation time the plants lost about 10% of their fresh weight. After stress treatment, the plants were returned to standard conditions for recovery. The way the drought stress was performed also induced a wind-chill effect due to strong evaporation and transpiration that cooled down the plant body. The drought/dehydration stress response is characterized by a biphasic expression kinetic (Figure 10.1). Within the ﬁrst hour of stress treatment there was a rapid increase in the number of responsive genes, then their number decreased and, at later time points, increased again. We identiﬁed 1322 drought stressresponsive genes increased in transcript abundance and 508 genes decreased (Table 10.1). The total number of 1562 differentially expressed genes after drought stress conditions is much lower than for most of the other stresses (Figure 10.1). Nonetheless, about 70% of the genes increased or decreased in transcript abundance during the drought stress recovery process were also increased or decreased during cold and salt stress conditions (Figure 10.2). Additionally, a considerable overlap of the upregulated genes with responsive genes upregulated after wounding or cold treatments could be found. Functional categorization, using GO terms [16], revealed that the drought stress recovery process involves signiﬁcantly more transcription factor genes and genes associated with the endomembrane system than could be expected on average (both p r 109). Drought treatment-responsive genes can be physiologically linked with functions in the wounding response and responses after jasmonic acid or ABA hormone treatment (all p r 108). Due to the overlap with genes responsive during other stress treatments, suitable molecular markers exclusively responding to drought stress treatment could not be identiﬁed. Many of the well and more speciﬁcally expressed genes also responded under salt, cold, genotoxic, and wounding stress [6, 8, 27]. On the basis of expression kinetics and of fold-difference in transcript abundance, we suggest a myrcene/ocimene synthase (TPS10, At2g24210), a glucosamine/galactosamine6-phosphate isomerase-related protein (At5g24420), a palmitoyl protein

10.4 The Nine Stresses

thioesterase family protein (At4g17470), and ATBETA-AMY (b-AMYLASE, At4g15210) as the most suitable drought-responsive marker genes (Table 10.2). 10.4.6 Heat Stress

The plants were heat treated at 38 1C for 3 h in a Perspex glass incubator cabinet. This corresponds to a 14 1C elevation in temperature compared to standard growth conditions. Although the incubator cabinet was placed inside the standard growth chamber, one also has to take changes in the light regime and humidity during treatment into consideration. After 3 h of stress treatment, the plants were returned for the next 21 h to standard conditions for recovery. The heat stress response is characterized by a continuous increase in the number of regulated genes over the 3-h treatment time. During the recovery period the number of responsive genes decreased rapidly (Figure 10.1). There were 2242 and 1807 genes found regulated in the shoot and root tissue, respectively. About the same number of genes exhibited an increased (2090) or decreased (2128) transcript abundance (Table 10.1). A comparison of heat stress-responsive genes with the genes responding to the other stresses disclosed little overlap (Figure 10.2). However, it is noteworthy to mention that a considerable number of downregulated genes were also downregulated during the salt and osmotic stresses. Functional categorization, using GO terms [16], revealed that the heat stress involves signiﬁcantly more transcription factor genes and genes associated with endomembrane system than could be expected on average (both p r 109). It is not surprising that heat treatment-responsive genes can be physiologically linked with heat shock protein activity ( p r 109) and responses to heat ( p r 108). Several genes could be identiﬁed as possible molecular markers for heat stress response. On the basis of signal intensities and on fold-difference in transcript abundance, we propose HSFA2 (HEAT SHOCK FACTORA2, At2g26150), HSP70 (HEAT SHOCK PROTEIN70, At3g12580), a peptidyl–prolyl cis–trans isomerase (ROF1, At5g48570), and HSP81-1 (HEAT SHOCK PROTEIN81-1, At5g52640) as suitable heat stress marker genes [29–31] (Table 10.2). 10.4.7 Wounding Stress

The plant shoots were wounded by punctuation of the leaves with a custom made pin-tool consisting of 16 needles (about 2 needles cm2). Three consecutive applications pierced an average of 3.6 distinct holes per leaf [9]. Previous studies have revealed that these conditions evoked the fewest dehydration responses using transgenic marker genes lines [32]. After wounding, the plants were returned to standard growth conditions for recovery. As this treatment affected the shoot tissue, it was not surprising that 1299 of a total of 1771 genes responded in the shoots (Table 10.1). There were almost three

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| 10 Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress times as many genes induced after wounding (1463) compared to the number of genes that decreased in transcript abundance (513). The wounding stress response is characterized by a biphasic expression kinetic (Figure 10.1). Within the ﬁrst hour of stress treatment a rapid increase in the number of responsive genes was observed, then their number decreased and at later time points increased again. A comparison of genes responding to wounding with genes responding to other stresses revealed only a little overlap (Figure 10.2). Still, it is noteworthy that the overlap found is almost restricted to genes upregulated after the wounding treatment. Functional categorization, using GO terms [16], revealed that the wounding recovery process involves signiﬁcantly more transcription factor genes and genes associated with endomembrane system than could be expected on average (both p r 109). Additionally, many wounding-responsive genes can be associated with hydrolase activity and response to heat (both p r 109). Fortunately, there is no broad overlap with the other stress responses. Several genes could be identiﬁed as molecular markers for the wounding response. We recommend a DNAJ heat shock N-terminal domain-containing protein (At2g21510) and DDF2 (DWARF AND DELAYED FLOWERING2, At1g63030) as suitable wounding-speciﬁc marker genes (Table 10.2). 10.4.8 Genotoxic Stress

The plant roots were continuously exposed to genotoxic stress by incubation in MS liquid media supplemented with bleomycin and mitomycin C to ﬁnal concentrations of 1.5 and 22 mg/ml [33, 34]. The genotoxic stress response is characterized by an almost stable number of genes regulated in the root tissue and a continuously increasing number of regulated genes affecting the shoot (Figure 10.1). Interestingly, the transcript abundance of responsive genes increased almost exclusively in the shoot. An analysis of genotoxic stress-responsive genes with the genes responding to the other stresses showed only a little overlap (Figure 10.2). Only downregulated genes signiﬁcantly overlapped with downregulated genes responsive in osmotic, salt, and heat stress. Functional categorization, using GO terms [16], revealed that the genotoxic stress involves signiﬁcantly more genes associated with endomembrane system than could be expected on average (p r 109). Moreover, many genotoxic stressresponsive genes can be associated with peroxidase activity and response to oxidative stress (both p r 106). Due to the small overlap with the other stress responses, several genes could be identiﬁed as highly speciﬁc molecular markers for genotoxic stress. On the basis of signal intensities and on fold-difference in transcript abundance, we propose a C3HC4-type RING ﬁnger family protein/BRCT domain-containing protein (At4g21070) and the DNA repair protein RAD51 (At5g20850) are suitable and highly speciﬁc marker genes for genotoxic stress [33, 34] (Table 10.2).

10.5 Signal Integration

10.4.9 Oxidative Stress

The plant roots were continuously exposed to oxidative stress by incubation in MS liquid media supplemented with methyl viologen to a ﬁnal concentration of 10 mM [35, 36]. Compared with the other eight stresses of the AtGenExpress abiotic stress dataset, the oxidative stress response is characterized by the smallest number of responsive genes (Table 10.1). Only 315 genes responded in the root and 213 in the shoot tissue. The total number of upregulated transcripts was almost identical in shoots and roots (198 and 196, respectively). Moreover, the oxidative stress response in the roots follows a biphasic expression kinetic (Figure 10.1). A comparison of oxidative stress-responsive genes with the total number of genes responding in the other stresses revealed only a little overlap (Figure 10.2). Nonetheless, a considerable number of upregulated genes were in common with the genes upregulated during the other stresses. Moreover, most of the genes that were found most responsive during oxidative stress treatment were also responsive to the other stress treatments. This is interesting and might be indicative of the overlapping nature of oxidative stress and the production of ROS as signaling molecules in the other stresses [1, 37, 38]. Functional categorization, using GO terms [16], revealed that oxidative stress involves signiﬁcantly more genes associated with the endomembrane system than could be expected on average (p r 109). It is not surprising that many oxidative stress-responsive genes can be associated with peroxidase activity and response to oxidative stress (both p r 107). Although there was no signiﬁcant overlap in numbers with the other stresses, suitable marker genes were very difﬁcult to ﬁnd. For example, fold-differences were too similar and signal intensities too low to be followed with standard molecular methods. Finally, only one single gene represents a suitable and highly speciﬁc marker gene: We recommend the expressed protein (At3g20340) of unknown function as a molecular marker to trace the oxidative stress response [36] (Table 10.2).

10.5 Signal Integration

Previously, 59 genes have been identiﬁed to be responsive in most of the stresses as early response genes [9]. Of these, 21 represent transcriptional regulators comprising, for example, members of the ERF or MYB gene families. Some of them have already been identiﬁed to be early stress-responsive and involved in Ca2 þ signaling, such as AZF2, ZAT10, ZAT12, and ERF5 [22, 39–41]. We successfully used vector analysis (VA) [42] in our previous study on cold, drought, and UV-B light stress to show the speciﬁc but time-dependent expression changes of the 59 common stress-responsive genes [9]. As we consider these genes

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Figure 10.3 VA of 59 common stress-responsive genes in the shoot during heat, wounding and UV-B light stress. The VA of four distinct time points of the shoot data was performed on 59 common stress-responsive genes that had been identiﬁed in a previous analysis [9]. Displayed are the VA scatter plots for heat versus UV-B light stress (a), UV-B light stress versus wounding (b), and wounding versus heat stress (c). All procedures and calculations were conducted according to Kilian et al. [9] and Breitling et al. [42].

Figure 10.3 (Continued)

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| 10 Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress to constitute general stress integrators of the ﬁrst wave of early response signals, we extended our analysis and performed VA on heat, UV-B, and wounding stress (Figure 10.3). Together with the previous study, VA provides an insight into the highly dynamic processes governed by the common stress-responsive genes. Since VA enables only two-dimensional approaches, we have to present the stresses in pairs and follow the expression vectors of the genes at four consecutive points in time (0.5, 1, 3, and 12 h). The comparison of heat, UV-B light, and wounding stress indicates that, at early time points, UV-B light and wounding stresses dominated over the regulatory inﬂuence of heat stress. Moreover, the heat stress vector is recessive at all time points compared with the UV-B light stress. In contrast, the response vectors of the 59 genes are highly variable when heat and wounding stresses are compared. The variations over time might be explained by the biphasic expression characteristics of the wounding stress response (Figure 10.1). When expression vectors of UV-B light and wounding stress are compared, both were induced at the early 0.5-h time point, which is even more pronounced at 1 h. In contrast, the UV-B light stress dominates the regulatory inﬂuence of wounding stress at the 3-h time point. Although the expression vectors of most of the 59 responsive genes no longer exhibit regulatory inﬂuence at the 12-h time point, a minority of the genes is still upregulated in both UV-B light stress and wounding (Figure 10.3). So far, we have shown the partial overlap of responsive genes during different stresses, the importance of 59 genes as common stress integrators, and, by VA, that some of the stresses dominate over the regulatory inﬂuences of other stresses. To gain insight into the main regulatory components underlying and dominating the regulatory processes, we have applied a principal component analysis (PCA) to all 22 747 genes normalized to the controls and 118 conditions present in the dataset. PCA is a method that reduces data dimensionality and is used to search for underlying patterns that signiﬁcantly account for the variances observed in a dataset [9, 43]. We have used the information of the ﬁrst nine components that cover more than 66% of the entire variance (Figure 10.4), which corresponds to an area of 71 standard error of the variance’s mean. Compared with other expression proﬁle experiments, coverage of 66% variance from nine components is low [9– 11], which is indicative of high variability and speciﬁcity in gene expression in each of the abiotic stresses despite their overlap in responsive genes and shared signaling cascades. principal componentPC1 contributes to 27.71% to the entire variability and thereby dominates the dataset. PC2 and PC3 cover 7.90 and 7.60% of the experiment’s variance, respectively. Figure 10.4 displays the components variance of PC1 and PC2 plotted according to the experimental conditions. Two general observations can be made. (i) Several of the data points cluster together, indicating a high similarity in the transcriptional responses to the different stresses [9, 43]. Taking a closer look one can attribute these similarities to the immediate early responsive genes that overlap between the stress conditions. (ii) A bifurcate distribution pattern is seen, which can be explained by PC2 that splits the variance of PC1 into an upper and a lower part.

10.5 Signal Integration

A possible explanation for this dominating inﬂuence is that PC2 covers most of the variance seen between the two different tissues (i.e., roots and shoots). This assumption can be further supported in a comparison of PC2 with PC3. As can be seen in Figure 10.4, the conditions exhibit a tissue-speciﬁc distribution along PC2, with the shoots having positive and the roots negative values. PC1 and PC3 are representative of general and condition wise stress-speciﬁc patterning. Stress- and tissue-dependent expression trajectories can be seen, when all nine PCs are plotted according to all genes. We have already mentioned that PC1 and PC2 represent a part of the variability seen between the stress conditions. While the same expression trajectories for PC1 and PC2 are observed in the shoot tissue, there are reversal and anti-correlating (r ¼ 0.93) trajectories in the roots (Figure 10.4). Noteworthy are PC3, PC5, and PC6, which contribute the majority of variance from the mean during osmotic (PC3), cold (PC5), and heat (all three) stress responses. To gain further insight into how the stress information is integrated and transduced to form a concerted response, we have searched the promoters of the stress-responsive genes for CREs over the expected background average using Athena [44]. For reasons of clarity, we have focused our analysis on the 40 most frequently observed CREs only (Table 10.3), irrespective of their position or orientation [45]. Several DNA motifs already known for their involvement in stress signaling are identiﬁed in all of the stresses. Amongst the most frequently observed are the ACGT-core containing elements, which constitute basic leucine zipper domain binding sites [46]. Additionally, enriched in all datasets are MYB transcription factor binding elements and the conserved TTGACY consensus of the W-box [33, 47–49]. On the other hand, there are elements enriched that link the stress responses with developmental processes. For example, the T-box, the LEAFYATAG, the EveningElement, the CCA1 binding site, the ATHB2 binding site or the ARF binding site TGTCTC are important integrators of information derived from the circadian clock, auxin signaling or for homeotic transformation [45, 50, 51]. In addition to auxin as a phytohormone, small signaling molecules play an important role in the transduction of abiotic stress information. Hence, it was not surprising to ﬁnd the GCC-box and the ABRE binding sites signiﬁcantly enriched in the promoters of the differentially regulated genes of all stresses (Table 10.3). On the one hand, the GCC-box is known to be the important element for the transcriptional responses upon ethylene and methyl jasmonate – both essential phytohormones for the systemic transduction of stress information [32]. On the other hand, the ABRE binding site has been found involved in ABA signal transduction and the integration of cytoplasmic calcium signaling information [11, 22, 39], both of which are important second messengers during stress. While in most of the stresses the 40 CREs under investigation have been more frequent in the datasets than could be expected from their background frequencies, the ACGT-core containing motifs responsible for ABA and cytoplasmic calcium signal transduction are lacking in the promoters of genotoxic and oxidative stress-responsive genes (Table 10.3). This is interesting and implies that

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Figure 10.4 PCA was performed on 22747 genes (a) and 118 conditions (b). The nine components found constitute more than 66% of the experimental variance. All data has been normalized to the controls, which are not displayed. PCA on genes (a) displays the expression trajectories of indicated components during the nine stresses. PCA on conditions (b) displays the variances in expression for shoots (triangle) and roots (circle) of PC1 versus PC2 (bottom left) and PC3 versus PC2 (bottom right). It can be inferred from both scatter plots that PC2 comprises the variance in gene expression between shoot and root. All procedures and calculations were conducted according to Kilian et al. [9] and Schoelkopf et al. [43].

Figure 10.4 (Continued)

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| 10 Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress Table 10.3 CREs Enriched in the Promoters of Stress-Responsive Genes.

Motif

UV-B Osmotic Salt Cold Wound Drought Heat Genotoxic Oxidative

ABF binding site ABRE binding site ABREATRD22 ABRE-like ACGT ABRE motif AGCBOXNPGLB ARF binding site ATHB2 binding site AtMYB2BS in RD22 AtMYC2BS in RD22 Box II promoter motif CACGTG motif CARGCW8GAT CCA1 binding site DRE core motif DREB1a/CBF3 EveningElement GADOWNAT Gap-box GAREAT GBF1/2/3BS in ADH GBOXLERBCS GCC-box I-box L1-box LEAFYATAG LTRE MYB binding site MYB1 binding site MYB1AT MYB1LEPR MYB2AT MYB4 binding site MYCATERD1 RAV1 binding site RY repeat SV40 core promoter T-box W-box Z-box

Forty CREs have been found for at least one of the stresses signiﬁcantly enriched in the promoters of stress-responsive genes. Filled circles indicate those cis elements more frequent at p r 0.00 001 than their respective background frequencies. Open circles indicate cis elements that do not exhibit a signiﬁcant enrichment under high stringent scoring conditions.

10.7 Conclusions

signaling via ROS does not integrate on these CREs, but involves other signaling cascades [33, 38, 46].

10.6 Novel Approaches and Future Developments

We already pointed out that the AtGenExpress abiotic stress experiment constitutes the ﬁrst four-dimensional expression proﬁle dataset in plant science. The four dimensions are composed of a gene condition time tissue matrix. At present, there are only a few algorithms designed for the analysis of multidimensional expression datasets. We and others have undertaken only preliminary steps in the analysis of expression datasets with three dimensions [13–15]. Suitable approaches for datasets of four or more dimensions are lacking [12]. We successfully applied a split three-dimensional analysis on the four-dimensional abiotic stress experiment, by evaluating the two tissues independently [14, 15]. However, there are already experiments of a complex three dimensional composition that cannot be sufﬁciently analyzed with present day methods. These are still challenging tasks for the future, as costs for the microarrays are decreasing, and more and more scientists have access to the technology. Additionally, we have to develop methods for the direct comparison of two or more independent expression datasets conducted by different persons. Although simple data normalization makes us believe in the intercomparability of expression data and several databases exist that perform such experiments, there is enough evidence from bioinformatics showing that most of the informative and meaningful data is missed [14, 30, 52]. Existing tools and algorithms can properly evaluate on the expression trajectories with the strongest differences from the average expression, but consequently miss small expression signatures that are important and equally informative. We have shown that the more expression proﬁles are combined, the less different the gene expression gets, due to normalization processes [12]. This is an important observation as we can not add up several hundreds of experiments, because most of the information will be lost. However, one successful approach termed FARO has been made using the overlap between lists of differentially expressed genes as a measure of relative distance between the conditions and experiments [8]. This approach strictly depends on the ﬁlters used and does not necessarily maintain the experimental design of complex experiments.

10.7 Conclusions

To summarize our ﬁndings, we have shown that stress signal transduction relies on general stress response patterns that are shared between the stress conditions. Nevertheless, stress- and tissue-speciﬁc signal integration modules can be found accompanied by systemic responses in the untreated tissues. ROS might be of

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| 10 Insights into the Arabidopsis Abiotic Stress Response from the AtGenExpress general importance as second messenger molecules in all of the stresses. Changes in cytoplasmic calcium and the involvement of phytohormones appear to have a rather speciﬁc function in the stresses and follow time dependent trajectories. All AtGenExpress expression datasets provide an invaluable atlas of publicly available expression information. Its value, in particular of the abiotic stress dataset, for the scientiﬁc community is veriﬁed by broad acceptance and the many publications that make use of the experiment’s data. Moreover, the AtGenExpress abiotic stress dataset can serve as model experiment for bioinformatics investigations of a multidimensional expression proﬁle dataset. The AtGenExpress approach provides a good basis for introducing community standards in how to perform and evaluate microarray expression experiments.

Acknowledgments

We are grateful to Felicity de Courcy for proofreading the manuscript and the members to the AtGenExpress consortium for providing data. The work was supported by a Deutsche Forschungsgemeinschaft grant to K.H. (AFGN/HA2146/5).

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17 Ulm, R., Baumann, A., Oravecz, A., Mate, Z., Adam, E., Oakeley, E.J., Schafer, E., and Nagy, F. (2004) Genome-wide analysis of gene expression reveals function of the bZIP transcription factor HY5 in the UV-B response of Arabidopsis. Proc. Natl. Acad. Sci. USA, 101, 1397–1402. 18 Oravecz, A., Baumann, A., Mate, Z., Brzezinska, A., Molinier, J., Oakeley, E.J., Adam, E., Schafer, E., Nagy, F., and Ulm, R. (2006) CONSTITUTIVELY PHOTOMORPHOGENIC1 is required for the UV-B response in Arabidopsis. Plant Cell, 18, 1975–1990. 19 Stoop, J.M.H., Williamson, J.D., and Pharr, D.M. (1996) Mannitol metabolism in plants: a method for coping with stress. Trends Plant Sci., 1, 139–144. 20 Watanabe, S., Kojima, K., Ide, I., and Sasaki, S. (2000) Effects of saline and osmotic stress on proline and sugar accumulation in Populus euphratica in vitro. Plant Cell Tissue Organ Cult., 63, 199–206. 21 Prata, R., Williamson, J.D., Conkling, M.A., and Pharr, D.M. (1997) Sugar repression of mannitol dehydrogenase activity in celery cells. Plant Physiol., 114, 307–314. 22 Ranf, S., Wunnenberg, P., Lee, J., Becker, D., Dunkel, M., Hedrich, R., Scheel, D., and Dietrich, P. (2008) Loss of the vacuolar cation channel, AtTPC1, does not impair Ca2 þ signals induced by abiotic and biotic stresses. Plant J., 53, 287–299. 23 Jiang, Y. and Deyholos, M.K. (2006) Comprehensive transcriptional proﬁling of NaCl-stressed Arabidopsis roots reveals novel classes of responsive genes. BMC Plant Biol., 6, 25. 24 Sakamoto, H., Maruyama, K., Sakuma, Y., Meshi, T., Iwabuchi, M., Shinozaki, K., and Yamaguchi-Shinozaki, K. (2004) Arabidopsis Cys2/His2-type zinc-ﬁnger proteins function as transcription repressors under drought, cold, and highsalinity stress conditions. Plant Physiol., 136, 2734–2746. 25 Novillo, F., Alonso, J.M., Ecker, J.R., and Salinas, J. (2004) CBF2/DREB1C is a

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negative regulator of CBF1/DREB1B and CBF3/DREB1A expression and plays a central role in stress tolerance in Arabidopsis. Proc. Natl. Acad. Sci. USA, 101, 3985–3990. Novillo, F., Medina, J., and Salinas, J. (2007) Arabidopsis CBF1 and CBF3 have a different function than CBF2 in cold acclimation and deﬁne different gene classes in the CBF regulon. Proc. Natl. Acad. Sci. USA, 104, 21002–21007. Sakuma, Y., Maruyama, K., Osakabe, Y., Qin, F., Seki, M., Shinozaki, K., and Yamaguchi-Shinozaki, K. (2006) Functional analysis of an Arabidopsis transcription factor, DREB2A, involved in drought-responsive gene expression. Plant Cell, 18, 1292–1309. Zarka, D.G., Vogel, J.T., Cook, D., and Thomashow, M.F. (2003) Cold induction of Arabidopsis CBF genes involves multiple ICE (inducer of CBF expression) promoter elements and a cold-regulatory circuit that is desensitized by low temperature. Plant Physiol., 133, 910–918. Lohmann, C., Eggers-Schumacher, G., Wunderlich, M., and Schofﬂ, F. (2004) Two different heat shock transcription factors regulate immediate early expression of stress genes in Arabidopsis. Mol. Genet. Genomics, 271, 11–21. Larkindale, J. and Vierling, E. (2008) Core genome responses involved in acclimation to high temperature. Plant Physiol., 146, 748–761. Busch, W., Wunderlich, M., and Schofﬂ, F. (2005) Identiﬁcation of novel heat shock factor-dependent genes and biochemical pathways in Arabidopsis thaliana. Plant J., 41, 1–14. Rushton, P.J., Reinstadler, A., Lipka, V., Lippok, B., and Somssich, I.E. (2002) Synthetic plant promoters containing deﬁned regulatory elements provide novel insights into pathogen- and wound-induced signaling. Plant Cell, 14, 749–762. Chen, I.P., Haehnel, U., Altschmied, L., Schubert, I., and Puchta, H. (2003) The transcriptional response of Arabidopsis to genotoxic stress – a high-density colony array study (HDCA). Plant J., 35, 771–786.

34 Chen, I.P., Mannuss, A., Orel, N., Heitzeberg, F., and Puchta, H. (2008) A homolog of ScRAD5 is involved in DNA repair and homologous recombination in Arabidopsis. Plant Physiol., 146, 1786–1796. 35 Desikan, R., A.-H.-Mackerness, S., Hancock, J.T., and Neill, S.J. (2001) Regulation of the Arabidopsis transcriptome by oxidative stress. Plant Physiol., 127, 159–172. 36 Luhua, S., Ciftci-Yilmaz, S., Harper, J., Cushman, J., and Mittler, R. (2008) Enhanced tolerance to oxidative stress in transgenic Arabidopsis thaliana plants expressing proteins of unknown function. Plant Physiol., 148, 280–292. 37 Volkov, R.A., Panchuk, II., Mullineaux, P.M., and Schofﬂ, F. (2006) Heat stressinduced H2O2 is required for effective expression of heat shock genes in Arabidopsis. Plant Mol. Biol., 61, 733–746. 38 Rentel, M.C. and Knight, M.R. (2004) Oxidative stress-induced calcium signaling in Arabidopsis. Plant Physiol., 135, 1471–1479. 39 Kaplan, B., Davydov, O., Knight, H., Galon, Y., Knight, M.R., Fluhr, R., and Fromm, H. (2006) Rapid transcriptome changes induced by cytosolic Ca2 þ transients reveal ABRE-related sequences as Ca2 þ -responsive cis elements in Arabidopsis. Plant Cell, 18, 2733–2748. 40 Vanderbeld, B. and Snedden, W.A. (2007) Developmental and stimulusinduced expression patterns of Arabidopsis calmodulin-like genes CML37, CML38 and CML39. Plant Mol. Biol., 64, 683–697. 41 Molinier, J., Lechner, E., Dumbliauskas, E., and Genschik, P. (2008) Regulation and role of Arabidopsis CUL4-DDB1ADDB2 in maintaining genome integrity upon UV stress. PLoS Genet., 4, e1000093. 42 Breitling, R., Armengaud, P., and Amtmann, A. (2005) Vector analysis as a fast and easy method to compare gene expression responses between different experimental backgrounds. BMC Bioinformatics, 6, 181.

References 43 Scholkopf, B., Smola, A., and Muller, K.R. (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput., 10, 1299–1319. 44 O’Connor, T.R., Dyreson, C., and Wyrick, J.J. (2005) Athena: a resource for rapid visualization and systematic analysis of Arabidopsis promoter sequences. Bioinformatics, 21, 4411–4413. 45 Berendzen, K.W., Stuber, K., Harter, K., and Wanke, D. (2006) Cis-motifs upstream of the transcription and translation initiation sites are effectively revealed by their positional disequilibrium in eukaryote genomes using frequency distribution curves. BMC Bioinformatics, 7, 522. 46 Schutze, K., Harter, K., and Chaban, C. (2008) Post-translational regulation of plant bZIP factors. Trends Plant Sci., 13, 247–255. 47 Stracke, R., Werber, M., and Weisshaar, B. (2001) The R2R3-MYB gene family in Arabidopsis thaliana. Curr. Opin. Plant Biol., 4, 447–456. 48 Hartmann, U., Sagasser, M., Mehrtens, F., Stracke, R., and Weisshaar, B. (2005) Differential combinatorial interactions of cis-acting elements recognized by R2R3MYB, BZIP, and BHLH factors control light-responsive and tissue-speciﬁc

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11 Integrative Approaches to Elucidate and Analyze Protein Interaction and Signaling Networks Sergio de la Fuente van Bentem, Alberto de la Fuente, and Heribert Hirt

Abstract

The introduction of high-throughput techniques that generate large data sets has urged the development of systematic approaches to interpret these data and transform them into comprehensible models. The formulation of network models from global protein studies is essential to understand the functioning of organisms. Currently, several network concepts have emerged in the ﬁeld of proteomics. It is important to highlight the differences between these concepts, since different representations allow distinct insights into functional organization. One such concept is the Protein Interaction Network (PIN), which contains proteins as nodes and undirected edges representing binding events observed in large-scale protein–protein interaction studies. A second concept is the Protein Signaling Network (PSN), in which the nodes correspond to levels of post-translationally modiﬁed forms of proteins and directed edges to causal effects through post-translational modiﬁcation, such as phosphorylation. The plant ﬁeld has recently employed high-throughput methods such as mass spectrometry and protein chips to study protein–protein interactions and signaling pathways. Data from nonplant model systems have been used to predict the ﬁrst global protein interaction networks in plants. These data can be integrated with data collected by other large-scale approaches, such as metabolomics and transcriptomics. Such integrative approaches should aid in understanding the ﬂexibility of plant responses triggered by a frequently changing environment.

11.1 Introduction

Advanced mass spectrometry (MS)-based methods enable high-throughput identiﬁcation of protein and peptide sequences [1]. Researchers can identify up to Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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| 11 Integrative Approaches to Elucidate and Analyze Protein Interaction and Signaling Networks thousands of proteins isolated from gels or from gel-free samples. This has brought the analysis of interactions and post-translational modiﬁcations of proteins to a higher level. Tandem afﬁnity puriﬁcation (TAP) is an established technique to purify protein complexes. TAP technology has allowed the dissection of hundreds of protein complexes from yeast [2–4]. The TAP method enables the elucidation of native protein complexes (if not disturbed by the TAP tag itself) by pulling down a TAP-tagged bait protein from cell extracts and determining its copurifying partners by MS. Although studies toward identiﬁcation of plant protein complexes have remained small scale, this strategy recently enabled the puriﬁcation of a large number of Arabidopsis proteins [5]. Powerful alternatives to the TAP method for studying protein–protein interactions are protein chips. They consist of arrays of up to thousands of proteins individually spotted onto a carrier such as a glass slide. Protein chip experiments allow the quantitative assessment of protein interactions by applying prey proteins on the chip and measuring the binding afﬁnities to each of the bait proteins on the chip [6]. Protein arrays can also be used for many other purposes (e.g., discovery of protein kinase substrates, see Section 11.4.4). The major drawback is the lack of physiological context in this in vitro approach. Although high-throughput experimental techniques have greatly increased our knowledge, understanding the global organization of proteomes is still by far incomplete. A global view on the proteome is hampered by the complexity – there are tens of thousands of proteins and potentially hundreds of thousands of relations between them. Abstract representations of the proteome and the relationships are needed to be able to analyze and interpret such huge collections of data [7]. This chapter summarizes and discusses the current status of network formulation and analysis in the ﬁeld of proteomics. The goal of this chapter is to enlighten researchers that measure interactions between proteins with current experimental techniques with concepts from Complex Network Analysis (CNA), and to highlight the importance of formulating and analyzing networks. Therefore, we start out by introducing the basic concepts of protein networks and CNA – a quantitative framework to investigate large complex networks.

11.2 Protein Networks

11.2.1 Introduction to Protein Networks

Why networks? To understand living cells, it has become apparent that one must regard them as systems rather than a collection of individual molecules. The study of systems consisting of thousands of interacting molecular species is

11.2 Protein Networks

very complicated and simplifying abstractions are necessary. The abstraction of intracellular processes into networks is particularly fruitful [8, 9]. Networks provide a clear representation of complicated relationships between large numbers of elements, and are used in scientiﬁc disciplines as diverse as sociology, epidemiology, molecular biology, and physics. The network approach to complex systems has led to insights into the evolution of networks, and shed light on the interplay between structure and function. The main goal is to relate the structure, or ‘‘topology,’’ of networks to the biological function. Insights into the global topological organization of networks summarizing relationships between proteins will provide insights into functional organization of proteomes. Future advances might enable to understand complex diseases in terms of complex networks [10, 11]. We discuss two main network models. The ﬁrst is the Protein Interaction Network (PIN), which summarizes protein–protein binding events on a proteomewide scale. PINs constitute the ﬁrst network-oriented approach to proteomics resulting in a huge body of literature. The formulation of PINs opened doors to novel research and insights into large-scale organization and evolution that cannot simply be obtained without an explicit network perspective. We give an unambiguous deﬁnition for PINs. Experimental procedures to discover protein–protein binding interactions are reviewed and computational approaches for network ﬁnetuning using information from different data sources are discussed. The second network model we discuss is the Protein Signalling Network (PSN), in which the nodes correspond to levels of post-translationally modiﬁed forms of proteins and directed edges to causal effects through post-translational modiﬁcation, such as phosphorylation [7, 12]. We review several experimental approaches for high-throughput discovery of phosphorylation events and the formulation of PSNs. The application of tools from CNA to PSNs is not as extensive as to PINs, but this will change in the future as PSNs are more interesting than PINs in terms of intracellular information processing. Network models of the proteome enable the application of CNA, a quantitative framework to investigate large complex networks, which has already provided many insights into the functional organization of the proteome [7]. Although PINs and PSNs are both formulated as networks, the concepts represent widely different physical systems. Therefore caution should be taken when applying relevant topological analysis. We discuss recent literature formulating and analyzing such networks. 11.2.2 CNA

Biological systems are complex, with many components (genes, proteins, protein complexes, transcription factors, etc.) interacting and reciprocally regulating in an orchestrated way. At an abstract level we can simplify these systems and represent them as a collection of nodes, representing the interacting elements, connected by edges, representing the pair-wise interactions between the nodes.

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| 11 Integrative Approaches to Elucidate and Analyze Protein Interaction and Signaling Networks Nodes represent the system components, the variables, the actors. Nodes are graphically often depicted as small circles. Edges represent certain relationships, or interactions, between the nodes, and are sometimes called ‘‘connections,’’ or ‘‘links.’’ Depending on the nature of the interaction, the edges may be directed, distinguishing between a source (or regulator) and a target (or regulated), or undirected. A network with directed edges is called a directed network, while one with undirected edges an undirected network. Directed edges are often depicted as arrows starting in the source node and ending in the target node. Undirected edges are simply lines drawn between two nodes (or sometimes as edges with arrowhead at both nodes, see, e.g., Table 11.2). CNA provides a quantitative framework to understand the topological characteristics of networks. The goal is to relate such characteristics to functional and dynamical characteristics of the systems represented by the networks. In order to be able to apply such tools in proteomics we need reliable representations of relationships between proteins as networks. One such representation is the PIN.

11.3 PINs

PINs are networks in which the nodes represent proteins and undirected edges represent physical binding interactions between them [7]. Two proteins that were observed in an experiment to physically bind to each other will be connected by an undirected edge. PINs are sometimes referred to as ‘‘interactomes’’ [13, 14] to indicate that they are collections of interactions at a proteome-wide scale. PINs have been compiled for a wide variety of organisms from all kingdoms of life, from bacteria such as Escherichia coli [15] to the yeast Saccharomyces cerevisiae [16, 17], the fruit ﬂy Drosophila melanogaster [18, 19] and the worm Caenorhabditis elegans [20] to the primate Homo sapiens [21–23]. The most predominantly used techniques for PIN formulation are yeast two-hybrid (Y2H) and TAP strategies. The ﬁrst time a network was explicitly compiled from protein–protein interaction data was for yeast [24]. The networks obtained from TAP studies are different from the PINs as deﬁned above. This is due to the fact that the authors assume edges between the bait and any other protein that is copuriﬁed with it. This way, proteins within the same complex will be joined by edges, although this does not necessarily mean direct physical binding between them. It was shown that computational discovery of protein complexes from TAP-derived networks is more accurate than from Y2Hderived networks [4] by comparing predicted complexes to the ones present in the MIPS database hosted by the Munich Information Center for Protein Sequences. This is expected since the TAP-derived networks explicitly include information about protein complexes, through the additional indirect edges. While for this purpose TAP-derived networks are superior, investigations into the large-scale organization of the proteome requires networks that reﬂect precisely the ‘‘wiring’’ structure of physical binding (i.e., PINs such as deﬁned above), with only edges that correspond to direct physical binding.

11.3 PINs

Most PINs have been constructed from studies on yeast and animal systems [7], and plant PINs have remained scarce [25]. However, several recent studies have reported on PINs built on both predicted and experimental data, as discussed in Section 11.3.1 and 11.3.2.

11.3.1 Toward Global Arabidopsis PINs

The ﬁrst attempts toward global Arabidopsis thaliana PINs were published by Geisler-Lee et al. [26] (Figure 11.1) and Cui et al. [28]. Both PINs are computationally predicted. The PIN by Geisler-Lee et al. [26] is solely based on the idea of conservation of interaction (i.e., interologs) and conﬁdence in interaction is obtained through a conﬁdence score CV ¼ N E S, where N is the total number of datasets the interaction has been observed, E is the total number of distinct experimental techniques used to generate datasets in which the interaction has been observed and S is the number of different species (in the reference set consisting of yeast, worm, fruit ﬂy, and human) in which the interaction has been identiﬁed. The problem faced by the interolog method is that only interactions between proteins that are highly conserved between animals/yeast (where

Figure 11.1 An Arabidopsis PIN. The largest connected component of the Arabidopsis PINs by Geisler-Lee et al. [26] laid out using the force-directed algorithm in Cytoscape [27].

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| 11 Integrative Approaches to Elucidate and Analyze Protein Interaction and Signaling Networks the experimental data comes from) and plants can be predicted. It is interesting that a good correlation between subcellular localization and proteins that interact was observed. Proteins located in the Golgi, however, were found to be more likely interacting with Golgi, vacuolar, or endoplasmic reticulum proteins, indicating potential trafﬁcking events between these compartments [26]. While the conﬁdence score employed by Geisler-Lee et al. [26] is attractive as it is easy to interpret, it is rather ad hoc. Cui et al. [28] employed a more formally justiﬁed approach. Their PIN was obtained by employing a ‘‘naı¨ve Bayesian approach’’ to integrate many sources of information, such as Gene Ontology, gene coexpression, genomic location as well as the conservation of interacting proteins, providing a more formally motivated conﬁdence score. Given the different means of obtaining the networks it is of interest to see how similar/dissimilar they are. At the same time we continue the introduction to CNA along these interesting examples. The two large PINs were obtained from the web: (i) the PIN by Geisler-Lee et al. [26] was provided as supplementary information to the publication and (ii) the PIN by Cui et al. [28] was obtained from the AtPID database in July 2008. We will refer to the PINs by Geisler-Lee et al. [26] and Cui et al. [28] as PIN_GL and PIN_C, respectively. The networks are very different in size: PIN_GL contains 3884 different proteins, while PIN_C contains many more 11 706. Interestingly, although the number of nodes is very different, the number of interactions is relatively similar, PIN_GL 20 677 edges versus PIN_C 24 418, indicating that PIN_C is much sparser than PIN_GL (‘‘sparseness’’ refers to the number of edges in the network relative to the total number of possible edges: both PIN_GL and PIN_C are thus extremely sparse, but PIN_C is sparser). PIN_GL contains 698 self-interactions, while no such interactions are present in PIN_C. The simplest way of comparing networks is by matching the pairs in both networks (i.e., ﬁnding out how much overlap there is in interacting pairs). Employing the ‘‘Merge Networks plug-in’’ in Cytoscape [27] the overlap (intersection) between the networks was obtained. The overlap is strikingly low. Only 2603 of the proteins occur in both networks and 3052 interacting pairs are in common. Of these proteins, only 1377 have at least one common link in both networks. We further compare some general topological properties as listed in Table 11.1 using the online tool tYNA [29]. Again, very different properties are observed in both networks. As can be seen from the number of connected components, the networks are quite fragmented, especially PIN_C. Each connected component is a subnetwork completely disconnected from other components, that is, there are no edges between nodes in one component with nodes in other components. Each of the components may correspond to a multiprotein complex that does not interact with any of the other proteins or, what most likely is the case, these are disconnected because the networks are incomplete, due the inability of the algorithms to predict certain edges. The largest connected component in PIN_GL contains 3664 nodes and 19 855 edges. The other 128 components are much smaller: the second largest has

11.3 PINs

19 nodes; the third largest seven and all other contain four or less nodes. The largest connected component in PIN_C contains 5506 nodes and 18 264 edges. The other 2127 components are much smaller: the second largest has 78 nodes, then six components between 50 and 20, then 722 components with 20 to three nodes, and 1399 components of only two nodes. Needless to say, many of the properties that we will discuss below are determined largely by the largest connected component. Nevertheless, all calculations were repeated for the largest components alone, but because the results were qualitatively the same, we report only the analysis of the entire networks. The clustering coefﬁcient is a measure of the network’s ‘‘cohesiveness,’’ indicating how densely connected the neighborhood of a given node is. The clustering coefﬁcient [30] of node i is deﬁned as the ratio between the number of edges of nodes adjacent to i and the total possible number of edges among them. It quantiﬁes how close is the neighborhood of node i is to a clique (a fully connected network, i.e., all nodes are connected to all other nodes). To use a sociological metaphor, it quantiﬁes how well for each node the statement ‘‘my friends are also friends of each other’’ applies. As seen in Table 11.1 the average clustering coefﬁcient (simply by taking the average over all nodes) of PIN_C is higher than PIN_GL. This is surprising since PIN_C is much sparser than PIN_GL. Nevertheless, PIN_C seems to be more cohesive. The shortest path length between two nodes is deﬁned as the minimum number of edges that must be crossed in order to reach one node from the other. The largest of all shortest paths between any pair of nodes is deﬁned as the network diameter. The diameter and the average shortest path length then provide an idea of how close all nodes are in the network. Distances between proteins in PIN_C are on average about twice as those in PIN_GL. Also the diameter is doubled (see Table 11.1). The degree of a node is simply the number of edges attached to it. By averaging over all the nodes in the network it is immediate to get the average degree. The maximum degree is deﬁned as the maximum value of all node degrees. Both average and maximum degree in PIN_GL are about three times larger than in PIN_C (Table 11.1). The degree distribution gives the probability distribution of degrees in a network. This distribution can be estimated for a given network by calculating the relative frequencies of each degree value in the network (how many nodes have degree ¼ 1, how many nodes have degree ¼ 2, etc., divided by the total number of nodes). Several authors have shown that the degree distributions of most PINs are well ﬁt by a ‘‘power law,’’ P(k) B ka, with a typically between 1 and 3, indicating these are ‘‘scale-free networks’’ in which most proteins have a small number of neighbors while a small number of proteins are ‘‘hubs’’; they have a large number of neighbors [31–34]. Others have found a slightly faster decaying tail, that is, a power law with exponential cutoff [35, 36], which shows fewer and smaller hubs than in case of a pure power law. Whether PINs are truly scale-free is thus not clear. In addition, there is currently a hot discussion about the interpretation of the power law observed in the degree distribution of most of the real-world data. The

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Node counts

3884 11706

Property

PIN_GL PIN_C

20677 24418

Edge count

129 2128

Connected components

10.47 4.17

14.96 6.03

172 56

0.12 0.39

0.21 0.43

SD

1.00 1.00

Maximum

Average

Maximum

Average

SD

Clustering coefﬁcients

Degrees

Table 11.1 Standard topological indices calculated for the two Arabidopsis PINs.

4.06 8.27

Average

1.16 2.71

SD

Shortest path lengths

13 26

Maximum

11.3 PINs

point in discussion is that real-world data are noisy and inaccurate (particularly for the higher degree), incomplete, and data are ‘‘sampled’’ from a potentially much wider network. To assess the validity of the power law ﬁndings, some authors demonstrated that sampling from a scale-free network can result in a non-scalefree network [37]. More importantly, it was shown that a power-law tail can be observed in networks obtained by sampling from networks having degree distributions very distinct from power laws [31]! More precisely, these authors generated four theoretical interaction networks with quite different topologies (random graph, exponential, power law, and truncated normal). A partial sampling of these networks resulted in subnetworks with topological characteristics that were virtually indistinguishable from those of current (partial) PINs. Their conclusion was that, with the current limited coverage levels, the observed scalefree topology of existing PINs cannot be conﬁdently extrapolated to complete PINs. Still, they pointed out that it is more likely that the current results are due to the fact that complete PINs are truly scale-free rather than having other degree distributions (see also [34]). The scale-free distribution is not as sensitive to false positives (erroneous links) in the network as they are to false negatives (missing links) [38]. Purely scale-free or not, the fact is that there are hubs with many more edges than the average degree. It has been computationally shown that networks with scale-free degree distributions are more robust toward random node removal than networks with homogeneous degree distributions and more sensitive to targeted attacks of the high-degree nodes [39]. This observation provides a link between network topology and the phenomenon of robustness of biological systems. This then suggests that highly connected nodes in PINs are more important than lowly connected nodes. Indeed, Jeong et al. showed a positive correlation, though not very large, between node degree and lethality in yeast PIN obtained mostly by Y2H experiments [36]. Knockout mutants missing a gene coding for a high-degree protein were lethal with higher probability than low-degree protein knockout mutants indicating that hubs indeed play important physiological roles. Figure 11.2 shows the degree distributions of both PINs for Arabidopsis. Both degree distributions that are well ﬁt by a power law (R2 for goodness of ﬁt is comparable to that observed for PINs from other organisms [31]): these networks too, appear to be ‘‘scale-free.’’ Many real-world networks also show a clustering coefﬁcient versus degree relationship with power law tails, C(k) B kb, suggesting that lower degree node neighborhoods are highly cohesive, while highly connected nodes have sparse neighborhoods; nodes with fewer edges tend to have higher clustering coefﬁcients than nodes with high degree [40]. Such networks are said to have a hierarchical topology, because the networks are built up from tightly connected regions, kept together by nodes with higher degree, forming less dense regions, which in turn are kept together by nodes with higher degree, and so on. Using the webtool visANT [41] we calculated the average clustering coefﬁcient at each degree. Plotting the average clustering coefﬁcients at each degree for the PINs under consideration shows that PIN_C does not have any hierarchical structure (in the above

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Figure 11.2 Degree distributions of the two Arabidopsis PINs. Circles: PIN_GL, squares: PIN_C. k stands for the number of connections and P(k) for the probability of ﬁnding a node having k number of connections. P(k) is estimated by the relative frequency observed in the networks. Lines show power-law ﬁts with accompanying equations and goodness of ﬁt index R2.

Figure 11.3 Clustering versus degree plot of the two Arabidopsis PINs. Circles: PIN_GL, squares: PIN_C. k stands for the number of connections and C(k) for the average clustering coefﬁcient of the nodes that have k number of connections. Lines show power-law ﬁts with accompanying equations and goodness of ﬁt index R2.

sense: its clustering coefﬁcient versus degree relationship is ﬂat) while there is a small but signiﬁcant negative slope for PIN_GL (Figure 11.3). Network motifs are small subgraphs that are over-represented in a network when compared to a null model [42]. The null model could, for instance, be a random graph or, arguably better, a randomized version of the same network keeping the degree distribution intact. Some authors believe that motifs are the ‘‘atomic’’

11.3 PINs

constituents of networks with speciﬁc functional properties. Motifs and similar local connection patterns can be used to classify and compare different networks [43, 44]. Motifs can be identiﬁed in directed as well as undirected networks. Obviously, there are many more directed subgraphs than undirected. For example, there are 13 unique directed three-node motifs, while there are just two undirected (see Table 11.2, ﬁrst two rows). For a yeast PIN, Wuchty et al. [45] showed that speciﬁc subgraphs contain more conserved proteins than expected by chance. They identiﬁed highly conserved proteins by using InParanoid, a database of orthologs [46], taking into account conservation of the yeast proteins with orthologs in the ﬁve eukaryotes H. sapiens, Arabidopsis, C. elegans, Mus musculus, and D. melanogaster. This conservation can be considered as suggestive evidence of the functional biological role of these small subgraphs, since evolution preserves modules with speciﬁc biological function [47]. The frequencies and signiﬁcance of small subgraph occurrence was compared between the two PINs under investigation. We employed the fast software FANMOD to count the number of three-node and four-node subgraphs in PIN_GL and PIN_C as well as in ensembles of 1000 randomized versions of both networks in order to demonstrate how different the counts are from those expected by chance in networks with the same number of nodes and edges, and the same degree distributions. The Z-score is used to show the signiﬁcance. It can be clearly seen that by chance in networks of these sizes and degree distributions almost all threenode subgraphs are expected to be of the ‘‘linear’’ type (Table 11.2, ﬁrst row) and only very few instances of completely connected triplets (Table 11.2, ﬁrst row). However, in both PIN_C and PIN_GL the frequencies of the clique motifs are rather high, especially in PIN_C (the fully connected four-node motif was not encountered once in the 1000 randomized networks, while it makes up about 21% of the motifs in PIN_C). In both networks the distribution of the motifs is completely different than expected by chance (as evidenced by the extremely high Z-scores). The extremely high occurrence of the cliques in PIN_C may be due to the incorporation of gene coexpression information in the algorithm to predict edges in PIN. In coexpression networks many such cliques are usually present. Nevertheless, even in PIN_GL, which was created without such information, the occurrence of the clique is very high. While both networks have been ‘‘evaluated’’ by their creators using Gene Ontology information, topological analysis could be another way of validation. Computationally predicted PINs should display properties that are widely observed in PINs derived on low or high throughput experiments (or stated differently: the computationally predicted PINs should conserve those properties of the PINs from other organisms that are used to make the predictions). We showed here that both PINs are very different, both in terms of direct matching of pairs as well as largescale topological properties. The only strong similarity is the degree distribution that in both cases resembles a power law. Standard topological indices are very different. Also the distribution of subgraphs is different, though in the same directions. A very important difference is the fact that PIN_C does not have any

| 237

238

17818

155

175.39

0.00063781 7 2.46 105

56.32 7 0.0015884

43.608 7 0.0016004

43.757

31.701

15.538

1

1

1

17818

99.999 7 2.46 105

56.243

1

Z-score

Frequency randomized PIN_C (%)

Frequency PIN_C (%)

Subgraph

34.284

53.804

5.1295

94.87

Frequency PIN_GL (%)

38.53 7 0.0016976

60.637 7 0.0011251

0.007304 7 2.9 104

99.993 7 2.9 104

Frequency randomized PIN_GL (%)

ensembles of 1000 randomized networks were created. The Z-score provides the number of standard deviations that the results found in PIN_GL and PIN_C are deviated from the mean of the randomized ensemble.

Table 11.2 Motif analysis of the two Arabidopsis PINs. To obtain a distribution of ‘‘expected by chance’’ values of frequencies,

25.013

60.738

176.04

176.04

Z-Score

239

6.9227

21.133

1

24.01

0.69494

1

1

1

249.97

2767.6

1

1

0.069856 7 2.5006 105

0.0021431 7 8.6745 105

3.239 106 7 2.0431 1073

070

0.21029

1.0864

10.073

0.54237

246.89

6246.1

7.17 106 7 3.3667 107

39.353

2.9047

0.0025142 7 4.3902 105

0.16479 7 0.0025179

0.66558 7 0.0004218

240

| 11 Integrative Approaches to Elucidate and Analyze Protein Interaction and Signaling Networks hierarchical structure whatsoever, while PIN_GL does. Based on the results of the topological analysis we would favor PIN_GL over PIN_C, in spite of the use of a more formal method to obtain PIN_C. This section also emphasizes one of the pitfalls of CNA: different networks, that are supposedly models of the same system, could have very different topological organization. Caution has to be taken before drawing conclusions, as the networks under study are usually largely incomplete as well as contain many erroneous edges. 11.3.2 An Arabidopsis PIN of Calmodulin- and Calmodulin-Like-Binding Proteins

Protein chips recently identiﬁed large numbers of calmodulin (CAM)- and CAMlike (calmodulin-likeCML)-binding proteins [5]. Interactions of several CAM and CML proteins with 1133 recombinant Arabidopsis protein preparations (about 80% successful puriﬁcations of full-length proteins) were tested. Instead of using a bacterial or yeast expression system, all were puriﬁed as TAP-tagged proteins expressed in Nicotiana benthamiana leaves. The chips were individually probed with three CAMs and four CMLs, and identiﬁed 77–122 targets per CAM/CML, adding up to 173 in total for all seven CAMs/CMLs. Among the main CAM/CMLbinding proteins are transcription factors and protein kinases such as receptor-like kinases and calcium-dependent protein kinases. The ﬁrst successful puriﬁcation of a large number of proteins from plants by the TAP method has revealed many novel connections between CAMs/CMLs and their binding partners. Moreover, it cleared the way to dissect in vivo plant protein complexes. Both the TAP puriﬁcation protocol and the protein chips will provide a rich source for upcoming studies on protein interactions/modiﬁcations and protein complexes.

11.4 PSNs

11.4.1 Introduction to PSNs

Although large-scale high-throughput experimental techniques have greatly increased our knowledge, our understanding of signal processing by cells is still by far incomplete. Multiple post-translational modiﬁcations can transform each protein in the proteome into a dynamic and multifunctional unit [48]. Most studies on signaling networks have focused on one particular post-translational modiﬁcation to decrease complexity. Evidently, combination of data sets from different large-scale approaches will enhance construction of entire signaling networks. Molecular networks have been constructed based on physical and functional interactions [49–51]. Large-scale analysis revealed signaling events that underlie

11.4 PSNs

apoptosis on a systems level [52]. Signal transduction pathways can be modeled at different levels of detail [53, 54] ranging from detailed mathematical models to graphical representations. Several mathematical models based on Ordinary Differential Equations have been formulated and their parameters optimized in order to ﬁt experimental observations [55–58]. While studies with such models provide many insights into the dynamics and function of signal transduction pathways, formulating such detailed models is a difﬁcult problem requiring a huge amount of experimental data, which is not commonly available, certainly not at a proteome-wide scale. The ﬁrst requirement of such a modeling approach is the knowledge of the pathway structure – which are the targets of kinases, phosphatases, and so on, and which reactions are involved. Inferring interaction structure at the proteome wide scale requires an abstraction of signal transduction pathways into PSNs. PSNs are networks in which the nodes correspond to levels of post-translationally modiﬁed states of proteins and directed edges to causal effects, indicating that the post-translationally modiﬁed state of one protein changes the post-translationally modiﬁed state of another [7, 12]. Nodes thus represent quantitative variables, that is, concentrations of the post-translationally modiﬁed states. A wide variety of post-translational modiﬁcations have been discovered of which phosphorylation is the most studied one [59]. Source nodes in PSNs will often be kinases with activating edges pointing out of them, but note that phosphatases (which reduce the level of the phosphorylated state of proteins) could be presented by inhibiting nodes. In PSNs no reactions appear like in the classical diagrams depicting signal transduction pathways. The networks described below almost exclusively involve protein phosphorylation. An even greater task is to unravel the function of each phosphosite in the phosphoproteome. Clever tricks are needed to facilitate the analysis of individual sites in a high throughput manner. One possible way is to regard sites as nodes in a signaling pathway and identify the most important regulatory sites in these pathways. Quantitative phosphoproteomic analysis followed by partial leastsquares regression analysis has allowed screening for key phosphosites in a signal transduction pathway [60]. Ultimately, all post-translational modiﬁcations will be included in PSNs as complete models for information processing by proteomes [7, 12]. 11.4.2 From Perturbations and Responses to PSNs

As PSNs are directed networks dealing with information ﬂow, inferring such networks requires an experimental setup involving targeted ‘‘perturbations’’ (interventions, disturbances) to the system under consideration. The simple train of thought behind this approach is that when a systems’ component is perturbed (e.g., the activity of a kinase is reduced by adding a speciﬁc inhibitor) the downstream effects of the kinase will respond to this perturbation (i.e., their phosphorylation states change with respect to the unperturbed situation). In this way

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| 11 Integrative Approaches to Elucidate and Analyze Protein Interaction and Signaling Networks the primary cause–effect relations can be established, but in a next step one has to distinguish between direct and indirect effects [61] as the directed edges in PSNs only represent ‘‘direct’’ causal effects (direct in the sense that the effects are not mediated by any of the other proteins that are explicitly considered in the PSN, but may be indirect through unobserved proteins, i.e., nodes that are ‘‘hidden’’ to the formulation of the PSN). Two recent studies outline how PSNs can be obtained in vivo through quantitative experimentation and perturbation analysis. Santos et al. [62] gave a proof of principle on a small network of three interacting human mitogen-activated protein kinases (MAPKs) (MAPK kinase kinase, MAPK kinase and MAPK). These authors employed a perturbation strategy initially proposed to infer the structure of Gene Networks [63, 64] and later adapted for signaling networks [65, 66]. Perturbing the concentration of each of the kinases by RNA interference and measuring the response of the other kinases enabled them to resolve the interaction structure using a linear algebra approach [63–66]. Interestingly, they could show that the network structure differed upon stimulation by different hormones. Sachs et al. [67] studied a signaling network of 11 proteins. In their approach the systems’ components are speciﬁcally perturbed and responses are measured in a large number of replicates (each replicate about 700–900 times) on a singlecell level [67]. Then Bayesian Networks are employed to identify the best network model ﬁtting all perturbation data. Comparing the inferred network to the known pathway it was concluded that the inference was highly reliable. The approach was unable to detect the feedback loops owing to the inability of Bayesian Networks to discover cyclic dependencies. This is a severe limitation of Bayesian Networks, as PSNs (and biological networks in general) contain many feedbacks (the presence of feedback in biological networks is arguably the reason that organisms are alive!). 11.4.3 High-Throughput Approaches to Create Perturbations and to Measure Responses

11.4.3.1 Quantitative Proteomics to Study the Effect of Stresses on Plant Proteomes Creating speciﬁc perturbations is a difﬁcult task. Pathway information could also be extracted using more general perturbations, of for instance whole pathways instead of single components of such pathways. Several groups have looked for proteins that change in abundance when a plant (cell) encounters stress [68–77]. These approaches were mainly gel-based approaches that identify regulated proteins by isolating proteins from the gel and sequencing them by MS. Jones et al. [72] have performed an iTRAQ-based, gel-free MS approach to search for changes in the Arabidopsis proteome upon infection with the pathogenic bacterium Pseudomonas syringae. Although the number of identiﬁed changes was low, this type of analysis is expected to be optimized and used for large-scale quantitative

11.4 PSNs

proteomics in the near future. Concluding, proteomic approaches can be undertaken to put proteins in PSNs. 11.4.3.2 Chemical Genetics to Identify Components of Trafﬁcking and Signaling Pathways An alternative approach to classical genetics to study plant processes is chemical genomics/genetics [78]. Chemical genomics screens have identiﬁed compounds that mark vesicular trafﬁcking and auxin signaling pathways [79–81]. Christian et al. [82] have identiﬁed novel chemicals that functionally mimic auxin. Importantly, chemical genetics can reveal novel functions for genes studied by other methods such as knockout or overexpression [83]. Small molecules can inhibit different protein family members, thereby circumventing problems such as embryo lethality or redundancy, which are encountered by a traditional genetic approach. However, in spite of some success [84], it remains difﬁcult to identify the protein component(s) that are targeted by the bioactive molecules. To identify the component by a chemical genetic screen for insensitivity/hypersensitivity will suffer from redundancy, as with traditional genetic screens. However, the chemical genetic screen does not suffer from embryo lethality. Moreover, afﬁnity puriﬁcation with immobilized molecules can identify the actual target(s) [85]. The growing availability of chemicals for screens makes this an attractive method to tackle plant pathways and molecular networks. Chemical genetics can elucidate the function of single protein kinases by introducing a speciﬁc mutation that can be used to speciﬁcally inhibit the mutant kinase. The mutation of a single residue in the ATP-binding pocket, termed the gatekeeper, has been shown to control sensitivity to a wide range of small-molecule inhibitors [86]. An ATP analog that is only bound by the mutant kinase and no wild-type kinase can reveal its true function, since no prior compensatory mechanism is activated as during gene deletion or overexpression. The mutant kinase can be effectively inactivated both rapidly and transiently, uncovering novel functions of protein kinase activities that are impossible to detect using kinasedepleted cells [87]. However, some kinases are intolerant to this mutation, but other mutations have been identiﬁed that should facilitate chemical genetic analysis of the majority of protein kinases [88]. 11.4.4 A NetworKIN Approach to Construct Plant Phosphorylation Networks

Protein and peptide chips have not only been used to obtain PINs (see Section 11.3), but also to identify plant protein kinase targets [89–92]. Although this is an in vitro method, data obtained from such approaches can be superimposed with in vivo phosphorylation sites obtained through MS approaches. Together with kinase motifs (i.e., the motif that each kinase targets) identiﬁed by peptide arrays [93, 94], potential kinase networks can be built. A relatively rapid and easy method recently described can be used to test whether phosphorylation events are regulated by stress [95].

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| 11 Integrative Approaches to Elucidate and Analyze Protein Interaction and Signaling Networks Data sets from quantitative phosphoproteomic approaches are crucial to put phosphorylation events in speciﬁc or general signaling pathways. Different MSbased methods exist to monitor phosphorylation sites during stress treatments [96, 97]. Several different approaches for quantitative MS have revealed phosphorylation sites regulated by environmental cues [98–101]. High-throughput techniques like protein and peptide arrays will identify in vitro kinase motifs and kinase targets. As this data, however, is insufﬁcient to predict in vivo kinase-substrate connections, more information is needed such as knowledge on protein–protein interactions and subcellular localization. This so-called ‘‘contextual information’’ is crucial to obtain in vivo kinase–substrate connections, as shown in the recently established networKIN approach that with quite high conﬁdentiality (60%) predicts these connections [102]. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) algorithm [103] is a rich source to provide such contextual information [102]. For instance, genes that act in the same pathway are often coexpressed [104]. Among the STRING parameters are gene coexpression data and physical protein–protein interactions. Different approaches might constitute a NetworKIN-like approach toward plant phosphorylation networks (Figure 11.4). The goal is to link all in vivo

Figure 11.4 A NetworKIN-like approach toward plant PSNs. Potential inputs to obtain a plant kinase network according to an approach based on NetworKIN [102]. The application of protein chips (to identify in vitro substrates), peptide arrays (kinase motifs), global MS (in vivo sites), STRING (contextual information like gene coexpression), quantitative MS (signaling pathway), and MS analysis of knockouts/ overexpressors/chemical genetic kinase mutants (in vivo kinase substrate candidates) will all aid in generation of a plant kinase signaling network. In the center, a example of a PSN is shown, which was drawn in Cytoscape [27].

References

phosphorylation sites identiﬁed by MS to the responsible kinases. Arabidopsis PINs are therefore crucial components of a plant NetworKIN-like approach. As it is absent in STRING, information on subcellular localization of plant proteins [105] could aid the prediction tool. However, substrates can trafﬁc to another compartment upon phosphorylation and therefore appear to localize differently. In addition, it has been very difﬁcult to overlap the subcellular location of kinases with that of their substrates [102]. Functional data can be obtained by analysis (e.g., molecular and phenotypic) analysis of kinase knockout, overexpressor lines, and lines that express a kinase mutant that can be inhibited by small molecules (see Section 11.4.3.2). Integrating all data from these sources will eventually enable a NetworKIN-like approach in plants to predict in vivo kinase–substrate connections (Figure 11.4) and in which biological processes they act. 11.5 Future Outlook on Plant Networks

Progress toward inferring protein complexes from three-dimensional structure of individual proteins has been made [106]. Additionally, protein chips can be used for studying other post-translational modiﬁcations such as protein glycosylation and protein interactions with, for instance, speciﬁc DNA sequences or phospholipids [107]. Considering all the technologies (e.g., MS and protein chips) available for research on Arabidopsis, it promises to become an important model for studying protein interaction and signaling networks. To obtain comprehensive models, systems biology needs to integrate large-scale experimental data. Such research will lead to a better understanding of plant processes and could eventually have an important impact on necessary improvements in agriculture to feed a growing human population, and might even improve human health [108].

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Index a A20/AN1 zinc ﬁnger domain-encoding genes, analysis of, 49 ABA. See Abscisic acid ABA-independent pathway – DREB/CBF transcription factors, 49 245 ABA-inducible genes, 141 Abiotic and biotic stress signaling networks – convergence points in, 69 –– roles of ROS at, 73–74 – hormone signaling and –– ABA and ET, 71–72 –– JA, 72 – transcription factors in cross-talk between –– ATAF genes, 74 –– ATAF2 overexpression, 75 –– OsNAC6 expression, 74 –– TSI1 expression, 75–76 Abiotic stress, 37, 138 – AtGenExpress, experiment, 200–201 – cold. See Cold stress – drought. See Drought stress – encountered by plants, 38 – freezing stress response, 29–30 – gene expression and, 201–204 – genome-wide transcriptional proﬁling techniques –– microarray approach, 39–40 –– during panicle initiation stage (P1) of rice, 40 –– rapid gene identiﬁcation, 39 –– SAGE and MPSS, 39 – genotoxic. See Genotoxic stress – heat. See Heat stress – osmotic. See Osmotic stress – oxidative. See Oxidative stress – response and stress-induced genes, 139–141 – role of microRNAs in, 41–42

– salt. See Salt stress – UV-B light. See UV-B light stress – wounding. See Wounding stress Abiotic stress-responsive genes – analysis using proteomic approaches, 42–44 – cross-talk between biotic and, 74 – expression in knockout plants, 127 Abiotic stress sensors – AtHK1, histidine kinase, 44–45 – CREI, cytokinin receptor gene, 45 Abiotic stress signaling, networking during plant – ABA-independent pathway, 49 – calcium and calcium-sensing proteins –– in Arabidopsis, 45–46 –– in rice, 46–47 –– in tomato, 45 – glyoxalase pathway, 48 – MAPK signaling cascade, 47–48 – sensing systems –– AtHK1, histidine kinase, 44–45 –– CREI, cytokinin receptor gene, 45 – SnRKs, 49 – transcription factors, 49–51 – transgenic approach, 51–52 Abiotic stress tolerance – BR signaling and, 128 –– in B. napus and Arabidopsis, 130–131 –– and hormones, 131 – OsISAP1 overexpression and, 49 – quantitative trait loci for, 44 – ZAT7 role in, 74 Abscisic acid (ABA), 139, 208 – cold stress and, 29–30 – in defense signaling, 71–72 – de novo synthesis and accumulation of, 25 – disease resistance and, 71–72 – growth inhibition by, 25

Plant Stress Biology. Edited by H. Hirt Copyright r 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32290-9

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| Index – signal transduction pathways, transcription factors in, 40 – treatment, structural and anatomical consequences of, 26 – vegetative stress tolerance and, 25–26 Abscisic acid-responsive element binding factors, 27 ACC. See 1-aminocyclopropane-1-carboxylic acid ACGT-core ABRE motif, 28 Acid resistance, 6–7 AFGN. See Arabidopsis Functional Genomics Network 1-aminocyclopropane-1-carboxylic acid (ACC), 169 Angiosperms – as desiccation-tolerant plants, 25 Antiadaptor protein – IraP (YaiB), 10 Antioxidant-responsive element (ARE), 168 APX. See Ascorbate peroxidase Arabidopsides – bound OPDA, 97–98 – and their constituents, 98 Arabidopsis, 141 – FAD8 gene, 142 – transcript proﬁles of various genes, 146 Arabidopsis Functional Genomics Network (AFGN), 200 Arabidopsis thaliana, 19, 169, 200 – ABA treatment of, 40–41 – ATAF2 functions in, 75 – BR role in abiotic stress tolerance of, 130–131 – CAM genes, 45 – CBL isoforms in, 47 – CDPK genes, 46 – DREB/CBF transcription factors in, 49–50 – expression of CIPK3 from, 47 – global, PINs, 231–240 –– of CAM/CML binding proteins, 240 –– degree distributions of, 235, 236 –– motif analysis of, 238–239 –– standard topological indices calculated for, 234 – humidity and temperature effects on slh1 mutant in, 78 – MAPK signaling cascade in, 47–48 – MKK1/MKK2–MPK4 pathway, 76 – MPK3 and MPK6 activity in, 78 – sfr (sensitivity to freezing) mutant in, 49 – transcript analysis under abiotic stress conditions, 40 – WIN1/SHN1 overexpression in, 68

ARE. See Antioxidant-responsive element AREB factors. See Abscisic acid-responsive element binding factors Arthrobacter globiformis – in rice, 150 Ascorbate–glutathione cycle, 182 Ascorbate peroxidase (APX), 182 ATAF2 overexpression, 75 AtGenExpress abiotic stress, experiment, 200–201 – ﬁndings, 201–204 – future developments, 221 AtHK1, histidine kinase, 44–45 AtMYC2, role in hormone signaling pathways, 72 AtNHX1 overexpression, 51

b BABA-induced resistance to oomycetes, 72 Bacteria – nutrient starvation, 4 BAK1 (BRI1-associated receptor kinase1) – and BR signaling, 120 – role in cell death –– defense-related genes, 128 –– link between BAK1 and PCD, 128 –– upregulation of stress-responsive genes, 129 – role in innate immunity –– host immune responses, 128 –– PRRs in Arabidopsis, 127–128 Bayesian networks, 242 Biotic and abiotic stress signaling networks – convergence points in, 69 –– roles of ROS at, 73–74 – hormone signaling and –– ABA and ET, 71–72 –– JA, 72 – transcription factors in cross-talk between –– ATAF genes, 74 –– ATAF2 overexpression, 75 –– OsNAC6 expression, 74 –– TSI1 expression, 75–76 Biotic stress, 138 Biotic stress responses – effects of humidity and temperature on, 78–79 Blue revolution, 138 Brassica napus hsp90, 142 Brassinosteroids – anticancer and antiviral effects, 126 – developmental pathway with stressresponsive pathways, 129

Index – gene expression regulation by, 119 – growth-promoting properties of, 119 – role in abiotic stress tolerance –– in B. napus and Arabidopsis, 130–131 –– and hormones, 131 – role in plant stress responses, 126 –– OsGSK1 knockout mutants, 127 – signaling pathway –– BZR1 and BES1, 120–121 –– components, 120 – stress-protective properties of –– drought stress, 123 –– pathogen attack, 124–125 –– salt stress, 123 –– temperature stress, 121–123 –– UV-B stress, 126 BR-regulated genes, 120 BRs. See Brassinosteroids Bryophytes – haploid gametophyte generation, 20 – poikilohydry, 24 – primitive traits, 18–19 – subgroupings, 17

c Caenorhabditis elegans, 230 Calcium and calcium-sensing proteins – in Arabidopsis, 45–46 – in rice, 46–47 – in tomato, 45 Calcium signaling – in cold stress, 144 – in salinity stress, 147–148 Calmodulin (CAM)-binding protein – Arabidopsis PIN of, 240 Calmodulin-like (CML)-binding proteins – Arabidopsis PIN of, 240 CAM - binding protein. See Calmodulin-binding protein CAMP/CRP-binding sites, 7 CAMs, role in abiotic stress signaling, 45–46 CAT. See Catalases Catalases (CAT), 182 CBL–CIPK interaction, 46 CDPK(s) – genes, 141 – role in abiotic stress signaling, 46 Cell death, BAK1 role in – defense-related genes, 128 – link between BAK1 and PCD, 128 – upregulation of stress-responsive genes, 129 Chaperones, 162

Chemical genetics – auxin signaling pathways and, 243 – vesicular trafﬁcking and, 243 Chilling stress. See Cold tolerance Cis-regulatory element (CRE), 201 – stress-responsive genes and, 217, 220 ClpXP protease, 9–10 Clustering coefﬁcient, 233 – versus degree relationship, of Arabidopsis PINs, 236 CML - binding proteins. See Calmodulin-likebinding proteins CNA. See Complex network analysis COI1-JAZ-JA-Ile-mediated signaling, 101–104 Cold acclimation, 142 54 cold-inducible genes, 141 Cold-regulated genes – in freezing tolerance, 142–143 Cold stress, 141, 209–210 – and abscisic acid, 29–30 – in calcium signaling, 144 – damage due to, 38 – functional categorization, using GO terms, 209 – generic pathway for plant, 145 – versus other stress-responsive genes, 209 Cold tolerance, 38 Compatible solutes, 149 Complex network analysis (CNA), 229–230 Contextual information, 244 Copper stress, 165 Coronatine (COR), 70 Craterostigma plantagineum, 154 CRE. See Cis-regulatory element CREI, cytokinin receptor gene, 45 Cuticle – composition of, 68 – permeability in lacs2 mutant, 68–69 Cytoscape, 232

d DAG. See Diacylglycerol Degree distribution, 233 – of Arabidopsis PINs, 235, 236 Dehydration. See Osmotic stress Dehydration stress. See Draught stress ‘‘Dehydrins’’, 28 Dehydroascorbate (DHA), 182 Desiccation tolerance – deﬁnition, 24 – in seeds, 25 Detoxiﬁcation – of ROS, 181–183

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| Index DHA. See Dehydroascorbate Diacylglycerol (DAG), 144 Directed network, 230 DNA damage – SOS regulon triggered by, 3 Draught stress, 210–211 – functional categorization, using GO terms, 210 DREBs/CBFs (dehydration-responsive element binding protein/C-repeat binding factor), 49–50 – DREB2A transcript, 75 Drosophila melanogaster, 230 299 drought-inducible genes, 140 Drought stress, 151–152 – and BRs, 123 – phospholipid signaling in, 154 – on photosynthesis, 152–153 – on stomata, 152–153 – responses, 29 – sugars and other osmolytes in, 153–154 Drought-tolerant IR62266 and CT9993 cultivars, proteome of, 43 DsrA and rpoS translation, 8

e Edges, biological systems, 229, 230 EIN2. See Ethylene-insensitive2 ERF – family and JA signaling, 105 – transcription factor, 75–76 Escherichia coli, 230 – antiadaptor protein in, 10 – proline and glycine betaine uptake in, 6 – rpoS mRNA, 8 – sS regulatory network –– gene expression by, 4 –– and global regulons, 4 –– metabolic regulation during stationary phase, 4–5 –– role in acid osmotic resistance, 6–7 –– role in shock osmotic resistance, 5–6 – stress responses, 3 – trehalose synthesis in, 5–6 Ethylene-insensitive2 (EIN2), 169 Evaporative water loss, 18 Exopolyphosphatase, overexpression of, 8 Expressed sequence tag (EST) collections, 23–24

f FANMOD, 237 Fenton reaction, 166, 180 Ferric reductase-defective3 (FRE3), 163

Floral transition-related genes, transcript levels of, 40 Food, reasons for discord between demand and supply of, 37 Fossil sporangia, 17 FRE3. See Ferric reductase-defective3

g GadA and gadBC genes, 6 GadX regulator, 7 Gametophores, 22 Gametophyte generation, 20 – haploid nature of dominant, 22 Gene clusters – deﬁned, 190 Gene expression – abiotic stress and, 201–204 Gene ontology (GO), 204 – cold stress and, 209 – draught stress and, 210 – genotoxic stress and, 212 – heat stress and, 211 – osmotic stress and, 206–207 – oxidative stress and, 213 – salt stress and, 208 – UV-B light stress and, 204 – wounding stress and, 212 General stress response. See Stress response, general Genotoxic stress, 212 – functional categorization, using GO terms, 212 – versus other stress-responsive genes, 212 GFP. See Green ﬂuorescent protein g -glutamylcysteinyl glycine (g-Glu–Cys– Gly), 182 Glutaredoxins (GRX), 182 Glycine betaine, 138 – uptake, 6 Glyoxalase genes, overexpression of, 48 GO. See Gene ontology GPCR. See G-protein-coupled receptors G-protein-coupled receptors (GPCR), 139 GPX. See glutathione peroxidase Green ﬂuorescent protein (GFP), 181 GRX. See Glutaredoxins

h Haber–Weiss reactions, 180 Heat shock proteins (HSP), 165 Heat stress, 211 – functional categorization, using GO terms, 211

Index – versus other stress-responsive genes, 211 – versus UV-B light stress, 216 Heavy metal ATPases (HMA), 162 213 high salinity-inducible genes, 141 HMA. See Heavy metal ATPases Homo sapiens, 230 Hordeum vulgare proline transporter (HvProT), 150 HSP. See Heat shock proteins HvProT. See Hordeum vulgare proline transporter Hydrogen peroxide signaling – in methyl viologen, 187

i Innate immunity, BAK1 role in – host immune responses, 128 – PRRs in Arabidopsis, 127–128 Inositol triphosphate (IP3), 144 Interactomes, 230 IP3 . See Inositol triphosphate IraM protein, 10 IraP (YaiB), antiadaptor protein, 10

j JA. See Jasmonic acid JA methyltransferase (JMT), 95 Jasmonates, 91, 170 Jasmonic acid, 91 – biosynthesis of –– in chloroplast and peroxisome, 93 –– enzymes of, 92 –– fatty acid bb-oxidation, 94 –– mutants of, 98–101 –– regulation, 94–95 – metabolism –– hydroxylation, 97 –– metabolites, 95–96 –– 12-OH-JA, 96 – role in developmental processes –– ﬂower development, 107–108 –– root growth, 106–107 – signaling of –– COI1–JAZ–JA-Ile-mediated, 101–104 –– mutants of, 98–101 –– repressor model in, 103 –– transcription factors involved in, 104–106 – signaling properties of, 95–96 JAZ–COI1-directed proteasome, 102

k KIN1 genes, 141

l ‘‘Late embryogenesis abundant’’ (LEA) proteins, 25 – protective functions of, 28–29 Late genes, 138 LEA/dehydrin-type gene, 140 LEA proteins, 142 Lipid peroxidation – in methyl viologen, 186 Low-temperature stress, 152 – on plant physiology, 141–142

m MAPK. See Mitogen-activated protein kinase MAPK cascades. See Mitogen-activated protein kinase cascades MAPK kinase, 142 MAPK signaling cascade, 47–48 Massively parallel signature sequencing (MPSS), 39 MDHA. See Monodehydroascorbate MEKK1–MKK2–MPK4/MPK6 cascade, 48 Metal-induced oxidative stress, 166–167 – parameters affected in plants, 167 Metal stress – affects plant physiology, 163–164 – cellular responses of, 165 – in plants, overview, 161 – signaling under, 167–170 Methyl viologen – antioxidative network upon, 186–187 – degradation of, 184 – hydrogen peroxide signaling in, 187 – lipid peroxidation in, 186 – oxidative stress of, 186 – superoxide anion-mediated signaling in plants, 187 – toxicity in plants and animals, mechanism of, 185 Microarray analysis – pollination/fertilization in rice, 40 – transcriptome proﬁle during stress response, 39 Microorganisms, growth of, 3 MicroRNAs – role in abiotic stresses, 41–42 Mitogen-activated protein kinase (MAPK), 168, 181, 242 – cascades –– and cross-talk between biotic and abiotic stress signaling, 76, 77 –– MEKK1 expression in, 77 –– MPK3 and MPK6, 78 –– role in hormone signaling, 77–78

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| Index MKK9–MPK3/MPK6 pathway, 78 Model systems – ﬂowering plants, 19–20 – importance of, 19 – Physcomitrella –– advantages of, 22–23 –– for comparative genomic analysis, 23 –– genome sequence assembly, 23–24 – for poikilohydry. See Tortula ruralis – rice, 19 Molecular markers – genes as, identiﬁcation of –– for cold stress response, 209 –– for drought stress response, 210 –– for heat stress response, 211 –– for osmotic stress response, 207 –– for oxidative stress response, 213 –– for salt stress response, 208 –– for UV-B light stress response, 204 –– for wounding stress response, 212 Monodehydroascorbate (MDHA), 182 Mosses – cell types in, 20 – desiccation tolerance, 24 – and ﬂowering plants, difference between, 20 – poikilohydric, 25 – rudimentary conducting tissues in, 20, 22 – stages in development of, 20–21 MPSS. See Massively parallel signature sequencing MYC2 and JA signaling, 104–105 MYC-like sequence-binding proteins, 50 MYC/MYB transcription factors, 149 MYC-type bHLH transcription factors, 50

n NAC – family transcription factors, 74 – transcription factor, 50 NADPH oxidases – with ROS, 180 Naı¨ve Bayesian approach, 232 NetworKIN – to construct plant phosphorylation networks, 243–245 Network motifs, 236–237 Nicotiana benthamiana leaves, 240 Nitric oxide (NO), 181 – and stomatal closure, 69–70 NO. See Nitric oxide Nodes, biological systems, 229, 230 – degree of, 233 NOS. See NO synthase

NO synthase (NOS), 181 NtCDPK2 signal transduction pathways, 48 Nutrient starvation – bacteria, 4

o 12-O-glucosyl-JA (12-O-Glc-JA), 95–96 Oligonucleotides, 140 OsCIPK genes, 47 OsLEA3-1 gene overexpression, 51 OsMAPK5, kinase activity of, 48 Osmolytes, 5 Osmoprotectants – uptake by ProP transport system, 6 Osmotic shock resistance, 5–6 Osmotic stress, 206–208 – functional categorization, using GO terms, 206–207 – versus other stress-responsive genes, 206 – in plants, 140 OsNAC6, NAC transcriptional activator, 74 OtsAB operon – induction of, 6 – trehalose production by, 5 Oxidative stress, 213 – functional categorization, using GO terms, 213 – metal-induced, 166–167 – versus other stress-responsive genes, 213 – parameters affected in plants, 167 Oxylipins, 170

p Paraquat, 183 Parsley cells, 191 Pathogen attack and BRs, 124–125 PCA. See Principal component analysis P5CS gene, 149 Perturbations, PSN, 241–242 Phosphatidylinositol bisphosphate (PIP2), 144 Phospholipase C (PLC), 144 Phospholipid signaling – in drought stress, 154 Photosysnthesis – in drought stress, 154 Physcomitrella patens, 19 – ABI3 paralogs, 28 – advantages of, 22–23 – cold stress and abscisic acid, 30 – for comparative genomic analysis, 23 – development of genomic resources for, 30–31 – effects of stress and ABA treatment on

Index –– growth arrest, 27 –– promoter motifs regulation, 27–28 –– structural and anatomical consequences, 26 – genome sequence assembly, 23–24 – stages in development of, 21 – vegetative stress tolerance, 25–26 Physical stress – deﬁned, 138 Phytoalexins, 191 Phytohormone, 148 PIN. See Protein interaction network PIP2. See Phosphatidylinositol bisphosphate Plant cells – antioxidative network in, 181–183 Plantglutathione peroxidase (GPX), 183 Plant metal uptake – bioavailable fraction of, 162 Plant phosphorylation networks – NetworKIN approach to construct, 243–245 Plant physiology – affected by metal stress, 163–164 – low-temperature stress on, 141–142 PLC. See Phospholipase C Poikilohydric mosses – Tortula ruralis, 25 Poikilohydry, 24 Post-translational modiﬁcations, 241 Power law, 233 (p)ppGpp and polyphosphate, link between, 8 Principal component analysis (PCA), 216 – performed on genes, 217–219 Proline, 149 – salinity stress in, 149–150 – uptake, 6 ProP transport system, 6 Protein interaction network (PIN), 229, 230–231 – clustering coefﬁcient of, 233 – by Cui, 232–233 – degree distribution, 233 – degree of node, 233 – by Geisler-Lee, 232–233 – global Arabidopsis thaliana, 231–240 –– of CAM/CML binding proteins, 240 –– degree distributions of, 235, 236 –– motif analysis of, 238–239 –– standard topological indices calculated for, 234 – naı¨ve Bayesian approach and, 232 – network motifs, 236–237 – shortest path length, 233

Protein networks – clustering coefﬁcient of, 233 – CNA, 229–230 – degree distribution, 233 – degree of node, 233 – future outlook on, 245 – network motifs, 236–237 – overview of, 228–229 – PIN. See Protein interaction network – PSN. See Protein signaling network – shortest path length, 233 Protein proﬁling under salt stress, 42 Protein signaling network (PSN), 229 – overview of, 240–241 – perturbations, 241–242 Proteomes – effect of stress on, quantitative proteomics to study, 242–243 PRX. See Type II peroxiredoxin Pseudomonas syringae, 242 PSN. See Protein signaling network

q Quantitative phosphoproteomic analysis, 241 Quantitative proteomics – effect of stress on proteomes, 242–243 Quantitative trait locus (QTLs) – for abiotic stress tolerance, 44

r Reactive nitrogen species (RNS) – with NO, 181 – of plant cells, 180 Reactive oxygen species (ROS), 139, 199 – biological relevance of, 179–181 – detoxiﬁcation of, 181–183 – generation of, 179–181 – NADPH oxidases with, 180 – salinity stress in, 150–151 ‘‘Resurrection plants’’, 24 Rice – A20/AN1 zinc ﬁnger domain-encoding genes in, 49 – ABA-activated SnRK2, 49 – CAM-encoding genes in, 45 – CDPK genes of, 46 – MAPK signaling cascade in, 48 – OsCIPK genes of, 47 – proteins, yeast two-hybrid analysis of, 43 – seedlings, low-temperature stress on, 42 – SNAC1 and SNAC2 transcription factors from, 50 RNAP and promoters, competition for, 10–11

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| Index RNS. See Reactive nitrogen species Roche 454 GS-FLX DNA sequencing procedure, 31 ROS. See Reactive oxygen species ROS production and ABA signaling, 73 ROS-scavenging enzymes, 73 RpoS mRNA intramolecular base pairing, inhibitors of, 8 RpoS promoter, 7 – expression and polyphosphate, 7–8 – translation –– DsrA role in, 8 –– by OxyS sRNA, 9 R2R3MYB transcription factors, 72 RssB (SprE), adaptor protein, 10

s SA- and JA-dependent signaling, cross-talk between, 98 Saccharomyces cerevisiae, 230 SAGE. See Serial analysis of gene expression Salicylic acid-induced protein kinase (SIPK), 48 Saline soils, 144 Salinity stress, 144–146 – ABA in, 148–149 – calcium signaling in, 147–148 – GB in, 149–150 – negative impact of, 146–147 – proline, 149–150 – ROS in, 150–151 – SOS pathways in, 147–148 – transcription factors in, 148–149 Salt overly sensitive (SOS) pathway, 46–47, 138 – in salinity stress, 147–148 Salt overly sensitive (SOS) pathway regulon – triggered by DNA damage, 3 Salt stress, 138, 208–209 – and BRs, 123 – functional categorization, using GO terms, 208 – versus other stress-responsive genes, 208 – protein proﬁling under, 42 Scale-free networks, 233 Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) algorithm, 244 Second green revolution, 138 Serial analysis of gene expression, 39 Serine/threonine kinases, 169 Sfr (sensitivity to freezing) mutant, 49 Shortest path length, of network, 233 Sigma factors, 4

Signaling cross-talk – deﬁnition, 68 – in stomatal guard cells, 70 Signal integration – stress-responsive genes and, 213–221 Signal transduction pathways, 68, 241 SNAC1 and SNAC2 transcription factors, 50 SnRKs, protein kinase, 49 SOD. See Superoxide dismutase Sodic soils, 144 SOS. See Salt overly sensitive SRK2E/OST1, terminal regulatory domain of, 49 sS-controlled genes – expression and stresses, 4, 6 – instability in exponential phase, 9 – regulation of –– post-translational, 9–10 –– RNAP core enzyme and, 10–11 –– transcriptional, 7–8 –– translational, 8–9 sS regulatory network – gene expression by, 4 – and global regulons, 4 – metabolic regulation during stationary phase, 4–5 – modules within, 6–7 – role in acid osmotic resistance, 6–7 – role in shock osmotic resistance, 5–6 Stomata – of ABA-insensitive stomata mutant, 70 – in drought stress, 154 – role in host defense, 70 Stomatal closure – MAMP-triggered, 70 – during water stress, 69 Stomatal regulatory pathway – signaling components, 69 Stress-induced miRNAs, 41 Stress-protective properties of BRs – drought stress, 123 – pathogen attack, 124–125 – salt stress, 123 – temperature stress, 121–123 – UV-B stress, 126 Stress response, general, 3 Stress-responsive genes, 201. See also speciﬁc stresses – CRE and, 217–220 – osmotic, comparison of, 206 – signal integration and, 213–221 – VA of 59 common, 213–216 Stress sensors, abiotic

Index – AtHK1, histidine kinase, 44–45 – CREI, cytokinin receptor gene, 45 Stress signal transduction pathways, 139 STRING algorithm. See Search Tool for the Retrieval of Interacting Genes/Proteins SUB (SUBMERGENCE TOLERANCE) gene, 44 Superoxide anion – signalling in plants, 190–191 – in signal transduction processes, 187–190 Superoxide dismutase (SOD), 182

t Tandem afﬁnity puriﬁcation (TAP), 228 TAP. See Tandem afﬁnity puriﬁcation Temperature stress and BRs, 121–123 Terrestrial ﬂora – angiosperms, 18–19 Tortula inermis, 29 Tortula ruralis, 25 – applications of, 31 – gene expression associated with desiccated state, 28 Transcription factors – in ABA signal transduction pathways, 40 – in cross-talk between biotic and abiotic signalling –– ATAF genes, 74 –– ATAF2 overexpression, 75 –– OsNAC6 expression, 74 –– TSI1 expression, 75–76 – induction of, 40–41 – involved in JA signaling –– ERF family, 105 –– MYC2, 104–105 –– WRKYs, 106 Transgenic crop plants, stress response gene characterization – AtNHX1 overexpression, 51 – OsLEA3-1 gene, 51 – ZmPLC1 lines, 52 Trehalose-6-phosphate phosphatase gene, 146 Trehalose synthesis – in Escherichia coli, 5–6 TSI1 expression, 75–76

Tuberonic acid, 96 Type II peroxiredoxin (PRX), 182

u Ultraviolet (UV)-B light, 200 Undirected network, 230 UV-B light. See Ultraviolet B light UV-B light stress, 204–206 – functional categorization, using GO terms, 204 – heat stress versus, 216 – versus other stress-responsive genes, 204 – wounding stress versus, 216 UV-B stress and BRs, 126

v Vascular plants, 18 Vector analysis (VA) – of stress-responsive genes, 213–216 Vigna aconitifolia – in tobacco, 154

w Water-deﬁcit stress. See Drought stress Water stress, 149 Wounding stress, 211–212 – functional categorization, using GO terms, 212 – versus other stress-responsive genes, 212 – versus UV-B light stress, 216 WRKYs and JA signaling, 106 WRKY transcription factors, 170

y Yeast two-hybrid (Y2H), 230 Y2H. See Yeast two-hybrid

z Zinc- and iron-regulated protein (ZIP), 162 Zinc ﬁnger proteins – ZAT expression, 73 – ZAT7 expression, 74 – ZAT12 expression, 73–74 Zinc stress, 165 ZmPLC1 lines, 52 Z-score, 237

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