Genes for Plant Abiotic Stress

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Genes for Plant Abiotic Stress

Genes for Plant Abiotic Stress Editors Matthew A. Jenks and Andrew J. Wood © 2010 Blackwell Publishing ISBN: 978-0-813-81502-2


Editors MATTHEW A. JENKS Professor Horticulture and Landscape Architecture Center for Plant Environmental Stress Physiology Purdue University

ANDREW J. WOOD Professor Stress Physiology and Molecular Biology Department of Plant Biology Southern Illinois University

A John Wiley & Sons, Inc., Publication

Edition first published 2010 © 2010 Blackwell Publishing Blackwell Publishing was acquired by John Wiley & Sons in February 2007. Blackwell’s publishing program has been merged with Wiley’s global Scientific, Technical, and Medical business to form Wiley-Blackwell. Editorial Office 2121 State Avenue, Ames, Iowa 50014-8300, USA For details of our global editorial offices, for customer services, and for information about how to apply for permission to reuse the copyright material in this book, please see our website at www.wiley.com/wiley-blackwell. Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by Blackwell Publishing, provided that the base fee is paid directly to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. For those organizations that have been granted a photocopy license by CCC, a separate system of payments has been arranged. The fee codes for users of the Transactional Reporting Service are ISBN-13: 978-0-8138-1502-2/2010. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought. Library of Congress Cataloging-in-Publication Data Genes for plant abiotic stress / editors, Matthew A. Jenks, Andrew J. Wood. p. cm. Includes bibliographical references and index. ISBN 978-0-8138-1502-2 (hardback : alk. paper) 1. Crops–Effect of stress on. 2. Crop improvement. 3. Crops and climate. 4. Crops–Physiology. 5. Crops–Development. I. Jenks, Matthew A. II. Wood, Andrew J. SB112.5G46 2009 632′.1–dc22 2009031844 A catalog record for this book is available from the U.S. Library of Congress. Set in 10.5 on 12 pt Times by SNP Best-set Typesetter Ltd., Hong Kong Printed in the Singapore 1

2010

Contents

Contributors Preface Section 1 Chapter 1

Chapter 2

ix xiii

Genetic Determinants of Plant Adaptation under Water Stress Genetic Determinants of Stomatal Function SONG LI and SARAH M. ASSMANN

3 5

Introduction Arabidopsis as a Model System How Do Stomates Sense Drought Stress? Signaling Events inside Guard Cells in Response to Drought Cell Signaling Mutants with Altered Stomatal Responses Transcriptional Regulation in Stomatal Drought Response Summary References

5 7 7 11 15 22 24 25

Pathways and Genetic Determinants for Cell Wall–based Osmotic Stress Tolerance in the Arabidopsis thaliana Root System HISASHI KOIWA

35

Introduction Genes That Affect the Cell Wall and Plant Stress Tolerance Genes and Proteins in Cellulose Biosynthesis Pathways Involved in N-glycosylation and N-glycan Modifications Dolichol Biosynthesis Sugar-nucleotide Biosynthesis Assembly of Core Oligosaccharide Oligosaccharyltransferase Processing of Core Oligosaccharides in the ER Unfolded Protein Response and Osmotic Stress Signaling N-glycan Re-glycosylation and ER-associated Protein Degradation N-glycan Modification in the Golgi Apparatus Ascorbate as an Interface between the N-glycosylation Pathway and Oxidative Stress Response Biosynthesis of GPI Anchor Microtubules

35 35 36 38 38 39 40 40 42 42 44 44 46 46 47 v

vi

Chapter 3

CONTENTS

Conclusion References

48 49

Transcription and Signaling Factors in the Drought Response Regulatory Network MATTHEW GEISLER

55

Introduction Drought Stress Perception Systems Biology Approaches Transcriptomic Studies of Drought Stress The DREB/CBF Regulon ABA Signaling Reactive Oxygen Signaling Integration of Stress Regulatory Networks Assembling the Known Pathways and Expanding Using Gene Expression Networks’ Predicted Protein Interactions Acknowledgments References

Section 2 Chapter 4

Chapter 5

Genes for Crop Adaptation to Poor Soil Genetic Determinants of Salinity Tolerance in Crop Plants DARREN PLETT, BETTINA BERGER, and MARK TESTER

55 55 56 63 66 71 72 72 74 75 75

81 83

Introduction Salinity Tolerance Conclusion References

83 85 100 100

Unraveling the Mechanisms Underlying Aluminum-dependent Root Growth Inhibition PAUL B. LARSEN

113

Introduction Mechanisms of Aluminum Toxicity Aluminum Resistance Mechanisms Aluminum Tolerance Mechanisms Arabidopsis as a Model System for Aluminum Resistance, Tolerance, and Toxicity Aluminum-sensitive Arabidopsis Mutants The Role of ALS3 in A1 Tolerance ALS1 Encodes a Half-type ABC Transporter Required for Aluminum Tolerance Other Arabidopsis Factors Required for Aluminum Resistance/Tolerance Identification of Aluminum-tolerant Mutants in Arabidopsis The Nature of the alt1 Mutations Conclusions References

113 114 117 120 121 121 122 126 128 129 132 138 138

CONTENTS

Chapter 6

vii

Genetic Determinants of Phosphate Use Efficiency in Crops FULGENCIO ALATORRE-COBOS, DAMAR LÓPEZ-ARREDONDO, and LUIS HERRERA-ESTRELLA

143

Introduction Why Improve Crop Nutrition and the Relationship with World Food Security? Phosphorus and Crops: Phosphorus as an Essential Nutrient and Its Supply as a Key Component to Crop Yield Phosphorus and Plant Metabolism: Regulatory and Structural Functions Phosphate Starvation: Adaptations to Phosphate Starvation and Current Knowledge about Phosphate Sensing and Signaling Networks during Phosphate Stress Nutrient Use Efficiency Genetic Determinants for the Phosphate Acquisition Genetic Determinants for Pi Acquisition by Modulating Root System Architecture Genetic Determinants Involved with Phosphorus Utilization Efficiency Genetic Engineering to Improve the Phosphate Use Efficiency Conclusions References

143 143 144 145

146 150 150 153 155 156 158 158

Color Plate Section Chapter 7

Section 3 Chapter 8

Genes for Use in Improving Nitrate Use Efficiency in Crops DAVID A. LIGHTFOOT

167

Introduction The Two Forms of NUE: Regulation of Nitrogen Partitioning and Yield in Crops Mutants as Tools to Isolate Important Plant Genes Transcript Analysis Metanomic Tools for Extending Functional Genomics Transgenics Lacking A Priori Evidence for NUE Microbial Activity Nodule Effects and Mycorrhizal Effects Water Effects Conclusions References

167

Genes for Plant Tolerance to Temperature Extremes

169 169 174 174 175 176 178 178 178 179 183

Genes and Gene Regulation for Low-temperature Tolerance MANTAS SURVILA, PEKKA HEINO, and E. TAPIO PALVA

185

Introduction Protective Mechanisms Induced during Cold Acclimation Regulation of Gene Expression Cross Talk between Abiotic and Biotic Stress Responses

185 188 192 207

viii

Chapter 9

Section 4 Chapter 10

Chapter 11

Index

CONTENTS

Conclusions and Future Perspectives Acknowledgments References

207 209 209

Genetic Approaches toward Improving Heat Tolerance in Plants MAMATHA HANUMAPPA and HENRY T. NGUYEN

221

Introduction Thermotolerance High Temperature Impact and Plant Response to Heat Stress Mechanism of Heat Tolerance in Plants Genetic Approaches to Improve Heat Tolerance in Crops The Effect of Stress Combination Evolving Techniques Conclusion and Perspectives References

221 221 223 230 235 244 246 247 247

Integrating Plant Abiotic Stress Responses

261

Genetic Networks Underlying Plant Abiotic Stress Responses ARJUN KRISHNAN, MADANA M.R. AMBAVARAM, AMAL HARB, UTLWANG BATLANG, PETER E. WITTICH, and ANDY PEREIRA

263

Introduction Plant Responses to Environmental Stresses Transcriptome Analysis of Abiotic Stress Responses Gene Network of Universal Abiotic Stress Response Conclusions References

263 264 270 274 276 276

Discovering Genes for Abiotic Stress Tolerance in Crop Plants MICHAEL POPELKA, MITCHELL TUINSTRA, and CLIFFORD F. WEIL

281

Introduction Salt Stress Heat Stress Oxidative Stress Nutrient/Mineral Stress Plant Architecture and Morphology Evolutionary Conservation and Gene Discovery Conclusion References

281 286 287 288 289 290 291 292 292 303

Contributors

Fulgencio Alatorre-Cobos

Laboratorio Nacional de Genómica para la Biodiversidad, Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional Campus Guanajuato 36821 Irapuato, Guanajuato, México [email protected]

Madana M.R. Ambavaram Virginia Bioinformatics Institute Virginia Tech Washington Street Blacksburg, VA 24061 USA Sarah M. Assmann

Biology Department Penn State University 208 Mueller Laboratory University Park, PA 16802 USA

Utlwang Batlang

Virginia Bioinformatics Institute Virginia Tech Washington Street Blacksburg, VA 24061 USA

Bettina Berger

Australian Centre for Plant Functional Genomics and University of Adelaide, PMB1 Glen Osmond, SA Australia, 5064

Matthew Geisler

Southern Illinois University Department of Plant Biology 403 Life Science II Building 1125 Lincoln Drive Carbondale, IL 62901 USA ix

x

CONTRIBUTORS

Mamatha Hanumappa

Division of Plant Sciences and National Center for Soybean Biotechnology University of Missouri Columbia, MO 65211 USA

Amal Harb


Pekka Heino

Department of Biological and Environmental Sciences Division of Genetics University of Helsinki P.O. Box 56, FIN-00014 Helsinki Finland

Luis Herrera-Estrella

Laboratorio Nacional de Genómica para la Biodiversidad, Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional Campus Guanajuato 36821 Irapuato Guanajuato, México [email protected]

Hisashi Koiwa

Department of Horticultural Sciences 2133 Texas A&M University College Station, TX 77843-2133 USA

Arjun Krishnan


Paul B. Larsen

Department of Biochemistry University of California Riverside, CA 92521 USA 951-827-2026 [email protected]

CONTRIBUTORS

Song Li

Biology Department Penn State University 208 Mueller Laboratory University Park, PA 16802 USA

David A. Lightfoot

Department of Plant Soil and Agricultural Systems Southern Illinois University Carbondale, IL 62901 USA

Damar López-Arredondo

Laboratorio Nacional de Genómica para la Biodiversidad, Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional Campus Guanajuato 36821 Irapuato Guanajuato, México [email protected]

Henry T. Nguyen

Division of Plant Sciences and National Center for Soybean Biotechnology University of Missouri Columbia, MO 65211 USA

E. Tapio Palva

Department of Biological and Environmental Sciences Division of Genetics, University of Helsinki P.O. Box 56, FIN-00014 Helsinki Finland

Andy Pereira

Virginia Bioinformatics Institute Virginia Tech Washington Street Blacksburg, VA 24061 USA [email protected], 540-231-3784

Darren Plett


xi

xii

CONTRIBUTORS

Michael Popelka

Agronomy Department Purdue University 915 West State Street West Lafayette, IN 47907 USA

Mantas Survila

Department of Biological and Environmental Sciences Division of Genetics, University of Helsinki P.O. Box 56, FIN-00014 Helsinki Finland

Mark Tester


Mitchell Tuinstra


Clifford F. Weil


Peter E. Wittich


Preface

Conventional crop-breeding strategies have made limited progress in enhancing harvest indices in regions commonly beset by abiotic environmental stresses, such as those caused by drought, salinity, toxic metals, cold, heat, and nutrient-limiting soils. Worldwide, crop yield losses to abiotic stress range from minor to total, with actual losses being influenced by the timing, intensity, and duration of the stress. A major constraint to improving yield under abiotic stress is our limited understanding of the diverse genes and their alleles that underlie stress tolerance, as well as the difficulties faced by breeders and biotechnologists seeking to combine favorable alleles to create the desired stress-adapted high-yielding genotypes. Moreover, crop domestication has narrowed the genetic diversity for stress adaptation available within crops, and thus, limited options for traditional crop breeding. Consequently, a better understanding of gene function in plant-stress adaptation and the means to use these genes to enhance crop performance are needed if we are to realize the full potential of our efforts in crop improvement. Recent studies of gene function have revealed highly complex and surprisingly integrated genetic and metabolic networks for plant response to abiotic stress. These findings are revealing a new paradigm for effective crop improvement, one that adapts a systems-based approach that closely integrates new discoveries in fundamental biology with newly developed methods in plant breeding and biotechnology. This book integrates a broad cross-section of scientific knowledge and expertise around the key genetic determinants of plant abiotic stress adaptation, with gene function discussed in a way that bridges the physiological, biochemical, developmental, and molecular levels, and gives special consideration to the importance of signaling networks. New and creative approaches for manipulating these determinants for germplasm improvement are also discussed. Information presented in this book will be especially useful to agronomists and horticulturists, crop breeders, biotechnologists, and molecular geneticists, and serve as an important scholarly text for researchers, educators, and post-graduate students.

Matthew A. Jenks and Andrew J. Wood

xiii



Section 1 Genetic Determinants of Plant Adaptation under Water Stress


1

Genetic Determinants of Stomatal Function Song Li and Sarah M. Assmann

Introduction

Water is the major constituent of land plants, comprising 80–90% of the fresh weight of most herbaceous plants (Kramer and Boyer, 1995). As a small polar molecule with a high dielectric constant, water is a good general purpose solvent for many organic and inorganic ions. Since most mineral nutrients, photosynthetic products, and other biomolecules are charged molecules, water serves as the nutrient carrier and also the solvent for most biochemical reactions. Water has high heat content and high heat of vaporization, which also makes water an ideal substance for temperature regulation such as cooling through transpiration. Water is of particular importance to land plants in two other aspects. First, turgor pressure exerted against cell walls helps plants maintain their form and facilitates cell expansion and growth. Second, water is an essential reactant in photosynthesis, the principal mechanism of biomass accumulation for plants (Kramer and Boyer, 1995). Because water molecules are highly integrated into plant physiology, water-limiting conditions, normally defined as drought, are some of the major abiotic stresses that limit crop productivity (Bhatnagar-Mathur et al., 2008). In natural environments, drought stress is often accompanied by and complicated by a number of other abiotic stresses, such as high temperatures, cold or salinity. High temperatures increase the driving force for transpiration while cold and soil salinity limit soil water availability to roots. To prevent water loss, land plants have evolved vapor-resistant cuticles on their aerial surfaces and use stomatal apertures to regulate transpirational water loss and CO2 uptake (Jenks and Hasegawa, 2005; Kunst and Samuels, 2003). A stomatal complex consists of a pair of guard cells surrounding a microscopic pore called a stoma or stomate; in some species, stomatal complexes also include neighboring subsidiary cells (Willmer and Fricker, 1996). When ample water is present in the environment, stomatal pores can open in response to light to facilitate CO2 uptake and photosynthesis. When water is limited, stomata close to prevent further water loss and to maintain leaf water potential. Stomatal pores are regulated by swelling or shrinking of guard cells through changing cellular water content, which in turn is driven by changes in cellular concentrations of osmotically active solutes such as K+, Cl− and malate2− (MacRobbie, 1998). Guard cells respond to many different drought signals, including the best-characterized drought hormone, abscisic acid (ABA). Molecular details of drought and ABA response in stomata are better characterized in the model species Arabidopsis as compared to drought responses in other tissues and organs in Arabidopsis or in other species (Nilson and Assmann, 2007). This chapter describes recent advances in our understanding of genetic regulators of stomatal drought responses, with a focus on molecular mediators identified in Arabidopsis (see Table 1.1). We will also discuss recently identified genetic regulators of stomatal development in Arabidopsis. Other aspects of plant drought responses are reviewed elsewhere in this book and are not the focus of this chapter. Genes for Plant Abiotic Stress Editors Matthew A. Jenks and Andrew J. Wood © 2010 Blackwell Publishing ISBN: 978-0-813-81502-2

5

6

At5g67300 Human Gene At4g09570 At1g35670 At4g34000 At3g19290 At3g55530 At4g26070 At1g04120 At1g30270 At2g13540 At1g48270 At4g17615 At5g47100 At5g40280 At1g12110

MYB44 PI5P CPK4 CPK11 ABF3 ABF4 SDIR1 MEK1 AtMRP5 CIPK23 ABH1 GCR1 CBL1/CBL9 1 2

OE OE OE OE RN RN RN OE T-DNA Knockout T-DNA Knockout T-DNA Knockout T-DNA Knockout T-DNA Knockout

Mutant type

NT: Not tested in published results RN: Reduced number of open stomata WT: wild-type response OE: Overexpression 1. Fast neutron-generated mutant 2. γ-irradiation generated mutant 3. Better survival rate without rewatering

ERA1 AtCHL1

AGI locus identifier

Gene Name

Smaller Smaller

Smaller Smaller NT NT NT WT

Smaller WT NT NT

Hypersensitive WT

Hypersensitive Hypersensitive NT Hypersensitive NT NT NT WT Hyposensitive Hypersensitive Hypersensitive Hypersensitive Hypersensitive NT WT

NT Hyposensitive NT Hyposensitive NT NT NT NT NT Hypersensitive NT Hypersensitive NT

Inhibition of opening

NT Less

Less Less Less Less Less Less Less NT Less Less NT Less Less

Excised leaf water loss

Promotion of closure

Light

ABA

Leaf phenotype

Stomatal phenotype

Less Less

NT Less Less Less NT NT NT NT Less NT Less Less Less

Wilting

Whole plant phenotype

NT NT

Better NT Better Better Better Better Better Better 3 NT Better NT Better NT

Rewater survival rate

Table 1.1 Mutants with both stomatal phenotypes and increased drought tolerance. Among all genes mentioned in this chapter, not all have published results from whole plant drought response experiments. This table lists all published mutants for which both increased whole plant drought tolerance phenotypes and altered stomatal phenotypes have been reported. Among these 16 mutants, 8 are 35S driven overexpression mutants, 4 are T-DNA single gene knockouts, and 1 is a T-DNA double mutant. In response to light, 5 mutants have intrinsically smaller stomatal apertures, and 3 mutants have a smaller fraction of open stomata as compared to wild type. Nine mutants exhibit hypersensitivity in stomatal ABA responses. One mutant (MRP5) is insensitive for ABAinduced stomatal closure. All mutants tested for excised leaf water loss showed reduced water loss.

GENETIC DETERMINANTS OF STOMATAL FUNCTION

7

Arabidopsis as a Model System

Plant reactions to drought stress have been studied in many experimental species including crop species, trees, desiccation-resurrection plants, and many plant model species. In recent years, the model species Arabidopsis has become the most widely used system to dissect the genetic basis of plant drought tolerance (Nilson and Assmann, 2007). Arabidopsis has the advantage of a short life cycle and small stature. Also, Arabidopsis is easy to transform as compared to many other experimental and crop species. These features have allowed the creation and maintenance of large collections of mutants (Alonso et al., 2003), which are valuable tools for genetic analyses of stomatal drought response. The genome of Arabidopsis has been fully sequenced and is the best annotated plant genome to date (Arabidopsis Genome Initiative, 2000; Swarbreck et al., 2008). This unparalleled richness of genomic information underlies many research technologies. Because stomata can only be observed under a microscope, forward-genetic screens based on stomatal physiology traits have proven somewhat difficult. In fact, many genetic regulators of stomatal functions were first identified by forward genetic screens on more easily scored traits, such as ABA sensitivity in seed germination, rather than by scoring stomatal ABA responses. In reverse genetic approaches, gene functions are first predicted by sequence homology to known genes and then tested by phenotyping plants harboring mutations in the target gene. Alternatively, many cell physiological processes involved in stomatal drought responses were first characterized in species with large stomata by chemical inhibitor analyzes, and then the regulatory genes were identified in Arabidopsis by reverse genetic approaches (Negi et al., 2008; Vahisalu et al., 2008; Wang et al., 2001; Kwak et al., 2002). Further, the well-annotated Arabidopsis genome provides information regarding gene family sizes and sequence similarities between gene family members; hence double mutants can be generated based on the evolutionary relationships between family members to evaluate functional redundancy (Mori et al., 2006). A fully sequenced genome also helps in the application of microarray (Leonhardt et al., 2004) and newly developed sequencing-by-synthesis technologies (Cokus et al., 2008). Because naturally occurring Arabidopsis variants have different degrees of drought tolerance (Hausmann et al., 2005), both microarray and sequencing technologies provide opportunities to find novel genes and polymorphisms, which are correlated with drought tolerance traits (Nordborg and Weigel, 2008; Bouchabke et al., 2008). The rich information generated by novel technologies has already been used to identify new molecular mediators of drought tolerance and to correlate drought tolerance phenotypes with novel genetic loci at the whole plant level (Ossowski et al., 2008; Seki et al., 2002).

How Do Stomates Sense Drought Stress?

Drought causes decreases in soil water availability and, in some geographic regions, reduced air relative humidity, both of which are known to reduce stomatal conductance in multiple crop species (Kramer and Boyer, 1995). Given their location in aerial surfaces of plants, stomata cannot directly sense water-limiting conditions in the soil. Instead, stomata close in response to chemical signals such as ABA. ABA is known to be synthesized in roots and transported in xylem sap to leaves when roots sense drought in the soil (Davies and Zhang, 1991). Ample evidence suggests that stomata can also directly respond to the changes in leaf water status, and such responses may correlate with the accumulation of ABA (Comstock, 2002). In addition, stomatal humidity sensing is also connected to the ABA pathway by several recently published reports (Xie et al., 2006; Lake

8

GENETIC DETERMINANTS OF PLANT ADAPTATION UNDER WATER STRESS

Humidity

Drought

Cytokinins MeJA

ABA

Ethylene

Stomatal Movement

Stomatal Development

Figure 1.1 Stomatal drought response is mediated by plant hormones. Solid lines indicate regulation of stomatal opening and closure. Dashed lines indicate regulation of stomatal development. See text for details.

and Woodward, 2008). Because of the importance of ABA as a drought signal, a number of genetic regulators of ABA biosynthesis have been identified, and they will be discussed in this section. Cytokinins, ethylene, and jasmonates are known to be drought signals that act on guard cells, although none of these hormones has been found to override the dominant role of ABA in stomatal drought response (Lake et al., 2002; Acharya and Assmann, 2008). Molecular components mediating stomatal response to other hormones and interactions between ABA and other hormones during drought will also be discussed (see Figure 1.1).

ABA as a Drought Hormone

ABA is a 15 carbon (C15) molecule derived from the cis-xanthophyll (C40) carotenoid precursor zeaxanthin. The Arabidopsis gene ABA1 is a zeaxanthin epoxidase, which catalyzes the conversion


9

of zeaxanthin to violaxanthin (Xiong et al., 2002; Nambara and Marion-Poll, 2005). Both drought and ABA induce ABA1 gene expression. Violaxanthin is converted to neoxanthin and then 9′-cisneoxanthin and 9′-cis-violaxanthin, which are both cis-xanthophylls (C40). ABA4 was identified by map-based cloning as a positive regulator of neoxanthin synthase (North et al., 2007). Unlike ABA1, ABA4 is not induced by drought, but knockout of either of these two genes eliminates dehydration-induced ABA accumulation (North et al., 2007). Consistent with these results, both aba1 and aba4 mutants showed lower leaf temperatures than wild type after three days of drought stress (North et al., 2007). The ABA specific metabolic pathway starts with the cleavage of cis-xanthophylls into C15 xanthoxin and a C25 by-product catalyzed by 9-cis-epoxycarotenoid dioxygenases (NCED) (Nambara and Marion-Poll, 2005). In Arabidopsis, tissue specificity of ABA biosynthesis emerges at this step, since a family of nine NCED genes has been identified in the genome (Iuchi et al., 2001). Among these nine genes, five NCEDs have been studied by promoter-GUS assay (Tan et al., 2003). AtNCED2 is expressed in guard cells from senescing leaves, while AtNCED3 is expressed in guard cells in cotyledons, hypocotyls, and petioles. Both AtNCED2 and AtNCED3 are expressed in roots, implying that both of these genes play roles in root ABA biosynthetic processes. Surprisingly, none of the five AtNCED genes was found by reporter gene analysis to be expressed in guard cells from rosette leaves or cauline leaves. ABA2 and AAO3 are enzymes downstream of AtNCEDs and catalyze the transformation of xanthoxin to abscisic aldehyde and abscisic aldehyde to abscisic acid, respectively (Cheng et al., 2002b; Koiwai et al., 2004). AAO3 activity also requires another gene, ABA3, which encodes a MoCo sulfurase; and the homolog of this gene, flacca, was one of the first ABA biosynthetic genes found by genetic approaches in tomato, where its mutation results in a wilty phenotype (Sagi et al., 2002). Among all ABA biosynthesis genes in Arabidopsis, only NCED3 was strongly induced by drought in leaf vascular tissue (Iuchi et al., 2001; Endo et al., 2008). The expression of AAO3 was detected in guard cells and significantly induced in guard cells upon drought treatment (Koiwai et al., 2004), consistent with a previous report that guard cells can themselves synthesize ABA (Cornish and Zeevaart, 1986). ABA catabolism includes both conjugation and oxidation into inactive forms. The predominant pathway of ABA oxidation is thought to be 8′-hydroxylation. This process is catalyzed by a family of four CYP707A genes, which can be induced by seedling dehydration (Kushiro et al., 2004). CYP707A1 and CYP707A3, but not CYP707A2, were shown to be induced in guard cells and vascular tissue, respectively, by high humidity. A cyp707a1 cyp707a3 double mutant showed stronger defects in stomatal opening responses to high humidity than either cyp707a1 or cyp707a3 single mutants, suggesting that these two genes have synergistic functions (Okamoto et al., 2008). Because high air humidity could be the first sign of drought release due to precipitation, these results highlight the role of ABA oxidation in drought release responses of guard cells. ABA remobilization from conjugated forms seems to be involved in stomatal regulation. In Arabidopsis, ABA was shown to be released from a biologically inactivated, glucose conjugated form by a β-glucosidase, BG1 (Lee et al., 2006). Dehydration, and even moderate changes in air humidity, can rapidly activate BG1 by inducing BG1 protein polymerization. Under darkness, T-DNA insertional mutant bg1 showed defective stomatal closure, which could be rescued by exogenous ABA (Lee et al., 2006). The bg1 mutant loses more water in detached rosettes and is more sensitive to drought stress than wild type (Lee et al., 2006). Further experiments on the temporal expression patterns of genes involved in ABA biosynthesis and catabolism, their responses to different levels of drought stresses, their response to drought

10


release, and effects of ABA metabolism on stomatal movement will provide valuable insight regarding the interconnection of ABA metabolic pathways and stomatal drought responses.

Other Hormones in Stomatal Drought Responses

Besides ABA, cytokinins are also well-accepted root-to-shoot signaling hormones that participate in plant drought response (Schachtman and Goodger, 2008). In multiple species, drought stresses decrease cytokinin concentrations in xylem sap, while high concentrations of cytokinins antagonize ABA effects on stomatal closure and can induce stomatal opening (Wilkinson and Davies, 2002). In Arabidopsis, three cytokinin-receptor histidine kinases (CRHK) mutants, ahk2, ahk3, and cre1, are hypersensitive to ABA and osmotic stress and thus more tolerant to drought stresses (Tran et al., 2007). These CRHKs also showed functions antagonistic to AHK1, an osmotic-sensing receptor histidine kinase (Tran et al., 2007). Both of these experiments were carried out at the whole plant level; therefore, whether any of these cytokinin receptors directly function in guard cells remains unknown. Cytokinins inhibit ABA-induced stomatal closure in wild type but not in the ethylene insensitive mutant ein3-1. In the cytokinin over-producing mutant, amp1-1, ABA-induced stomatal closure is impaired but can be restored through inhibition of ethylene biosynthesis (Tanaka et al., 2006). Although these results suggest that cytokinins inhibit ABA-induced stomatal closure through ethylene biosynthesis, the possibility that cytokinin receptor kinases are also activated in guard cells cannot be ruled out. The role of ethylene in plant drought response is unclear. In different species, ethylene production differentially depends on the duration and intensity of drought (Acharya and Assmann, 2008). In Arabidopsis, ethylene inhibits ABA-induced stomatal closure (Tanaka et al., 2005), and also mediates an antagonistic effect of cytokinin on ABA-induced stomatal closure as described above (Tanaka et al., 2006). However, ethylene alone was shown to induce stomatal closure in Arabidopsis, and this process is mediated by H2O2 (Desikan et al., 2006). Ethylene receptor histidine kinase ETR1 and signaling proteins EIN2 and ARR2 are involved in ethylene-induced stomatal closure; and AtrbohF, a NADPH oxidase, seems to be an intermediate protein ABA and ethylene cross talk in guard cells (Desikan et al., 2006). Another histidine kinase, AHK5, is downstream of ethylene-induced reactive oxygen species (ROS) production but not ABA-induced stomatal closure (Desikan et al., 2008). In addition to regulating stomatal aperture, ethylene-treated plants or ethylene over-production mutants showed increased stomatal density in Arabidopsis (Serna and Fenoll, 1996). Jasmonic acid and methyl jasmonate (MeJA), collectively called jasmonates (JA), increase rapidly in response to drought (Creelman and Mullet, 1995). Unlike drought-induced ABA production, plant JA content drops after an initial peak, which suggests a transient role of JA in plant drought response (Creelman and Mullet, 1995, 1997; Deng et al., 2008). MeJA can induce stomatal closure in Arabidopsis, but such effects may depend on the plant growth conditions (Zhao et al., 2008). MeJA-induced stomatal closure is mediated by a number of common intracellular components of stomatal ABA responses, but is also mediated by several MeJA-specific components. In terms of common mediators, both MeJA and ABA induce stomatal closure by inducing ROS and nitric oxide (NO) production, which in turn activate membrane Ca2+ permeable channels, anion channels, and outward K+ channels in guard cells (Evans, 2003; Munemasa et al., 2007). Also, the induction of ROS in guard cells by either MeJA or ABA is dependent on protein phosphatase RCN1 and AtrbohD/F (Saito et al., 2008). However, MeJA but not ABA regulation of ROS, NO,


11

and ion channel activities is impaired in two JA insensitive mutants, jar1 and coi1 (Munemasa et al., 2007), while ABA but not JA-induced ROS and NO production is mediated by protein kinase OST1 (Suhita et al., 2004).

Signaling Events inside Guard Cells in Response to Drought

Drought stress is perceived by guard cells as a composite signal from multiple plant hormones but predominantly from ABA, which activates a wide range of intracellular messengers such as calcium, nitric oxide (NO), reactive oxygen species (ROS) phosphatidic acid (PA), and intracellular pH changes. A number of genetic regulators upstream of these intracellular messengers have been identified in Arabidopsis. In addition, many of these intracellular messengers can modulate membrane permeability through direct or indirect regulation of plasma membrane and vacuolar membrane ion channels, transporters, and pumps. Some of these intracellular messengers, such as calcium and proton concentrations, are themselves regulated by membrane channels and pumps. Membrane channels and transporters mediate the uptake and release of osmotically active solutes such as K+, Cl− and malate2− from guard cell cytosol and vacuoles, resulting in changes of cellular water content and guard cell volume, and eventually leading to changes in stomatal apertures (see Figure 1.2).

Ion Channels and Other Membrane Transport-related Proteins

Light activation of membrane H+ ATPases causes hyperpolarization of the plasma membrane, which is the driving force for ion flow into guard cells. Two dominant mutants ost2-1D and ost22D, which each harbor mutations in the guard cell–expressed H+ ATPase gene, AHA1, were found to have reduced leaf surface temperatures by a thermal imaging based screen. Stomata of ost2 mutants cannot close in response to ABA, due to constitutively active H+ ATPase (Merlot et al., 2007). This result suggests that downregulation of H+ ATPase activity is an important step in stomatal drought response (Goh et al., 1996). KAT1 is a well-characterized inward K+ channel subunit highly enriched in guard cells (Nakamura et al., 1995), which forms functional multi-protein complexes with KAT2 (Pilot et al., 2001) and potentially several other K+ channel proteins in guard cells (Szyroki et al., 2001; Ivashikina et al., 2005). At least five K+ channel proteins are expressed in guard cells (Szyroki et al., 2001), and knockout of KAT1 does not impair stomatal opening. However, plants that overexpress dominant negative alleles of KAT1 or KAT2 have reduced inward K+ current and reduced water loss (Kwak et al., 2001; Lebaudy et al., 2008), suggesting that K+ influx mediated by inward K+ channels is important for light-induced stomatal opening and plant drought response. ABA regulates inward K+ channel activity not only through secondary messengers, but also through vesicle trafficking to and from the cell membrane (Sutter et al., 2007). Two syntaxin proteins have been studied in detail in Arabidopsis. SYP121 was found to be directly involved in KAT1 membrane trafficking (Sutter et al., 2006), while SYP61 is enriched in guard cells, and is important for osmotic stress response (Zhu et al., 2002). In Arabidopsis, GORK is the major outward K+ channel gene expressed in guard cells. T-DNA knockout and dominant negative mutants of GORK showed impaired outwardly rectifying K+ channel activity (Hosy et al., 2003). Gork mutants also have increased water loss in both excised

Signaling

Protein Kinases

Enzymes

Proteins GPCRs

and Phosphatases InsP5-ptase

CIPK

CDPK

CBL

PP2C

PP2A

SnRK2

PTP

MAPK

Myrosinase HT G proteins PLD ROP G Proteins

LCBK SphK Rboh NR

ABA

Drought

LRR-RLKs

PLC

Membrane Transport

Secondary Messengers

TF and RNABinding Proteins

H+ATPase Ca2+in K+in K+out CHX

NRT

Ca2+

InsPs

phytoS1P S1P NO

PA

ROS

PIPs

bZIP bHLH Myb

MRP Anion Channels

isothiocynanates

CBP

Stomatal Drought Response Figure 1.2 Cellular mediators of stomatal drought response. The ABA-mediated drought stimulus acts on signaling proteins and membrane-transport–related proteins. Signaling proteins regulate membrane transport and intracellular enzymes. Although in mammalian systems, membrane-signaling proteins can directly activate intracellular protein kinases, this type of connection has not yet been found in guard cells. Changes in secondary messengers are regulated by enzymes that catalyze the formation of signal molecules or by membrane transport. Secondary messengers regulate many downstream signaling components. Protein kinases and phosphatases are also involved in stomatal drought response. Protein kinases and phosphatases can regulate membrane transporters, which is omitted from this figure for clarity. Transcription factors regulate both drought-induced gene expression and stomatal development. mRNA-binding proteins are involved in stomatal ABA and humidity responses. Ht G proteins: Heterotrimeric G proteins NR: Nitric reductases InsPs: Inositol phosphates CHX: Cation/H+ exchanger NRT: Nitrate transporter PIPs: Phosphatidylinositol phosphates MRP: ATP-binding cassette (ABC) transporters CBP: mRNA cap-binding proteins Rboh: NADPH oxidases

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leaf and whole plant experiments (Hosy et al., 2003). Besides plasma membrane K+ channels, plants also have ion channels localized in internal membranes, including AtTPK1, which is a two-pore potassium channel mainly located in the tonoplast. Knockouts of AtTPK1 have reduced stomatal closure rate as compared to wild type, which is consistent with AtTPK1-mediated K+ release from vacuole to cytoplasm (Gobert et al., 2007). Besides K+ channels, anion channels are also important for stomatal movement in response to environmental stimuli. SLOW ANION CHANNEL-ASSOCIATED 1 (SLAC1) was recently cloned and proposed to encode a guard cell anion channel (Negi et al., 2008; Vahisalu et al., 2008). Knockouts of SLAC1 showed impaired stomatal responses to a range of environmental signals including ABA and humidity. Although knockouts of SLAC1 showed abolished anion channel activity in planta, heterologous expression of SLAC1 in yeast cannot complement a yeast malate uptake deficient mutant, and expression of SLAC1 in Xenopus oocytes does not result in anion channel activity (Negi et al., 2008; Vahisalu et al., 2008); one explanation is that additional components are required for SLAC1 anion channel activity. ATP-binding cassette proteins have been found to modulate guard cell anion channel activity and thus are potential partners for SLAC1 (Leonhardt et al., 1999). Two ATP-binding cassette (ABC) transporters, AtMRP4 and AtMRP5, have been studied in detail in Arabidopsis. Mutants of AtMRP4 and AtMRP5 have opposite phenotypes. In atmrp4 mutants, stomatal apertures are larger than wild type under both light and dark while stomatal ABA response is similar to wild type; therefore, plants lose more water under drought conditions (Klein et al., 2004). In contrast, atmrp5 mutants showed reduced water loss (Klein et al., 2003). Although ABA-induced stomatal closure is impaired in atmrp5 mutants, lightinduced stomatal opening is also reduced, which could be the main reason for the reduced water loss observed at the whole plant level (Klein et al., 2003). Besides the well-known K+ channels and anion channels, a number of other relevant membrane transporters have also been identified. For example, AtCHX20, a cation/H+ exchanger, was found to be a positive regulator of light-induced stomatal opening (Padmanaban et al., 2007). Knockouts of a nitrate transporter, AtCHL1/NRT1, implicated in NO3− uptake during stomatal opening, have enhanced drought tolerance (Guo et al., 2003). In addition, a PLEIOTROPIC DRUG RESISTANCE 3 transporter, AtPDR3 (Galbiati et al., 2008), and a sucrose transporter, AtSUC3 (Meyer et al., 2004), are both highly expressed in guard cells, although their roles in guard cell physiology remain unknown.

Calcium

ABA induces cytosolic Ca2+ increases through the activation of Ca2+ permeable channels in the plasma membrane and through the activation of Ca2+ release from internal stores. Elevated cytosolic Ca2+ ions can inhibit inward K+ channels and H+ pumps at the plasma membrane while activating anion channels (MacRobbie, 1998). Arabidopsis CYCLIC NUCLEOTIDE GATED CHANNEL2 (CNGC2/DND1) mediates cAMPinduced Ca2+ influxes through plasma membrane channels (Ali et al., 2007). Despite the importance of Ca2+ channels in stomatal ABA response, guard cell phenotypes have not yet been tested under ABA or drought treatment in the dnd1 mutant. Ca2+ release from internal stores may be partially mediated by a tonoplast channel, the two pore channel TPC1 (Furuichi et al., 2001; Peiter et al., 2005; but see Ranf et al., 2008). In addition, a thylakoid-localized calcium sensor (CAS1) may participate in Ca2+-induced Ca2+ release from chloroplasts (Han et al., 2003; Nomura et al., 2008). Both the cas1 mutant and the tpc1 mutant showed impaired extracellular Ca2+-induced stomatal

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closure, but these two mutants did not have impaired stomatal ABA response (Han et al., 2003; Peiter et al., 2005). Little is known about the importance of Ca2+-induced closure under physiological conditions, thus the role of these two proteins in plant drought response is not clear (Ranf et al., 2008; Nomura et al., 2008).

ROS and NO

ABA activates plasma membrane Ca2+ channels by inducing cytosolic H2O2 production by the NADPH oxidases, AtrbohD, and AtrbohF in Arabidopsis guard cells (Kwak et al., 2003; Murata et al., 2001; Pei et al., 2000). AtrbohD/F-mediated ROS production is also involved in stomatal ethylene (Desikan et al., 2006) and jasmonate signaling (Munemasa et al., 2007). Other evidence also suggests that a small GTPase ROP2 and the phosphatidic acid (PA) pathway is involved in ABA-induced ROS production (Park et al., 2004). ROS also inhibit the enzymatic activities of other mediators of stomatal ABA responses, such as plasma membrane H+ ATPase activation (Zhang et al., 2004b), and protein phosphatases ABI1 and ABI2 (Meinhard and Grill, 2001). Further, an Arabidopsis glutathione peroxidase, AtGPX3, interacts with ABI1 and ABI2 and acts as a positive regulator of stomatal ROS responses (Miao et al., 2006). In Arabidopsis, ABA-induced NO production in guard cells is mainly mediated by NIA1 and NIA2 (Desikan et al., 2002), and also requires H2O2 production from the AtrbohD/F pathway (Bright et al., 2006). NO activates anion channels and inhibits inward K+ channels through cADPRand cGMP-dependent intracellular calcium release (Garcia-Mata et al., 2003; Sokolovski et al., 2005), whereas NO inhibition of outward K+ channels may be through direct protein modification (Sokolovski and Blatt, 2004).

Other Small Intracellular Molecules

ABA-induced cytosolic Ca2+ oscillation and stomatal closure are partially mediated by phospholipase C (PLC), which hydrolyzes phosphatidylinositol-4,5-bisphosphate (PIP2) into inositol-1,4,5trisphosphate (InsP3) (which induces Ca2+ release) and diacylglycerol (Staxen et al., 1999; Cousson, 2003). Inositol polyphosphate 5-phosphatase (InsP 5-ptase) catalyzes the hydrolysis of InsP3 and terminates the ABA induced InsP3 signaling (Perera et al., 2008). Transgenic plants with overexpression of InsP 5-ptase showed better whole plant drought tolerance and less water loss from excised leaves than wild type. Transgenic plants are hypersensitive to ABA-induced stomatal closure but insensitive to ABA inhibition of stomatal opening (Perera et al., 2008). These results suggest that, under this particular experimental condition, hypersensitivity to ABAinduced stomatal closure is more important for the observed whole plant drought tolerance phenotype. In a functional proteomic study, TGG1 was identified as one of the most abundant proteins in guard cells (Zhao et al., 2008). TGG1 encodes a myrosinase, which cleaves glucosinolates to produce toxic compounds such as isothiocyanates. T-DNA knockouts of tgg1 are hyposensitive to ABA-inhibited stomatal opening, consistent with a lack of ABA inhibition of inward K+ channels in guard cells of tgg1 mutants (Zhao et al., 2008). The glucosinolate-myrosinase system is well known for its function in plant biotic stress response (Barth and Jander, 2006). Because of the dual role of TGG1 in both biotic and abiotic stress responses, TGG1 is a good candidate for engineering pest and drought resistant crop plants.


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Cell Signaling Mutants with Altered Stomatal Responses

Stomatal drought response involves not only ion channels and secondary messengers, but also a large number of cell-signaling proteins including protein kinases, protein phosphatases, G-proteins, and farnesyl transferase. Interestingly, many of these signaling proteins are also related to stomatal humidity response, which is also discussed in this section (see Figure 1.2).

Protein Kinases SnRK Protein Kinases Among many different types of protein kinases, members of the SNF1-

related protein kinase (SnRK) family are conserved mediators of ABA and osmotic stress responses in different plant species. Proteins from this family, including Vicia faba AAPK, barley PKABA, soybean SPK1/2, tobacco OSAK, rice SAPK, and those from Arabidopsis (Li et al., 2000; GomezCadenas et al., 2001; Monks et al., 2001; Mustilli et al., 2002; Kelner et al., 2004; Kobayashi et al., 2004) are activated by ABA, osmotic stress or both in various plant tissues. Among these genes, OST1 and AAPK are highly expressed in guard cells and play roles in stomatal function. AAPK was first identified as an ABA-activated protein kinase by in-gel kinase assay followed by mass spectrometry–based protein identification, while its ortholog OST1 was isolated in Arabidopsis by a thermal imaging-aided forward genetic screen. Originally, two mutant alleles of OST1, ost1-1 and ost1-2, were isolated by map-based cloning. Both mutants contain point mutations, and the mutant plants lack stomatal ABA responses but not stomatal light and CO2 responses (Mustilli et al., 2002). OST1 protein is activated by ABA in guard cells and roots in a calcium independent manner. This activation is blocked by the abi1-1 mutation but not by the abi2-1 mutation (Yoshida et al., 2006a). Also, ABA-induced ROS production in guard cells is interrupted by ost1 mutation, while direct application of ROS and calcium can cause stomatal closure in ost1 mutants. This evidence indicates that OST1 is upstream of ROS production and downstream of abi1-1 (Mustilli et al., 2002). Subsequent experiments showed that the ABI1 protein physically interacts with the C-terminal domain II of OST1, which is required for OST1 function in guard cell ABA responses. On the other hand, the OST1 C-terminal domain is not required for ABA response but is required for osmotic activation of OST1 kinase activity (Yoshida et al., 2006a). OST1 and several other SnRK2-like proteins phosphorylate AREB1, a transcription factor that binds to ABA-responsive elements (ABRE). These results support a critical role of the OST1 kinase in guard cell signaling. LRR Receptor Kinases Receptor-like kinases (RLK) are transmembrane kinases that function in

cell-to-cell communication and environmental signal perception in many eukaryotes (Dievart and Clark, 2004). The Arabidopsis genome contains more than 400 RLKs, with 216 Leucine-rich repeat (LRR)–containing receptor kinases representing the largest RLK family. In general, LRR receptor kinases have three domains: one LRR-containing extracellular domain, one intracellular serine/ threonine (ser/thr) protein kinase domain, and one single-pass transmembrane domain (Dievart and Clark, 2004). Arabidopsis RPK1 is an LRR receptor kinase that mediates ABA responses in plant germination, growth, and stomatal responses. The transcript level and protein level of RPK1 are both upregulated by ABA. In two independent mutant alleles of RPK1, ABA-induced stomatal closure was impaired (Osakabe et al., 2005). However, components downstream of RPK1 and the effects of rpk1 mutation on plants under drought conditions remain unknown.

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The Arabidopsis ERECTA (ER) gene is a well-known LRR-receptor kinase that controls plant lateral organ size and flower development by regulating plant cell proliferation (Shpak et al., 2004). By quantitative trait loci (QTL) analysis, the Arabidopsis ERECTA gene was found to be a genetic regulator of transpirational efficiency (TE) (Masle et al., 2005), which is a measure of plant water use efficiency. All three erecta mutants used in the study showed increased stomatal conductance, partly due to the increased number of stomata on the leaf surface. Erecta mutants also have fewer mesophyll cells and reduced photosynthetic capacity as compared to plants with a functional ERECTA gene. Both increased stomatal conductance and decreased photosynthetic capacity negatively affect TE; consequently, erecta mutants have lower TE in both well-watered and drought conditions (Masle et al., 2005). Because the ABA sensitivities of erecta mutants have not yet been tested, to date, evidence suggests that ERECTA affects plant TE through multiple morphological changes. CDPK and CIPK Network Cytosolic Ca2+ is one of the key secondary messengers for multiple

cellular responses in guard cells. Ca2+-dependent protein kinases (CDPK) (Cheng et al., 2002), and calcineurin B–like (CBL) proteins (Cheong et al., 2007) are important intracellular calcium sensors, propagating the cytosolic Ca2+ signal to downstream targets such as membrane ion channels and transcription factors. The Arabidopsis genome encodes 34 CDPKs, each of which contains a kinase domain, an autoinhibitory domain and a calmodulin-like domain (Cheng et al., 2002). The calmodulin-like domain binds Ca2+ when the cytosolic free Ca2+ concentration is elevated, thus causing a conformational change that releases the autoinhibitory domain from the kinase domain and activates the CDPK. Recently, four Arabidopsis CDPKs were found to be related to stomatal functions. CPK3 and CPK6 were first identified by PCR from a guard cell–enriched cDNA library, and their expressions in guard cells were later confirmed by both RT-PCR and microarray analysis (Mori et al., 2006). In both single mutants, cpk3 and cpk6, and the double mutant cpk3cpk6, Ca2+ activation of slowanion channels was impaired. Furthermore, in cpk3cpk6 double mutants, ABA activation of S-type anion channel was strongly inhibited. In guard cells, one way of increasing cytosolic Ca2+ concentration is through activation of plasma membrane Ca2+ permeable-channels, which can be activated by ROS. In the cpk3cpk6 double mutant, ABA but not ROS activation of Ca2+ channels was inhibited. Finally, in both single mutants and the double mutant, ABA-induced stomatal closure was impaired (Mori et al., 2006). The functions of two other CDPKs, CPK4 and CPK11, have been characterized for various ABA responses in different organs and tissues (Zhu et al., 2007). For drought-related functions, the single mutants and double mutant of these two CDPKs are less sensitive to ABA-induced stomatal closure as compared to wild type, while overexpression of either CDPK conveys ABA hypersensitivity. In water-loss experiments, single and double mutants lost more water, while overexpression lines conserved more water than wild-type plants. Furthermore, in drought-rewater experiments, overexpression lines had higher survival rates than wild-type plants whereas all single and double mutants died under the same condition. Two ABA response transcription factors, ABF1 and ABF4 can be phosphorylated by these two CDPKs, which connects these CDPKs’ functions to transcriptional regulation (Zhu et al., 2007). Unlike the CDPKs, which can be directly activated by Ca2+, CBL proteins do not contain kinases domains and cannot directly phosphorylate downstream effectors. Instead, after Ca2+ binding at EF-hand domains, CBL proteins can bind and activate CBL-interacting protein kinases (CIPK). The Arabidopsis genome encodes 10 CBL proteins and 25 CIPKs (Kolukisaoglu et al., 2004), which form an interconnected network, with multiple CBLs interacting with the same


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CIPK. T-DNA insertional mutants for all 25 CIPKs in Arabidopsis were screened for their phenotypes under drought conditions (Cheong et al., 2007). Among the CIPK mutants tested, the cipk23 mutant had the best survival rate (70%) while wild-type mutants had only a 20% survival rate under the same experimental condition. A number of drought- or ABA-inducible genes were induced to a similar level in wild-type and cipk23 mutants, and ABA content was not elevated in the cipk23 mutant. However, cipk23 mutant stomata are hypersensitive in both ABA-induced stomatal closure experiments and ABA-inhibited stomatal opening experiments. Earlier publications had shown that the upstream factors of CIPK23 are CBL1 and CBL9 (Kolukisaoglu et al., 2004), both of which can interact with CIPK23 and in turn, activate a K+ channel, AKT1, in roots (Xu et al., 2006). The double mutant, cbl1cbl9, but not cbl1 or cbl9 single mutants, conserved more water in water-loss experiments as compared to wild type (Cheong et al., 2007). Since CIPK23, CBL1, and CBL9 coexpress in guard cells (Cheong et al., 2007), these results suggest that CBL1 and CBL9 function synergistically upstream of CIPK23 in the regulation of stomatal ABA response and plant drought tolerance. In another study, CBL1 (called SCaBP5 in the publication) and CIPK15 (called PKS3) were shown to interact in yeast two hybrid experiments. RNA interference (RNAi) lines of cbl1 or cipk15 were hypersensitive to ABA in stomatal closure, and thus lost less water as compared to wild type (Guo et al., 2002). However, the effect of CBL1 RNAi lines, stronger than knockout lines, could be due to off target effects of RNAi. CIPK15 can interact with an APETALA2/EREBPtype transcription factor, AtERF7, which is a negative regulator in stomatal ABA responses (Song et al., 2005). MAP Kinases Cascade Mitogen-activated protein kinase (MAPK) cascades are conserved signaling modules, consisting of three types of protein kinases: MAPK, MAPK kinase (MAPKK), and MAPKK kinase (MAPKKK). As indicated by their names, MAPKKKs phosphorylate MAPKKs and MAPKKs phosphorylate MAPKs, which propagate signals to downstream effectors. Plant MAPK cascades are known to be involved in multiple biotic and abiotic stress responses, hormonal responses, and plant development (Colcombet and Hirt, 2008). Recently, two members of Arabidopsis MAPK cascades, MPK3 (Gudesblat et al., 2007) and MEK1 (Xing et al., 2007), were found to be related to stomatal ABA responses. Antisense lines of MPK3 were found to be less sensitive to ABA inhibition of stomatal opening under normal conditions, while ABA promotion of stomatal closure was reduced in these lines if cytosolic pH was also clamped. Further experiments showed that stomata in these RNAi lines are less sensitive to H2O2-induced stomatal closure and inhibition of opening. However, ABA-induced H2O2 production was not affected. These results suggest that MPK3 is involved in cytosolic pH regulation and is downstream of H2O2 production (Gudesblat et al., 2007). Unlike MPK3, MEK1 was found to be upstream of H2O2 production in Arabidopsis. A mek1 T-DNA insertional mutant is less sensitive to ABA-induced closure and hypersensitive to drought stress, while overexpression lines of MEK1 are more resistant to drought treatment (Xing et al., 2007). Although MPK3 and MEK1 are related to stomatal ROS response, several other members of MAPK cascades have been found to be related to stomatal development and will be discussed in a later section.

Phosphatases

Plant protein phosphatases catalyze dephosphorylation reactions. Members from three categories of protein phosphatases, protein phosphatases 2C (PP2C), protein phosphatases 2A (PP2A), and

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phosphotyrosine phosphatase (PTP) are involved in stomatal drought response (Farkas et al., 2007; Luan, 2003). PP2C Type 2C protein phosphatases are important modulators in stomatal ABA responses.

So far, four Arabidopsis PP2Cs—ABI1, ABI2, HAB1 and AtPP2CA—have been studied in detail. By forward genetic screen, dominant mutants abi1-1 and abi2-1 were both found to be ABA insensitive in germination assays (Koornneef et al., 1984). These loci were later found to encode two closely related PP2Cs, ABI1 and ABI2, whose phosphatase activities were diminished by point mutations at the same conserved amino acid in abi1-1 and abi2-1 (Leung et al., 1994; Meyer et al., 1994; Bertauche et al., 1996; Leung et al., 1997; Rodriguez et al., 1998a). Although phenotypes of these mutants suggest that both genes are positive regulators for various plant ABA response, experiments using recessive and loss of function mutants of ABI1 and ABI2 indicate that both genes are negative regulators of ABA signaling (Gosti et al., 1999; Merlot et al., 2001). Due to this difference in ABA sensitivities between different types of mutants, one should be cautious about the placement of these two proteins within the ABA signaling network. However, biochemical experiments using wild-type ABI1 and ABI2 showed that both proteins can be modulated by a number of signaling molecules downstream of ABA. For example, ABA causes increased cytosolic ROS, which inhibits the phosphatase activity of ABI1 and ABI2, while ABA also causes a cytosolic pH increase, which increases the phosphatase activity of ABI1 and ABI2 (Leube et al., 1998; Meinhard and Grill, 2001; Meinhard et al., 2002). In yeast, two hybrid experiments, ABI2, and, to a lesser extent, ABI1, bind to protein kinase CIPK15, which is a negative regulator of stomatal ABA response (Guo et al., 2002). Also, an important ABA-induced secondary messenger, phosphatidic acid (PA), interacts with ABI1 and represses ABI1 activity (Zhang et al., 2004). This process is related to ABA and G protein regulation of stomatal aperture and will be discussed in detail in a later section. Genetic analysis of abi1 and abi2 recessive mutants also showed that these two genes have synergistic functions in ABA-induced stomatal closure and hence plant drought response (Merlot et al., 2001). Interestingly, such synergistic interaction was also observed between ABI1 and another Arabidopsis PP2C, HAB1 (Saez et al., 2006). As a highly ABA-induced ABI1/2 homologue (Rodriguez et al., 1998), HAB1 is expressed ubiquitously in all plant tissues and is highly expressed in ABA target tissues, including guard cells, as shown by reporter gene (Saez et al., 2004) and microarray (Leonhardt et al., 2004) analyzes. Although stomata are hypersensitive to ABA promotion of closure in one hab1 knockout line (Leonhardt et al., 2004), another knockout line shows a wild-type response in water loss assays (Saez et al., 2004). Double mutants of both hab1 and one of the abi1-2 or abi1-3 knockout lines showed stronger stomatal ABA response and better drought tolerance than single recessive mutants of either gene or wild type (Saez et al., 2006). Such synergistic interactions between PP2C genes are likely due to their redundant yet not completely overlapping functions in plant ABA and drought response. The last PP2C protein discussed in this chapter is AtPP2CA, which interacts with K+ channels AKT2 and AKT3, and thus may be involved in regulation of plant K+ homeostasis (Vranova et al., 2001; Cherel et al., 2002). Atpp2ca knockout plants are hypersensitive to ABA promotion of stomatal closure, but perform similarly to wild-type plants in water-loss experiments. Overexpression of AtPP2CA causes stomatal ABA insensitivity and increased plant water loss (Kuhn et al., 2006). Interestingly, plants with overexpression of HAB1 also showed ABA insensitivity while knockout lines of the same gene were not significantly different from wild type. These results again suggest redundant functions between PP2C genes. Finally, a mutant with


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reduced AtPP2CA phosphatase activity also has increased ABA accumulation in seeds (Yoshida et al., 2006b). Whether mutants of other PP2C genes also affect ABA accumulation is not known. PP2A RCN1 is a guard cell–enriched protein phosphatase 2A that is also expressed in other tissues including mesophyll cells, as revealed by promoter GUS analysis (Kwak et al., 2002). In guard cells, an rcn1 T-DNA knockout impairs two important intermediate ABA responses, cellular ROS increase and intracellular Ca2+ increase; loss of these responses in turn causes impaired anion channel and inward K+ channel responses to ABA. Thus, rcn1 mutant guard cells are less sensitive to ABA-induced stomatal closure (Kwak et al., 2002). MeJA-induced stomatal closure is also impaired in the rcn1 mutant; and MeJA no longer promotes ROS production, which suggests ABA and MeJA cross talk in guard cells is mediated by a RCN1-ROS pathway (Saito et al., 2008). PTP Experiments using a protein tyrosine phosphatase (PTP) inhibitor, phenylarsine oxide, supported a role of PTP in stomatal ABA response (MacRobbie, 2002). In a recent study, a T-DNA insertional mutant, phs1-3, which has reduced expression of tyrosine phosphatase PHS1, was found to have closed stomata under light when the measurement was taken immediately after harvest. When stomata of phs1-3 were opened using an opening medium under light, they showed hypersensitivity in ABA inhibition of light-induced stomatal opening (Quettier et al., 2006). Further investigation using knockout lines of PHS1 may provide more insight into the function of PTP in stomatal ABA response.

G Proteins

Several plant G-proteins, including members of the heterotrimeric G-protein complex and three small GTPases, have been found to mediate stomatal drought response. Heterotrimeric G-proteins are comprised of three subunits, Gα, Gβ, and Gγ, whereas proteins in the small GTPases family are monomeric proteins (Assmann, 2005). Heterotrimeric G Proteins Canonical heterotrimeric G-protein complexes are signaling mediators that transduce extracellular signals perceived by G-protein–coupled receptors (GPCR) to intracellular effectors such as phospholipases and ion channels. The GDP-bound Gα subunit, Gβ subunit, and Gγ subunit form a heterotrimer, which binds to cytosolic regions of the GPCR. Activation of the GPCR causes the exchange of GDP for GTP on the Gα subunit, and also the dissociation of GTP-bound Gα from the Gβγ dimer. Both the Gα-GTP and the Gβγ dimer can activate downstream effectors until the intrinsic GTPase activity of Gα hydrolyzes the GTP into GDP. Then, inactivated Gα recruits Gβγ back into the inactive heterotrimeric complex (Jones and Assmann, 2004; Assmann, 2005). The Arabidopsis genome encodes one canonical Gα subunit, GPA1, one Gβ subunit, AGB1 and two known Gγ subunits, AGG1 and AGG2 (Ma et al., 1990; Weiss et al., 1994; Mason and Botella, 2000). T-DNA insertional mutants of GPA1 and AGB1 are both insensitive to ABA inhibition of stomatal opening, but not ABA induction of stomatal closure (Wang et al., 2001; Fan et al., 2008). These phenotypes are consistent with the electrophysiological evidence that gpa1 and agb1 mutants are both insensitive to ABA inhibition of inward K+ channels and conditionally insensitive to ABA activation of anion channels (i.e., insensitive under strong cytosolic pH buffer). In addition, the gpa1 mutant has wider stomata in normal growth conditions and loses more water as compared to

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wild-type plants. This evidence in total supports a role of GPA1 and AGB1 in stomatal drought response (Wang et al., 2001; Fan et al., 2008). However, the knockout mutants of two Gγ subunits, AGG1 and AGG2, do not show the same stomatal phenotype as gpa1 and agb1 in response to ABA (Trusov et al., 2008). Such an observation may suggest plant heterotrimeric G-proteins function differently from mammalian G proteins or that additional Gγ subunits exist. Sphingosine-1-phosphate (S1P) is one signaling molecule upstream of GPA1-mediated stomatal drought responses (Ng et al., 2001; Coursol et al., 2003; Worrall et al., 2008). Sphingosine kinase (SPHK) catalyzes S1P production, and SPHK activity is enhanced by ABA (Coursol et al., 2003). S1P inhibits stomatal opening and promotes stomatal closure by inhibiting inward K+ channels and activating outward anion channels in wild-type plants, whereas in the gpa1 mutant, S1P cannot regulate ion channels or stomatal apertures (Coursol et al., 2003). In addition to S1P, GCR1 is another upstream component of GPA1-mediated stomatal signaling. GCR1 has sequence homology to one class of mammalian GCPR and was shown to interact with GPA1 both in vitro and in vivo. Leaves from gcr1 mutants lose less water as compared to wild type. Also, gcr1 mutants showed better tolerance to drought treatment and higher survival rate after re-watering. In contrast to the gpa1 mutant, stomata of gcr1 mutant are hypersensitive to both ABA and S1P, which suggests that GCR1 is a negative regulator of G protein signaling in guard cells (Pandey and Assmann, 2004). Recently, several other candidate plant GPCRs have been cloned (Gookin et al., 2008). GGR2 had been proposed as an ABA receptor that interacts with GPA1 (Liu et al., 2007). However, the function of GCR2 is still under debate (Gao et al., 2007; Guo et al., 2008; Illingworth et al., 2008). Besides GCR2, two novel GPCR-Type G proteins, GTG1 and GTC2, have been identified and shown to bind to ABA (Pandey et al. 2009). The double mutant gtg1 / gtg2 is hyposensitive to ABA in assays of ABA-induced stomatal closure, but has a wild type response in ABA inhibition of stomatal opening. GTG1 and GTG2 each can interact with GPA1 in vitro and in vivo, which supports their roles in G protein signaling (Pandey et al., 2009). Phospholipase D (PLD) activity and the resultant product, phosphatidic acid (PA), are also related to GPA1 function in stomatal ABA response. In wild-type plants, ABA promotes PLD activity and PA production, which in turn promote stomatal closure and inhibit stomatal opening. Arabidopsis PLDα1 physically interacts with GPA1 and can stimulate the GTPase activity of GPA1. While GPA1 binding activates PLDα1 activity, adding GTP into the reaction negatively regulates GPA1-PLDα1 binding (Zhao et al., 2004). Stomatal ABA and PA responses were tested in two single mutants, pldα1 and gpa1, and double mutant pldα1gpa1 (Mishra et al., 2006). In these mutants, plants harboring gpa1 mutation are hyposensitive to ABA and PA inhibition of stomatal opening. Plants harboring pldα1 mutation are hyposensitive to ABA-induced stomatal closure, and this insensitivity can be rescued by exogenous PA treatment (Mishra et al., 2006). These results suggest PA is upstream of GPA1 in ABA inhibition of stomatal opening but not in ABA-induced stomatal closure. The observed stomatal phenotype of pldα1 knockout mutants can be partially explained by the interaction between PA and ABI1, because the latter is a negative regulator of stomatal ABA response. PA interacts with ABI1 and reduces the effect of ABI1 by both inhibition of phosphatase activity and by relocation of ABI1 from cytosol to cell membrane (Zhang et al., 2004). ABA induced a similar level of stomatal closure in wild type and abi1 knockout mutant, but not in an abi1 mutant expressing PA insensitive ABI1R73A protein. Also, ABA failed to induce stomatal closure in the pldα1 mutant, but this phenotype was reversed in the pldα1; abi1 double mutant. All the evidence above implies that the ABA-PLD-PA-ABI pathway is important for ABA-induced stomatal closure. However, both abi1 knockout and ABI1R73A mutants showed wild-type ABA response in opening experiments, suggesting that ABI1 is not related to the ABA inhibition of opening pathway (Mishra et al., 2006).


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Small GTPase Two small GTPases, AtRAC1 (ROP6) and ROP10, have been found to be negative regulators of stomatal ABA responses. As a guard cell–enriched ROP GTPase, AtRAC1 mediates stomatal ABA responses through regulation of the guard cell actin cytoskeleton. In transgenic plants expressing dominant-positive (i.e., constitutively active) AtRAC1 (DP-RAC1), ABA-induced actin disruption as well as ABA-induced stomatal closure were impaired. These impaired phenotypes were also observed in the abi1-1 mutant, in which AtRAC1 cannot be activated by ABA. Furthermore, dominant negative AtRAC1 (DN-RAC1) transgenic plants showed stomatal closure even without ABA treatment, both in the wild-type background, and in an abi1-1 mutant background (Lemichez et al., 2001). However, as discussed above, current evidence is not sufficient to infer the relationship between AtRAC1 and ABI1 in the wild-type signaling network. Compared to AtRAC1, enrichment of ROP10 in guard cells is less pronounced, based on promoter GUS analysis. A null mutant of ROP10 is hypersensitive to ABA-induced stomatal closure and is more drought resistant than wild type. ROP10 protein localization at the guard cell membrane is weakly disrupted in an ABA-hypersensitive mutant, era1-2 (Zheng et al., 2002). Another small GTPase, ROP2, is related to the stomatal light response. Plants transformed with constitutively active ROP2 protein have large stomatal apertures under darkness and smaller stomatal apertures under light than wild type, while stomata of a dominant negative ROP2 mutant as well as an rop2 knockout mutant showed faster and larger opening under light. Further, thermal imaging showed that constitutively activated ROP2 (CA-ROP2) plants have higher leaf temperatures due to smaller stomatal apertures (Jeon et al., 2008). These results support a role of ROP2 in light responses of stomata; however, earlier results also suggest that ROP2 is involved in PA-induced ROS production in guard cells (Park et al., 2004). Because PA and ROS are positive regulators of ABA-induced stomatal closure, it can be postulated that ROP2 is also involved in stomatal ABA responses.

Farnesyl Transferase

ERA1 is the β-subunit of Arabidopsis farnesyl transferase (Cutler et al., 1996). In yeast and mammalian systems, farnesyl transferase catalyzes lipid modifications of signal transduction proteins to target them to the plasma membrane. Arabidopsis era1 mutants are hypersensitive to ABA-induced stomatal closure, partly due to reduced ABA activation of Ca2+ channels and anion channels in era1-2 guard cells (Pei et al., 1998; Allen et al., 2002). The era1-2 mutant has a reduced wilting phenotype under drought conditions but also has a reduced growth compared to wild type. The effect on crop drought tolerance of changing farnesyl transferase expression was tested in Brassica napus (Wang et al., 2005). The drought-inducible promoter used in this study provides reversible and conditional expression of an antisense ERA1 construct, which overcomes the pleiotropic effects of constitutive silencing of ERA1. Seed yield was increased in the transgenic plants under drought conditions, but was not affected under normal growth conditions (Wang et al., 2005). The conditional knockout strategy can be applied to crops using other regulators of stomatal ABA response to improve crop drought tolerance.

Genes Related to Humidity Sensing

Genetic regulators of plant humidity responses have begun to emerge in recent years. Early experiments showed that two Arabidopsis MAP kinases, AtMPK4 and AtMPK6, were not responsive at

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the transcript level, but were activated by humidity and osmotic stresses (Ichimura et al., 2000). Later, a protein kinase SnRK2e (Yoshida et al., 2002), subsequently called OST1 (Xie et al., 2006), was not only activated by ABA but also by low humidity in plants. By a reverse genetic approach, a T-DNA insertional mutant of SnRK2e was isolated and young mutant plants showed a wilting phenotype under rapid low humidity treatment. In the same study, it was found that several other protein kinases were activated by low humidity or ABA. The identities of these other proteins remain elusive (Yoshida et al., 2002). OST1 was also identified, as was ABA2, from a forward genetic screen for humidity response mutants, using thermal imaging of leaf surface temperature on an EMS-mutagenized population (Xie et al., 2006). Another ABA catabolism gene, CYP707A1, is also induced at the transcript level by high humidity in guard cells in Arabidopsis (Okamoto et al., 2008). An mRNA cap binding protein mutant, abh1, also exhibits an altered response to humidity (Hugouvieux et al., 2002). Under low humidity growth conditions, abh1 mutants have smaller stomatal apertures as compared to wild-type plants, while subsequent electrophysiology experiments correlated that phenotype to enhanced slow anion channel activity and reduced inward K+ channel activity. The abh1 mutant is also hypersensitive to ABA-inhibited seed germination and has strong whole plant phenotypes (Hugouvieux et al., 2001). The Arabidopsis anion channel mutant, slac1, also has altered humidity responses (Vahisalu et al., 2008). Another experiment showed that LCBK, a sphingoid long-chain base (LCB) kinase, is slightly activated by humidity (Imai and Nishiura, 2005). As discussed above, sphingosine signaling has been found to be involved in stomatal ABA response (Coursol et al., 2003). All of these experiments imply a connection between ABA signaling and humidity responses; however, none of these results can rule out the possibility of a humidity specific pathway that is independent from ABA signaling (Assmann et al., 2000). Finally, a novel hat1 mutant was found to survive low humidity for 6 days while wild-type plants died in 24 hours. Hat1 maps to a 168kb region in chromosome 5, which contains 21 genes (Yan et al., 2006). Cloning of HAT1 may provide new insight into Arabidopsis humidity responses.

Transcriptional Regulation in Stomatal Drought Response

In addition to inducing fast and reversible responses such as stomatal closure, drought stress also causes dynamic changes in gene-expression patterns, which are considered to be slower than most of the above discussed signaling processes. Using microarray and sequencing technologies, thousands of gene-expression changes were found in response to drought or ABA at the whole plant level (Seki et al., 2002; Hoth et al., 2002) and in guard cells (Leonhardt et al., 2004). A protein phosphatase 2C gene, AtPP2C-HA, was identified based on the results from the guard cell microarray (Leonhardt et al., 2004). In this section, several transcriptional regulators involved in stomatal drought response will be discussed. Because drought and other environmental factors can also modulate stomatal size and distributions in developing leaves, recent advances in the stomatal development and patterning will also be discussed in this section.

Transcription Factors in Stomatal Drought Response

ABF2 (Kim et al., 2004), ABF3, and ABF4 (Kang et al., 2002) are basic leucine zipper (bZIP) proteins that can bind to ABREs of promoters. All three genes have strong expression in guard cells, suggesting functions in regulation of stomatal gene expression. Transgenic plants with overexpression of any of these three genes showed reduced water loss from excised leaves and increased


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drought tolerance, which is likely due to reduced stomatal opening and changes in expression of genes such as ABI1, ABI2, KAT1, and KAT2 (Kang et al., 2002; Kim et al., 2004). A RING finger E3 ligase, SDIR1, is a putative upstream regulator of ABF3 and ABF4, because overexpression of ABF3 or ABF4 can partially rescue the ABA insensitive phenotype of an sdir1 knockout mutant (Zhang et al., 2007). SDIR1 is expressed in all tissues tested and significantly induced by drought in guard cells. Plants with SDIR1 overexpression are more tolerant to drought while sdir1 knockout plants are more sensitive to drought as compared to wild type. Under normal watering, SDIR1 overexpression lines also have smaller stomatal apertures and are hypersensitive to ABA-induced stomatal closure, while knockout lines have larger stomata and are less sensitive to ABA compared to wild type and sdir1 knockout lines (Zhang et al., 2007). Three R2R3-Myb transcription factors, AtMYB44, AtMYB60, and AtMYB61, are also related to stomatal function. All three Myb transcription factors are highly expressed in guard cells as shown by promoter reporter gene assays. However, each of these three genes acts on different aspects of stomatal responses. AtMYB44 overexpression lines have smaller stomatal apertures in normal conditions and faster closure rate under ABA treatment as compared to wild type, while knocking out AtMYB44 does not result in altered phenotypes (Jung et al., 2008). Unlike AtMYB44, AtMYB60 and AtMYB61 are not involved in stomatal ABA responses, but in light regulation of stomatal movements. T-DNA insertional mutant atmyb60 has smaller stomatal openings under light as compared to wild type, while a myb61 mutant showed defects in dark-induced stomatal closures (Newman et al., 2004; Cominelli et al., 2005). Drought stress responses were directly tested for AtMYB60 and AtMYB44. Both the atmyb60 mutant and AtMYB44 overexpression plants showed increased drought tolerance, which is consistent with their roles in stomatal regulation (Newman et al., 2004; Jung et al., 2008). ABO1 is homologous to a subunit in yeast and human elongator complex, which is a multiprotein complex functioning in transcript elongation. abo1-1 mutants lose water more slowly than wild-type plants, and are hypersensitive to ABA-induced stomatal closure. The abo1-1 mutation also affects stomatal development, because almost half of identified guard cell pairs form immature stomata with closed apertures (Chen et al., 2006).

Stomatal Development and Drought Responses

Stomata develop from leaf protodermal cells through multiple steps including stomatal spacing divisions, guard mother cell division, and guard cell maturation. Stomatal spacing determines stomatal distribution and density, while stomatal sizes are determined by guard cell maturation (Bergmann et al., 2004). Sizes and distribution of stomatal complexes on mature leaf surfaces are fixed, restricting acclimation to environmental changes to the changes of stomatal apertures. However, both the sizes and distribution of stomata as determined during leaf maturation determine the capacity of stomatal conductance (Spence et al., 1986). Many environmental signals can modify the density and sizes of stomata during leaf development. Under water-limiting conditions, cotton plants develop smaller and denser stomata in contrast to well-watered conditions (Cutler et al., 1977), and treatment of Tradescantia virginiana plants with ABA causes similar phenotype (Franks and Farquhar, 2001). Both the effects of CO2 and humidity on stomatal development are blocked in an ABA biosynthesis mutant, aba1, implying that both regulatory effects are mediated by ABA (Lake and Woodward, 2008). A number of genetic regulators of stomatal patterning have been discovered in recent years. A subtilisin-like serine protease, SDD1, and a secretory peptide (Berger and Altmann, 2000; Groll et al., 2002), EPIDERMAL PATTERNING FACTOR 1 (EPF1) (Hara et al., 2007), were hypoth-

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esized to be involved in extracellular signaling during stomatal development. Transmembrane receptor-like protein TMM (Geisler et al., 2000), and transmembrane receptor-like kinases, ER, ERL1, and ERL2 (Shpak et al., 2005), are intermediate components between extracellular signals and intracellular signaling pathways of stomatal development. An MAPK cascade, including YODA, MKK4/MKK5, and MPK3/MPK6, acts downstream of membrane receptor kinases to regulate stomatal development (Bergmann et al., 2004; Wang et al., 2007). One member in this cascade, MPK3, is also a positive regulator of stomatal ABA and ROS responses (Gudesblat et al., 2007). Downstream of this MAPK cascade, a subfamily of basic helix-loop-helix (bHLH) transcription factors, SPCH, MUTE and FAMA, are involved in the control of stomatal lineage transitions (Ohashi-Ito and Bergmann, 2006; Pillitteri et al., 2007; MacAlister et al., 2007; Lampard et al., 2008). Another subfamily of bHLH transcription factors, ICE1 and SCRM2, form dimers with SPCH, MUTE, and FAMA, and thus control stomatal initiation, proliferation, and differentiation (Kanaoka et al., 2008). Interestingly, ICE1 was previously found to be important for plant cold tolerance (Chinnusamy et al., 2003). Finally, two genes, FLPS and Myb88, which are paralogous proteins of one subfamily of Myb transcription factors, also affect stomatal development (Lai et al., 2005). Because both ICE1 and MPK3 are also involved in plant response to environmental signals, these two genes are potential candidates for further analysis of how environmental factors modulate stomatal developmental programs (Casson et al., 2008).

Summary

Stomata are microscopic pores on leaf surfaces flanked by pairs of guard cells. Guard cells respond to a range of different environmental stimuli, such as light, humidity, temperature, and CO2 concentration, and also a number of internal stimuli mediated by plant hormones. The main role of stomata in plant drought response is to limit transpirational water loss, through reducing stomatal apertures in response to the stress hormone ABA. In Arabidopsis, many positive and negative regulators of guard cell drought responses have been identified by genetic and transgenic approaches. Mutants in these mediators sometimes showed improved plant drought tolerance, mainly due to increased guard cell ABA sensitivity (see Table 1.1) or increased ABA biosynthesis. However, as a general stress hormone, ABA also has other functions such as inhibiting seed germination and restricting plant growth. These traits, especially ABA inhibition of seed germination, are observed in many mutants that have hypersensitive stomatal ABA responses. ABA hypersensitive traits in organs other than guard cells could be detrimental to crop plants. Mutants with defects in lightinduced stomatal opening could also have better drought tolerance due to their intrinsically smaller stomatal apertures under light. However, the photosynthesis rate and total assimilation, although not measured in most published papers, is likely to be reduced in these mutants, because the reduced apertures can limit CO2 diffusion into the leaves. Therefore, more analyzes of transpiration efficiency are needed to assess the utility of these mutations for crop improvement. Some genes, for example Er family genes, simultaneously modify stomatal development and other aspects of leaf morphology, which both modulate plant water use efficiency. Genetic regulators with similar dual or multiple functions could be potential targets for further experimental analysis and for crop breeding. Arabidopsis heterotrimeric G proteins are good candidates, because G protein mutants, gpa1 and agb1, have both altered stomatal ABA responses, stomatal density changes, and leaf morphology changes. G protein mutants are hypersensitive to ABA inhibition of germination and root growth, but hyposensitive to ABA-inhibited stomatal opening (Pandey et al., 2006). Such opposite functions of G proteins imply that wild-type plants have longer root growth,


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better germination rates, and better stomatal response as compared to mutants that lack G protein components. Thus, it can be postulated that G proteins are global positive regulators of plant growth. Despite the enormous progress made in understanding the genetic regulators of stomatal drought response in Arabidopsis, we are far from understanding the complex interactions between these regulators. To apply our knowledge in Arabidopsis in improving crop drought tolerance, one strategy would be generating overexpression or RNAi transgenic plants for all of the known regulators and then testing these plants under identical conditions, which is unfeasible for most other species. The alternative strategy would be to first synthesize our knowledge about the interconnection of these regulators into a network, and then use computational simulation to test the synthesized network until it can reproduce the observed interactions and responses in stomata (Li et al., 2006). We also need to keep in mind that those key regulators discovered in Arabidopsis, may have altered their function or gained functional redundancy in other species. As more fully sequenced plant genomes become available, comparative genomics can also help us to identify conserved genes that are universally involved in stomatal functions. Because drought is a complex stress interweaving with many other stresses, studies of cross talk are also of great importance for practical concerns such as crop breeding. During the last two decades, the Arabidopsis community has accumulated an unprecedented number of wellcharacterized gene-centric mutants involved in both drought response and other aspects of plant physiology. When studying stomatal drought responses, these well-characterized mutants are valuable biological tools for understanding cross talk between cellular processes under drought or between drought and other stresses.

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Pathways and Genetic Determinants for Cell Wall–Based Osmotic Stress Tolerance in the Arabidopsis thaliana Root System Hisashi Koiwa

Introduction

The growing tips of the plant roots are critical tissues for establishing a root network in the soil. The root tip contains the root apical meristem, where directional cell divisions continuously produce new cells (Benfey and Scheres, 2000). Subsequently, the elongation and differentiation of these cells produce ordered root cell profiles. The developmental program that regulates the plant root architecture has been studied intensively and involves layers of functions of multiple transcription factors (Nakajima and Benfey, 2002; Petricka and Benfey, 2008). When plants are exposed to environmental stress, the root tips are usually the first to encounter the new environment. Therefore, the cell divisions and differentiations at the root tips have greater ability to respond to environmental signals. Primary roots of wild-type Arabidopsis under salt stress reduce the growth rate by reducing the number of dividing cells in the meristems and producing smaller mature cells without changing duration of the cell cycle (West et al., 2004). In contrast, salt-sensitive mutant stt3a undergoes cell cycle arrest under the salt stress (Koiwa et al., 2003), indicating that sustaining cell cycle progression is an integral part of stress tolerance mechanism in plant roots. Furthermore, the developmental program that determines the identity of the cells determines the stress response outputs of individual root cells (Dinneny et al., 2008). Growth and differentiation of plant cells determines production and deposition of cell wall materials. At the same time, cell walls can influence the rate of cell division, elongation, and morphology of the cells. Because of this greater degree of coordination, growth, development, and cell wall biosynthesis of growing plant tissues are prone to environmental perturbations. Tobacco cultured cells exposed to salinity produce cell walls much weaker than those of unstressed cells (Iraki et al., 1989). This is accompanied by a decrease in crystalline cellulose in the primary cell wall and a decrease in hydroxyprolines in cell wall proteins, suggesting a cellulosic-extensin framework of cells are compromised during the salt stress. Few other studies indicate that osmotic stress tolerance of plants is directly affected by cell wall components (Amaya et al., 1999; Shi et al., 2003). Cell wall proteins undergo various post-translational modifications that directly influence the protein functions. This chapter will provide an overview of functions of cell wall proteins and their post-transcriptional modification pathways in stress tolerance. Genes That Affect the Cell Wall and Plant Stress Tolerance

The cell wall is composed from carbohydrate components and protein components. Carbohydrate components include crystalline cellulose microfibrils, hemicelluloses, xyloglucans, and pectin Genes for Plant Abiotic Stress Editors Matthew A. Jenks and Andrew J. Wood © 2010 Blackwell Publishing ISBN: 978-0-813-81502-2

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matrix (O’Niell and York, 2003). Biosynthetic enzymes for carbohydrate polymers are often found on plasmamembranes or in the Golgi apparatus. Cell wall proteins such as extensins, glycine-rich proteins, hydroxyproline-rich glycoproteins, and arabinogalactan proteins are thought to be involved in structural frameworks, whereas enzymes such as expansins, xyloglucan endo-transglycosylase modulate the degree of cross-linking in the cell wall to allow cells to expand during growth and development. These functions provide an additional framework that complements the polysaccharide network and reinforces the assembly of wall materials. The first molecular genetic evidence that connects the cell wall and plant stress tolerance was provided when overexpression of cell wall peroxidase in tobacco plants improved the seed germination of transgenic plants under various osmotic stresses (Amaya et al., 1999). The authors proposed that increased linkage in cell walls could alter the pore size of the cell wall and increase the amount of water retained in the seed cell wall. Later, Shi and others reported that cell wall protein SOS5 was important for salt tolerance of Arabidopis (Shi et al., 2003). SOS5 encodes an arabinogalactan protein (AGP), an extensively glycosylated Hyp-rich proteoglycans. AGP family proteins are found in various cellular components including cell walls, plasma membranes, and vesicles (Majewska-Sawka and Nothnagel, 2000). Under salt stress, the sos5 mutant root tip swells and its growth arrests. SOS5 protein contains two arabinogalactan protein domains, two fasciclin-like domains, and C-terminal glycosylphosphatidylinositol anchor sequence. Since fasciclin domains are found in cell adhension proteins, the authors hypothesized that SOS5 is involved in cell-to-cell adhesion. The cell walls of sos5 are thinner and disorganized, implying that the normal cell wall structure is important for sustaining root growth under salt stress. Cellulose is a major component of cell walls. Forward genetic approaches identified a series of genes that are involved in cellulose biosynthesis (Somerville, 2006). Importance of cellulose biosynthesis in the plant stress tolerance was first indicated by the study of Arabidopsis leaf wilting 2 (lew2) mutants, which are enhanced for drought and osmotic stress tolerance (Chen et al., 2005). lew2 plants are tolerant to drought, sodium chloride (NaCl), and other osmotic stresses and accumulate more abscisic acid and compatible solutes that have been proposed to protect cells under osmotic stresses. Surprisingly, LEW2 is identical to the CesA8/IRX1 gene that encodes a subunit of cellulose synthase complex. In this instance, LEW2/CesA8/IRX1 functions in secondary cell wall formation in xylem and the mutant shows collapse of xylem structure (Taylor et al., 2000). Constant wilting phenotype and drought tolerance phenotype of lew2 have been attributed to the impeded water transport through xylem, which imposes constant water stress to the shoot tissue. This triggers biosynthesis of ABA and other stress response in the plants and thus preconditions the plants for more severe drought stress. More recently Kang and others showed enhanced salt sensitivity of rsw1 and rsw2, other cellulose biosynthesis mutants (Kang et al., 2008). These findings indicate that the cellulose biosynthesis pathway is a determinant of plant tolerance to osmotic stress.

Genes and Proteins in Cellulose Biosynthesis

Crystalline cellulose microfibrils are produced by cellulose synthase complex. The complex has been localized on the plasmamembranes of plant cells and has been observed as “rosettes” by the freeze fracture electron microscope technique. The cellulose synthase rosette structure consists of six globular units, each of which in turn consists of six subunits of cellulose synthases. It has been proposed that during cellulose synthesis, each subunit of rosette containing six subunits of cellulose synthase produces six strands of β-1,4-glycans that co-crystallize into a 36-chain microfibril/rosette (Herth, 1983; Somerville, 2006).

PATHWAYS AND GENETIC DETERMINANTS

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A large number of cellulose synthase (CesA family) and cellulose synthase-like (CslA-CslG families) proteins are present in the Arabidopsis genome (Dhugga, 2001). CesA family proteins are shown to function in cellulose biosynthesis in primary and secondary wall formation. Mutant phenotype analysis, tissue specific expression profiles, and protein-protein interaction assays established that CesA1, 3, and 6 form a complex that function in the primary cell wall formation (Desprez et al., 2007; Persson et al., 2007; Wang et al., 2008). Null mutations in CesA1 and 6 cause lethality, whereas partial redundancy is observed among CesA6, 2, 5, and 9 (Desprez et al., 2007; Persson et al., 2007). Study of secondary wall biosynthesis revealed that CesA4, 7, and 8 are simultaneously required for the secondary wall biosynthesis (Taylor et al., 2000; Taylor et al., 2003). Inhibition of cellulose biosynthesis in the primary cell wall induces phenotypic abnormality similar to phenotype induced by salt stress. Arabidopsis roots treated with isoxaben or dichlorobenzonitrile cause radial swelling of the root tip (Arioli et al., 1998). Several Arabidopsis mutant plants selected based on aberrant swollen root morphology are defective in cellulose biosynthesis. For example, cellulose synthase mutations, namely, rsw1 (CesA1) (Arioli et al., 1998) and quill (Hauser et al., 1995) were identified in this approach. In addition, several other genes, which are not homologous to CesA family proteins, but shown to affect cellulose biosynthesis, were identified as the root morphology mutants. RSW2/LION’S TAIL is allelic to KORRIGAN1, and encodes β-1,4glucanase (Lane et al., 2001). RSW3 encodes α-glucosidase I in the endoplasmic reticulum involved in the processing of N-glycans of glycoproteins (Burn et al., 2002). COBRA encodes a glycosylphosphatidylinositol-anchored plasmamembrane protein (Schindelman et al., 2001; Roudier et al., 2005). KOBITO1/ELONGATION DEFECTIVE1/ABA INSENSITIVE 8 encodes a plasma membrane/cell wall protein of unknown function (Pagant et al., 2002; Lertpiriyapong and Sung, 2003; Brocard-Gifford et al., 2004). Mutations in these genes all resulted in decreased cellulose content in the mutant cell walls (Peng et al., 2000; Schindelman et al., 2001; Pagant et al., 2002). Similarity between root morphologies induced by salt stress and by cellulose deficiency prompted Kang and others (2008) to analyze their functional relationship. This study led to identification of RSW2/LIT/KOR1 as a functional link between cellulose biosynthesis, osmotic stress tolerance, and protein N-glycosylation (Kang et al., 2008). RSW2 belongs to the family of class II membrane proteins and contains a luminal catalytic domain with eight potential N-glycosylation sites (Nicol et al., 1998). N-glycosylation of Brassica RSW2 homolog is essential for its catalytic activity in vitro (Molhoj et al., 2001), and combining mutations in N-glycosylation or modification pathways with conditional rsw2-1 mutation induced strong constitutive growth defects in the double mutants (Kang et al., 2008). Further supporting evidence for this linkage became available with analysis of cobra mutants. COBRA protein has two kinds of modifications, N-glycosylation and GPI anchor. A severe mutant allele of cobra causes a cellulose deficiency. Combining N-glycosylation and modification mutations with mild conditional cobra mutation alleles causes constitutive growth defects similar to the results obtained with the rsw2-1 mutation (Koiwa et al., unpublished). Furthermore, a mutation in PEANUT/PIG-M, an endoplasmic reticulum-localized mannosyltransferase that is required for synthesis of the GPI-anchor resulted in a seedling lethal phenotype with a decrease of crystalline cellulose. These findings indicate close relations among the protein N-glycosylation, the GPI anchor addition, cellulose biosynthesis, and plant osmotic stress tolerance (Gillmor et al., 2005). Interestingly, the kobito1 mutant affected in cellulose biosynthesis and the aba-insensitive8 were allelic suggesting a direct connection between cell wall formation and ABA/ osmotic stress signaling (Pagant et al., 2002). In the following section, biosynthetic pathways that are required for these protein modifications are discussed.

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Pathways Involved in N-glycosylation and N-glycan Modifications

In eukaryotes, secreted and membrane-bound proteins are post-transcriptionally modified by N-glycosylation. Proteins that enter the endoplasmic reticulum (ER) receive core oligosaccharides at conserved N-glycosylation motifs (N-X-S/T) (Helenius and Aebi, 2001). The core oligosaccharide has a structure of Glc3Man9GlcNac2, which is transferred from dolichol to amino group of proteins by oligosaccharyltransferase in the ER. Maturation of core oligosaccharides to highmannose and complex-type oligosaccharides requires a series of glucosidase and glycosyltransferases in the ER and in the Golgi apparatus.

Dolichol Biosynthesis

Core oligosaccharides are assembled on membrane dolichol-phosphate molecules. Dolichols are unsaturated polyisoprenoids that contain 15–23 units of isoprenoid units. The biosynthesis of dolichols mainly occurs in the ER. The initial step of dolichol biosynthesis is production of isopentenyl diphosphate (IPP) via the mevalonate pathway (Grabinska and Palamarczyk, 2002) (Figure 2.1). IPP is used by farnesyl diphosphate synthase, which conjugates three IPP to produce farnesyl diphosphate (FPP). The formation of longer chain polyprenyldiphosphate is catalyzed by cis-prenyltransferases. Cis-prenyltransferases catalyzes the sequential addition of IPP to FPP via electrophillic addition of carbocations (Cunillera et al., 2000; Oh et al., 2000). Data from yeasts and animals indicate that the resulting polyprenol is converted to dolichol by α-saturation reactions (Sagami et al., 1993; Szkopinska et al., 1996). Resulting dolichol is phosphorylated by dolichol kinase (Rymerson et al., 1992) to produce dolichyl phosphate that functions in protein N- and O-glycosylations and GPI-anchor biosynthesis.

Figure 2.1 Biosynthetic pathway of dolichols.


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Arabidopsis LEAF WILTING1 (LEW1) encodes an isoform of cis-prenyltransferases. Although T-DNA insertion in LEW1 caused lethality, a missense mutation of LEW1 gene resulted in a decrease of dolichol content by 85% and defects in protein N-glycosylation (Yang et al., 2008). Surprisingly, the lew1 mutant exhibited greater drought tolerance than wild type. Lew1 exhibited a greater hydraulic conductivity in the root system, which could be accounted for by enhanced aquaporin activity. In leaves, lew1 plants show increased electrolyte leakage, decreased turgor, and increased stomatal closure. Apparently, moderate stress at the cell membrane induced stomatal closure and drought tolerance in lew1 mutants.

Sugar-nucleotide Biosynthesis

Sugar substrates for assembly and modification of N-glycans are provided in forms of dricholphosphate-linked sugars and sugar nucleotides. The biosynthetic pathways for these substrates are summarized in Figure 2.2. In cytosol, UDP-glucose is produced from sucrose and/or Glucose

Figure 2.2 Integration of nucleotide-sugar substrates and ascorbate biosynthesis pathway. Steps in parentheses are implied by yeast pathway. Compounds used in protein glycosylations are boxed. ALG5, glucosylphosphodolichol synthase; SS, sucrose synthase; UGP, UDP-glucose pyrophosphorylase; UDP-Glc-DH, UDP-glucose dehydrogenase; UXS, UDP-xylose synthase; PGM, phosphoglucomutase; PGI, phosphoglucoisomerase; GFA1, glucosamine 6-P synthase; GNA1, glucosamine 6-P acetyltransferase; AGM1, phosphoacetyl-glucosamine mutase; UAP1, UDP-N-acetylglucosamine pyrophosphorylase; PMI, phosphomannoisomerase; PMM, phosphomannomutase; VTC1/CYT1, GDP-mannose pyrophosphorylase; GME, GDP-mannose epimerase; MUR1, GDP-mannose-4,6-dehydratase; GER1, GDP-4-keto-6-deoxy-D-mannose 3,5-epimerase-4-reductase; DPM1, dolichol phosphate mannose synthase; VTC2, GDP-galactose phosphorylase; VTC4, galactose-1-P phosphatase; GalDH, galactose dehydrogenase; GLDH, galactono-1,4-lactone dehydrogenase.

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1-phosphate, and is converted to UDP-glucuronic acid (UDP-GlcA) by UDP-glucose dehydrogenase (Tenhaken and Thulke, 1996). UDP-GlcA is then converted to UDP-xylose by UDP-GlcA decarboxylase (Pattathil et al., 2005). Biosynthesis of UDP-GlcNac has not been studied in plants, but it appears to be produced from fructose 6-P by a conserved four-step version of the Leloir pathway (Milewski et al., 2006). GDP-mannose and GDP-galactose also are produced from fructose 6-P as an intermediate of Wheeler-Smirnoff pathway of ascorbate biosynthesis (Wheeler et al., 1998). GDP-mannose also serves as a precursor for the production of GDP-L-fuc (Zablackis et al., 1996; Bonin and Reiter, 2000).

Assembly of Core Oligosaccharide

This process has not been characterized well in plants except for a few genes. However, the conserved pathway is well studied in yeasts (Figure 2.3). At the cytoplasmic side of the ER, assembly of core oligosaccharide starts with the transfer of GlcNAc1P from UDP-GlcNAc to dolichyl-P to form GlcNAc-PP-dolichol. This reaction is catalyzed by the enzyme UDP-GlcNAc:dolichol phosphate GlcNAc-1-P transferase (GPT), which is a target for an antibiotic tunicamycin. Overexpression of GPT increased tunicamycin tolerance of transgenic Arabidopsis (Koizumi et al., 1999). Subsequently, a series of reactions add another GlcNAc (by ALG13/ALG14 complex) and five mannose residues (by ALG1, ALG2, ALG11 mannosyltransferases) using UDP-GlcNAc and GDPMan as substrates and produce Man5-GlcNAc2-PP-Dol structure (Snider et al., 1980). The heptasaccharide-PP-Dol undergoes “flipping” across the ER membrane (Helenius and Aebi, 2002). Upon translocation of heptasaccharide-PP-Dol to the luminal side of the ER, the second phase of the core oligosaccharide starts. The oligosaccharide receives four more mannose and three glucose residues to form Glc3Man9GlcNAc2-PP-Dol (Lehle et al., 2006). In contrast to the cytoplasmic reactions, the reactions in ER use substrates attached to dolichol phosphates, such as Glc-P-Dol and Man-PDol. Dolichol-linked substrates are produced from UDP-Glc and GDP-Man by dolichyl phosphoglucose synthase (Heesen et al., 1994) and dolichyl phosphomannose synthase (Orlean et al., 1988). Similar to Man5-GlcNAc2-PP-Dol, Glc-P-Dol and Man-P-Dol are produced on the cytoplasmic side of the ER and must be flipped into the ER in order to serve as saccharide donors for the lumenoriented transferases (Rush et al., 1998). Three mannosyltransferases (ALG3, ALG9, ALG12) extend mannose branches whereas three glycosyltransferases (ALG6, ALG8, ALG10) cap the α-1,6 branch and complete the biosynthesis of the core oligosaccharides. Mutation in Arabidopsis ALG3 does not affect production of complex-N-glycans, but eradicates the production of high-mannosetype N-glycans (Henquet et al., 2008). The core oligosaccharide produced in the ER of alg3 has structure of Glc3Man5GlcNac2. Apparently, this structure is still transferred to nascent peptides; however, alg3 mutant activates unfolded protein response (see below).

Oligosaccharyltransferase

Oligosaccharyltransferase (OST) is a multisubunit enzyme that catalyzes en bloc transfer of core oligosaccharides to nascent peptides in the ER. OST consists of eight subunits in yeast (OST1, OST2, OST3/OST6, OST4, OST5, STT3, WBP1, SWP1) (Kelleher and Gilmore, 2006). Four genes encoding three subunits of OST have been reported in Arabidopsis thaliana. DAD1 was first reported as a suppressor of cell death phenotype of CHO cell dad1 mutant (Gallois et al., 1997). Later, DAD1 was found to be a homolog of yeast OST2 subunit. Arabidopsis STT3a and its paralog


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Figure 2.3 Biosynthetic pathway of core-N-glycan assembly in the cytosol and in the ER. Only GPT, ALG3, and OST subunit genes were characterized in plants. Other ALG gene products refer to corresponding proteins in Saccharomyces cerevisiae. GPT, UDP-GlcNAc:dolichol phosphate GlcNAc-1-P transferase.

STT3b were found by forward genetic screening for salt-sensitive root growth (Koiwa et al., 2003). Root growth of stt3a mutant was sensitive to various solutes, such as NaCl, potassium chloride (KCl), and mannitol. Stt3a mutation causes underglycosylation of glycoproteins and induces expression of ER chaperon, BiP. Mutation in STT3b by itself did not cause any phenotypic abnormality, however, stt3a stt3b double mutant was gamete lethal. This indicates that STT3a and STT3b redundantly exert essential cellular functions. Arabidopsis DGL1 is homologous to WBP1 in yeast (Lerouxel et al., 2005). A severe loss of function mutation in DGL1 causes lethality. Plants

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homozygous for partial loss-of-function allele of DGL1 exhibit a decrease in protein N-glycosylation that is associated with defects in cell elongation and altered cell wall composition. Interestingly, overexpression of DAD1 and its paralog DAD2 in Arabidopsis can suppress program cell death induced by UV irradiation. The suppression was observed in the presence or absence of tunicamycin, indicating DAD1 has anti-PCD function independent of oligosaccharyltransferase activities (Danon et al., 2004).

Processing of Core Oligosaccharides in the ER

Figure 2.4 summarizes the processing and maturation processes of core oligosaccharides in the ER and in the Golgi. Upon translocation of the core oligosaccharide from dolichol-PP to a nascent peptide, the core oligosaccharide undergoes a series of trimmings by α-glucosidase I and II that remove glucose residues from the core oligosaccharide (Boisson et al., 2001; Burn et al., 2002; Gillmor et al., 2002). This process is closely associated with chaperon-assisted protein folding of the newly synthesized proteins, as the terminal glucose of the core oligosaccharide is recognized and bound by ER chaperons, calnexin and calreticulin (Crofts and Denecke, 1998). A successfully folded protein loses all the glucose residues from its N-glycans (Man9GlcNAc2) whereas N-glycans of misfolded proteins receive glucoses and re-enter the folding cycle. Ultimately, the N-glycans of correctly folded proteins are processed by ER-mannosidase I (ER manI). Trimming of N-glycans by ER ManI to Man8GlcNAc2 isomer B removes the glycoprotein from reglucosylation and calnexin binding cycles. ER ManI is concentrated together with the ERAD substrate in the pericentriolar ER-derived quality control compartment (ERQC). Proteins with Man8GlcNAc2 structure exit from the ER-quality control pathway, and the glycoproteins will be exported to the Golgi apparatus.

Unfolded Protein Response and Osmotic Stress Signaling

Overexpression or mutation of secreted proteins, mutations in the N-glycosylation enzymes, tunicamycin treatment, and changes in redox status in the ER can induce accumulation of unfolded and misfolded proteins in ER. The accumulation of unfolded proteins in the ER triggers the unfolded protein response (UPR). During the UPR, expressions of genes encoding proteins that promote protein foldings, such as calnexin, calreticulin, binding protein (BiP, ER isoform of HSP70) protein disulfide isomerase, are induced (Martinez and Chrispeels, 2003). In mammals, the activation of UPR is triggered by parallel signaling pathways (Kohno, 2007), but only the evolutionarily conserved IRE1 pathway has been characterized in Arabidopsis (Koizumi et al., 2001). IRE1 is an ER-localized transmembrane protein with luminal sensor domain and cytoplasmic kinase and RNAse domains. In the absence of unfolded proteins, the sensor domain binds to BiP and is inactive. Upon accumulation of unfolded protein, the BiP dissociates with the sensor domain and the sensor domain binds to unfolded proteins and induces dimerization and activation of IRE1 (Kimata et al., 2007). The signal-induced dimerization/oligomerization of IRE1 promotes transphosphorylation of the kinase domain and activation of nuclease domain. In yeasts, the activated nuclease domain catalyzes splicing of Hac1 transcription factor that activates transcription of UPR-target genes. Proper regulation of protein quality control via UPR is important for adaptation to water stress. Analysis of transgenic tobacco plants overexpressing and silenced for BiP demonstrated that BiP

Figure 2.4 Plant N-glycosylation pathway in the ER and in the Golgi apparatus. OST, oligosaccharyltransferase; GnTI, b1,2N-acetylglucosaminyltransferase I; GnTII, b1,2-N-acetylglucosaminyltransferase II.

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is essential for normal plant growth and water-stress tolerance (Alvim et al., 2001). Under progressive drought stress, BiP-overexpressing plants maintain the shoot turgor and water content better than wild-type plants. Induction of endogenous BiP expression was observed in wild type during the course of water stress, suggesting that BiP has protective function during water stress. In transgenic potato plants expressing antisense-BiP, cDNA showed increased sensitivity to water deficit. Similarly, transgenic plants expressing antisense α-glucosidase II showed severe growth reduction and less tuber production in the field though the plants did not show the phenotype when grown in the greenhouse. This was accompanied by an increase in BiP expression. Cells of antisense plants show spontaneous plasmolysis, similar to the lew1 mutant, suggesting a similar constitutive stress response in these plants. Gene expression profiling of plants undergoing osmotic stress or UPR identified a group of transcripts that are synergistically regulated by these two stresses (Costa et al., 2008). This signaling pathway was termed the integrated pathway. Of the genes regulated by the integrated pathway are asparagine-rich protein (NRP) A and B, which can promote program cell death when overexpressed in soybean protoplasts. The symptoms induced by NRPs can be ameliorated by addition of zeatin, implying that both osmotic stress and ER-stress induce senescence-like PCD via common pathway.

N-glycan Re-glycosylation and ER-associated Protein Degradation

As described above, proteins that failed to fold in the calnexin chaperon system can re-enter another round of folding. The decision whether or not to enter another folding cycle is rendered, in yeasts and animals, by a folding sensor, UDP-glucose:glycoprotein glucosyltransferase, which recognizes unfolded structure of released polypeptides and adds back a glucose residue to N-glycans of unfolded proteins (Cannon and Helenius, 1999; Trombetta and Helenius, 2000). On the other hand, proteins that failed to fold in multiple rounds of folding cycle become a substrate of slow-acting ER-manI that removes single mannose residue from Man9GlcNAc2 and produces Man8GlcNAc2 isomer B (Jakob et al., 1998; Cabral et al., 2000). Unfolded protein with Man8GlcNAc2 isomer B structure is recognized by EDEM (ER degradation enhancing α-mannosidase–like protein) family proteins and targeted for translocation via the Sec61 translocation complex (Molinari et al., 2003; Oda et al., 2003; Wang and Hebert, 2003). Misfolded proteins are then deglycosylated by peptide: N-glycanases (Diepold et al., 2007) and degraded by the ubiquitin-proteasome system (Plemper and Wolf, 1999). This process is known as ER-associated degradation (ERAD) (Vembar and Brodsky, 2008).

N-glycan Modification in the Golgi Apparatus

Upon successful folding of polypeptides and trimming of single mannose residue from N-glycans, the glycoproteins are exported to the Golgi apparatus. Further modifications with glycosyltransferases and glycosylhydrolases in the Golgi apparatus produce mature complex N-glycans. In the cis-Golgi, Golgi α-mannosidase I (MANI) hydrolyzes the terminal α1,2-linked mannose residues (Mast and Moremen, 2006), and therefore converts the high-mannose-type oligosaccharide, Man8GlcNAc2, to Man5GlcNAc2. The Arabidopsis genome contains two redundant MANI genes, which are essential for the processing of Man8GlcNAc2 (Kajiura et al, unpublished


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data). N-acetylglucosaminyltransferase I (GnTI) then add single GlcNAc to Man5GlcNAc2 (Wenderoth and Schaewen, 2000). Resulting GlcNAcMan5GlcNAc2 is trimmed by Golgi αmannosidase II to produce GlcNAcMan3GlcNAc2 (Strasser et al., 2006); then single GlcNAc is added by N-acetylglucosaminyltransferase II (GnTII) to produce a common intermediate of eukaryotic complex N-glycan biosynthesis pathway, GlcNAc2Man3GlcNAc2 (Strasser et al., 1999). The unique structures of plant complex N-glycans are produced in the late N-glycan modification pathway, which employs plant-specific glycosyltransferases such as β1,2-xylosyltransferase (XYLT) (Strasser et al., 2000), two α1,3-fucosyltransferase (FUCT) (Bondili et al., 2006), β1,3galactosyltransferase (GALT) (Strasser et al., 2007) and α1,4-FUCT (Léonard et al., 2002). The functions of individual N-glycan modification enzymes are actively studied in Arabidopsis. The reactions by MANI and GnTI are prerequisite for the following modifications. The first Arabidopsis thaliana mutant lacking complex N-glycans (cgl1, complex glycan 1) was reported in 1993 (von Schaewen et al., 1993). CGL1 encode the Golgi GnTI, completely lack complex N-glycans, and accumulate oligomannosidic N-glycans, predominantly Man4GlcNAc2. Since then, several mutants and transgenic plants altered in N-glycan maturation in the Golgi apparatus have been reported. Arabidopsis hgl1 mutant lacking functional α-mannosidase II does not produce complex N-glycans, however, instead of accumulating high-mannose-type glycans, hgl1 plants accumulate hybrid type N-glycans. This indicates that mannose trimming from α-1,3mannose branch is not essential for subsequent modifications (Strasser et al., 2006). The following step is the parallel reactions by GnTII, XYLT, and FUCTa/FUCTb. Mutations in the genes encoding individual enzymes cause production of complex N-glycans lacking one or more residues (Strasser et al., 2004; Kang et al., 2008 von Schaewen, unpublished data). A triple mutant lacking XYLT, and FUCTa/FUCTb produces a complex N-glycans predominantly GlcNAc2Man3GlcNAc2. Arabidopsis GALT is expressed mainly in stem tissues where glycoproteins with antenna modified complex N-glycans accumulate. Complex N-glycans in galT-1 mutant plants lack Lewis a epitope structure. Since GALT reaction is a prerequisite for the FUCTc reaction to occur, galT-1 plants are not capable of generating any antenna modifications. Furthermore, a recent study indicates that manIamanIb double mutant, which cannot produce complex glycans, but accumulate the Man8GlcNAc2 form (Kajiura et al., unpublished data). The N-glycosylation in the ER and N-glycan modification in the Golgi apparatus is strongly connected with root growth under osmotic stress (Kang et al., 2008). Cgl1, hgl1, and fucTa fucTb xylT triple mutant were all salt sensitive like stt3a mutants. Unlike stt3a mutants, the mutations in the Golgi N-glycan modification enzymes did not activate the BiP promoter, confirming the prediction that these proteins function after the protein quality control step. In contrast, individual mutations in FUCTa/FUCTb, XYLT, GnTII, FUCTc did not cause salt sensitivity (Kang et al., 2008; Koiwa, unpublished results). This indicates that certain structures of complex N-glycan are required to sustain root growth under salt stress. Salt sensitivity of fucTa fucTbxylT triple mutant but not fucTa fucTb or xylT mutants indicates that the core β1,2-xylose and α1,3fucose redundantly function in salt tolerance. However, hgl1 mutant that produces hybrid N-glycans is salt sensitive in spite of the fact that the β1,2-xylose and α1,3-fucose modifications occur in this mutant (Strasser et al., 2006; Kang et al., 2008). Interestingly, gnTII mutant did not show salt sensitivity. GnTII does not modify α-1,3-mannose branch of N-glycans after trimming by αmannosidase II (HGL1) but has core β1,2-xylose and α1,3-fucose. Thus, gnTII is different from hgl1 only by the absence of α-1,3 and α-1,6-mannose extensions in the α-1,3-mannose branch. Further detailed analyzes are necessary to understand the function of fine N-glycan structures in salt tolerance.

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Ascorbate as an Interface between the N-glycosylation Pathway and Oxidative Stress Response

Ascorbate is a major antioxidant in the ascorbate-glutathione cycle. Decrease of ascorbate in Arabidopsis results in sensitivity to various oxidative stresses including exposure to ozone, UV-B irradiation, and sulfur dioxide (Conklin et al., 1996). In plants, ascorbate is produced via WheelerSmirnoff pathway (Wheeler et al., 1998). The early part of this pathway is shared by the ascorbate biosynthesis pathway and protein glycosylation and cell wall biosynthesis pathways (Figure 2.2). The common intermediate is GDP-mannose produced from hexose-phosphate via mannose-1phosphate. Indeed, mutation in UDP-mannose pyrophosphorylase has been isolated as vtc1 mutant decreased in ascorbate as well as cyt1 mutant decreased in cellulose content (Conklin et al., 1996; Nickle and Meinke, 1998; Conklin et al., 1999; Lukowitz et al., 2001). Furthermore, compromising phosphomannomutase, which is a step upstream of VTC1 reaction, causes a decrease in ascorbate content in gene-silenced Nicotiana benthamiana (Qian et al., 2007), and protein underglycosylation and cell death in Arabidopsis pmm-12 mutant (Hoeberichts et al., 2008).

Biosynthesis of GPI Anchor

Glycosylphosphatidylinositol (GPI) anchors are eukaryotic mechanisms to attach proteins to the surface of the membranes (Paulick and Bertozzi, 2008). The GPI moiety of the anchor can be cleaved by phospholipase D, which allows regulated release of proteins from the cell surface. The core structure of GPI anchor (EtNP-6Manα1-2Manα1-6Manα1-4GlcNα1-6-inositol-phospholipid) is highly conserved among eukaryotes. Variety of side chain modifications exists for the mannosides and inositol moiety, resulting in the production of isoforms with microheterogeneity (Oxley and Bacic, 1999). GPI-anchor biosynthesis is poorly characterized in plants, however, the limited information available suggests that the pathway is similar to yeast and mammals (Lalanne et al., 2004; Gillmor et al., 2005). Figure 2.5 summarizes the biosynthesis pathway of the core GPIanchor structure in animals. The biosynthetic pathway of GPI starts on the cytosolic side of the ER with addition of N-acetylglucosamine to phosphatidylinositol (PI) by a GPI-Nacetylglucosaminyltransferase (GPI-GnT) complex. GPI-GnT is composed of six subunits, namely, PIG-A, PIG-C, PIG-H, GPI1, PIG-P, and DPM2. Mutations in Arabidopsis genes SETH1 and SETH2, encoding PIG-C and PIG-A homolog, respectively, affect pollen germination and tube growth, and cause very low male transmission of the mutated genes (Lalanne et al., 2004). Mutant pollen has normal cellulose and pectin staining profile, however, it was enhanced for the callose staining. The following step is catalyzed by GlcNAc-PI de-N-acetylase (PIG-L). The resulting GlcN-PI is flipped into the luminal side of the ER, and the acyl-group is added by inositol acyltransferase (PIG-W). In the ER, stepwise action of GPI-α-1,4-mannosyltransferase (PIG-M/PIG-X) (Ashida et al., 2005), phosphoethanolamine transferase (PIG-N), GPI-α-1,6-mannosyltransferase (PIG-V) (Kang et al., 2005), GPI-α-1,2-mannosyltransferase (PIG-B) (Takahashi et al., 1996), phosphoethanolamine transferase (PIG-F/PIG-O) (Inoue et al., 1993; Hong et al., 2000) completes the assembly of GPI precursor. Mutation in Arabidopsis PIG-M homolog results in peanut1 mutation that causes embryonic swelling, associated with cellulose deficiency (Gillmor et al., 2005). Other peanut mutants (pnt2-pnt5) may encode other enzymes in the same pathway. In addition, a homolog of PIG-F was identified in rice, and its expression profile has been characterized (Lee and Kang, 2008). The pre-assembled GPI is transferred en bloc to proteins that have the C-terminal GPI attachment signal sequence (CAAX) and single transmembrane domain. The transmembrane domain is pro-


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Figure 2.5 The biosynthetic pathway of the core GPI-anchor in the ER. The pathway is inferred by animal studies, and characterized plant homologs are indicated in the parenthesis.

teolytically cleaved, the C-terminus is attached to the GPI anchor by an amide bond formed between the carboxyl terminus, and phosphoethanolamine, which is attached to the third mannose of the anchor. This reaction is catalyzed by GPI transamidase complexes and consists of four subunits (GPI8, GAA1, PIG-S, PIG-T, and PIG-U) (Hong et al. 2000, 2003; Vainauskas and Menon, 2004). After attaching to protein, a GPI inositol-deacylase (PGAP1) removes the acyl group from the inositol moiety (Tanaka et al., 2004). To date, no plant GPI transamidase has been characterized.

Microtubules

The microtubule cytoskelton is an essential component of growth and morphology of the plant cells. The cortical microtubules form a structure under plasma membrane and guide patterning of

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the cellulose microfibrils deposited to the cell wall. This in turn determines the expansion and morphology of the cells. Another linkage that relates cell wall function and salt tolerance was provided by recent studies of microtubules. Shoji and others (2006) reported that a helical growth phenotype and microtubule orientation of spiral1 can be suppressed by imposing salt stress or combining spr1 with either Na+/H+ antiporter mutant sos1 or with its regulatory protein mutant sos2 (Shoji et al., 2006). Spr1 is defective in microtubule organization and displays a right-handed helical growth phenotype. Furthermore, salt stress by itself alters the helical growth pattern of Arabidopsis roots. Based on this observation, Shoji and others proposed that cytoplasmic salt imbalance compromises cortical microtubule functions in which microtubule-localized SPR1 is specifically involved (Shoji et al., 2006). The significance of the above study in salt tolerance could be provided by the study of Wang and others (2007). In their study, dynamics of microtubule organization during the salt stress and effects of microtubule-altering drugs on salt tolerance were determined. They found that upon exposure to the salt stress, the cells undergo depolymerization and re-organization of the cortical microtubules. Inhibition of depolymerization by pacritaxel decreases the salt tolerance, but promoting depolymerization by oryzarin improved salt tolerance both in wild type and in the sos1 mutant. Interestingly, free cytoplasmic calcium concentration increased upon depolymerization of the microtubules, and the addition of calcium to the growth medium promoted the recovery of the cortical microtubule organization. In contrast, calcium deficiency inhibited the recovery of the microtubule organization. These results suggest calcium mediated reorganization of microtubule cytoskelton is essential for the salt tolerance (Wang et al., 2007). It has been known that orientation of microtubule and cellulose microfibrils correlates through the developmental stage. In the developing root, the cortical arrays in the meristem region are transversely oriented until the root hair emerges and alignment of cellulose microfibrils show similar direction to microtubules. Disruption of cortical microtubule arrays by genetic mutation or by oryzarin treatment causes radial swelling of the root cells that is accompanied with loss of alignment of microfibrils (Baskin et al., 2004). This suggests that microtubules guide the cellulose synthase complex on the cell surface; indeed, an elegant study using fluorescently tagged tubulin and CesA6 suggested that CesA6 travels on the microtubules (Paredez et al., 2006). Localization of KOR1 protein is also regulated by microtubules (Robert et al., 2005). Conversely, cellulose biosynthesis influences cortical array organization because a genetic screen to identify oryzarin hypersensitive mutants identified CesA6 and KOR1 (Paredez et al., 2008).

Conclusion

Recent advances in Arabidopsis genetic analysis demonstrated that formation of proper cell wall structure is essential for sustaining root growth under salt/osmotic stress. Multiple regulatory mechanisms for cell wall formation are also connected to the stress tolerance, such as protein N-glycosylation/modification of RSW2/KOR1 or COBRA that are involved in cellulose biosynthesis, and regulation of microtubule orientation by SOS1 Na+/H+ antiporter. Bidirectional regulation has been shown for microtubule cortical array formation and cellulose biosynthesis. Similarly, salt stress and SOS1 affect microtubule cortical array formation but establishment of salt tolerance requires microtubule cortical array rearrangement. Apparently, cell walls not only provide structural integrity during osmotic stress, but also likely function as a scaffold to mediate various signaling events juxtaposed to the plasma membranes that operate during the osmotic stress.


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Transcription and Signaling Factors in the Drought Response Regulatory Network Matthew Geisler

Introduction

Drought, even if only one day mild water stress, affects the expression of more than 1,000 genes in Arabidopsis and rice. Some of these expression changes are the unplanned consequences of drought, as the loss of water leads to damage or malformation of proteins and membranes, which in turn impacts metabolism, cell division, and other cellular processes. Other changes to gene expression are part of a highly regulated survival strategy that confers at least some drought tolerance, avoidance, or resistance. Here we separate tolerance as the ability to continue biological processes during water deficit; resistance as the ability to survive drought such as by shifting to a dormant state; and avoidance as an escape mechanism either by alleviating the drought symptoms, or by giving up on the somatic tissue, speeding up reproduction and surviving as seeds. Plants may possess one or more of such strategies, and the regulatory system is thus faced with a decision as to which survival strategy to employ. This decision is probably based on the severity of the drought, but also could involve other factors, such as the current developmental stage, the level of metabolic reserves, and also the ability of the plant to predict the nature of the stress it faces using memory (physiological or genetic) and environmental indicators other than the drought stress itself (i.e., light level, temperature, time of year, etc). Although most of the components of drought signaling and response seem to be conserved across land plants, the wiring of these decision-making circuits, the weights assigned to each input value, and the threshold values for arriving at different decision states are probably quite variable. Thus, each species may have a unique policy for dealing with drought stress. Drought Stress Perception

A number of different pathways for the sensing and signaling of drought stress have been explored. These involve signaling molecules such as reactive oxygen species (ROS), calcium, the sugar trehalose, and the hormones abscisic acid and methyl jasmonate (Bartels and Sunkar 2005; Kaur and Gupta, 2005; Huang et al., 2008; Wasilewska et al., 2008; Paul 2008; Sakamoto et al., 2008; Ge et al., 2008). A number of signaling proteins have also been identified, including members of many known transcription factor families (i.e., AP2, basic leucine zipper [bZIP], ZAT, WRKY; Uno et al., 2000; Kim et al., 2004; Amoutzias et al., 2006; Li et al., 2008; Schutze et al., 2008; Jiang and Deyholos, 2009) and protein-protein interactors (i.e., map kinases, calcium-binding proteins; Bartels and Sunkar 2005; Menges et al., 2008). Despite identification of many of the signaling components and downstream regulators, attempts to discover the drought sensor itself have only recently been successful. Genes for Plant Abiotic Stress Editors Matthew A. Jenks and Andrew J. Wood © 2010 Blackwell Publishing ISBN: 978-0-813-81502-2

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Although it is possible that there are many ways in which plants sense drought, at least one of the drought sensors appears to be a histidine kinase (AtHK1), part of a two component signaling complex also found in other organisms as a membrane-bound sensory system (Urao et al., 1999). Overexpression of AtHK1 increases drought resistance, while knockout mutants are less tolerant (Wohlbach et al., 2008). The expression of the AtHK1 gene is osmotically regulated and abundant in roots. The orthologous gene in yeast (Sln1) also appears to encode an osmotic stress sensor. A transcriptomic study of knockout and overexpression lines showed that AtHK1 is a regulator of NCED4, a gene controlling a critical step in ABA biosynthesis, and ABI2, involved in ABA regulation, linking AtHK1 to ABA. Other ABA biosynthetic genes and ABA responsive genes are also affected by overexpression or loss of AtHK1. Meta-analysis of expression patterns of more than 400 stress microarrays showed co-expression of AtHK1 with several ARR (Arabidopsis response regulator) genes, which interact with the (pseudo response regulator) PRR genes central to the circadian clock (Harrisingh and Nitabach, 2008). This is not surprising because ABA responses are clock gated, and co-expression of the drought sensor with the circadian clock may explain some of this gating, especially if the expression or output of the receptor was gated by or under the direct control of the circadian clock rather than the other way around (co-expression does not inform us of the direction of causality). Early stages of the drought signaling and tolerance in Arabidopsis use many of the same genes found in the desiccation tolerance strategy of seeds (Santos-Mendoza et al. 2008). If all seeds require a desiccation-tolerant dormant phase, this would mean all seed plants have the potential genes for drought tolerance. Differences between drought tolerant and drought sensitive seed plants would be in the vegetative regulation of these pre-existing genes. The AtHK1 receptor is involved in the activation of ABA biosynthetic genes in late seed development (Wohlbach et al., 2008). ABA treatment of seedlings, other abiotic stresses, including cold, osmotic, salt, ozone, and biotic stresses like infection by bacterial or fungal pathogens, induce global gene expression patterns that are highly correlated to drought stress, especially early in stress response (see Table 3.1). This indicates that many of the genes involved in ABA response and these other stresses are the same. It is likely that a single complex network of decision-making regulators chooses a survival strategy by integrating the inputs from multiple stress sensors. This circuit is also connected to the circadian clock and likely receives inputs by determining the level of metabolic reserves (sucrose, starch) through trehalose signaling (Kamin et al., 2007; Iordachescu and Imai, 2008; Ge et al., 2008; Paul et al., 2008), and current stage of development for example through ABA and auxin signals via ABI3, ABI5, LEC, and FUS genes (Santos-Mendoza et al., 2008). As we will delve into the regulatory circuitry discovered, we must necessarily include intersections with other networks that directly integrate with drought, and perhaps begin detailing this single decision-making network.

Systems Biology Approaches

The application of bioinformatics analysis to whole genome and post-genome high throughput data has made possible a top-down view of biological systems (see Yuan et al., 2008 for overview). This is a comprehensive view of all genetic and metabolic components simultaneously as the plant is perturbed by stresses. It suffers from false positive discoveries (incorrect identification of a component or interaction) just as bottom up approaches suffer from false negatives (missing or leaving out major components). The systems biology approach to drought-stress regulation begins by first identifying all components involved. The easiest way to do this would seem to be identifying all genes that are drought induced or suppressed (Figure 3.1, bottom left). This assumes (wrongly) that

Table 3.1 Correlation of drought stress transcriptome with other stresses. For color detail, please see color plate section.

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Individual microarray experiment


Normalization

Microarray matrix

PCC

Global similarity


SA HC

Regulated gene list

NV

Co-expressed network

SOR

Regulatory motifs

Co-expressed genes

Figure 3.1 A typical top-down transcriptomic meta-analysis workflow. Individual microarray experiments undergo statistical analysis (SA) to determine which genes have had significant changes to expression between experiment and control groups. In order to compare microarrays, they are normalized and globally scaled so that the genomic average expression is set to an arbitrary value (usually 100 units). The microarray matrix is the assembly where each column is an individual scaled microarray dataset (there can be up to thousands of such datasets), and each row is one gene. The contents of each element in the matrix (X, Y position) are either the normalized scaled signal, or the log (base 2) of the ratio of experiment over control (known as the m-value). Columns can be run through pairwise linear regression using the Pearson correlation coefficient (PCC) to determine the similarity of global expression pattern (all genes) between experiments. Hierarchical clustering (HC) can be used on rows to determine which genes have similar patterns of expression. Network visualization (NV) of co-expressed genes usually employs a distance cutoff to decide if an edge (co-expression) should be drawn between two nodes (genes), and then presents the entire genome as a network.

any gene thus regulated must be part of the survival strategy. Thousands of genes are affected by drought, and there are different cohorts of genes induced or suppressed at different times after the onset of water deficit. Some of these genes must be part of the drought regulatory network (Shinozaki et al., 2003) and others part of the survival strategy, so it is likely this gene list is enriched for such genes; however, identifying candidate genes from the background of genes changing expression indirectly or as a result of dysfunction is a challenge. A more focused strategy groups genes by expression pattern across an expression matrix, either a time course or an array of different stresses and tissues to produce a gene co-expression network (Figure 3.1 lower right). These co-expressed genes thus have similar responses to abiotic stimuli, and are assumed to be part of the same regulon (group of co-regulated genes), and thus may have the same transcription factor or factors controlling them. Co-expression maps have been generated for Arabidopsis, and are becoming a tool for systems biology approaches, with the caution that genes with co-related expression are not necessarily working for the same cause. A co-expression map was employed to study co-regulated components of the drought sensor ATKH1 (Wohlbach et al., 2008). This was done by assembling a meta-database of some 444 different microarray experiments on which ATHK1 gene was present. The expression

TRANSCRIPTION AND SIGNALING FACTORS IN THE DROUGHT RESPONSE REGULATORY NETWORK

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values (M-values is the log base 2 of the expression ratio of treated versus control) were globally scaled and normalized to allow comparison as a matrix of 444 arrays by 22,800 genes. Hierarchical clustering of this matrix identifies small co-expressed clades (groups) surrounding ATHK1, with the presumption that any gene that is tightly regulated with the drought sensor must either be drought regulated (i.e., downstream) or else regulating the sensor itself (i.e., upstream). This tightly regulated cluster included several response regulator genes (ARR4, 5, 6, 8, and 9), an unknown gene (At4g37080), and an 18S ribosomal RNA. The co-expression of ARRs, known to interact with the circadian clock (Harrisingh and Nitabach 2008; Más 2008) leads to the assumption that ATHK1 is then connected to the clock, and thus drought regulation is connected to this sensor. This type of transcriptomic meta-analysis study (as outlined in Figure 3.1) is increasingly common, especially for regulatory systems and metabolic pathways. An individual microarray experiment can be used to generate lists of induced and suppressed genes. After normalization, microarrays can be merged from all available sources into a single matrix. Currently there are now more than 2,000 different published array datasets for Arabidopsis, and more than 100 for rice either at TAIR (www. arabidopsis.org), NASC (www.arabidopsis.info), or gene expression omnibus (GEO; http://www. ncbi.nlm.nih.gov/geo/). Linear regression is employed to compare experiments, and yields a ranking of similarity of the global expression pattern of genes for different treatments. Thus, different treatments, mutants, and tissues can be evaluated and compared on a global level (most or all genes in the genome). Pearson correlation (or Spearman’s correlation) coefficient is then used as a metric for hierarchical clustering of genes, generating tables of co-expressed groups of genes. Genes that are co-regulated are assumed to be part of the same biological process or pathway. The strength of co-expression becomes the edge value to tie genes (nodes) into a network model of gene regulation. Co-expressed genes can also be mined for statistically over-represented sequences in promoters using algorithms such as Gibbs sampling (Thijs et al. 2001) and hidden Markov models (Bailey and Gribskov 1998). These efforts can identify cis-regulatory elements, but often discover non-regulatory motifs such as microsattelites. See Figure 3.2. An examination of all regulatory clusters of Arabidopsis has revealed a higher-level organization for nuclear and plastid regulons, and identified some 998 clusters of co-regulated genes overall (Mentzen and Wurtele, 2008). Maps of co-regulated genes using different matrixes of microarray-based expression data and different metrics (measurements of similarity of expression pattern) have been generated by several groups (Rhee et al., 2006; Kilian et al., 2007; Horan et al., 2008; Less and Galili, 2008; Matsui et al., 2008). These so far have been presented as unbiased tools available to researchers of regulatory systems to make candidate lists of potential pathway members. All of these tools so far come with the caveat that the lists of genes they generate are based on inference, and not direct experimental evidence, so caution is urged when drawing conclusions from such datasets. A complementary approach is to create a map of protein-protein interactions using high throughput yeast-2-hybrid, co-precipitation, or other methods to capture physical interactions between proteins. This effort is an attempt to capture some of the regulatory steps that do not rely on changes to the mRNA level of the regulators. Currently there is no large-scale dataset available for plants, however a predicted interactome has been constructed based on protein orthology (Geisler-Lee et al., 2007). The assumption is that if two proteins interact in yeast and their orthologs interact in Drosophila, these are conserved interactions, and the orthologs in plants probably also interact. As it turns out, and this is true in all eukaryotes studied so far, all genes in a genome are part of the same interacting pathway! There is a single interconnected web of interacting proteins, not discreet smaller pathways each belonging to a biological function. We find transcription factors, metabolic enzymes, cytoskeletal proteins, DNA replication machinery, and everything else to be part of the same pathway (e.g., compare Figure 3.3 to Figure 3.4A). Interesting, but this resulting map is not

Figure 3.2 The DREB/CBF circuit anchored with cis-regulatory elements. This view of the CBF portion of cold/drought response is centered on 4 members of DREB A-1 subfamily. Genes are shown as proteins (colored ovals) and promoters (black lines) with cis-regulatory elements (colored small rectangles). Regulators and elements are color matched. The ICE protein (inducer of CBF; blue oval) binds to the ICEr3 and ICEr4 elements (blue rectangles) in ZAT12, NAC72, HOS9, and CBFs 1-3. HOS9 binds to the HOS9r1 element (yellow ovals and rectangles), possibly acting as a repressor by displacing ICE since the elements are adjacent on the promoter. CBFs and DREB2 all bind to the DRE element (green) to induce downstream genes (light green) and the growth regulator ZAT10 (pink). CBF2 suppresses CBF1 and 3, but probably does not bind to the DRE element itself. ZAT10, which is also induced by ROS and other abiotic stresses, has 2 ICE elements, and suppresses the activity of DREbinding genes through an unknown mechanism. NAC72 is both upstream and downstream of CBFs, having both DRE and ICE elements. It binds to NAC72r1 element in CBF2, 3 and HOS9 to suppress expression. DREB2 is activated by drought, CBF4 is activated by increased ABA and interacts with ABRE binding factors (ABF). GA oxidases possess DRE elements and are activated by CBF1 to reduce GA levels, which leads to DELLA mediated dwarfism. For color detail, please see color plate section.

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Figure 3.3 Assembly of osmosensor, ABA and DREB/CBF regulatory circuits. Individual interactions identified in this chapter were assembled into an excel file, identifying the locus identifier for each gene, and downloading attributes from the Arabidopsis information resource (TAIR), the subcellular localization database for Arabidopsis (SUBA), and input into the network visualization tool cytoscape 2.6.1 (www.cytoscape.org) using the automated hierarchical layout. Shapes represent genes (triangles as hormones), colors are localizations (blue = nucleus; red = endomembrane; green = chloroplast, tan = cytostole, pink = unknown. Edges represent interactions (arrowhead = induces or activates, bar = suppresses). For color detail, please see color plate section.

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Figure 3.4 Systems level view of drought/cold stress network. Genes described in this chapter were identified in the predicted interactome of Arabidopsis (Geisler-Lee et al. 2007), and all interaction between, and were captured with first neighbors using cytoscape. This predicted and neighbor ’s network was then merged with known interactions from Figure 3.3 to produce a single network of 323 genes and 1,638 interactions. Genes are identified by shape (octagon = known transcription factor or DNA binding; hexagon = protein, metabolite, or hormone binding; parallogram = structural or transporter; triangle = nucleotide binding; circle = everything else or unknown) and color (blue = nucleus, light blue = mitochondria; green = chloroplast; tan = cytosol; red = endomembrane; pink = unknown). The whole network is shown (A) without gene labels, and two smaller portions show interactions near the CBF/DREB regulatory circuit (B) and the osmosensing ATHK1, ARR phosphor-relay genes and PRR genes of the circadian clock (C). For color detail, please see color plate section.

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itself very useful in trying to identify the connections related to drought stress regulation or any other discreet process. The best that systems biologists can do to subdivide this map into manageable portions is to identify the first neighbors (with direct interactions) of “bait” genes that we know are part of the system we are interested in (e.g., Figure 3.4B, 3.4C). Bioinformatic approaches can also attempt to identify molecular function and potentially subcellular localization of genes by discovering domains or family alignments. These are patterns within the coding sequence that are either of known molecular function (in the case of domains), or are correlated with a known function but lack direct evidence. In this way, genes are assigned a function without any direct evidence, and grouped into families. For example, the MYB transcription factor family is populated by 128 genes in Arabidopsis, all classified as transcription factors although experimental evidence exists for less than half of the members (based on TAIR 8.0 classification at www.arabidopsis.org). The remaining genes are assumed to be transcription factors as well because they have a MYB domain. Other gene families are curated with much less support. Networks in systems biology often use domain or functional assignment by alignment rather than by evidence, typically using the classifications found in the gene ontology (GO) database. These GO codes are often used as a post-hoc test of network validity in protein-protein interaction and co-expression networks. Ontology can also be used to create a network, but these are probably even more prone to false positives than co-expression. Even noisier are the bibliographic or text-mining networks that look for the co-occurrence of search terms (proteins, metabolites, signaling molecules) in the abstracts of all scientific literature and web pages (Rihn et al. 2003). This is usually a tool for curators to identify papers of interest, but lately has been proposed as a crude network reconstruction method if one wants to quickly evaluate the information contained in tens of thousands of papers without having to read each one.

Transcriptomic Studies of Drought Stress

Numerous transcriptomic studies have been carried out by subjecting Arabidopsis and other model plant species to various abiotic stresses, and then capturing a snapshot of the mRNA levels of each gene in the genome. (For recent drought transcriptome papers, see Matsui et al., 2008, Huang et al., 2008, Degenkolbe et al., 2009.) The best of these so far from a systems standpoint was carried out by the Arabidopsis Genome Expression project (AtGen Express; see http://www.arabidopsis. org/portals/expression), which conducted a series of abiotic stress time courses with root and shoot tissues harvested separately at six different time points (0.5, 1, 3, 6, 12, and 24 hours) (Kilian et al., 2007). These series, conducted in magenta boxes on agar media, included a 4 °C cold shift, mechanical wounding of leaves, lid-off dehydration, shift to media with either 150 milliMole (mM) NaCl, 300 mM mannitol, or 10 mM of one of the plant hormones. RNA extracted from these samples was then hybridized to the ATH1 DNA microarray to get a comprehensive picture of gene mRNA levels during the first day of stress. Using global scaling normalization, it is possible to directly compare the results of these experiments (see Denby and Gehring, 2005 for example). The genes induced by abiotic stress are numerous. Up to 25% of the genome is differentially regulated by any given stress. The first changes to the mRNA expression level were rapid, occurring within the first hour. Surprisingly a cohort of 59 genes including 21 transcription factors was induced no matter what stress was applied. These universal stress genes include known stress regulators ZAT10 and ERF5 as well as members of the MYB, AP2, and C3H2 zinc finger families, proteins involved in calcium ion signaling, and other signaling genes (Kilian et al., 2007; Khandelwal et al., 2008). This observation suggested that the first response to all abiotic stresses is universal, like a person

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might say “ahhh” if suddenly wounded, burnt, frozen or shocked, in simple recognition that a stress has been encountered. In longer exposures to cold or drought (6−24 hours), the fraction of genes that respond to all stresses equally diminishes, and more stress specific genes are expressed. The function assignment of most genes in Arabidopsis is still hypothetical, predicted based on homology of sequence or domain motif or frequently completely unknown. There are clear patterns to expression however. Genes are grouped by the similarity of expression patterns across several abiotic stresses using hierarchical or k-means statistical clustering methods (Horan et al., 2008). In this way, 206 new genes were identified specific to a particular stress, and 104 new genes were found responsive to multiple stresses. This top-down annotation by co-regulation may help expand the list of genes that need to be considered. This list is already daunting, with more than 877 annotated stress responsive genes and 210 novel ones found by Horan and others (2008). There is a clear separation of the timing of response, with early-induced and late-induced transcription factor gene groups. Roots and shoots have the same stress expression profile for many differentially regulated genes; however, there are clear-cut groups of root-only and shoot-only responding genes. Each of these transcriptomic profiles can be thought of as the mixture of response and reaction to drought. Mathematically, a result set is a series of numbers corresponding to each gene, and can thus be directly compared to other transcriptomic profiles via Pearson correlation (PCC) or Spearman’s rank correlation (SRC) to estimate the similarity of responses. A matrix table of transcriptomes (Table 3.1) gives us a map of which stress, tissue, and time point is most similar to another. Hierarchical clustering these treatments, a dendrogram shows which stresses are most related, that is, those having the most similar patterns of gene expression. Comparing a drought time-course to other stresses, it is clear that time is the most important factor in the pattern of whole genome expression in response to stress (blue boxes in Table 3.1). Just looking at correlations within drought stress (Table 3.1 top brown rows), expression is most similar at nearby time points (compare shoot at 0.5 hours and shoot at 1.0 hours), and roots and shoot expression patterns coalign to the same time points, indicating that neither is “out of phase” (i.e., by delays caused by signal propagation). Comparing drought stress to salt stress (Table 3.1 yellow rows), there is strong correlation (PCC > 0.4) between genome expression along the same time points. That is, whatever drought stress is regulating at 1 hour, salt stress for 1 hour regulates many of the same genes the same way. This is true for cold and osmotic (blue rows) but has an especially strong correlation with wounding stress (green rows). Indeed the global expression pattern of wounding stress more closely resembles drought than any other stress, but only along the time course. Salt, cold, and osmotic stresses across all time points more broadly correlate to drought stress for 0.5 to 1 hour (notice the faint blue vertical rectangular boxes indicating high correlation across all time points in salt versus 0.5 and 1 hour in drought). Drought stress at the 0.5- to 1-hour time point also correlates (more weakly) with ozone gassing and ABA application (Table 3.1 pink rows), indicating that genes induced early in drought stress are similar to ABA- and ROS-induced genes. Methyl jasmonate correlates with drought at 3 hours, after the peak correlating time points for ABA application indicating that jasmonate-induced genes activate later in the drought response than ABAinduced genes. Pathogen infestation, by either bacteria or fungi at 24 hours post inoculation, correlate weakly with all time points in drought stress (Table 3.1, faint blue horizontal rectangles in the purple rows). This would mean that genes turned on after a day of infection are similar to genes expressed throughout drought stress, many of these being WRKY type transcription factors. The time course profile of a stress response might indicate more than just the progress of signal propagation. There are internal timekeeping mechanisms centered on the cell cycle and circadian clock. Cell cycle is tied directly to proliferation, growth and production of new organs, and organ size. Mutations reducing cell cycle result in plants with smaller and misshapen organs and abnor-


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malities in development. Severe stresses often result in similar phenotypes, and may either directly affect cell cycle, or represent the indirect effect of stress pathology, that is, the stress reduces photosynthetic and metabolic efficiency, diverts energy toward stress abatement, and results in fewer resources for growth, thus reducing cell division rather indirectly. The circadian clock is a vital part of plant short-term memory, allowing it to turn on and off genes in anticipation of abiotic events, including light, temperature, water availability, and potentially the activity cycle of herbivores and pathogens (Gardener et al., 2006). Plants with intact circadian clocks are healthier and survive better than those with mutations in clock genes (Green et al., 2002; Dodd et al. 2005). Approximately one-third of all Arabidopsis genes are influenced by the circadian clock (Covington et al. 2008), and in particular the response to ABA and MeJA hormones is highly dependent on the position of the circadian clock (Mizuno and Yamashino, 2008). The clock itself is a free-running cascade of 5 PSEUDO-RESPONSE REGULATOR (PRR) genes, which each reciprocally activate the next gene in the cascade and inhibit the previous gene (McClung 2006; Más 2008). The PRR genes encode the central pacemaker of the clock, and it is entrained daily through a phytochrome-based light signal proteins PhyA and B and PIF3 (phytochrome interacting factor) interacting with TOC1 (Más 2008). Martin-Tryon and Harmer (2008) identified XAP5 CIRCADIAN TIMEKEEPER (XCT), which also connects the light signal to the circadian clock. Mutants of XCT have altered circadian rhythm, and show different photomorphogenic phenotypes depending on light color. When plants are subjected to a cold temperature shift, the clock stops, clock-type genes and clock-regulated genes exhibit constitutive expression (Ibañez C et al. 2008; Bieniawska et al. 2008). Thus, circadian rhythm is a major factor in the regulatory pattern of cold-induced genes. Given the high degree of overlap between cold and drought stress (Table 3.1), and that ABA response is clock-gated, it is very likely that drought stress also is directly tied to the circadian clock. This may go a long way in explaining the timeresponse correlation between abiotic stresses. The controls in the Killian and others (2007) experiments were plants grown in ideal conditions and harvested at the same time as the treated plants. If the clock has been stopped by cold stress, then the differences in gene expression are a combination of stress response, and the diverging clock positions. If this is causing the correlation of stresses across the time course, then it is likely that all abiotic stresses interfere with the circadian clock, because this correlation appears systemically throughout the dataset no matter what stress was applied. Covington and others (2008) determined that a third of all expressed genes are influenced by the circadian rhythm in Arabidopsis. Sorting by hormone, ABA-responsive and methyl jasmonate (MeJA)-responsive genes were found to oscillate diurnally, but not ethylene, brassinolide, auxin, or cytokinin responsive genes (Mizuno Yamashino, 2008). Thus, ABA- and JA-mediated pathways should both be strongly altered by changes to circadian rhythm. We saw this pattern as well in the global correlation of transcriptomes (Table 3.1). Why only these hormones are tied to the clock is unknown, and as responses to ABA and JA depend on time of day, extra care must be taken in experimental design to produce repeatable results. Since drought is strongly regulated by ABA, experiments following the time course and application of drought stress must consider the time of day the stress was applied. If the processes being observed are programmed responses to drought, or to clock phase, this likely will have different outcomes depending on when the experiment is begun. Two controls for each drought experiment are thus necessary, an untreated control and a zero time point (taken at the moment just before the stress is applied). The untreated control will continue to cycle through the circadian rhythm. If drought alters the phase, the two clocks will get increasingly “out of sync,” and differences between drought and control will be due to clock cycle. The zero time control is a snapshot of the “un-droughted” plant at the same clock phase as the

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treatment (initially). If the clock stops in drought-treated plants, this control will stay in sync with the drought stress, but not the untreated control. Between these two controls, it will be possible to separate clock-independent drought stress regulation from circadian clock components.

The DREB/CBF Regulon

One of the most clearly understood abiotic stress regulatory networks from both the cis-regulatory and transcription factor side is mediated through the dehydration-responsive element (DRE) and several layers of interconnecting transcription factors. Much of our understanding of this circuit has been built from the bottom-up by linking the results of promoter binding assays and mutant analysis (Riera et al., 2005; Nakashima et al., 2006; Yamaguchi-Shinozaki and Shinozaki, 2006). Studies from the top down using DNA microarrays have largely supported the established components and wiring of the CBF regulon and have added new putative components. Cold, salt, and drought stress all cause rapid (1–2 hour) expression of related AP2 type transcription factors called DREB1A-D (drought element binding) or CBF1-4 (C-repeat binding) and DREB2 (Agarwal et al., 2006). The AP2-ERBP family of transcription factors (146 members in Arabidopsis) is responsible for a diverse range of biological functions including flower organ identity (APETELA2, for which the family is named), ethylene, JA, and cytokinin response (ERF, JRF, CRF), and still has many members of unknown roles (Riechmann and Meyerowitz, 1998; Pierik et al., 2007; Nakano, 2006). The CRF (cytokinin response) type AP2 transcription factor is regulated by a histidine kinase two-component system, similar to that identified as the drought stress receptor (Rashotte, et al. 2006). A subgroup with a single AP2 repeat includes DREB (drought response), ERF, TINY (tiny root hairs), and ABI4 (abscisic acid insensitive) genes. Within this subgroup DDF1 and 2 (DWARF AND DELAYED FLOWERING), regulators of both gibberellins and salt tolerance are part of DREB subfamily A-1, which includes all four CBFs. Over-expression of either DDF gene results in reduced gibberellins, dwarfism, and increased salt tolerance. Overexpression of CBF1-4 (DREB1A-D) results in increased cold, salt, and drought tolerance through increases in proline and cytosolic sugars and expression of drought resistance genes such as LEA, late embryogenesis abundant (Gilmour et al., 2004; Kasuga et al., 1999, Huang et al., 2008; Hundertmark and Hincha, 2008; Gutha and Reddy, 2008). Overexpression of CBF genes also results in GA loss and dwarfism, similar to that of DDF genes, indicating that all subfamily members may compete for the same binding sites, and have significantly overlapping regulons. DREB2 belongs to a related subfamily (A-2) of eight genes, including two that are drought induced (DREB2A and B) and are regulators of drought tolerance genes. The cis-regulatory element DRE or LTRE is the promoter target for CBF, DREB2 (and likely DDF) transcription factors. It is specifically recognized by CBF proteins, but 1-base mismatches of the primary motif sequence are not bound in vitro. Cold and drought regulated genes are enriched for DRE elements in their promoter in Arabidopsis, rice, and the moss Physcomitrella patens (Maruyama et al., 2004; Liu et al., 2007). DREB/CBF homologs have been found in virtually all plant species indicating that this pathway is potentially common to all land plants. Using microarray data in the reverse correlation, all genes with DRE elements were first obtained based on pattern search, then that group of DRE-containing genes was assayed on several microarrays for changes in group-expression patterns using statistical analysis. DRE-containing genes are not only enriched for cold induction, but also for drought, wounding, osmotic, heat, and salt stresses, as well as the hormone ABA, sfr6 (sensitive to freezing), and cbf2 mutants, during seed


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imbibitions and strangely, in atrichoblast cells (Geisler et al., 2006; Table 3.2). The SRF genes (there are seven) all protect plants from freezing either as regulators or as cryoprotectors (i.e., SFR2; Fourrier et al., 2008). SRF6 acts post-translationally on CBF proteins or downstream as overexpression of CBF1 and does not induce the DRE regulon in sfr6-1 mutants (Knight et al., 2009). However, cold stress still does induce the DRE regulon in sfr6-1 mutants (see Table 3.2) indicating that alternative transcription factors such as CBF4 and DREB2 are unaffected. SFR6 is linked to the circadian clock through the presence of an evening element, in the promoter (Knight et al., 2008). The expression pattern for DRE-regulon (DRE containing genes), known as a cis-regulatory fingerprint (Table 3.2) describes the collective behavior for the element itself, without knowledge or bias as to the binding transcription factor. It is likely that all DREB subfamily A-1 members will bind to DRE, but so will other transcription factors, each with different binding strengths, connecting that element/gene to different pathways. By calculating the expression pattern from the perspective of the cis-regulatory element, we get a complementary view of gene regulation. This will help flesh out regulatory pathways and identify the interconnections of different signals. Using the same strategy, the allowable sequence variants were determined by a bioinformatic process called in silicio mutagenesis, in which all genes with a naturally occurring variant sequence (one base mismatch) were identified in Arabidopsis, and this variant subgroup was analyzed for enrichment of induction or suppression by cold (Geisler et al., 2006). Allowable sequence variants determined by this method agreed in in vitro binding results, in that GCCGAC and ACCGAC sequences both showed cold induction, while TCCGAC and CCCGAC did not. The orientation of the element to the gene, however, did not make a difference. The DRE element can be located up to 400 bp upstream, in the 5′UTR, or in the first few introns and still show correlation to cold induction. The correlation disappears any further upstream. The ABRE element by contrast, can be located up to 1,200 bp upstream and still show significant correlation to ABA induction, but it cannot be located in the UTR or introns (Geisler et al., unpublished results). Using this bioinformatic method, correlating the presence, orientation, position, and naturally occurring variations in sequence, a great deal can be learned about the function, mechanism, and responses of a cis-regulatory element, and a large number of false-positives can be removed (elements that occur in the wrong position and are non-regulatory). The comprehensive identification of downstream targets of CBF genes will be greatly facilitated by the analysis of DRE cis-regulatory elements. CBF genes show some interactions amongst themselves (Figure 3.2). CBF2 is a negative regulator of its paralogs, knockout of CBF2 increases expression of CBF1 and 3, but CBF2 may not bind members of the same downstream regulon (CBF1 and 3 have no DRE element). CBF2 is expressed later than CBF1 and 3 and may act to control the timing of the cold/drought response (Novillo et al., 2007). CBF3 was thought to suppress the activity of CBF2 (due to increased expression in an ice1 mutant; Chinnusamy et al., 2007); however, individual knockouts of CBF1 and 3 do not show this (Novillo et al., 2007). CBF2 is thus likely directly regulated by ICE1. CBF1 and 3 regulons significantly overlap, with some genes requiring both transcription factors for expression, but do not affect the regulation of other CBF genes. Knockouts of either CBF1 or 3 result in reduced cold and drought resistance, indicating that they do not act redundantly and that both genes are required for resistance. CBF4, a drought and ABA-induced member, is not affected by knockout or overexpression of CBF2. Upstream of the CBF gene is the ICE (Inducer of CBF) gene, a c-MYC-like basic helix-loop-helix that is phosphorylated in cold temperatures (but with no change in transcription), and binds to the ICE box (ICEr3/ICEr4) found in CBF1, 2, and 3 (Zarka et al., 2003; Benedict et al., 2006). Other ICE elements (ICEr1 and 2) overrepresented in the ICE regulon were subsequently shown not to be bioactive (Benedict et al, 2006). Light and circadian

Table 3.2 Regulatory fingerprint of drought responsive element (DRE). Experimental design

Overall p-value

Induced genes

Ind. enrich

Ind. p-value

Supp. genes

Supp. enrich

Supp. p-value

Cold stress 24 hrs shoot Cold stress 12 hrs shoot Cold stress 24 hrs root Cold stress 12 hrs root Freezing 3 hrs Cold stress 24 hrs on soil Freezing 3 hrs cold acclimated 35S:CBF2 Wounding of shoot 6 hrs sfr3 mutant 24 hrs cold stress dormant seed of cvi ecotype sfr6 mutant 24 hrs cold stress seed imbibed in light vs dark cvi ecotype fad2 mutant in cold 3 hrs dry seed vs imbibed cvi ecotype prolonged imbibition vs dry cvi ecotype sfr6 mutant 24 hrs cold wounding 3 hrs Salt stress 3 hrs shoot 3 μm ABA 24 hrs 30 °C heat shock wounding 12 hrs fad3/7/8 mutant 3 hrs cold stress Atrichoblast cells vs whole root Salt stress 24 hrs shoot 30 um ABA 24 hrs gpa1 mutant w/ABA Drought stress 0.5 hr Osmotic stress 3 hrs Drought stress 1 hr Whole root vs lateral root cap Osmotic stress 6 hrs shoot Greenhouse vs growth chamber

2.82E-34 4.12E-26 1.66E-25 6.23E-20 3.81E-18 1.64E-17 1.71E-15 1.77E-14 2.56E-12 2.02E-11 1.42E-10 1.68E-10 2.79E-10 5.20E-10 6.42E-10 6.42E-10 2.46E-09 1.44E-08 1.56E-08 1.74E-08 6.42E-08 1.22E-07 1.96E-07 2.36E-07 4.44E-07 5.11E-07 5.84E-07 6.45E-07 7.41E-07 1.79E-06 1.96E-06 2.15E-06 2.39E-06

206 158 159 126 123 133 114 105 109 131 204 132 149 72 193 171 205 80 113 172 109 107 80 166 128 183 108 111 132 105 149 151 87

2.15 2.17 2.11 2.15 2.10 1.98 2.00 2.04 1.89 1.73 1.52 1.66 1.64 2.02 1.50 0.79 1.47 1.90 1.69 0.82 1.68 1.65 1.75 1.48 1.54 0.86 1.63 1.55 1.49 1.52 0.91 1.44 1.49

3.21E-33 9.93E-26 5.77E-24 5.72E-20 1.62E-18 4.50E-17 4.04E-15 9.08E-15 2.34E-12 2.61E-11 4.62E-11 5.25E-10 6.55E-11 3.96E-10 7.25E-10 0.00037 9.19E-10 1.96E-09 3.49E-09 0.00290 8.66E-09 4.51E-08 1.54E-07 3.60E-08 2.12E-07 0.026 8.62E-08 8.98E-07 8.60E-07 5.95E-06 0.20 1.03E-06 9.72E-05

91 72 87 62 56 58 36 34 50 67 170 65 112 31 171 193 55 49 56 209 93 39 21 114 55 192 80 14 51 11 191 87 21

0.61 0.65 0.62 0.69 0.72 0.65 0.61 0.68 0.70 0.71 0.81 0.68 0.83 0.68 0.79 1.5 0.74 0.91 0.82 1.43 0.94 0.75 0.64 0.89 0.70 1.41 0.96 0.54 0.71 0.46 1.40 0.80 0.48

2.99E-07 0.0001 1.22E-06 0.002 0.011 0.0006 0.0020 0.02 0.008 0.003 0.002 0.001 0.03 0.02 0.0003 7.25E-10 0.02 0.50 0.12 7.50E-09 0.53 0.074 0.03 0.195 0.041 1.13E-07 0.763 0.018 0.0124 0.0076 3.00E-07 0.0297 0.00044

The regulatory fingerprint for extended DRE ([A/G/T][A/G]CCGACN[A/T]) in the 500-bp upstream promoter region. There are 2,381 genes with the extended DRE element in the 500-bp upstream of the transcription start site, the region in which DRE is statistically enriched and demonstrated to be functional. Looking at each transcriptome (microarray experiment), of these DRE genes, 851 are present on microarrays. The number of induced, suppressed, and neutral genes with DRE is compared to expected values based on the genome as a whole. The enrichment is the ratio of observed versus expected genes. An overall p-value is determined from a chi-squared test (2df) between observed and expected numbers of genes, and specific p-values (1df) were calculated for induced/not induced and suppressed/not suppressed genes. Treatments with a significant p-value are thus enriched (enrichment >1), or depleted (enrichment

Genes for Plant Abiotic Stress

Plant Abiotic Stress (Biological Sciences Series)

Plant Responses to Abiotic Stress (Topics in Current Genetics)

Plant Stress And Biotechnology

Abiotic Stress Adaptation in Plants: Physiological, Molecular and Genomic Foundation

Abiotic Stress Adaptation in Plants: Physiological, Molecular and Genomic Foundation

Improving Crop Resistance to Abiotic Stress: Omics Approaches

Abiotic Stress Responses in Plants: Metabolism, Productivity and Sustainability

Abiotic Stress Adaptation in Plants: Physiological, Molecular and Genomic Foundation

Plant Stress Tolerance: Methods and Protocols

Ion Channels and Plant Stress Responses

Handbook of Plant and Crop Stress

Stress Management for Teachers

Stress

Stress

Murder Genes

Murder Genes

Pandora's Genes

Good Genes

Genes VIII

Genes IX

Genes VIII

Immunoglobulin Genes

Essential genes

Genes VII

Red Genes, Blue Genes: Exposing Political Irrationality

Tele-Stress - Relief For Call Center Stress Syndrome

Good Genes

Pandora's Genes

Deadly Genes

HOX Genes

Genes for Plant Abiotic Stress

Plant Abiotic Stress (Biological Sciences Series)

Plant Responses to Abiotic Stress (Topics in Current Genetics)

Plant Stress And Biotechnology

Abiotic Stress Adaptation in Plants: Physiological, Molecular and Genomic Foundation

Abiotic Stress Adaptation in Plants: Physiological, Molecular and Genomic Foundation

Improving Crop Resistance to Abiotic Stress: Omics Approaches

Abiotic Stress Responses in Plants: Metabolism, Productivity and Sustainability

Abiotic Stress Adaptation in Plants: Physiological, Molecular and Genomic Foundation

Plant Stress Tolerance: Methods and Protocols

Ion Channels and Plant Stress Responses

Handbook of Plant and Crop Stress

Stress Management for Teachers

Stress

Stress

Murder Genes

Murder Genes

Pandora's Genes

Good Genes

Genes VIII

Genes IX

Genes VIII

Immunoglobulin Genes

Essential genes

Genes VII

Red Genes, Blue Genes: Exposing Political Irrationality

Tele-Stress - Relief For Call Center Stress Syndrome

Good Genes

Pandora's Genes

Deadly Genes

HOX Genes

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