Advances in
BOTANICAL RESEARCH Series Editors JEAN-CLAUDE KADER
Laboratoire Physiologie Cellulaire et Mole´culaire des Plantes, CNRS, Universite´ de Paris, Paris, France
MICHEL DELSENY
Laboratoire Ge´nome et De´veloppement des Plantes,
CNRS IRD UP, Universite´ de Perpignan, Perpignan, France
Academic Press is an imprint of Elsevier 32 Jamestown Road, LondonNW17BY,UK 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands First edition 2010 Copyright Ó 2010 Elsevier Ltd. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email:
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CONTRIBUTORS TO VOLUME 53
PIERRE ABAD INRA, UMR 1301, 400 route des Chappes, F-06903 Sophia-Antipolis, France; CNRS, UMR 6243, 400 route des Chappes, F-06903 Sophia-Antipolis, France; UNSA, UMR 1301, 400 route des Chappes, F-06903 Sophia-Antipolis, France SE´LASTIQUE AKAFFOU URES Daloa, B150, Daloa, Coˆte d’Ivoire ALAN C. ANDRADE Laboratory of Molecular Genetics (LGM-NTBio), Embrapa Genetic Resources and Biotechnology, CP 02372, 70770-900 Brası´lia-DF, Brazil TODD BLEVINS Department of Biology, Indiana University, 915 E. Third Street, Bloomington, IN, 47405, USA JORDI BOU-TORRENT Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB, c. Jordi Girona, 18-26, 08034-Barcelona, Spain ´ NGELES CALATAYUD Department of Horticulture, Instituto ValenA ciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Naquera km. 4.5, 46113 Moncada, Valencia, Spain CLAUDINE CAMPA UMR DIAPC, GECOFA; Centre IRD de Montpellier, BP64501, 34394 Montpellier Cedex, France ABDELLY CHEDLY Laboratoire d’Adaptation des Plantes aux Stress Abiotiques, Centre de Biotechnologie a` la Technopole de Borj Ce´dria (CBBC), BP 901, 2050 Hammam-lif, Tunisia; Institute for Plant Ecology, Justus-Liebig University, Giessen, Heinrich-Buff-Ring 26-32, D-35392 Giessen, Germany ´ S CIFUENTES-ESQUIVEL Centre for Research in Agricultural NICOLA Genomics (CRAG) CSIC-IRTA-UAB, c. Jordi Girona, 18-26, 08034Barcelona, Spain DOMINIQUE CROUZILLAT Nestle´ R&D Tours, 101 Av. G. Eiffel, Notre Dame d’Oe´, BP 49716 37097, Tours, Cedex 2, France ALEXANDRE DE KOCHKO UMR DIAPC, GECOFA; Centre IRD de Montpellier, BP64501, 34394 Montpellier Cedex, France; URES Daloa, B150, Daloa, Coˆte d’Ivoire, Laboratory of Molecular Genetics (LGM-NTBio), Embrapa Genetic Resources and Biotechnology, CP 02372, 70770-900 Brası´lia-DF, Brazil MARC ¸ AL GALLEMI´ Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB, c. Jordi Girona, 18-26, 08034-Barcelona, Spain ANAHIT GALSTYAN Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB, c. Jordi Girona, 18-26, 08034-Barcelona, Spain ix
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CONTRIBUTORS
ELISA GORBE Department of Horticulture, Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Naquera km. 4.5, 46113 Moncada, Valencia, Spain ROMAIN GUYOT UMR DIAPC, GECOFA; Centre IRD de Montpellier, BP64501, 34394 Montpellier Cedex, France PERLA HAMON UMR DIAPC, GECOFA; Centre IRD de Montpellier, BP64501, 34394 Montpellier Cedex, France SERGE HAMON UMR DIAPC, GECOFA; Centre IRD de Montpellier, BP64501, 34394 Montpellier Cedex, France KOYRO HANS-WERNER Institute for Plant Ecology, Justus-Liebig University, Giessen, Heinrich-Buff-Ring 26-32, D-35392 Giessen, Germany JAIME F. MARTI´NEZ-GARCI´A Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB, c. Jordi Girona, 18-26, 08034-Barcelona, Spain; Institucio´ Catalana de Recerca i Estudis Avanc¸ats, Passeig Lluı´s Companys 23, 08010-Barcelona, Spain RAY MING Department of Plant Biology, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA LUKAS A. MUELLER Boyce Thompson Institute for Plant Research, Tower Road, Ithaca, NY 14853-1801, USA CRAIG S. PIKAARD Department of Biology, Indiana University, 915 E. Third Street, Bloomington, IN, 47405, USA ´ RIE PONCET UMR DIAPC, GECOFA; Centre IRD de MontpelVALE lier, BP64501, 34394 Montpellier Cedex, France FRE´DE´RIC PONTVIANNE Department of Biology, Indiana University, 915 E. Third Street, Bloomington, IN, 47405, USA KSOURI RIADH Laboratoire d’Adaptation des Plantes aux Stress Abiotiques, Centre de Biotechnologie a` la Technopole de Borj-Ce´dria (CBBC), BP 901, 2050 Hammam-lif, Tunisia ` SALLA-MARTRET Centre for Research in Agricultural GenoMERCE mics (CRAG) CSIC-IRTA-UAB, c. Jordi Girona, 18-26, 08034Barcelona, Spain CHRISTINE TRANCHANT-DUBREUIL UMR DIAPC, GECOFA; Centre IRD de Montpellier, BP64501, 34394 Montpellier Cedex, France MEGDICHE WIDED Laboratoire d’Adaptation des Plantes aux Stress Abiotiques, Centre de Biotechnologie a` la Technopole de Borj-Ce´dria (CBBC), BP 901, 2050 Hammam-lif, Tunisia VALERIE M. WILLIAMSON Department of Nematology, University of California, Davis, CA 95616, USA
CONTENTS OF VOLUMES 35–52 Series Editor (Volumes 35–44) J.A. CALLOW School of Biosciences, University of Birmingham, Birmingham, United Kingdom
Contents of Volume 35 Recent Advances in the Cell Biology of Chlorophyll Catabolism H. THOMAS, H. OUGHAM AND S. HORTENSTEINER The Microspore: A Haploid Multipurpose Cell A. TOURAEV, M. PFOSSER AND E. HEBERLE-BORS The Seed Oleosins: Structure Properties and Biological Role J. NAPIER, F. BEAUDOIN, A. TATHAM AND P. SHEWRY Compartmentation of Proteins in the Protein Storage Vacuole: A Compound Organelle in Plant Cells L. JIANG AND J. ROGERS Intraspecific Variation in Seaweeds: The Application of New Tools and Approaches C. MAGGS AND R. WATTIER Glucosinolates and Their Degradation Products R. F. MITHEN
Contents of Volume 36 PLANT VIRUS VECTOR INTERACTIONS Edited by R. Plumb xi
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Aphids: Non-Persistent Transmission T. P. PIRONE AND K. L. PERRY Persistent Transmission of Luteoviruses by Aphids B. REAVY AND M. A. MAYO Fungi M. J. ADAMS Whitefly Transmission of Plant Viruses J. K. BROWN AND H. CZOSNEK Beetles R. C. GERGERICH Thrips as Vectors of Tospoviruses D. E. ULLMAN, R. MEIDEROS, L. R. CAMPBELL, A. E. WHITFIELD, J. L. SHERWOOD AND T. L. GERMAN Virus Transmission by Leafhoppers, Planthoppers and Treehoppers (Auchenorrhyncha, Homoptera) E. AMMAR AND L. R. NAULT Nematodes S. A. MacFARLANE, R. NEILSON AND D. J. F. BROWN Other Vectors R. T. PLUMB
Contents of Volume 37 ANTHOCYANINS IN LEAVES Edited by K. S. Gould and D. W. Lee
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Anthocyanins in Leaves and Other Vegetative Organs: An Introduction D. W. LEE AND K. S. GOULD Le Rouge et le Noir: Are Anthocyanins Plant Melanins? G. S. TIMMINS, N. M. HOLBROOK AND T. S. FEILD Anthocyanins in Leaves: History, Phylogeny and Development D. W. LEE The Final Steps in Anthocyanin Formation: A Story of Modification and Sequestration C. S. WINEFIELD Molecular Genetics and Control of Anthocyanin Expression B. WINKEL-SHIRLEY Differential Expression and Functional Significance of Anthocyanins in Relation to Phasic Development in Hedera helix L. W. P. HACKETT Do Anthocyanins Function as Osmoregulators in Leaf Tissues? L. CHALKER-SCOTT The Role of Anthocyanins for Photosynthesis of Alaskan Arctic Evergreens During Snowmelt S. F. OBERBAUER AND G. STARR Anthocyanins in Autumn Leaf Senescence D. W. LEE A Unified Explanation for Anthocyanins in Leaves? K. S. GOULD, S. O. NEILL AND T. C. VOGELMANN
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Contents of Volume 38 An Epidemiological Framework for Disease Management C. A. GILLIGAN Golgi-independent Trafficking of Macromolecules to the Plant Vacuole D. C. BASSHAM Phosphoenolpyruvate Carboxykinase: Structure, Function and Regulation R. P. WALKER AND Z.-H. CHEN Developmental Genetics of the Angiosperm Leaf C. A. KIDNER, M. C. P. TIMMERMANS, M. E. BYRNE AND R. A. MARTIENSSEN A Model for the Evolution and Genesis of the Pseudotetraploid Arabidopsis thaliana Genome Y. HENRY, A. CHAMPION, I. GY, A. PICAUD, A. LECHARNY AND M. KREIS
Contents of Volume 39 Cumulative Subject Index Volumes 1–38 Contents of Volume 40 Starch Synthesis in Cereal Grains K. TOMLINSON AND K. DENYER The Hyperaccumulation of Metals by Plants M. R. MACNAIR Plant Chromatin — Learning from Similarities and Differences J. BRZESKI, J. DYCZKOWSKI, S. KACZANOWSKI, P. ZIELENKIEWICZ AND A. JERZMANOWSKI
CONTENTS OF VOLUMES 35–52
The Interface Between the Cell Cycle and Programmed Cell Death in Higher Plants: From Division unto Death D. FRANCIS The Importance of Extracellular Carbohydrate Production by Marine Epipelic Diatoms G. J. C. UNDERWOOD AND D. M. PATERSON Fungal Pathogens of Insects: Cuticle Degrading Enzymes and Toxins A. K. CHARNLEY
Contents of Volume 41 Multiple Responses of Rhizobia to Flavonoids During Legume Root Infection JAMES E. COOPER Investigating and Manipulating Lignin Biosynthesis in the Postgenomic Era CLAIRE HALPIN Application of Thermal Imaging and Infrared Sensing in Plant Physiology and Ecophysiology HAMLYN G. JONES Sequences and Phylogenies of Plant Pararetroviruses, Viruses, and Transposable Elements CELIA HANSEN AND J. S. HESLOP-HARRISON Role of Plasmodesmata Regulation in Plant Development ARNAUD COMPLAINVILLE AND MARTIN CRESPI
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Contents of Volume 42 Chemical Manipulation of Antioxidant Defences in Plants ROBERT EDWARDS, MELISSA BRAZIER-HICKS, DAVID P. DIXON AND IAN CUMMINS The Impact of Molecular Data in Fungal Systematics P. D. BRIDGE, B. M. SPOONER AND P. J. ROBERTS Cytoskeletal Regulation of the Plane of Cell Division: An Essential Component of Plant Development and Reproduction HILARY J. ROGERS Nitrogen and Carbon Metabolism in Plastids: Evolution, Integration, and Coordination with Reactions in the Cytosol ALYSON K. TOBIN AND CAROLINE G. BOWSHER
Contents of Volume 43 Defensive and Sensory Chemical Ecology of Brown Algae CHARLES D. AMSLER AND VICTORIA A. FAIRHEAD Regulation of Carbon and Amino Acid Metabolism: Roles of Sucrose Nonfermenting-1-Related Protein Kinase-1 and General Control Nonderepressible-2-Related Protein Kinase NIGEL G. HALFORD Opportunities for the Control of Brassicaceous Weeds of Cropping Systems Using Mycoherbicides AARON MAXWELL AND JOHN K. SCOTT Stress Resistance and Disease Resistance in Seaweeds: The Role of Reactive Oxygen Metabolism MATTHEW J. DRING
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Nutrient Sensing and Signalling in Plants: Potassium and Phosphorus ANNA AMTMANN, JOHN P. HAMMOND, PATRICK ARMENGAUD AND PHILIP J. WHITE
Contents of Volume 44 Angiosperm Floral Evolution: Morphological Developmental Framework PETER K. ENDRESS Recent Developments Regarding the Evolutionary Origin of Flowers MICHAEL W. FROHLICH Duplication, Diversification, and Comparative Genetics of Angiosperm MADS-Box Genes VIVIAN F. IRISH Beyond the ABC-Model: Regulation of Floral Homeotic Genes LAURA M. ZAHN, BAOMIN FENG AND HONG MA Missing Links: DNA-Binding and Target Gene Specificity of Floral Homeotic Proteins RAINER MELZER, KERSTIN KAUFMANN ¨ NTER THEIßEN AND GU Genetics of Floral Development in Petunia ANNEKE RIJPKEMA, TOM GERATS AND MICHIEL VANDENBUSSCHE Flower Development: The Antirrhinum Perspective BRENDAN DAVIES, MARIA CARTOLANO AND ZSUZSANNA SCHWARZ-SOMMER
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Floral Developmental Genetics of Gerbera (Asteraceae) TEEMU H. TEERI, MIKA KOTILAINEN, ANNE UIMARI, SATU RUOKOLAINEN, YAN PENG NG, URSULA MALM, ¨ NEN, SUVI BROHOLM, ROOSA LAITINEN, ¨ LLA EIJA PO PAULA ELOMAA AND VICTOR A. ALBERT Gene Duplication and Floral Developmental Genetics of Basal Eudicots ELENA M. KRAMER AND ELIZABETH A. ZIMMER Genetics of Grass Flower Development CLINTON J. WHIPPLE AND ROBERT J. SCHMIDT Developmental Gene Evolution and the Origin of Grass Inflorescence Diversity SIMON T. MALCOMBER, JILL C. PRESTON, RENATA REINHEIMER, JESSIE KOSSUTH AND ELIZABETH A. KELLOGG Expression of Floral Regulators in Basal Angiosperms and the Origin and Evolution of ABC-Function PAMELA S. SOLTIS, DOUGLAS E. SOLTIS, SANGTAE KIM, ANDRE CHANDERBALI AND MATYAS BUZGO The Molecular Evolutionary Ecology of Plant Development: Flowering Time in Arabidopsis thaliana KATHLEEN ENGELMANN AND MICHAEL PURUGGANAN A Genomics Approach to the Study of Ancient Polyploidy and Floral Developmental Genetics JAMES H. LEEBENS-MACK, KERR WALL, JILL DUARTE, ZHENGUI ZHENG, DAVID OPPENHEIMER AND CLAUDE DEPAMPHILIS Series Editors (Volume 45– ) JEAN-CLAUDE KADER Laboratoire Physiologie Cellulaire et Mole´culaire des Plantes, CNRS, Universite´ de Paris, Paris, France
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MICHEL DELSENY Laboratoire Ge´nome et De´veloppement des Plantes, CNRS IRD UP, Universite´ de Perpignan, Perpignan, France
Contents of Volume 45 RAPESEED BREEDING History, Origin and Evolution S. K. GUPTA AND ADITYA PRATAP Breeding Methods B. RAI, S. K. GUPTA AND ADITYA PRATAP The Chronicles of Oil and Meal Quality Improvement in Oilseed Rape ABHA AGNIHOTRI, DEEPAK PREM AND KADAMBARI GUPTA Development and Practical Use of DNA Markers KATARZYNA MIKOLAJCZYK Self-Incompatibility RYO FUJIMOTO AND TAKESHI NISHIO Fingerprinting of Oilseed Rape Cultivars ´ ˇ URN AND JANA ZˇALUDOVA VLADISLAV C Haploid and Doubled Haploid Technology L. XU, U. NAJEEB, G. X. TANG, H. H. GU, G. Q. ZHANG, Y. HE AND W. J. ZHOU Breeding for Apetalous Rape: Inheritance and Yield Physiology LIXI JIANG
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Breeding Herbicide-Tolerant Oilseed Rape Cultivars PETER B. E. MCVETTY AND CARLA D. ZELMER Breeding for Blackleg Resistance: The Biology and Epidemiology W. G. DILANTHA FERNANDO, YU CHEN AND KAVEH GHANBARNIA Development of Alloplasmic Rape MICHAL STARZYCKI, ELIGIA STARZYCKI AND JAN PSZCZOLA Honeybees and Rapeseed: A Pollinator–Plant Interaction D. P. ABROL Genetic Variation and Metabolism of Glucosinolates NATALIA BELLOSTAS, ANNE DORTHE SØRENSEN, JENS CHRISTIAN SØRENSEN AND HILMER SØRENSEN Mutagenesis: Generation and Evaluation of Induced Mutations SANJAY J. JAMBHULKAR Rapeseed Biotechnology VINITHA CARDOZA AND C. NEAL STEWART, JR. Oilseed Rape: Co-existence and Gene Flow from Wild Species RIKKE BAGGER JØRGENSEN Evaluation, Maintenance, and Conservation of Germplasm RANBIR SINGH AND S. K. SHARMA Oil Technology ¨ US BERTRAND MATTHA
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Contents of Volume 46 INCORPORATING ADVANCES IN PLANT PATHOLOGY Nitric Oxide and Plant Growth Promoting Rhizobacteria: Common Features Influencing Root Growth and Development ´ NICA CREUS, MARI´A CELESTE MOLINA-FAVERO, CECILIA MO LUCIANA LANTERI, NATALIA CORREA-ARAGUNDE, MARI´A CRISTINA LOMBARDO, CARLOS ALBERTO BARASSI AND LORENZO LAMATTINA How the Environment Regulates Root Architecture in Dicots MARIANA JOVANOVIC, VALE´RIE LEFEBVRE, PHILIPPE LAPORTE, SILVINA GONZALEZ-RIZZO, CHRISTINE LELANDAIS-BRIE´RE, FLORIAN FRUGIER, CAROLINE HARTMANN AND MARTIN CRESPI Aquaporins in Plants: From Molecular Structure to Integrated Functions OLIVIER POSTAIRE, LIONEL VERDOUCQ AND CHRISTOPHE MAUREL Iron Dynamics in Plants JEAN-FRANC ¸ OIS BRIAT Plants and Arbuscular Mycorrhizal Fungi: Cues and Communication in the Early Steps of Symbiotic Interactions VIVIENNE GIANINAZZI-PEARSON, NATHALIE SE´ JALON-DELMAS, ANDREA GENRE, SYLVAIN JEANDROZ AND PAOLA BONFANTE Dynamic Defense of Marine Macroalgae Against Pathogens: From Early Activated to Gene-Regulated Responses AUDREY COSSE, CATHERINE LEBLANC AND PHILIPPE POTIN
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Contents of Volume 47 INCORPORATING ADVANCES IN PLANT PATHOLOGY The Plant Nucleolus ´ EZ-VA ´ SQUEZ AND FRANCISCO JAVIER MEDINA JULIO SA Expansins in Plant Development DONGSU CHOI, JEONG HOE KIM AND YI LEE Molecular Biology of Orchid Flowers: With Emphasis on Phalaenopsis WEN-CHIEH TSAI, YU-YUN HSIAO, ZHAO-JUN PAN, CHIACHI HSU, YA-PING YANG, WEN-HUEI CHEN AND HONG-HWA CHEN Molecular Physiology of Development and Quality of Citrus ´ S, JOSE´M. FRANCISCO R. TADEO, MANUEL CERCO COLMENERO-FLORES, DOMINGO J. IGLESIAS, MIGUEL A. NARANJO, GABINO RI´OS, ESTHER CARRERA, OMAR RUIZ-RIVERO, IGNACIO LLISO, RAPHAE¨ L MORILLON, PATRICK OLLITRAULT AND MANUEL TALON Bamboo Taxonomy and Diversity in the Era of Molecular Markers MALAY DAS, SAMIK BHATTACHARYA, PARAMJIT SINGH, TARCISO S. FILGUEIRAS AND AMITA PAL
Contents of Volume 48 Molecular Mechanisms Underlying Vascular Development JAE-HOON JUNG, SANG-GYU KIM, PIL JOON SEO AND CHUNG-MO PARK Clock Control Over Plant Gene Expression ANTOINE BAUDRY AND STEVE KAY
CONTENTS OF VOLUMES 35–52
Plant Lectins ELS J. M. VAN DAMME, NAUSICAA LANNOO AND WILLY J. PEUMANS Late Embryogenesis Abundant Proteins MING-DER SHIH, FOLKERT A. HOEKSTRA AND YUE-IE C. HSING
Contents of Volume 49 Phototropism and Gravitropism in Plants MARIA LIA MOLAS AND JOHN Z. KISS Cold Signalling and Cold Acclimation in Plants ERIC RUELLAND, MARIE-NOELLE VAULTIER, ALAIN ZACHOWSKI AND VAUGHAN HURRY Genome Evolution in Plant Pathogenic and Symbiotic Fungi GABRIELA AGUILETA, MICHAEL E. HOOD, GUISLAINE REFRE´GIER AND TATIANA GIRAUD
Contents of Volume 50 Aroma Volatiles: Biosynthesis and Mechanisms of Modulation During Fruit Ripening BRUNO G. DEFILIPPI, DANIEL MANRI´QUEZ, KIETSUDA LUENGWILAI AND MAURICIO ´ LEZ-AGU ¨ ERO GONZA Jatropha curcas: A Review NICOLAS CARELS You are What You Eat: Interactions Between Root Parasitic Plants and Their Hosts LOUIS J. IRVING AND DUNCAN D. CAMERON
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Low Oxygen Signaling and Tolerance in Plants FRANCESCO LICAUSI AND PIERDOMENICO PERATA Roles of Circadian Clock and Histone Methylation in the Control of Floral Repressors RYM FEKIH, RIM NEFISSI, KANA MIYATA, HIROSHI EZURA AND TSUYOSHI MIZOGUCHI
Contents of Volume 51 PAMP-Triggered Basal Immunity in Plants ¨ RNBERGER AND BIRGIT KEMMERLING THORSTEN NU Plant Pathogens as Suppressors of Host Defense JEAN-PIERRE ME´TRAUX, ROBERT WILSON JACKSON, ESTHER SCHNETTLER AND ROB W. GOLDBACH From Nonhost Resistance to Lesion-Mimic Mutants: Useful for Studies of Defense Signaling ANDREA LENK AND HANS THORDAL-CHRISTENSEN Action at a Distance: Long-Distance Signals in Induced Resistance MARC J. CHAMPIGNY AND ROBIN K. CAMERON Systemic Acquired Resistance R. HAMMERSCHMIDT Rhizobacteria-Induced Systemic Resistance ¨ FTE DAVID DE VLEESSCHAUWER AND MONICA HO Plant Growth-Promoting Actions of Rhizobacteria STIJN SPAEPEN, JOS VANDERLEYDEN AND YAACOV OKON
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Interactions Between Nonpathogenic Fungi and Plants M. I. TRILLAS AND G. SEGARRA Priming of Induced Plant Defense Responses UWE CONRATH Transcriptional Regulation of Plant Defense Responses MARCEL C. VAN VERK, CHRISTIANE GATZ AND HUUB J. M. LINTHORST Unexpected Turns and Twists in Structure/Function of PR-Proteins that Connect Energy Metabolism and Immunity MEENA L. NARASIMHAN, RAY A. BRESSAN, MATILDE PAINO D’URZO, MATTHEW A. JENKS AND TESFAYE MENGISTE Role of Iron in Plant–Microbe Interactions P. LEMANCEAU, D. EXPERT, F. GAYMARD, P. A. H. M. BAKKER AND J.-F. BRIAT Adaptive Defense Responses to Pathogens and Insects LINDA L. WALLING Plant Volatiles in Defence MERIJN R. KANT, PETRA M. BLEEKER, MICHIEL VAN WIJK, ROBERT C. SCHUURINK AND MICHEL A. HARING Ecological Consequences of Plant Defence Signalling MARTIN HEIL AND DALE R. WALTERS
Contents of Volume 52 Oxidation of Proteins in Plants—Mechanisms and Consequences LEE J. SWEETLOVE AND IAN M. MØLLER
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Reactive Oxygen Species: Regulation of Plant Growth and Development HYUN-SOON KIM, YOON-SIK KIM, KYU-WOONG HAHN, HYOUK JOUNG AND JAE-HEUNG JEON Ultraviolet-B Induced Changes in Gene Expression and Antioxidants in Plants S. B. AGRAWAL, SURUCHI SINGH AND MADHOOLIKA AGRAWAL Roles of -Glutamyl Transpeptidase and -Glutamyl Cyclotransferase in Glutathione and Glutathione-Conjugate Metabolism in Plants NAOKO OHKAMA-OHTSU, KEIICHI FUKUYAMA AND DAVID J. OLIVER The Redox State, a Referee of the Legume–Rhizobia Symbiotic Game DANIEL MARINO, CHIARA PUCCIARIELLO, ALAIN PUPPO AND PIERRE FRENDO
Arabidopsis Histone Lysine Methyltransferases
´ DE´RIC PONTVIANNE,1 TODD BLEVINS AND CRAIG S. FRE PIKAARD
Department of Biology, Indiana University, 915 E. Third Street, Bloomington, IN, 47405, USA
I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. HKMT Classification in Arabidopsis . . . . . . . . . . . . . . . . . . . . . . . . A. Gene Organization and Evolution . . . . . . . . . . . . . . . . . . . . . . . B. The Set Domain of HKMT Enzymes . . . . . . . . . . . . . . . . . . . . III. Class I–IV HKMTs and Their Roles in Plant Development . . . . . . . A. Class I HKMT Enzymes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Class II HKMT Enzymes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Class III HKMT Enzymes . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Class IV HKMT Enzymes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Class V HKMTs Mark Inactive Chromatin via Histone 3 Lysine 9 Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. SUVH Proteins. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. SUVR Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Conclusions and Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Corresponding author: E-mail:
[email protected]
Advances in Botanical Research, Vol. 53 Copyright 2010, Elsevier Ltd. All rights reserved.
0065-2296/09 $35.00 DOI: 10.1016/S0065-2296(10)53001-5
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ABSTRACT In eukaryotes, changes in chromatin structure regulate the access of gene regulatory sequences to the transcriptional machinery and play important roles in the repression of transposable elements, thereby protecting genome integrity. Chromatin dynamics and gene expression states are highly correlated, with DNA methylation and histone post-translational modifications playing important roles in the establishment or maintenance of chromatin states in plants. Histones can be covalently modified in a variety of ways, thereby affecting nucleosome spacing and/or higher-order nucleosome interactions directly or via the recruitment of histone-binding proteins. An extremely important group of chromatin modifying enzymes are the histone lysine methyltransferases (HKMTs). These enzymes are involved in the establishment and/or maintenance of euchromatic or heterochromatic states of active or transcriptionally repressed sequences, respectively. The vast majority of HKMTs possess a SET domain named for the three Drosophila proteins that are the founding members of the family: Suppressor of variegation, Enhancer of zeste and Trithorax. It is the SET domain that is responsible for HKMT enzymatic activity. Mutation of Arabidopsis HKMT genes can result in phenotypic abnormalities due to the improper regulation of important developmental genes. Here, we review the different classes of HKMTs present in the model plant Arabidopsis thaliana and discuss what is known about their biochemical and biological functions.
I. INTRODUCTION In eukaryotes, nuclear DNA is organized by histone proteins to form the fundamental unit of chromatin, the nucleosome. Each nucleosome is composed of 147 base pairs of DNA that is wrapped not quite twice around a histone octamer composed of two copies each of histone H2A, H2B, H3 and H4 (Luger et al., 1997). It is now clear that chromatin assembly exerts a major influence on gene expression by affecting the accessibility of the transcriptional machinery, including RNA polymerase complexes and transcription factors, to the DNA. As a consequence, changes in chromatin structure accompany a broad spectrum of important processes during development, including differentiation, embryonic stem cell maintenance and senescence (Baroux et al., 2007; He and Amasino, 2005; Hochedlinger and Plath, 2009; Kouzarides, 2007). Nucleosome positioning is highly dependent on the genome sequence itself (Kaplan et al., 2009; Segal et al., 2006). The accessibility of DNA sequences within each nucleosome is further modulated by covalent modifications of the histones, methylation of cytosines in the DNA that is wrapped around the histones and differential use of histone variants. In vertebrates and plants, post-translational modification of histones and DNA methylation regulate or reflect the chromatin condensation and transcriptional status of the associated DNA. Genes located in a condensed chromatin context
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(heterochromatin) are generally inactive or silenced, whereas those found in a decondensed chromatin context (euchromatin) are more likely to be transcribed (Jenuwein and Allis, 2001; Kouzarides, 2007). Heterochromatin is typically enriched in repetitive DNA, including transposable elements, centromeric repeats and excess, inactive ribosomal RNA (rRNA) gene repeats. Unlike constitutive heterochromatin, which remains condensed throughout the cell cycle, euchromatic regions undergo dynamic changes in chromatin condensation state and include intervals, such as intergenic sequences, that are often characterized by the presence heterochromatic marks (Bender, 2004a). Changes in DNA methylation or histone modification states are mediated by specific enzymes. With regard to histone post-translational modifications, the enzymes modifying histones H3 and H4, particularly within their N-termini that protrude from the nucleosome core, are best understood (Kouzarides, 2007). The modifications carried out by these enzymes include methylation, acetylation, phosphorylation, ubiquitination, sumoylation, citrullination and ADP-ribosylation. The large variety of histone modifications, potentially conferring regulatory information, have been hypothesized to constitute a so-called ‘histone code’ (Jenuwein and Allis, 2001; Kouzarides, 2007). Histone methylation occurs at both lysine and arginine amino acids and is used to mark both active and inactive chromatin, depending on context (Lachner and Jenuwein, 2002; Wang et al., 2007; Yu et al., 2006). For instance, Histone 3 Lysine 4 (H3K4) that is mono-, di- or trimethylated is present in nucleosomes associated with the promoter regions of active genes, whereas Histone 3 Lysine 9 (H3K9) mono-, di- and trimethylation occurs in nucleosomes associated with inactive genes located in euchromatic region and within highly condensed constitutive heterochromatin (Bernatavichute et al., 2008; Gendrel et al., 2002; Zhang et al., 2009). Repressive histone modifications and DNA methylation are mechanistically linked (Richards, 2002). For example, mutations in the cytosine methyltransferase, MET1, lead to decreased H3K9 dimethylation, whereas mutations disrupting the functions of the H3K9 methyltransferase, Kryponite/SUVH4 (SU(VAR)3–9 homologues 4) results in decreased cytosine methylation (Jackson et al., 2002, 2004; Tariq et al., 2003). Genomewide analyses have also revealed correlations between patterns of histone modification and cytosine methylation (Bernatavichute et al., 2008; Cokus et al., 2008; Gendrel et al., 2002; Lister et al., 2008; Zhang et al., 2009). In Arabidopsis, a family of genes encode putative histone methyltransferases. Some of these enzymes function as arginine methyltransferases, but the majority are believed to be histone lysine methyltransferases (HKMTs) (Baumbusch et al., 2001; Ng et al., 2007; Niu et al., 2007; Wang et al., 2007).
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Five lysine methylation sites have been identified so far in plants, namely lysines 4, 9, 27 and 36 of Histone 3 and lysine 20 of Histone 4 (Pfluger and Wagner, 2007; Zhang et al., 2007b). In other eukaryotes, methylation of H3K79, H4K59 and H1BK26 has also been reported (Trojer et al., 2007; Zhang et al., 2003). All known HKMTs in plants have a so-called SET (Suppressor of variegation, Enhancer of zeste and Trithorax) domain that is responsible for the catalytic activity of the enzymes. Thus, these proteins are members of the SET Domain Group (SDG) protein super-family (Gendler et al., 2008). Our review focuses on those classes of SDG proteins that are known, or thought, to possess HKMT activity.
II. HKMT CLASSIFICATION IN ARABIDOPSIS A. GENE ORGANIZATION AND EVOLUTION
In Arabidopsis thaliana, 49 genes encoding putative SET domain-containing proteins have been identified (www.chromDB.org; Baumbusch et al., 2001; Ng et al., 2007). Similarly, the human genome encodes 50 SDG proteins, including 24 HKMTs. By contrast, the budding yeast (Saccharomyces cerevisiae) genome encodes only four SDG proteins (Fig. 1) (Allis et al., 2007). Of the 49 A. thaliana SDG proteins, 31 are known, or thought, to have HKMT activity and can be divided into five classes (I to V), based on their domain architectures (Fig. 2) and/or differences in enzymatic activity (Fig. 3) (Baumbusch et al., 2001; Ng et al., 2007; Springer et al., 2003). A class VI, which contain SDG proteins with a disrupted SET domain, and a class VII, which include SDG proteins that methylate non-histone proteins, have also been described but are not discussed in this review (Ng et al., 2007). Phylogenetic analyses of A. thaliana and Zea mays genes have indicated that most of the gene duplication and functional diversification events that gave rise to the SDG protein family occurred prior to the divergence of monocotyledonous and dicotyledonous plants (Ng et al., 2007; Springer et al., 2003). However, there are exceptions, as exemplified by class III SDG genes that encode Trithorax-like (ATX) proteins. The Arabidopsis genome encodes five ATX genes, whereas rice and maize have only two or three, respectively (Ng et al., 2007; Springer et al., 2003). This observation suggests that duplication and diversification of some ATX genes occurred after the divergence of monocots and dicots, resulting in two sub-groups: sub-group 1 including ATX1 and ATX2 in Arabidopsis and two ATX2-like in maize and subgroup 2 including ATX3, ATX4 and ATX5 in Arabidopsis and a single copy of ATX4-like in maize (Fig. 1) (Avramova, 2009; Ng et al., 2007).
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Fig. 1. HKMTs organization and evolution. Relationships among HKMT Arabidopsis HKMT (grey), Human (Hs) HKMT and fission yeast (Sp) HKMT (dark) protein sequences determined by clustalW multiple alignment, followed by Neighbour Joining and Boostrap analysis. B. THE SET DOMAIN OF HKMT ENZYMES
Lysines can be mono-, di- or trimethylated, with differences in methylation state impacting or reflecting chromatin structure and gene transcriptional activity (Lachner and Jenuwein, 2002; Pfluger and Wagner, 2007). All known lysine methylation modifications, with the exception of Histone 3 Lysine 79 methylation (which has not been reported in plants), are carried out by methyltransferases that contain a SET domain. The SET domain encompasses approximately 130–150 amino acids that is thought to have
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F. PONTVIANNE ET AL. Formal name
Locus
SDG1 SDG10 SDG5
CLF SWN MEA
AT2G23380 AT4G02020 AT1G02580
SDG26 SDG8 SDG7 SDG24 SDG4
ASHH1 ASHH2 ASHH3 ASHH4 ASHR3
AT1G76710 AT1G77300 AT2G44150 AT5G59960 AT4G30860
III
SDG27 SDG30 SDG14 SDG16 SDG29 SDG2 SDG25
ATX1 ATX2 ATX3 ATX4 ATX5 ATXR3 ATXR7
AT2G31650 AT1G05830 AT3G61740 AT4G27910 AT5G53430 AT4G15180 AT5G42400
IV
SDG15 SDG34
ATXR5 ATXR6
AT5G09790 AT5G24330
SDG32 SDG3 SDG19 SDG33 SDG9 SDG23 SDG17 SDG21 SDG22 SDG13 SDG18 SDG20 SDG31 SDG6
SUVH1 SUVH2 SUVH3 SUVH4 SUVH5 SUVH6 SUVH7 SUVH8 SUVH9 SUVR1 SUVR2 SUVR3 SUVR4 SUVR5
AT5G04940 AT2G33290 AT1G73100 AT5G13960 AT2G35160 AT2G22740 AT1G17770 AT2G24740 AT4G13460 AT1G04050 AT5G43990 AT3G03750 AT3G04380 AT2G23740
Class
I
II
V
ChromDB ID
Pre-SET SET Post-SET WYILD
YDG-SRA AT-hook PHD PWWP
Protein composition
FYRC FYRN EZD SANT
CXC AWS zf-CW
Fig. 2. Domain architecture of histone HKMTs in A. thaliana. Abbreviations: EZD, E(Z) domain; SANT, SWI3, ADA2, N-CoR and TFIIIB” DNA-binding domain; CXC, cysteine-rich region; PHD, plant homeodomain; zf-CW, a zinc finger with conserved Cys and Trp residues; PWWP, domain named after a conserved Pro–Trp–Trp–Pro motif; FYRN, F/Y-rich N-terminus; FYRC, F/Y-rich C-terminus.
evolved from an ancient motif found in bacterial proteins (Alvarez-Venegas et al., 2007). Structural and functional analyses of the SET domain of the human protein SUV39H1, which methylates H3K9, identified a series of key amino acids that are conserved in all SET-domain group proteins (Rea et al., 2000). Crystal structures of SET domains of several HKMTs have been solved, providing insights into their catalytic mechanisms and protein
ARABIDOPSIS HISTONE LYSINE METHYLTRANSFERASES KYP/SUVH4 SUVH1 SUVH2 SUVH5 SDG4 II SUVH6 SDG8 SUVH3 ATX1 SUVH7 ATX2 SUVH8 ATX3 III SUVR4 ATX4 SUVR1 ATX5 SUVR2 SUVR5
K4
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K27
SDG25 III SDG8 SDG4 II SDG26
K36
Histone H3
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K20
Histone H4
Fig. 3. Diagram representing the H3 and H4 Lysines targeted by Arabidopsis HKMT proteins. The HKMTs whose activity has been biochemically demonstrated are highlighted using in black, whereas the HKMTs whose specificities have not been confirmed are shown in gray.
substrate specificities (Couture et al., 2005, 2006a, b; Xiao et al., 2003a, 2005). The SET domain possesses a unique fold dominated by 12 b-strands (Couture and Trievel, 2006). Two others domains, the pre-SET and the post-SET domains, sometimes flank the SET domain and may facilitate interactions with specific histone substrates. The hydroxyl group of a highly conserved tyrosine in the SET domain interacts with the substrate and transfers a methyl group to the lysine using S-adenosylmethionine (AdoMet) as the methyl group donor (Couture and Trievel, 2006; Rea et al., 2000; Xiao et al., 2003b).
III. CLASS I–IV HKMTS AND THEIR ROLES IN PLANT DEVELOPMENT A. CLASS I HKMT ENZYMES
Class I HKMTs are homologues of Enhancer of Zeste E(Z) from Drosophila that have H3K27 methyltransferase activity (Jones and Gelbart, 1993; Muller et al., 2002). The Arabidopsis genome encodes three E(Z)-like proteins: CURLY LEAF (CLF), MEDEA (MEA) and SWINGER (SWN), each of which contain a SET domain, two E(Z) domains, a SANT (SWI3, ADA2, N-CoR and TFIIIB DNA-binding) domain and a CXC (cysteine-rich) region. E(Z)-like proteins are components of Arabidopsis Polycomb Repressive Complex 2 (PRC2)-like complexes that function as
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transcriptional repressors in diverse eukaryotes (Baroux et al., 2007). Analysis of clf, mea and swn mutants suggests that PRC2 complexes involving these proteins are required for H3K27 trimethylation, but direct biochemical evidence is currently lacking (Fig. 3) (Gehring et al., 2006; Makarevich et al., 2006). CLF and MEA were the first HKMT genes described in plants, and helped underscore the importance of chromatin modification for proper plant development. CLF is required to repress FLOWERING LOCUS C (FLC) (Jiang et al., 2008; Wood et al., 2006). FLC, in turn, is a repressor of flowering, which is a process requiring a number of chromatin modifications, including histone methylation (He and Amasino, 2005). CLF is required for the methylation of H3K27 among histones associated with the FLC gene, as well as other developmentally important genes. Thus, clf mutations induce pleiotropic phenotypic defects in addition to altered flowering time, including altered leaf shape – hence the gene name (Katz et al., 2004; Makarevich et al., 2006; Schubert et al., 2006). MEA is necessary for proper seed development. Maternally inherited loss of function mea alleles cause embryo abortion and endosperm overproliferation (Grossniklaus et al., 1998; Kiyosue et al., 1999). Phylogenetic and molecular analyses have shown that MEA arose through duplication of an ancestral E(Z) homologue within the Brassicaceae family, indicating that MEA function came about relatively recently in angiosperm evolutionary history (Fig. 1) (Spillane et al., 2007). SWN, the third E(Z)-like protein in Arabidopsis, also appears to participate in trimethylation of H3K27 at loci important for flower development, including AGAMOUS and SHOOTMERISTEMLESS (STM) (Katz et al., 2004; Schubert et al., 2006). MEA, CLF and SWN are probably responsible for the regulation of many genes and it was shown that more than 4000 genes carry H3K27 trimethylation marks (Makarevich et al., 2006; Zhang et al., 2007a). The specific role of each of them might potentially be identified through genome-wide comparisons of histone modifications in wild-type plants and mea, clf and swn mutants, and it should be considered that CLF and SWN regulate genes involved in many different processes of the plant life cycle. B. CLASS II HKMT ENZYMES
Class II HKMTs are essentially implicated in the methylation of H3K36 (Fig. 3), a chromatin modification that is enriched within the region of actively transcribed genes (Lee and Shilatifard, 2007). Except for SDG4, class II HKMTs have a SET domain preceded by an AWS (Associated with SET) motif (Ng et al., 2007). The function of the AWS motif is unknown, but it is also found in mammalian class II HKMTs.
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Functional insights have been obtained for two class II HKMTs: SDG4 and SDG8. Mutation of SDG8 affects FLC expression and induces an early flowering phenotype (Zhao et al., 2005). SDG8 is implicated in H3K36 diand trimethylation, but sdg8 mutations do not affect H3K4, H3K9 or H3K27 methylation at the FLC locus (Zhao et al., 2005). Thus, changes in H3K36 methylation are sufficient to affect FLC expression. Several metabolic pathways are also perturbed in sdg8 mutants (Cazzonelli et al., 2009; Dong et al., 2008; Xu et al., 2008; Zhao et al., 2005). sdg8 mutants show altered expression of SPS/BUS (Supershoot/Bushy) and UGT74E2 genes, both of which affect shoot branching, a key process for plant biomass and seed production (Dong et al., 2008). Expression of CAROTENOID ISOMERASE, a gene required for carotenoid synthesis, is also perturbed in sdg8 mutants. Consequently, a lower accumulation of lutein is observed in sdg8 mutants, a carotenoid implicated in photosynthesis and photoprotection (Cazzonelli et al., 2009). SDG4 was recently shown to be involved in pollen and stamen development (Cartagena et al., 2008; Thorstensen et al., 2008). Deficiency of SDG4 leads to reduced expression of multiple genes, probably due to defects in H3K4 dimethylation and H3K36 trimethylation. The sdg4 mutation also affects fertility (Cartagena et al., 2008). C. CLASS III HKMT ENZYMES
Like other HKMTs, class III HKMTs have also been shown to be involved in flowering time regulation. Class III HKMTs consist of five Arabidopsis genes that encode homologues of Trithorax; they have therefore been named Arabidopsis Trithorax-like proteins 1-5 (ATX1-5) (Fig. 1) (Avramova, 2009). Class III proteins contain both SET and a post-SET domains, as well as PHD (plant homeodomain), PWWP (proline–tryptophane– tryptophane–proline), FYRN (F/Y-rich N-terminus) and FYRC (F/Y-rich C-terminus) domains (Fig. 2) (Alvarez-Venegas and Avramova, 2001). The PHD domain is thought to interact with trimethylated H3K4 (Pen˜a et al., 2006). The PWWP domain is present in diverse proteins involved in chromatin function, including histone-modifying enzymes, DNA-modifying enzymes and transcription factors and have been found to interact with both histone and DNA (Laue et al., 2008; Qiu et al., 2002; Stec et al., 2000; Wang et al., 2009). ATX1 and ATX2 form a protein sub-group: ATX1 mediates H3K4 trimethylation, whereas ATX2 mediates H3K4 dimethylation (Fig. 3) (Pien et al., 2008; Saleh et al., 2008). atx1 mutants display an early flowering phenotype and altered leaf morphogenesis (Alvarez-Venegas et al., 2003;
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Saleh et al., 2008). Intriguingly, the double atx1 atx2 mutant has an even more severe early flowering phenotype than atx1, suggesting that ATX1 and ATX2 activities overlap for proper expression of genes implicated in flowering time regulation (Pien et al., 2008; Saleh et al., 2008). Despite the evidence for partial redundancy in controlling flowering time, ATX1 and ATX2 do not appear to regulate the same pool of genes (Saleh et al., 2008). Transcriptome analysis revealed that 7% of overall gene expression is affected in atx1. By contrast, only 0.7% of all genes display a different pattern of expression in atx2 mutants compared with controls (AlvarezVenegas et al., 2006). To date, no functions have been reported for ATX3, ATX4 or ATX5. These proteins have very conserved amino acid sequences, suggesting that they may have redundant functions. Double and triple mutant combinations of these three genes might potentially reveal their functional significance in A. thaliana. ATXR3 and ATXR7 also belong to the class III AtKMTs; however, no data are available on the putative role and/or activity of ATXR3. However, ATXR7 (also known as SDG25) is able to specifically methylate Histone 3 in vitro and loss of function of SDG25 promotes flowering through reduction of FLC expression (Berr et al., 2009). Further analyses suggest that ATXR7 might be implicated in H3K36 dimethylation and its role might overlap with the class II HKMT, SDG26 (Berr et al., 2009). D. CLASS IV HKMT ENZYMES
There are two class IV HKMTs in Arabidopsis, ATXR5 and ATXR6. Both proteins possess a PHD domain associated with their SET domain. Class IV proteins have an additional motif that allows them to interact with proliferating cell nuclear antigen (PCNA) (Raynaud et al., 2006). PCNA is a processivity factor for DNA polymerase delta during DNA replication, which suggests a role for class IV HKMTs in cell cycle regulation (Raynaud et al., 2006). ATXR5 and ATXR6 were recently shown to carry out monomethylation of H3K27 (Fig. 3) (Jacob et al., 2009). ATXR5 and ATXR6 appear to act redundantly, because depletion of H3K27 monomethylation is only detectable in the atxr5 atxr6 double mutant (Jacob et al., 2009). Genome-wide analyses have revealed the presence of H3K27 monomethylation in heterochromatic chromocentres, whereas H3K27 di- and trimethylation are mainly present in euchromatic regions (Jacob et al., 2009; Zhang et al., 2007a). This suggests that distinct H3K27 methylation states correlate with different chromatin states. Interestingly, derepression of repetitive elements occurs in atxr5 atxr6
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double mutants (Jacob et al., 2009) and is correlated with reduced H3K27 monomethylation but not reduced DNA methylation or H3K9 methylation, confirming a key role for H3K27 monomethylation in gene silencing and genome stability (Jacob et al., 2009; Mathieu et al., 2005).
IV. CLASS V HKMTS MARK INACTIVE CHROMATIN VIA HISTONE 3 LYSINE 9 METHYLATION A. SUVH PROTEINS
1. Discovery of SUVH proteins In 2002, two independent mutant screens revealed roles for SUVH4/KRYPTONITE in H3K9 methylation and gene silencing in Arabidopsis (Jackson et al., 2002; Malagnac et al., 2002). In these screens, suvh4 mutations were identified by their effects on the expression of the SUPERMAN locus, thereby affecting the number of floral organs (Jackson et al., 2002), or by the derepression of silenced PHOSPHOANTHRINILATE ISOMERASE (PAI) genes (Bender, 2004b; Luff et al., 1999; Malagnac et al., 2002). Importantly, cytosine methylation patterns were also perturbed at the loci where H3K9 dimethylation was lost, revealing a link between histone methylation and DNA methylation (Jackson et al., 2002). In other studies, depletion of cytosine methylation in the methyltransferase mutant met1 correlates with altered H3K9 methylation (Tariq et al., 2003), further indicating a functional relationship between DNA methylation and H3K9 modification.
2. Characteristics of SUVH proteins SUVH proteins have a SET domain, a pre-SET domain and a post-SET domain. An additional motif, named the SET and RING finger-associated (SRA) domain, is also a characteristic of SUVH proteins (Fig. 2). There are 10 members of the SUVH protein family, which appears to be plant specific (Fig. 1). The SRA domain serves as a methylcytosine-binding motif in both animals and plants (Citterio et al., 2004; Kraft et al., 2008; Unoki et al., 2004; Woo et al., 2007, 2008). In Arabidopsis, point mutations in the SRA domain of SUVH2 or SUVH4 leads to reduced H3K9 dimethylation, and deletion of the motif in SUHV4 or SUVH6 results in a failure of the protein to bind methylated DNA in vitro (Johnson et al., 2007). Interestingly, different SRA domains preferentially bind methylated cytosines, in particular, DNA sequence contexts. In vitro, the SUVH2 SRA domain has highest affinity for symmetric, CG methylation, whereas the SUVH9 SRA domain has
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highest affinity for asymmetric, CHH methylation (Johnson et al., 2008). HKMT binding to DNA sequences displaying different cytosine methylation contexts would provide an elegant mechanism for transducing epigenetic information encoded by DNA methylation patterns into altered histone methylation states. 3. Activity of SUVH proteins The H3K9 methyltransferase activity of SUVH4 was first inferred from molecular genetic studies, and then confirmed by mass spectrometric analysis of in vitro methylated histones (Jackson et al., 2002, 2004; Johnson et al., 2004; Malagnac et al., 2002). In vitro activities for SUVH1, SUVH5 and SUVH6 indicate that these proteins are H3K9 methyltransferases, which raises the possibility that all SUVH proteins methylate H3K9 (Fig. 3) (Ebbs and Bender, 2006; Ebbs et al., 2005; Naumann et al., 2005). SUVH2 and SUVH9 arose via a recent duplication in the Arabidopsis genome (Blanc et al., 2000, 2003). Initial evidence suggested that SUVH2 possesses H4K20 and H3K9 methyltransferase activity (Naumann et al., 2005). However, a more recent study found that suvh2 and suvh9 mutants, and suvh2 suvh9 double mutants do not display detectably altered histone methylation patterns (Johnson et al., 2008). Moreover, unlike SUVH4, SUVH5 or SUVH6, no HKMT activity was detected for SUVH2 or SUVH9 in vitro (Johnson et al., 2008). Furthermore, the methyl group donor AdoMet does not bind to recombinant SUVH2 or SUVH9, whereas AdoMet binding to recombinant SUVH4, SUVH5 and SUVH6 is observed (Johnson et al., 2008). One possibility is that the biological functions of SUVH2 and SUVH9 depend primarily on SRA domain interactions with methylated DNA, rather than on putative HKMT activity. 4. Functions of SUVH proteins SUVH4 is involved mainly in maintenance of CHG methylation controlled by the cytosine methyltransferase CMT3 (chromomethylase 3), such that a loss of DNA methylation is observed in suvh4 mutants (Jackson et al., 2002; Malagnac et al., 2002). Jackson et al. (2002) showed that CMT3 does not interact with H3K9 dimethylation directly, and suggested that LHP1 was necessary to link SUVH4 and CMT3 activities (Jackson et al., 2002). However, subsequent work cast doubt on this hypothesis (Malagnac et al., 2002). It is now clear that SUVH4 is responsible for the majority of H3K9 dimethylation in heterochromatin (Jackson et al., 2004; Jasencakova et al., 2003; Johnson et al., 2002). Mutations in SUVH4 do not lead to a significant reactivation of repetitive elements, as is observed for cytosine
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methyltransferase mutants. Several HKMT proteins may act redundantly to silence these loci. Indeed, SUVH5 and SUVH6 have been shown to work together with SUVH4 to silence inverted repeats (Ebbs and Bender, 2006; Ebbs et al., 2005). Moreover, the triple mutant suvh2 suvh4 suvh9 shows altered expression of an F-box gene SUPPRESSOR of DRM1 DMR2 CMT3 (SDC), which induces a curly leaf phenotype, whereas no changes are observed in single mutants (Johnson et al., 2008). Over-expressing SUVH2 induces a general increase in heterochromatization mediated by an increase in repressive histone marks, including H3K9 dimethylation, H3K27 di- and trimethylation and H4K20 dimethylation (Naumann et al., 2005). This chromatin re-organization results in development changes, such as delayed leaf senescence (Ay et al., 2008). The function of SUVH proteins in mediating H3K9 methylation is linked to cytosine methylation. A close correlation between these two heterochromatic marks is observed genome-wide (Bernatavichute et al., 2008; Gendrel et al., 2002). Furthermore, similar molecular defects are observed in mutants deficient in DNA methyltranferases or histone methyltransferases (Cao et al., 2003; Chan et al., 2006; Ebbs and Bender, 2006; Ebbs et al., 2005; Jackson et al., 2002; Johnson et al., 2007, 2008; Malagnac et al., 2002). For example, the triple mutant drm1 drm2 cmt3 shows depletion of DNA methylation at CHG and CHH sites, as well as some reduction in CG methylation. A decrease in H3K9 dimethylation is also observed in drm1 drm2 cmt3, similar to the effect of suvh4 mutations (Johnson et al., 2007). Similarly, suhv4 suvh2 suvh9 and drm1 drm2 cmt3 triple mutants display similar derepression of the SDC locus that correlates with a loss of DNA methylation and H3K9 methylation (Johnson et al., 2008). Collectively, the available data reveal an important role for SUVH proteins in regulating the activity of loci present both in euchromatic and heterochromatic regions of the Arabidopsis genome. A recent study also analysed telomere length in the suvh4 mutant. Compared to wild-type plants, suvh4 plants have shorter telomeres, which suggest that heterochromatin maintenance affects telomere stability (Grafi et al., 2007).
B. SUVR PROTEINS
1. Characteristics of SUVRs Few studies have investigated the properties and functions of SUVR (SU (VAR)3–9 related) proteins. Like SUVH proteins, SUVR proteins have a SET domain which is associated with a pre-SET domain and a post-SET domain (Baumbusch et al., 2001; Thorstensen et al., 2006). However, in
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contrast to SUVH proteins, SUVRs lack an SRA domain. A novel N-terminal plant-specific domain, named WIYLD based on conserved residues, has been identified in SUVR1, SUVR2 and SUVR4 proteins (Fig. 2) (Thorstensen et al., 2006). The WIYLD domain is a conserved region (residues 21–77 in SUVR4) that possesses structural similarity to the C-terminal domain of RuvA, a DNA-binding protein implicated in homologous recombination in bacteria (Rice et al., 1997; Thorstensen et al., 2006). Five genes encoding SUVR proteins (SUVR1 to SUVR5) are present in the A. thaliana genome (Fig. 2). SUVR4 possesses H3K9 mono- and dimethylation activities, which suggests that this class of HKMTs is responsible for repressive chromatin marks (Fig. 3) (Thorstensen et al., 2006). 2. Functions of SUVR proteins The functions of SUVR proteins remain unclear. However, SUVR1, SUVR2 and SUVR4 proteins are most similar to HKMTs in humans that are implicated in heterochromatin formation. A recent report found that Arabidopsis SUVR5 (also known as AtCZS) interacts with the remodelling factor AtSWP1 and that both SUVR5 and AtSWP1 are required to downregulate FLC expression. This repression correlates with H3K9 dimethylation and H3K27 dimethylation at the FLC promoter (Krichevsky et al., 2007). As yet, no functions have been described for the other four SUVR proteins. SUVR proteins all display at least partial nucleolar localization, which has not been observed for other classes of HKMTs (Thorstensen et al., 2006). The nucleolus is best known as the site of ribosome biogenesis, but it is now clear that myriad aspects of RNA metabolism occur in the nucleolus (Boisvert et al., 2007), including processing of siRNAs involved in RNA-directed DNA methylation (RdDM) (Li et al., 2006; Pontes et al., 2006). This nucleolar processing centre generates heterochromatic, 24-nt small RNAs that target both RdDM and H3K9 methylation to specific genomic regions. The presence in the nucleolus of SUVR proteins that catalyse H3K9 methylation could reflect a role in rRNA gene modification or a potential link to the heterochromatic siRNA production machinery.
V. CONCLUSIONS AND PERSPECTIVES The analysis of Arabidopsis HKMTs remains challenging because of the large number of genes in this family. Of the 31 predicted histone HKMTs, half have assigned functions or lysine specificities (Fig. 3). Because different HKMTs can help activate or silence gene expression, they can have
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antagonistic roles in modulating gene activity. For instance, CLF is implicated in H3K27 methylation, a repressive chromatin mark, whereas ATX1 methylates H3K4, a mark of active chromatin. CLF and ATX1 can both modify the same chromatin region, with opposing effects on gene activity. Single clf and atx1 mutants show abnormal leaf development, which is rescued in the double mutant clf atx1, indicating that this antagonism is biologically significant (Saleh et al., 2007). Much effort is now focused on discovering specific functions for HKMTs. There are already several examples of HKMTs that seem to act preferentially at a specific tissue and time in development. An example is MEA, which is only expressed in the endosperm and the embryo (Grossniklaus et al., 1998; Kinoshita et al., 1999). Creating double and triple mutants for members of each Arabidopsis HKMT sub-group could reveal functions, and functional redundancies, among these proteins. However, this is a timeconsuming approach that requires loss-of-function mutations for all HKMT genes, which are not yet available for all of the genes. Artificial microRNAs that target one or more genes simultaneously might be a useful strategy for analysing the functions of potentially redundant HKMTs (Schwab et al., 2006). An important consideration for the interpretation of in vivo data concerning HKMT functions is that the enzymes may modify non-histone targets that are responsible for the observed phenotypes. For example, the plant protein Rubisco is targeted by a methyltransferase that possesses a SET domain (Trievel et al., 2002; Ying et al., 1999). Moreover, non-histone targets of HKMTs have been described in mammals, including transcription factors (Chuikov et al., 2004; Kouskouti et al., 2004). Genome-wide DNA methylation and histone modification data have revealed heterogeneity in H3K9 dimethylation distribution across the genome. H3K9 dimethylation tends to occupy larger regions in pericentromeric regions than it does in the chromosome arms, suggesting that distinct H3K9 methyltransferases, possibly the SUVR and SUVH proteins, could regulate the level of histone methylation in these different regions (Bernatavichute et al., 2008). Moreover, a genome-wide analysis of H3K27 trimethylation patterns also revealed that perhaps 4400 A. thaliana genes are impacted by this specific type of histone modification (Zhang et al., 2007b). This observation is consistent with the hypothesis that H3K27 methylation is a major silencing mechanism in plants. It is known that H3K27 and H3K9 methylation function independently as repressive chromatin marks (Mathieu et al., 2005). Recent data obtained for class IV HKMTs ATXR5 and ATXR6 confirm this hypothesis (Jacob et al., 2009). H3K9 methylation and DNA methylation are both critical for epigenetic regulation of gene expression in plants, but the mechanisms linking these
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interdependent processes remain to be fully determined. However, the SRA domain of SUVH proteins helps explain how H3K9 methyltransferase activity can be recruited to regions characterized by methylated DNA, as does the presence of a chromodomain in the DNA methyltransferase (cf. SUVHs proteins). CMT3 can potentially explain the recruitment of DNA methyltransferase activity to nucleosomal regions enriched for histones methylated on H3K9 or H3K27. Intriguingly, neither SUVH2 nor SUVH9 displays HKMT activity but both can interact with methylated DNA. Alignment of SUVH proteins reveals amino acids substitutions in the SUVH2 and SUVH9 SET domains that might explain their lack of activity. However, they retain conserved pre-SET, SET and post-SET domains. It would be interesting to test whether their SET domains can interact with methylated H3K9. If this were the case, it might be possible that SUVH2 and SUVH9 recognize methylated DNA, and, at the same time, protect methylated H3K9 from histone lysine demethylase activities.
ACKNOWLEDGEMENTS The authors would like to thank Yannick Jacob for critical reading of the manuscript. Research in the Pikaard lab is supported by United States NIH grants GM60380 and GM077590. TB is supported by a Ruth L. Kirschstein National Research Service Award from the NIH. The content of this paper is solely the responsibility of the authors and does not necessarily reflect the views of the NIH.
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Advances in Coffea Genomics
ALEXANDRE DE KOCHKO,*,1 SE´LASTIQUE AKAFFOU,† ALAN C. ANDRADE,‡ CLAUDINE CAMPA,* DOMINIQUE CROUZILLAT,§ ROMAIN GUYOT,* PERLA HAMON,* RAY MING,|| LUKAS A. MUELLER,{ VALE´RIE PONCET,* CHRISTINE TRANCHANTDUBREUIL* AND SERGE HAMON* *
UMR DIAPC, GECOFA; Centre IRD de Montpellier, BP64501, 34394 Montpellier Cedex, France † URES Daloa, B150, Daloa, Coˆte d’Ivoire ‡ Laboratory of Molecular Genetics (LGM-NTBio), Embrapa Genetic Resources and Biotechnology, CP 02372, 70770-900 Brası´lia-DF, Brazil § Nestle´ R&D Tours, 101 Av. G. Eiffel, Notre Dame d’Oe´, BP 49716 37097, Tours, Cedex 2, France || Department of Plant Biology, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA { Boyce Thompson Institute for Plant Research, Tower Road, Ithaca, NY 14853-1801, USA
I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Molecular Markers, Genetic Maps and Cytogenetics . A. Molecular Markers and Genetic Diversity . . . . . . B. Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . C. QTL Identification . . . . . . . . . . . . . . . . . . . . . . .
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Advances in Botanical Research, Vol. 53 Copyright 2010, Elsevier Ltd. All rights reserved.
0065-2296/09 $35.00 DOI: 10.1016/S0065-2296(10)53002-7
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III. Genomic Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Coffea Genome Size and Cytogenetics . . . . . . . . . . . . . . . . . . . . B. Expressed Sequence Tags in Coffea . . . . . . . . . . . . . . . . . . . . . . C. Bac Libraries in Coffea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Genes and Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Bioinformatics: Coffee Genomic Resources Available on the World Wide Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Toward the Whole Genome Sequencing of Coffee . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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ABSTRACT Coffee is the second most valuable commodity exported by developing countries. The Coffea genus comprises over 103 species but coffee production uses only two species throughout the tropics: Coffea canephora, which is self-sterile and diploid and better known as Robusta, and C. arabica, which is self-fertile and tetraploid. With the arrival of new analytical technologies and the start of genome sequencing projects, it was clearly time to review the state of the art of coffee genetics and genomics. In the first part of this chapter, we present the main results concerning genetic diversity and phylogeny – the most advanced fields – based on large molecular marker sets, such as random amplified polymorphic DNAs (RAPDs), amplified fragment length polymorphisms (AFLPs), intersimple sequence repeat (ISSR), single sequence repeats (SSRs), or conserved orthologue set (COS), which are mainly polymerase chain reaction (PCR) based. These markers also enable the construction of genetic maps and the identification of quantitative trait loci (QTLs) for both morphological and biochemical traits. In the second part, after reviewing current knowledge on variation in coffee genome size and insights into cytogenetics, we focus on currently available genomic resources and web facilities. Large sets of expressed sequences tags (ESTs) and bacterial artificial chromosome (BAC) libraries for both C. canephora and C. arabica have been obtained along with information on genes and specific metabolic pathways. In the final section, we describe recently designed tools and their ultimate goal, which is to facilitate the sequencing, assembly and annotation of the first Coffea genome. We are at the gate of a new era of scientific approaches to coffee that should lead to a better understanding of phylogenetic relationships and genome evolution within the genus. Finally, taken together, this information should help develop improved varieties to meet the new challenges represented by ongoing radical changes in the environment.
I. INTRODUCTION Coffee is the fourth most valuable traded agricultural commodity (FAO Statistics, 2004) but is the second most valuable commodity exported by developing countries, and more than 75 million people depend on coffee for all or most of their livelihood (Pendergrast, 2009). Coffee is currently
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B
A
Fig 1.
C
Coffee tree: Coffea canephora. (A) Tree carrying fruits. (B) Flowers. (C) Mature
fruits.
produced throughout the tropics, the two main cultivated species being Coffea canephora (better known as Robusta) and C. arabica (Fig. 1). The Coffea genus belongs to the Rubiaceae family, the largest flowering plant family comprising about 650 genera and 13,000 species (Rova et al., 2002). All Coffea species are native to Africa, Madagascar and Mascarenes. The genus, which was formerly divided into three botanical sections: Eucoffea (West and Central African species), Mozambicoffea (East African species) and Mascarocoffea (Malagasy and Mascarenes species) by Chevalier (1947) and Coste (1955), is now organized in at least 103 species (Maurin et al., 2007). All Coffea species, except C. arabica (amphidiploid with 2n = 4x = 44 chromosomes), are diploid (2n = 2x = 22 chromosomes). Their diploid genome size, estimated by flow cytometry (Cros et al., 1995; Noirot et al., 2003), varies from 1.03 to 1.76 pg of DNA per nucleus. From the early 1970s to the late 1980s, wild Coffea species were collected in contrasted habitats in Madagascar and African tropical forests with two main objectives: to preserve their genetic resources and to produce interspecific hybrids for breeding programs. The impressive diversity of the genus, with very extreme phenotypes ranging from small shrubs in East African dry forests to 20-m-high trees in West African tropical rain forests, makes the genus an ideal model to explore diversification and adaptation processes. During the 1980s, plant geneticists and breeders mostly focused on the construction of genetic linkage maps and the identification of quantitative
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trait loci (QTLs). Bernatzky and Tanksley (1986) first demonstrated that efficient linkage mapping strategies can be applied to plants with a high number of polymorphic loci. As far as coffee is concerned, breeders were mostly interested in acquiring a genetic map of C. arabica. However, the main problem with Arabica is that the cultivated forms come from a very narrow genetic base (foundation effect) and that the Ethiopian genetic resources are not available to geneticists. On the other hand, because of its lower coffee quality, C. canephora was not a priority for breeders despite its diploid status, naturally huge diversity, and the fact that it is the main source of instant coffee. This explains why coffee genetics was not at the cutting edge during this period. During the 1990s, there was an explosion in Arabidopsis research due to rapid progress in plant molecular science. The initial objectives, which were the free availability of most resources and the completion of the genome sequencing of Arabidopsis, rapidly stimulated projects focused on different types of plants. In parallel, the synteny concept was extended to address questions of chromosome homeology (mostly within the grass family), which led to the possibility of extrapolating results from one species to a relative species (Devos et al., 1995): Rice genetic maps were extrapolated to sorghum, sugarcane, etc. Arabidopsis data were extrapolated within the Cruciferaceae family, specifically within cultivated Brassica species. Similarly a strong colinearity between tomato and potato chromosomes (Solanaceae family) was described by Bonierbale et al. (1988). In the 1980s, 10 years after the pioneers, coffee researchers were only just beginning to develop their own molecular markers. In the early years of the 21st century, new sequencing possibilities have changed our vision of genomics, which has become crucial for coffee, a model plant like tomato. The SOLanaceae genome project (SOL network) chose a strategy based on the sequencing of 220 Mb of Euchromatin, which was believed to contain most genes and particularly those involved in traits such as fleshy fruit formation, which are not found in Arabidopsis or rice. Today, whole genome sequencing (WGS) is routinely performed and with the rapid emergence of new technologies, an increasing number of species, including C. canephora, will be sequenced in the next few years. In contrast to previous approaches, these new technologies offer access to all the genes of a genome; provide information about global genome structure and allow analysis of regulatory regions, transposable elements and noncoding sequences. Coupled with highly automated tools, these approaches have created unprecedented opportunities for generating and analyzing large biological data sets. The opportunity to systematically compare complete nucleic acid sequences from very different organisms has fundamentally modified the way biologists undertake genome studies.
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As far as coffee is concerned, an important goal remains the sequencing of the economically predominant species, C. arabica. But due to its allotetraploid nature and the subsequent difficulties to assemble properly the sequence of its genome, the scientific community finally agreed to concentrate its efforts first on C. canephora, the diploid cultivated species. This decision was facilitated by the availability of dihaploid genotypes of C. canephora obtained using the technique developed by Couturon (1986). The availability of dihaploid genotypes combined with the increase in new technologies suddenly opened the way to sequencing the whole Coffea genome. In this chapter, we present an up-to-date review of the main advances in coffee genomics with special emphasis on structural genomics, functional genomics, bioinformatics and WGS strategies.
II. MOLECULAR MARKERS, GENETIC MAPS AND CYTOGENETICS Initial coffee molecular genetic data were obtained using isozymes from 18 populations of wild species (Berthou et al., 1980). Restriction fragment length polymorphisms (RFLPs) were only used for a short period because of the large amounts of DNA required by such markers and the low yield of marking subsequently obtained. During the 1990s, the development of polymerase chain reaction (PCR)-based technologies paved the way for a great number of molecular markers such as random amplified polymorphic DNAs (RAPDs) and amplified fragment length polymorphisms (AFLPs). But the real change came with the advancement of single sequence repeats (SSRs), single nucleotide polymorphism (SNP) and conserved ortholog set (COS) markers. In coffee, like in other crops, molecular markers were mostly used to (i) assess the genetic diversity of the species, (ii) construct genetic maps, and (iii) identify QTLs. A. MOLECULAR MARKERS AND GENETIC DIVERSITY
The first paper reporting on the molecular analysis of DNA was published by Berthou et al. (1983). Chloroplast and mitochondrial DNA from nine species or taxa of coffee trees were compared with respect to their phylogenetic relationship using RFLP analysis. Three types of chloroplast DNA (cp DNA) were detected indicating the following relationships: (i) C. arabica, C. eugenioides; (ii) C. canephora, C. congensis, ‘nana’ taxon; and (iii) C. liberica. The mitochondrial DNA (mt DNA) separated into five types:
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(i) C. arabica, C. eugenioides, C. congensis; (ii) C. canephora, ‘nana’ taxon; (iii) C. excelsa; (iv) C. liberica; and (v) Paracoffea ebracteolata. The divergence in organelles containing DNAs agreed with the phylogenetic relationship deduced using the then conventional methods. Restriction patterns of the cp and mt DNAs isolated from a clone of C. arabusta (a hybrid between C. canephora and C. arabica) were compared to those of the parents and were found to be maternally inherited. RFLPs, involving DNA–DNA hybridization with homologous and/or homeologous probes, were the first molecular markers used to build plant genetic linkage maps (Bernatzky and Tanksley, 1986; McCouch et al., 1988). In coffee, they were also used to construct the first molecular linkage map (Paillard et al., 1996). The development of PCR-based markers, such as RAPDs, allowed researchers to get around the problem of the amount of DNA and, despite the low RAPD reproducibility often reported in the literature, these markers are still used today to assess the genetic diversity within C. arabica (Masumbuko and Bryngelsson, 2006; Sera, 2001; Silveira et al., 2003) and also in C. canephora (Ferrao et al., 2009; Tshilenge et al., 2009). The development of AFLPs by Vos et al. (1995) enabled numerous reproducible and informative dominant markers to be obtained in only a few experiments even if reproducibility between laboratories was not ideal. In addition, these markers are generally widely distributed throughout the genome and, mostly in C. arabica, are useful to (i) identify genetic diversity among cultivars (Dessalegn et al., 2008); (ii) detect introgression in cultivars derived from natural interspecific hybrids (Prakash et al., 2004; Steiger et al., 2002); (iii) analyze the genetic of resistance (Gichuru et al., 2008; Herrera et al., 2009); (iv) construct a genetic map of C. arabica (Pearl et al., 2004); and (v) clarify taxonomic debates between C. liberica var liberica and C. liberica var dewevrei, combined with the analysis of morphological traits and the study of male fertility in interspecific F1 hybrids (N’Diaye et al., 2005; Poncet et al., 2005). AFLPs are often used to analyze diversity (and for genetic mapping), but very little information is available on their sequence characteristics. Species-specific sequences have been analyzed in a single Coffea genome (C. pseudozanguebariae) associated with clustered or nonclustered AFLP loci of known genetic position. Conversion of these AFLP markers into sequence-characterized amplified region (SCAR) anchor markers enabled the determination of sequence conservation within Coffea species with respect to species relatedness (Poncet et al., 2005). Most recently, intersimple sequence repeat (ISSR) and inverse sequencetagged repeat (ISTR) markers have been used in several studies (Aga and Bryngelsson, 2006; Masumbuko and Bryngelsson, 2006; Ruas et al., 2003).
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However in coffee, these kinds of markers are not widely used. Nevertheless, they confirmed the low genetic diversity available among the cultivated C. arabica species in Ethiopia (Aga and Bryngelsson, 2006; Aga et al., 2003, 2005; Zeltz et al., 2005). SSR, microsatellite markers, appeared later in the development of new technologies, especially for DNA sequencing. They opened the way for the development of very informative molecular markers due to the high level of polymorphism of this type of marker also enabling the detection of all alleles present at a given locus. Hundreds of markers were obtained from microsatellite libraries, sequencing of bacterial artificial chromosome (BAC) ends, or expressed sequence tag (EST) libraries from both C. arabica (Baruah et al., 2003; Rovelli et al., 2000) and C. canephora (Dufour et al., 2001; Hendre et al., 2008; Leroy et al., 2005; Poncet et al., 2007). Primers derived from these sequences generally exhibit broad cross-species transferability (Baruah et al., 2003; Combes et al., 2000; Moncada and McCouch, 2004; Poncet et al., 2004). Despite their high efficiency, the SSRs confirmed the lower number of alleles per locus in C. arabica (cultivated and wild forms) compared to C. canephora (Cubry et al., 2008; Moncada and McCouch, 2004; Silvestrini et al., 2007). They also confirmed the considerable diversity of C. canephora and, more generally, of wild diploid species (Cubry et al., 2008; Gomez et al., 2009; Prakash et al., 2005). New SSR markers for diversity studies (Cubry et al., 2008) and BAC–FISH (fluorescent in situ hybridization) experiments have been also successfully developed (Guyot et al., 2009; Herrera et al., 2007). COS markers are a very promising new source of markers (i.e., SSRs or SNPs) (Fulton et al., 2002; Wu et al., 2006). Generated by the combined availability of information from characterized gene sequences in comparison with other unrelated species using bioinformatics, these markers are being already used in many studies. One study, whose aim was to develop widely applicable gene markers for phylogenetic reconstructions at low taxonomic levels, tested the low copy of nuclear COS genes (Li et al., 2008). The markers were found to be highly informative in phylogenetic reconstruction of congeneric species, where introns provide a higher proportion of parsimony informative sites than traditional phylogenetic markers such as intergenic transcribed sequence (ITS) and matK. At greater phylogenetic distances, where only coding regions could be aligned, the polymorphism levels of the COS ranged between those of ndhF and matK. The first paper describing the use of COSII markers for comparative mapping across Solanaceae (tomato) versus Rubiaceae (coffee) was published by Wu et al. (2006). The results indicate that some coffee genome areas correspond to specific segments of the tomato genome and imply
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that COS genes can be used for comparative mapping among Euasterid plant families. B. GENETIC MAPS
In the case of C. canephora, coffee genetic linkage mapping started at the same time as the development of molecular markers. A progeny of C. canephora doubled haploids was the first to be mapped with RFLPs and RAPDs (Paillard et al., 1996). Based on SSRs, SNPs and COSII, two genetic maps of C. canephora, are currently nearing completion by Centre de cooperation Internationale en Recherche Agronomique pour le De´veloppement (CIRAD) and Nestle´ (T. Leroy and D. Crouzillat, personal communication). Fig. 2 shows the C. canephora genetic map developed by Nestle´ (Lefebvre-Pautigny et al., submitted). Their comparison will be used to build a C. canephora consensus map, the future reference for the ongoing coffee genome sequencing project. In addition, in collaboration with Indonesian Coffee and Cocoa Research Institute (ICCRI) (Indonesia), a progeny will be available for the scientific community, as recommended by the International Coffee Genomics Network (ICGN; http://www.coffeegenome.org/). Interspecific crosses between C. canephora and wild species have also been undertaken but frequently showed segregation distortion (Ky et al., 2000). Most coffee interspecific genetic linkage maps were first constructed to identify QTLs involved in very contrasting traits existing in several wild species (Coulibaly et al., 2002, 2003a, b; Ky et al., 2000; N’Diaye et al., 2007). Now, the mapping of anchor markers, such as genes (BustamantePorras et al., 2007a; Campa et al., 2003; Mahesh et al., 2006), SSRs and EST–SSRs (Coulibaly et al., 2003b), provides valuable information on the organization of different sized genomes through comparative mapping. The construction of genetic maps using COSII markers by Nestle´ and Institut de Recherche pour le De´veloppement (IRD) for different interspecific progenies will soon allow comparison between the two closely related families, Solanaceae (tomato) and Rubiaceae (coffee). The first C. arabica genetic linkage map was constructed with AFLPs on a small pseudo-F2 population (Pearl et al., 2004). As mentioned above, due to the low genetic diversity of C. arabica, the combinations of 288 AFLP primers only generated 464 markers that were usable for mapping. A partial map of C. arabica was constructed, based on a backcross population and RAPD markers, by De Oliveira et al. (2007). From a total of 178 markers evaluated, only 134 that segregated 1:1 (P > 0.05) were used to build the map. Seventeen markers were not linked, while 117 formed 11 linkage
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Fig 2. Genetic map of Coffea canephora developed by Nestle´. The map contains 682 loci, 479 SSR, 199 SNPs (unigenes) and 4 BAC. It covers 1306 cM.
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F
Fig 2.
G
H
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groups, covering a genome distance of 1803.2 cM. The maximum distance between adjacent markers was 26.9 cM, and only seven intervals exceeded 20 cM. The markers were also used for assisting selection of the plants closest to the recurrent parent, to accelerate the introgression of rust resistance genes in the coffee breeding program. These results are a good
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testimony of the considerable efforts invested to acquire a saturated C. arabica genetic map. C. QTL IDENTIFICATION
In Coffea, the genetic control of useful traits (monofactorial or quantitative) such as S alleles, fruiting cycle length, biochemical contents and morphological traits, has mostly been studied using interspecific progenies. The S-locus was mapped using a C. canephora progeny derived from a doubled haploid (Lashermes et al., 1996) and an interspecific cross between C. canephora (self-incompatible) and C. heterocalyx (self-compatible) (Coulibaly et al., 2002, 2003a). In the latter progeny, three significant QTLs were also detected for pollen viability. In C. canephora, C. arabica and C. liberica, the period between flowering and ripening is very long (10–11 months). Following the identification of very shorter fruiting cycles (2–3 months) in wild species native to eastern Africa, the control of fruiting time (FT) and cycle length are very interesting characters to study. Using an interspecific backcross between C. pseudozanguebariae (2 months FT) and C. liberica Hiern var. dewevrei de Wild (10–11 months FTs), Akaffou et al. (2003) showed that FT is an additive trait. The bimodal distribution of the full growth period suggests the involvement of Ft1, a major gene. This gradient overlaps those of caffeine and chlorogenic acid (CGA) content, suggesting that long FT could control caffeine and CGA contents in coffee beans. There is a similar relation between FT and seed weight. The genetic control of several biochemical compounds was studied using the same progeny. It appears that the bean caffeine content and the quantity of an undetermined heteroside are oligogenic (Barre et al., 1998). One major gene with two alleles could be involved in the control of the biosynthesis of the two compounds; the absence of caffeine could be controlled by one recessive gene and heteroside content by one codominant gene. Feruloylquinic acid (FQA) isomer content appears to be controlled by one major gene with the dominant allele leading to the absence of 3-FQA (Ky et al., 1999). As far as trigonelline is concerned, a nucleocytoplasmic inheritance with one nuclear QTL was found (Ky et al., 2001). Morphological quantitative traits were also studied. The phenotypic (16 quantitative traits) and genetic differentiation between the two related Coffea species (C. liberica Hiern and C. canephora Pierre) were evaluated (N’Diaye et al., 2007). Eight QTLs for the petiole length, leaf area, number of flowers per inflorescence, fruit shape, fruit disc diameter, seed shape and seed length were identified and mapped. However, in all these cases, only
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major genes or QTLs with strong effects could be detected because of the small size of the available populations.
III. GENOMIC RESOURCES In the past few years, there has been a dramatic increase in coffee genomic tools and molecular resources for cultivated Coffea species. Understanding the composition, structure and evolution of the coffee genome is now possible. The Coffea research community has produced basic genomic resources such as large sets of EST sequences and genomic inserts into BAC libraries for both C. canephora and C. arabica. Here, we describe (i) current knowledge on coffee genome size and cytogenetics, (ii) Coffea EST resources; (iii) available BAC libraries; (iv) genes and metabolism; and (v) web facilities. A. COFFEA GENOME SIZE AND CYTOGENETICS
Coffee genome sizes were estimated using flow cytometry. Four main conclusions were drawn: (i) the genome size of diploid coffee varies from 1.03 (C. racemosa) to 1.76 pg (C. humilis) (Cros et al., 1995, 1998; Hamon et al., 2009; Noirot et al., 2003); (ii) species native to dry areas (mostly in East Africa) have a smaller genome size (<1.3 pg) than those native to evergreen forest (1.3 < x < 1.76 pg); (iii) a difference in genome size greater than 0.25 pg is associated with high rates failure in crosses and marked sterility of hybrids; (iv) by coupling flow cytometry tools with cytometry images, the 1C nuclear DNA content of C. canephora and C. arabica was evaluated and results confirmed the true allotetraploidy of C. arabica (Clarindo and Carvalho, 2008). As far as chromosome morphology is concerned, early observations clearly showed that diploid coffee chromosomes (2n = 2x = 22) are small, metacentric and submetacentric (Sybenga, 1960). For a long time, the limited number of metaphases in root meristem cells has strongly limited coffee cytogenetic investigations. But recently, the development of pachytene chromosome analysis (Pinto-Maglio and Da Cruz, 1987, 1998) and improved methods of chromosome preparations (Clarindo and Carvalho, 2008) enabled an overview of heterochromatin versus euchromatin distribution along the chromosomes and led to karyotyping of both C. arabica and C. canephora. FISH and heterochromatin staining techniques have been used for coffee (Barre et al., 1998; Hamon et al., 2009; Raina et al., 1998). These techniques enabled improved resolution for the physical mapping of ribosomal genes and heterochromatin AT- or GC-rich regions. This approach, which was extended to a large set of Coffea species (Hamon et al., 2009), showed that
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there is a correlation between the number of secondary constrictions (one or two satellite chromosomes (SAT-chromosomes)), the number of rDNA sites (5S and 18S), the geographic origin of the species (West and Central Africa vs. East Africa) and the genome size. However, it was impossible to assign a causality relationship between these traits and rDNA distribution patterns. FISH techniques also permitted visual location of BAC clones on C. arabica (Herrera et al., 2007) and C. canephora chromosomes (Guyot et al., 2009). Combined BAC–FISH technology for use on pachytene chromosomes is a very promising tool for complementary studies of the organization of the coffee tree genomes, as well as for the comparison of species genetic relationships and of physical maps. Genomic in situ hybridization (GISH) was used to study the genome organization of interspecific hybrids. In interspecific F1 hybrids, Barre et al. (1998) demonstrated that there is a linear relationship between the number of chromosomes of one parental species and the nuclear DNA content. It was also helpful to analyze the DNA content of the backcrossed derived hybrids and to monitor species evolution of C. Arabica. Lashermes et al. (1999) focused on the origin of the tetraploid Arabica and concluded on the allopolyploid origin of the species. Hamon et al. (2009) provided clear cytogenetic evidence that one C. arabica progenitor is native to East Africa and the other to West or Central Africa. More detailed analysis of the organization of the coffee genome will certainly be undertaken in the very near future by combining BAC–FISH, DNA fiber, FISH and genetic maps. The organization of gene-rich regions along C. canephora chromosomes remains unknown. However, previous cytological observations in C. canephora and C. arabica chromosomes by Pinto-Maglio and Da Cruz (1987, 1998) revealed the presence of intensely and lightly stained patterns in some regions indicating an overall chromosome organization in condensed heterochromatin and decondensed euchromatin regions. Similarly to the architecture of Medicago and tomato chromosomes (Kulikova et al., 2001; Lin et al., 2005), C. canephora heterochromatin regions are mainly located around centromeric regions, while euchromatin forms the distal parts of chromosomes. As for other studied plants, it is expected to find gene-rich regions in the distal euchromatine of the C. canephora genome. B. EXPRESSED SEQUENCE TAGS IN COFFEA
ESTs are high-throughput single-pass sequences produced from cDNA clones. These cDNA clones are generally organized in large libraries that provide a picture of gene expression in a specific tissue or an organ under particular physiological conditions. Despite their short sizes and their relatively low
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quality, ESTs are valuable resources that can be exploited in different ways. In the absence of complete genome sequence, large-scale ESTs are used to (i) discover the composition of genes, (ii) provide gene repertoires of a given species and (iii) study gene expression in particular tissues or under specific conditions (Van der Hoeven et al., 2002). EST sequences can also be used for comparative genomic applications including the determination of conserved genes between genomes to (i) perform phylogenetic studies (Vandepoele and Van de Peer, 2005), (ii) discover COS markers for comparative genetic mapping (Fulton et al., 2002) and (iii) investigate and study gene and genome duplications. More recently, the availability of large-scale EST sequences from different genotypes of the same organism facilitated the detection of new allelic variations such as SNP and InDels (Tang et al., 2006). Finally, ESTs are an invaluable complement to sequenced genomes in the validation of gene predictions, the identification of coding and noncoding transcribed regions and the identification of alternative splicing of genes (Rudd, 2003). As of July 2009, more than 62 million ESTs are publicly available on the dbEST database (GenBank; http://www.ncbi.nlm.nih.gov/dbEST/dbEST_summary.html) (Boguski et al., 1994). These EST sequences have increased dramatically by more than 20 million during the past 2 years (3 million between November 2008 and February 2009). Table I lists the top seven (T1–7)
TABLE I Summary of ESTs of a Selection of Plant Organisms Available in GenBank (as of July 2009) Species Top seven T1 – Zea mays (maize) T2 – Arabidopsis thaliana T3 – Glycine max (soybean) T4 – Oryza sativa (rice) T5 – Triticum aestivum (wheat) T6 – Brassica napus (oilseed rape) T7 – Hordeum vulgare (barley) Other examples Physcomitrella patens subsp. Patens Vitis vinifera (wine grape) Solanum lycopersicum (tomato) Medicago truncatula (barrel medic) Coffea species Coffea canephora (Coffee robusta) Coffea arabica (Coffee arabica) Source: http://www.ncbi.nlm.nih.gov/dbEST/dbEST_summary.html.
Nb of EST sequences 2,018,634 1,527,298 1,386,618 1,248,995 1,067,014 632,344 501,366 362,131 353,941 291,209 260,238 55,694 43,562
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plant species with the largest number of available ESTs in GenBank as well as other model plant species. Together, these top seven plant species represent more than 8.3 millions ESTs. Different research groups have produced large-scale sets of Coffea EST sequences. However, the number of publicly available ESTs remains dramatically low because most of these sequences are private property. Some institutions decided to keep their own resources confidential for a while (The Brazilian Coffee Genome Project, CENICAFE), while others (Nestle´, IRD) made them freely available. The Brazilian Coffee Genome Project has generated 130,792, 12,381 and 10,566 EST sequences from C. arabica, C. canephora and C. racemosa, respectively, assembled into 33,000 unigenes (Vieira et al., 2006). The CENICAFE research group produced 32,961 EST sequences from three different tissues (leaves, 31-week-old fruits and flowers) of C. arabica (cv. Catura) assembled into 10,799 unigenes (Montoya and Vuong, 2006). Neither project has yet released sequences to public databases1. Different research groups have produced large sets of EST sequences in C. canephora. At the French IRD, 10,420 EST sequences (assembled into 5534 potential unigenes) were produced from C. canephora fruit and leaf cDNA libraries (Poncet et al., 2006). Including the 47,000 ESTs, representing 13,175 unigenes, published by Nestle´ and Cornell University (Lin et al., 2005), a total of 55,694 sequences are currently available, comprising the main public resource for the scientific community (Table I). From two C. arabica cultivars (red Catai and red Bourbon), 1587 EST sequences were produced to develop a cDNA microarray containing 1506 ESTs from leaves and embryonic roots (De Nardi et al., 2006). Sequences are available at the coffeeDNA database (http://www.coffeedna.net/). This considerable number of sequences represents a valuable resource to establish an exhaustive gene catalog for the Coffea genus. Interestingly, the analysis showed that 22% of sequences had no similarity to released and known protein sequences in GenBank (BLASTX with a threshold of 10e–5 E-value). A significant fraction may represent noncoding transcribed sequences such as untranslated terminal region (UTR) or parts of transposable elements (TEs), which are the main component and one of the major forces driving the structure and evolution of plant genomes. GenBank offers access to 1577 ESTs for C. arabica and 55,694 ESTs for C. canephora. An EST database was generated at Cornell University (http:// www.sgn.cornell.edu/content/coffee.pl), grouping 47,000 ESTs from five C. canephora cDNA libraries organized by type of tissue with particular 1 Since the first submission of this manuscript, CENICAFE released 41,985 ESTs, bringing the total number of C. arabica ESTs to 43,562.
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attention to seed development (Lin et al., 2005). Following clustering and assembly, 13,175 unigenes were identified and used for comparative analysis with the gene repertoires of Arabidopsis and tomato (Solanum lycopersicum). C. canephora appeared to be more closely related to tomato (both from the Euasterid clade) than to Arabidopsis (Eurosid clade). Computational sequence comparison indicated a better conservation of the gene catalogs between C. canephora and tomato than between C. canephora and Arabidopsis. Such conservation of the gene repertoire associated with a similar genome size and chromosome karyotype and architecture promoted the use of tomato as a genomic model for Coffea species. Recently, another valuable application to the C. canephora EST sequences was demonstrated in the annotation of Coffea genomic sequences. In the absence of robust and specific gene prediction software for Coffea genes, EST alignments were used to validate and correct gene models predicted with gene prediction algorithms trained with Eurosids genes (Guyot et al., 2009). Beside the traditional exploitation of EST sequences, ESTs from the Brazilian Coffee Genome Project and from the publicly available C. canephora sequences were screened for the presence of TE insertions (Lopes et al., 2008). However, so far, the impact of such elements on the Coffea genus has not been investigated. In the work cited above, 140 transcripts from 39,312 Coffea unigenes were found to contain TE insertions (mainly long terminal repeat (LTR) retrotransposons) into protein coding regions called ‘TE-cassettes’. A total of 26 putative TE-encoded sequences were identified, suggesting that gene structures in Coffea may be modified through TE insertion by a molecular evolution process (Lopes et al., 2008). C. BAC LIBRARIES IN COFFEA
Large-insert genomic BAC libraries have become central and cost-effective tools in genomic research. BAC libraries have been constructed for many plant species including Arabidopsis (Mozo et al., 1998), rice (Wang et al., 1995), tomato (Budiman et al., 2000), papaya (Ming et al., 2001), grape (Adam-Blondon et al., 2005) and bread wheat (Safar et al., 2004). The appearance of high-quality BAC libraries in plants enabled the development of a wide variety of genomic applications including (i) the development of new genetic markers through BAC end sequencing for comparative genomics and to saturate genetic maps (Lai et al., 2006; Paux et al., 2006; Shultz et al., 2007); (ii) the construction of physical maps through BAC end sequencing (Lai et al., 2006; Mao et al., 2000) and high-throughput fingerprinting characterization of BAC clones (Chen et al., 2002; Moroldo et al., 2008; Mozo et al., 1999; Paux et al., 2008); (iii) the development of BAC-based FISH (BAC–FISH)
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allowing the integration of cytogenetic and physical maps (Cheng et al., 2001a, b); and (iv) the development of new strategies to facilitate chromosome and WGS projects (AGI, 2000; IRGSP, 2005; Paux et al., 2008). In the Coffea genus, five BAC libraries have recently been constructed so far exclusively concerning the cultivated Coffea species C. arabica (two libraries) and C. canephora (three libraries) (Table II). The first C. arabica BAC library was constructed using the C. arabica IAPAR-59 (Agronomic Institute of Parana´, Brazil) cultivar (Noir et al., 2004). This popular inbred commercial line in South America was selected for its useful agronomical traits such as resistance to root-knot nematodes (Meloidogyne exigua) and to leaf rust (Hemileia vastatrix) (Sera, 2001). This library is composed of 80,813 BAC clones (average clone size 130 Kb) and represents approximately eightfold the 1300 Mb C. arabica genome size (Table II). Recently, this BAC library was used to construct a physical map linked to the SH3 leaf rust resistance locus (Mahe´ et al., 2007). In addition, the synteny between the SH3 physical contig and the Arabidopsis reference genome was assessed. Relative conservation of the synteny was established between C. arabica and four different chromosomal segments in Arabidopsis. In the SH3 region, genomic sequences of Arabidopsis appeared to be a valuable tool to develop approaches based on marker saturation. The second C. arabica BAC library was constructed from the small bean and high-cup quality C. arabica Cv. Tall Mokka (Jones et al., 2006). In parallel, the Tall Mokka variety was crossed with the large bean, lowcupping quality, Arabica variety Catimor to generate a segregating population for the identification of QTLs. This BAC library consists of 52,416 clones with an average size of 94 Kb, providing a 4 genome equivalent (Table II). The main purpose of this BAC library is coffee improvement through cloning of genes controlling disease resistance and other important economic traits. In addition to applied research, the genomic library is currently being used for comparative orthologous sequence analysis to understand the evolution of the polyploid C. arabica genome from its two diploid progenitor genomes (R. Ming, Personal Communication). Perspectives in C. arabica genomics concern the development of a very high coverage (>10) large-insert genomic library (>100,000 clones) that could become the reference tool for physical mapping of this large complex genome. The construction of an integrated physical and genetic map will enable mapbased gene cloning and genome sequencing. In this process, the complete physical maps of C. canephora and C. eugenioides, the ancestral diploid progenitors of C. arabica, will be valuable resources to successfully complete the physical map of C. arabica. The presence in the C. arabica genome of two
TABLE II Available Coffea BAC Libraries (as of May 2009) Species
Genotypes/cultivars
Number of clones
Average size of clones Cloning site
Coverage estimation*
C. arabica C. arabica C. canephora
IAPAR 59 Hybrid Mokka–Catimor IF 126
80,813 52,416 55,296
130 kb 94 kb 135 kb
HindIII HindIII HindIII
~8 ~3.8 ~10.6
C. canephora
IF200 D.H.
36,864
150 kb
HindII
~7.9
C. canephora
IF200 D.H.
36,864
121 kb
BstYI
~6.9
*
Calculated for an average genome size of 1300 Mb for C. arabica and 700 Mb for C. canephora. D.H., doubled haploid.
Authors/references Noir et al. (2004) Jones et al. (2006) Leroy et al. (2005) de Kochko A., Lashermes P. and Wing R. A. de Kochko A., Lashermes P. and Wing R. A.
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highly similar subgenomes has greatly limited the use of chromosome walking and positional cloning approaches. To overcome this complexity and to access a wider range of genetic diversity, the research and development of resources has also focused on the cultivated ancestral diploid progenitor C. canephora. Different large-insert C. canephora BAC libraries have been produced. The first C. canephora BAC library was constructed in 2005, using the clone IF126, a hybrid between two distinct genetic groups within C. canephora: Congolese and Guinean (Moschetto et al., 1996). This library contained 55,296 clones (average size of 135 Kb), providing coverage of about 10 genome equivalents (Leroy et al., 2005). Initially, it was used to investigate the genome organization of genes involved in sugar metabolism (Leroy et al., 2005). Later, the characterization of CcEIN4 and CcCCoAOMT genes encoding an ethylene receptor and a caffeic acid O-methyltransferase, respectively, demonstrated its value in identifying genes of agronomical interest in C. canephora (Bustamante-Porras et al., 2007; Chabrillange et al., 2006; Guyot et al., 2009). The genomic sequence of the C. canephora CcEIN4 region was compared to that of several sequenced dicotyledonous genomes (such as Arabidopsis, Medicago truncatula, tomato and grape) that covered the Euasterid and Eurosid clades. Extensive conservation between C. canephora and most of the genomes studied was demonstrated; locally the gene content and order were shown to be highly conserved. The highest degree of microcollinearity was found between C. canephora and V. vinifera, which belong, respectively, to Euasterids and Eurosids, two clades that diverged more than 114 million years ago (Guyot et al., 2009). The two other C. canephora BAC libraries were constructed with the genomic DNA of a doubled haploid (DH) line derived from the clone IF200. The BAC libraries were prepared with two restriction enzymes HindIII and BstYI, respectively, and include 36,864 clones each (Table II). The two libraries were completed with a total ~16 genome equivalents, providing the basic resources for an international initiative for the construction of a physical map of C. canephora. Sequencing of the 72,000 BAC ends was recently initiated at Genoscope (France). Finally, a C. eugenioides (the putative maternal parent of C. arabica) BAC library was recently funded (http://www.fontagro.org/). It will be constructed with the aim of sequencing the BAC ends and constructing a physical map. Altogether these genomic resources will rapidly promote the development of homeologous comparative sequence analysis studies between C. arabica and its diploid progenitors. Comparative sequence mapping could answer several questions concerning C. arabica such as what are the consequences of interspecific hybridization on reshaping the genomes and on genome evolution.
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Research on coffee plants has mostly focused on agronomical improvement of the plant. Breeders are especially interested in selecting C. arabica plants with better disease resistance or C. canephora green beans that provide a high-quality coffee beverage. Breeding for complex metabolic compounds is, for the moment, less of a priority. Coffee aroma formation is a very complex process, involving Maillard and Strecker’s reactions (Maillard, 1913) as well as thermal degradation during roasting (De Maria et al., 1994). Some aroma precursors, such as sucrose and trigonelline, result in products with desirable flavor (Clifford, 1985; Dart and Nursten, 1985; De Maria et al., 1996; Feldman et al., 1969), while others, such as CGAs and caffeine, increase bitterness (Leloup et al., 1995; Voilley et al., 1977). Enhancing Robusta cup quality would thus imply increasing sucrose and trigonelline contents while decreasing CGA and caffeine contents. Improvement of coffee cup quality first requires an understanding of the mechanisms governing the accumulation, in beans, of the precursors that generate coffee aroma and taste. Among the 20 or so families of compounds involved in coffee cup quality (Flament, 1991), only four have been studied for their biochemical pathway and its regulation. These studies concerned purine alkaloids (caffeine), CGA (whose biosynthesis is controlled by the phenylpropanoid pathway), lipids and sugars. Due to its commercial importance, caffeine biosynthesis has been the most widely studied of the alkaloid biosynthetic pathways in the coffee plant. This purine alkaloid is produced in a variety of plants, including tea, kola nuts, guarana berries, Yerba mate and cacao beans (Ashihara and Crozier, 1999). In coffee plants, caffeine (1,3,7-trimethylxanthine) is synthesized in three methylation steps involving S-adenosyl-L-methioninedependent N-methyltransferases plus a step involving elimination of the ribose residue from xanthosine (Ashihara and Crozier, 1999). Structural studies of xanthosine (X), methyltransferase (XMT) and 1,7-dimethylxanthine methyltransferase (DXMT) revealed several elements that appear to be critical for substrate selectivity. Serine-316 in XMT appears to play a major role in the recognition of xanthosine (XR). Likewise, a change from glutamine-161 in XMT to histidine-160 in DXMT may have catalytic consequences. A change from phenylalanine-266 to isoleucine-266 in DXMT is also likely to be crucial for the discrimination between mono and dimethyl transferases in coffee (McCarthy and McCarthy, 2007). Several genes of this pathway have been cloned and characterized (Mizuno et al., 2003a, b; Ogawa et al., 2001; Uefuji et al., 2003). In vitro, recombinant methyltransferases obtained by heterologous expression of these genes converted
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xanthosine into caffeine (Uefuji et al., 2003). Silencing and overexpression approaches led to an overview of metabolic engineering of the caffeine biosynthetic pathway (Ogita et al., 2004). Tobacco plants (caffeine free in natural conditions) that simultaneously expressed the three methylation genes also produced caffeine (Uefuji et al., 2005). CGA biosynthesis is also the subject of active studies today. In coffee plants, among the many genes involved in the phenylpropanoid pathway, CcCCoAOMT was the first gene described as being involved in CGA biosynthesis (Campa et al., 2003; Lepelley et al., 2007). It encodes a methyltransferase that catalyzes an early step of the lignin biosynthesis. A gene encoding phenylalanine ammonia lyase (PAL), which catalyzes the first step of the phenylpropanoid pathway, was then described (Mahesh et al., 2006). The isolation of two C30 H genes encoding hydroxylases – one HQT and one HCT genes encoding transferases, all of which are involved in the last step of CGA formation – proved that the two routes coexist for CGA biosynthesis in coffee plants (Lepelley et al., 2007; Mahesh et al., 2007). Reducing sugars (mainly glucose) and amino acids (free or associated with proteins) are the most actively involved precursors of the aromatic (volatile) compounds formed during Maillard’s reactions and provided by the coffee cup. Despite the high importance of these compounds in coffee cup quality, studies on their genomics have only begun recently. The first coffee cDNA sequence from the sugar metabolism was cloned by Zhu and Goldstein (1994). The corresponding gene encodes an a-galactosidase that degrades galactomannans – complex cell wall polysaccharides – during seed germination. More recently, studies focused on the sucrose metabolism pathway. Using available ESTs from the Brazilian Coffee Genome Project (http:// www.lge.ibi.unicamp.br/cafe/), Geromel et al. (2006) isolated two full-length cDNAs (CaSUS1 and CaSUS2) expressed during C. arabica fruit development. These cDNAs encode sucrose synthase isoforms and their contrasting expression patterns in perisperm, endosperm and pericarp tissues pointing to the central role of these enzymes in sugar metabolism during sucrose accumulation in the coffee cherry. Furthermore, Privat et al. (2008) identified the complete set of genes encoding enzymes involved in sucrose synthesis/degradation in coffee beans. Transcriptomic and enzymatic analysis also revealed the important role of vacuolar invertases in sucrose accumulation. Beverage quality also depends on the length of the fruiting cycle, which influences the amount of compounds that accumulate. C. arabica at least, as a climacteric plant, shows a sudden increase in ethylene production and respiration during fruit ripening (Pereira et al., 2005). Some trials were conducted to act on coffee fruit ripening by adding exogenous ethylene (Rao, 1978; Rao et al., 1978; Winston et al., 1992) but, until recently, no molecular studies
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were carried out on ethylene biosynthesis or on its perception in coffee plants. Bustamante-Porras et al. (2005) were the first to describe a gene sensitive to ethylene encoding a transcription factor-like protein. The presence of cDNA corresponding to this gene was observed in leaves and mature fruits. A more extensive survey was then conducted to identify and characterize ethylene receptors encoding genes in C. canephora. To date, three such genes have been characterized (Bustamante-Porras et al., 2007a, b) named CcETR1, CcEIN4 and CcETR2 based on their homology with the corresponding Arabidopsis gene (O’Malley et al., 2005). The deduced encoded proteins presented the expected features of ethylene receptors with many conserved domains and more variable regions. Young transgenic Arabidopsis seedlings overexpressing CcEIN4 cDNA showed a loss of gravitropic regulation. Genetic mapping of CcETR1 and CcEIN4 revealed that both genes are localized on two different linkage groups in wild Coffea species (Bustamante-Porras et al., 2007a). BAC– FISH experiments indicated that the genome region carrying CcEIN4 is only present as one copy in the C. canephora genome (Guyot et al., 2009). In addition, expression studies using conventional techniques such as Northern Blot or real-time quantitative PCR (qPCR) have also been performed on coffee (Barsalobres-Cavallari et al., 2009; Cruz et al., 2009). Generally, expression studies resulted in the identification of genes involved in (i) plant response to biotic and abiotic stresses such as infection by the rust fungus (H. vastatrix) (Andrade, 2008; Fernandez et al., 2004; Ganesh et al., 2006; Petitot et al., 2008), coffee leaf miner (Mondego et al., 2005) and drought (Marracini et al., 2008); (ii) fruit development and maturation (Bustamante-Porras et al., 2007a; Hinniger et al., 2006; Salmona et al., 2008; Simkin et al., 2008); and (iii) particular biosynthetic pathways such as sugar (Geromel et al., 2006, 2008a, b; Privat et al., 2008), caffeine (Koshiro et al., 2006), carotenoids (Simkin et al., 2008), storage proteins and galactomannans (Marraccini et al., 2001; Pre et al., 2008). Isolation of full-length cDNA sequences enables the heterologous expression of key proteins and the determination of their structure and function. To this end, some enzymes of the phenylpropanoid pathway (Lepelley et al., 2007; Mahesh et al., 2007) or involved in caffeine biosynthesis (McCarthy and McCarthy, 2007) have been characterized. A genome-wide survey of gene expression levels will enable a better understanding of how transcriptional networks are interconnected in order to program different biological processes. In recent years, different techniques such as microarrays and qPCR have been used for transcript profiling. However, the building of a large EST data set is a prerequisite for the development of microarrays (Alba et al., 2004; Mitreva and Mardis, 2009). Initiatives for the generation of such arrays have already begun and some
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results have started to appear (De Nardi et al., 2006; Privat et al., 2008). Microarrays based on available coffee EST sequences were recently developed in France by a Nestle´/IRD/CIRAD scientific consortium granted by Ge´noplante. This project, named ‘PUCE CAFE’, is in part dedicated to large-scale transcriptomic analysis during grain development of C. canephora grown in different countries (Ecuador, French Guyana, Reunion Island). At the end of the project, the generated arrays should be available to the international scientific community working on coffee. To reconstruct the metabolic pathways involved in the biosynthesis of the main coffee seed storage compounds, Joet et al. (2009) conducted integrated transcriptome and metabolite analyses. The work was performed by combining real-time (RT)-PCR on 137 selected genes (of which 79 had not previously been characterized in Coffea) and metabolite profiling. Next-generation sequencing technologies might also be an interesting approach to perform transcriptome profiling of coffee. In the same way as serial analysis of gene expression (SAGE), shotgun libraries derived from mRNA or small RNAs are deeply sequenced using these novel sequencing technologies, and the counts (tags) corresponding to individual genes can be used for quantification (Mardis, 2008; Shendure and Ji, 2008). However, meaningful transcript profiling using these new sequencing technologies would depend on the availability of a reference sequence for the coffee genome. Although the generation of a high-quality reference sequence of the coffee genome would have been prohibitively costly only a few years ago, this can now be achieved rapidly and cheaply using hybrid assemblies of different sequencing technologies (Darby and Hall, 2008). Initial efforts by the coffee scientific community are underway to this end. E. BIOINFORMATICS: COFFEE GENOMIC RESOURCES AVAILABLE ON THE WORLD WIDE WEB
The volume of information related to molecular biology has grown exponentially over the years due to rapid developments in genomic and molecular research technologies. Bioinformatics plays a key role both in deciphering genomic, transcriptomic and proteomic data generated by high-throughput sequencing technologies and in managing information. The reduction in computing costs, access to Internet, database technologies and computational methods are constantly being improved which facilitate access to and the integration and retrieval of an increasing number of data. In the past few years, genomic information has been rapidly accumulated on Rubiaceae species and especially on those belonging to the Coffea genus. A number of bioinformatics resources have been developed for coffee. However,
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most bioinformatics resources are not accessible to the general public, as they are proprietary and confined to the respective projects that fund them. Nevertheless, there are some notable exceptions of data sets that have been put into the public domain. The most important coffee resources on the Web are summarized in Table III. The University of Trieste (Italy) has developed a coffee DNA database (http://coffeedna.net), a user-friendly site that contains genomic information on coffee, with particular focus on Arabica, including ESTs, marker information, coffee germplasm and transposable elements. IRD (France) has developed MoccaDB (http://moccadb.mpl.ird.fr/), an interactive online database that manages annotated and/or mapped microsatellite markers in Rubiaceae (Plechakova et al., 2009). In its current release, the database stores 638 markers, which were defined from 259 ESTs and 379 genomic sequences (Poncet et al., 2006, 2007). Marker information was retrieved from 11 published works and completed with original data on 132 microsatellite markers validated in the respective laboratories. DNA sequences were derived from three Coffea species/hybrids. Microsatellite markers were checked for redundancy, in vitro tested for crossamplification and diversity/polymorphism status in up to 38 Rubiaceae species belonging to the Cinchonoideae and Rubioideae subfamilies. Functional annotation was provided along with a number of associated markers to describe metabolic pathways. Users can search the database for markers, sequences, maps, or information on diversity through multioption query forms. The retrieved data can be browsed and downloaded, along with the protocols used, using a standard web browser. MoccaDB also includes bioinformatics tools (CMap viewer and local BLAST) and hyperlinks to related external data sources (NCBI GenBank and PubMed, SOL Genomics Network (SGN) database). SGN (http://sgn.cornell.edu/) is mainly devoted to Solanaceae genomics and includes other closely related Euasterids species such as coffee and snapdragon (Mueller et al., 2005). It currently provides the most complete public unigene builds of coffee ESTs from over 55,000 Nestle´ ESTs and almost 9000 ESTs from IRD (both from C. canephora). The unigene set is thoroughly annotated using BLAST against Arabidopsis and GenBank datasets, InterproScan protein domains and Gene Ontology terms. SGN also maintains a database of all loci that have been experimentally characterized among Euasterids. The database stores locus symbols and aliases, free text descriptions, relation to mutants (with mutant images), references to the literature, Gene and Plant Ontology annotations and other information (Menda et al., 2008). Currently, there are almost 6000 loci for all Euasterids in the database but only 112 are coffee loci. Researchers from the community can request editor privileges for a locus that
TABLE III Coffee Resources Available on the Web Name The Cenicafe coffee databases CENICAFE – Colombia TropGENE DB CIRAD – France
Ccmb coffee database CCMB – India
The Brazilian Coffee Genome EST project Brazilian Coffee Research and Development Consortium (CBP&DCafe´) – Brazil
Description An EST database containing 32,000 coffee (C. arabica, C. liberica) ESTs. It also includes 6000 Beauvaria bassiana ESTs and 4000 Hypothenemus hampei (coffee berry borer) ESTs. Login required for access to data. http://bioinformatics.cenicafe.org/ A crop information system created to store genetic and genomic information about tropical crops. A module for coffee has been implemented that manage data about C. canephora BAC library and SSR markers (55,296 and 253, respectively). http://tropgenedb.cirad.fr/en/coffee.html A database for the molecular characterization of the available coffee genepool in India and generating basic materials (molecular markers/mapping populations) as a prelude to molecular (DNA) marker-based coffee breeding program. Still under construction. http://www.ccmb.res.in/coffeegermplasm/index.htm The database and the interface have been developed to support the Brazilian Coffee Genome EST project. It manages 130,792, 12,381 and 10,566 sequences for C. arabica, C. canephora and C. racemosa, respectively, (37 cDNA libraries) assembled into 33,000 unigenes. Login required for access to data. http://www.lge.ibi.unicamp.br/cafe/
Data type
Reference
EST
Cristancho et al. (2006)
BAC ends, SSR
Leroy et al. (2005); Ruiz et al. (2004)
Molecular markers, diversity, genetic maps
EST, SSR, SNP, transposable elements
Vieira et al. (2006)
(continues )
TABLE III Name CoffeeDNA University of Trieste – Italy MoccaDB IRD – France
The SOL Genomics Network (SGN) Cornell University – USA
(continued )
Description
Data type
A database for coffee genomics (13,686 ESTs, 266 Microsatellites, 43 retrotransposon, taxons). Login required for access to private information and certain functions. http://www.coffeedna.net/ A comprehensive web resource for researchers working on Coffea genus, the Rubiaceae family or related species. It manages information about EST-SSR and SSR markers (to date, 638 markers). Markers were checked for redundancy, in vitro tested for crossamplification and diversity over up to 38 Rubiaceae species. MoccaDB includes Cmap and BLAST tools and links to other related databases (e.g., SGN, NCBI). http://moccadb.mpl.ird.fr/ The SOL Genomics Network is a genomics information resource for the Solanaceae family and related family in the Euasterid clade with the aim of building a comparative bioinformatics platform. SGN currently houses map and marker data for Solanaceae species, a large expressed sequence tag collection with computationally derived unigene sets, an extensive database of phenotypic information for a mutagenized tomato population, and associated tools such as realtime quantitative trait loci. About 47,000 coffee (C. canephora var robusta) EST sequences released by Cornell University and Nestle´ S.A. are freely accessible. http://sgn.cornell.edu/
EST, SSR, retrotransposons
Reference
SSR, DNA sequences, genetic map, diversity data
Plechakova et al. (2009)
EST, COS, map, phenotypes
Mueller et al. (2005)
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interests them and log in to edit and expand the annotations with simple web-based tools. While there are almost 100 annotators for more than 250 tomato loci, coffee researchers still need to come forward to claim their loci of interest. CoffeaCyc (http://solcyc.sgn.cornell.edu/) is a pathway database for Coffea, maintained at SGN as part of the SolCyc system and based on the SGN unigene assemblies and annotation. The database is currently based solely on C. canephora and comprises 200 pathways and 781 distinct compounds. Although little manual curation has been done on the database, some important coffee-specific pathways, such as caffeine biosynthesis, have been curated manually. The system is based on the Pathway Tools software (Paley and Karp, 2006), and most popular Pathway Tools features are available, such as the overview diagram, the Omics Viewer – which can be used to overlay expression and other data on the diagram – and the advanced search and browsing functions.
IV. TOWARD THE WHOLE GENOME SEQUENCING OF COFFEE The coffee plant is one of the major commodities in many tropical countries but for several reasons its genetics and genomics have not been on the cutting edge. One reason was its perennial status and the need to wait at least 4 years from seed to seed. Another was the extreme commercial value of Arabica and its low diversity. Native to Ethiopia, this species underwent two successive genetic bottlenecks: one its genetic origin (amphiploidy) and one created by human agriculture, i.e., the limited number of plants planted in early plantations. The final consequence was a very low level of polymorphism available for breeding. In coffee, the development of molecular markers (the first step in coffee genomics) was essential to (i) assess genetic diversity within the two main cultivated species C. canephora and C. arabica; (ii) analyze the diversity of wild-related species and detail phylogenetic relationships within the genus; (iii) detect introgressions; (iv) identify QTLs; and (vi) characterize major genes of interest. The main result was the significant difference between the two cultivated species with respect to their genetic diversity. C. canephora, diploid, with a wide geographic East to West distribution (from Uganda to Guinea and southward to Angola) has a high level of genetic diversity. The narrow genetic base of the cultivated C. arabica, amphidiploid, although higher among wild genotypes, appears clearly with all type of markers. This is due to the bottleneck constituted by its genetic origin, an interspecific hybridization involving unreduced gametes or followed by a chromosome
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doubling (Lashermes et al., 1999). Molecular markers and cytogenetic data have confirmed the early hypothesis of a hybridization involving two species related to the current C. canephora and C. eugenioides. Molecular markers were also used to construct genetic maps and identify QTLs. A sequence-tagged genetic map is essential for genome assembly and for tagging target genes of interest. A preliminary linkage map of C. Arabica has been constructed using AFLP markers (Pearl et al., 2004). Linkage mapping in coffee requires more efforts and is more costly than in annual crops due to the longer generation time, a low polymorphism rate, particularly in Arabica coffee and the absence of a large collection of DNA markers and genomic sequences. A high C. canephora density map will be available soon and will be helpful for integration of genetic and physical maps and assemble the genome sequence. When a draft sequence of the C. canephora genome is available, SSR markers from this diploid genome will be used to map the Arabica genome. A high-density map of C. eugenioides is also needed to assemble the Arabica genome. The species need to be compared to obtain good transferability of markers from one species to another. Perfect transferability across the Coffea genus, whatever the type of PCRbased molecular markers used, has already been demonstrated (Poncet et al., 2007). It is possible to extend this transferability to the Rubiaceae family. In fact, the genomic data available in public databases are mostly derived from the Coffea genus. This easy transferability of Coffea markers to other Rubiaceae genera makes the Coffea genus a model genus for the whole Rubiaceae family. Recently, using COS markers, a comparison was made with related families like Solanaceae and, to a certain extent, it should also be possible to extrapolate to more distant species. Evaluation of genome evolution/conservation requires the transfer of information from model species used as references to ‘orphan’ genomes lacking available resources, which could facilitate the identification of genes of interest through map-based cloning strategies. For years, it was assumed that the synteny decreased progressively, then disappeared along with species divergence. In coffee, it was shown that within genomes, in particular in areas of importance such as the CcEIN4 gene region, the genomic organization could remain more conserved than expected over longer evolutionary periods. An unexpected microcolinearity was found, for the region containing this ethylene receptor gene, between Coffea and Vitis, two genera not considered to display strong genetic similarities (Guyot et al., 2009). Similarly, with the rapid advance of genomic and transcriptomic projects, large amounts of sequence information are now available. Plant genomists have been experimenting alternative approaches to identify genes underlying all types of traits and biological processes.
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Over the last decade, the rate of generation of genome sequence data has far outstripped our ability to ascertain gene function. At the gene level, several genes have been isolated and characterized and in a more global way, several studies have focused on quality-related genes and their involvement in storage compound biosynthesis and accumulation during endosperm development. The aid provided by next-generation sequencing technologies will certainly advance functional analysis beyond model systems and permit a massive acceleration of our ability to assign biological roles to genes. Thus, further characterization of gene networks in coffee will certainly help to identify new targets for manipulation of physiological, biochemical and developmental processes in this very important crop species. The Coffea genome community now has all the competences and capacities it needs to tackle metabolic pathways. These resources have enabled basic knowledge to be acquired about Coffea genomes that is essential for the ongoing C. canephora large-scale genomic projects and comparative genomics. Comparative genomic studies are essential to investigate the conservation of gene order between closely and distantly related plant species. Large-insert genomic libraries are primary genomic resources for positional cloning, physical mapping, integration of genetic and physical maps and sequencing of the genomes. The most efficient physical mapping approach is direct fingerprinting of BAC clones followed by anchoring mapped ESTs or other types of DNA markers to confirm the contig maps and integrate genetic and physical maps. The creation of a high-coverage C. canephora BAC library from a DH genotype, and its forthcoming BAC end sequencing will (i) serve as support for resources for the whole C. canephora genome sequencing project and (ii) produce a valuable dataset for comparative genomics within the Rubiaceae family, since few noncoffee data are available today. Nevertheless, in the absence of a complete reference sequence, ESTs remain a key resource to understand the function and evolution of the Coffea genomes and to develop plant-breeding approaches. Nonetheless, efforts are still required to develop further resources and tools in Coffea including (i) the generation of large sets of ESTs and fulllength cDNA in cultivated and wild Coffea species; (ii) the development of genomic BAC libraries in C. eugenioides and closely related wild Coffea species; and (iii) the WGS of C. canephora as a model to understand genome structure and evolution in the Coffea genus and the Rubiaceae family. Unfortunately, despite extensive efforts to generate large sets of ESTs in the past few years, only a limited number of sequences are publicly available so far.
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Although the genomic data available on coffee plants are rapidly increasing, they are often isolated, and very few sequence resources are freely accessible. The SGN is an example of a database that is rapidly developing into a comprehensive resource for comparative biology between members of the Solanaceae family. This resource includes a great number of data of many different types. Due to the relative genetic proximity of the Solanaceae and Rubiaceae families (Euasterids), as reported earlier, data from SOL can be easily transferred to coffee trees. With the increasing use of new generation sequencing technologies, the availability of large quantities of biological information from multiple web resources will continue to explode. Furthermore, the nature of the data is becoming increasingly diverse. Although all these resources are highly informative individually, efficiently integrating and comparing data from a range of heterogeneous sources has become crucial to accelerate genomic research. The management and integration of these resources will require increasingly sophisticated electronic mechanisms. One solution to facilitate the cross-referencing of data sources is the use of controlled structured vocabularies (e.g., Gene Ontology, Plant Ontology) and standardized data formats. With this new generation of sequencing technologies, bioinformatics in general, but more specifically in Coffee with the coffee genome sequencing project, is facing new challenges to better manage, process and analyze these large quantities of biological information. The genomes of C. canephora and C. arabica are the targets of the coffee genome sequencing project. Given that C. canephora and C. eugenioides are progenitors of C. arabica, an ideal approach could have been to sequence these two diploid genomes first. However, even with the reduced cost of the next generation of sequencing technologies, sequencing and annotating a plant genome is not a trivial project and is still costly. Ultimately, the genome of C. eugenioides will be sequenced for a better assembly and annotation of the C. arabica genome. This project will enable a better understanding of the dynamics of genome evolution after the hybridization event between the two progenitors. The entire BAC libraries can be fingerprinted using an automated high-throughput fingerprinting technique. Sequencing the ends of BAC clones is an important step for any genome sequencing project. The paired BAC end sequences are critical for building scaffolds of the whole genome shotgun sequences of coffee genomes. Physical mapping information (contig and chromosomal location) of each BAC will be combined with BAC end sequence data to construct a comprehensive assembly of the coffee genomes. Sequencing a large set of ESTs from various organs and tissues at different developmental stages and stress conditions is still the most cost-effective way to validate expressed genes before the first release of the C canephora genome.
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The currently available Roche 454 Titanium sequencing technology makes it feasible to sequence the diploid C. canephora genome using the whole genome shotgun approach. With financial support from funding agencies, sequencing the tetraploid C. arabica genome is also achievable. The emerging SMRT sequencing technology from Pacific Biosciences will ensure the sequencing of all three target coffee genomes. The BAC-by-BAC genome sequencing approach is not suitable for sequencing the mediumsized genomes of Robusta (~700 Mb) and Arabica (~1.2 Gb), because of the high cost and long period of time required to complete the genomes. Specifically, genomic DNA can be isolated from young leaf nuclei of the selected Robusta coffee genotype, thereby reducing contamination of organellar DNA. To provide a near-saturated genome coverage and to reduce the cost, it is assumed that generating a 20 genome coverage of regular Roche 454 Titanium runs with 400 bp reads and a 10X genome coverage of paired end 454 runs with 200 bp reads is sufficient for a reasonable genome assembly and annotation. The whole genome shotgun sequence can be assembled using the recently developed public domain software packages (ARACHINE, MIT; PHUSION, Sanger Center, JAZZ, JGI and GS de novo Assembler, Roche 454). Annotation of the whole genome shotgun sequences focuses on the identification of genes, but also includes searches for uncharacterized transposable elements. Coffee unigenes from cDNA will be aligned with the unmasked genome assembly, which can be used in training ab initio gene prediction software. Finally, only 10 years after the first genome sequence of Arabidopsis, the Coffee community is ready for a new challenge: entering true Coffee genomics.
ACKNOWLEDGEMENTS The authors express their thanks to Drs. Maud Lepelley, James McCarthy and Isabelle Privat for their critical reading of the manuscript. A. C. Andrade acknowledges FAPEMIG, CNPq and FINEP for financial support.
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Regulatory Components of Shade Avoidance Syndrome
` JAIME F. MARTI´NEZ-GARCI´A,*,†,1 ANAHIT GALSTYAN,* MERCE * * ´ ¸ AL SALLA-MARTRET, NICOLAS CIFUENTES-ESQUIVEL, MARC GALLEMI´* AND JORDI BOU-TORRENT* *
Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTAUAB, c. Jordi Girona, 18-26, 08034-Barcelona, Spain † Institucio´ Catalana de Recerca i Estudis Avanc¸ats, Passeig Lluı´s Companys 23, 08010-Barcelona, Spain
I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. The Shade Avoidance Syndrome: A Set of Responses to Anticipate to Plant Canopy Shade. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. The Low Red to Far Red Ratio Light: A Reliable Signal of Plant Proximity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Photoreceptors Involved in the Regulation of SAS: The Phytochromes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. How and When: SAS Responses in Different Organs and Developmental Stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Molecular Mechanisms in SAS Signalling . . . . . . . . . . . . . . . . . . . . . A. Molecular Components Regulating SAS Responses . . . . . . . . . . B. Integration of SAS Regulatory Components into Transcriptional Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Cross Talk of SAS Signalling With Other Regulatory Pathways . . . . A. Circadian Clock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Low and High Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Hormones. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Advances in Botanical Research, Vol. 53 Copyright 2010, Elsevier Ltd. All rights reserved.
0065-2296/09 $35.00 DOI: 10.1016/S0065-2296(10)53003-9
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D. Integration of Hormone- and Shade-Regulated Transcriptional Networks in the SAS Control . . . . . . . . . . . . . . . . . . . . . . . . . . E. Plant Defence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII. Spatial and Temporal Aspects of the SAS Signalling. . . . . . . . . . . . . IX. Concluding Remarks: From Master Genes to Regulatory Modules of the SAS Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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ABSTRACT Competition for light has an important impact on plant development. Plants sense the presence of nearby competitor vegetation as a change in the light quality, i.e. a reduced red to far-red ratio. The responses to shade are generally referred to as the shade avoidance syndrome (SAS), and involve various developmental changes aimed to outgrow the neighbouring plants, and are characterized by enhanced elongation, reduced leaf expansion, decreased branching and ultimately early flowering. These responses can be detrimental in agriculture, because they induce reallocation of resources into elongation growth at the expense of harvestable organs, hence lowering the crop yield. Genetic analyses performed on the SAS response of seedlings have shown the involvement of several transcription factors in the regulation of this response. At least in a few cases, it has been shown that phytochrome rapidly regulates the expression levels of several modulators of hormone responsiveness, rapidly linking shade perception, massive changes in gene expression and modification of hormone sensitivity of the responsive tissues. Here we develop our view on how shade-modulated changes in the transcriptional profiles result in complex SAS responses.
I. INTRODUCTION In both natural and agricultural plant communities, resources are frequently limited, and competition between individuals often results in plastic developmental responses for adaptation to the specific resource shortage. Light is probably amongst the most important resources for plant growth and limitations in its supply compromise survival and growth. As a consequence, evolution has shaped plant mechanisms and strategies to maximize light acquisition and modify the patterns of development to reconcile their sessile nature with the variability in the environmental light supply. In natural conditions, one of the situations in which light might become limited is under high density, such as those found in forests and prairies, where a mixture of different species growing in dense conditions might eventually result in shading and, therefore, in a shortage of solar energy for photosynthesis.
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In a situation of light shortage, plants have evolved to either tolerate or avoid shading caused by nearby competitors. Shade tolerance is a concept that refers to the capacity of a given plant to tolerate low light levels. From a physiological point of view, shade tolerance of a given plant is defined as the minimum light quantity (see Section III) under which a plant can survive (Valladares and Niinemets, 2008). But from a biological point of view, to define a species as shade tolerant, the whole life cycle of the plant from early survival and growth to reproduction must be considered. Thus, although many plants can tolerate lowintensity light conditions, only a fraction of them can reproduce under these conditions. These include the elephant ear (Alocasia macrorrhiza) (Kirschbaum et al., 1988; Noguchi et al., 1996), holly (Ilex aquifolium L.) (Valladares et al., 2005), impatiens (Impatiens balsamina), and several coleus (Solenostemon scutellarioidies) and Fuchsia cultivars (http://www.extension.umn.edu/ distribution/horticulture/DG8464.html). Shade tolerance is a complex property of plants that is achieved by different sets of responses in different species, such as alterations in leaf physiology and biochemistry, leaf anatomy and morphology and/or plant architecture. In general, under low light, shade tolerants tend to adapt to a highly conservative utilization of resources, commonly accompanied by very low growth rates and by structural and biochemical changes intended to enhance the efficiency of photosynthetic energy transduction and to reduce respiration losses (Smith, 1982; Valladares and Niinemets, 2008). Morphologically, growth of shade-tolerant species under low light conditions typically results in thinner leaves, reduced apical dominance, high branching frequency and low elongation response. In addition, plants accumulate higher chlorophyll content per leaf area or leaf dry mass (Valladares and Niinemets, 2008). By contrast, shade-avoiding species growing under ‘shade’(see Section V) generally tend to adapt their development to favour internode extension at the expense of leaf development, and to increase apical dominance (which reduces branching frequency), allowing the young leaves to escape from shade.
II. THE SHADE AVOIDANCE SYNDROME: A SET OF RESPONSES TO ANTICIPATE TO PLANT CANOPY SHADE Plants detect different characteristics of the complex light signal, such as the quantity (intensity or amount of photons), quality (colour or wavelength of the photons), periodicity (relative duration of the light period in a day which changes throughout the year in many regions of the earth) and direction. Light quantity seems to be a major signal for shade tolerance (Smith, 1982). In shade avoidance, by contrast, both light quantity and light quality are
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important. The combination of these two aspects of the incoming light, in varying proportions depending on the location of the plant, determines the spectrum received. However, the modification of only one of these two aspects of the light environment can induce responses to avoid shade. For example, a plant that is not initially shaded can detect the proximity of other individuals by a specific alteration in light quality. The plant then initiates a set of responses, known as the shade avoidance syndrome (SAS), intended to overgrow neighbouring competitors and/or to adapt to the eventual shading. If despite these adaptive responses the neighbour individuals overgrow and shade the plant, light quantity becomes limiting, that is, there is a reduction in the amount of radiation active for the photosynthesis, resulting in additional SAS responses. This distinction is important because responses to neighbouring vegetation take place before a plant is actually covered by a plant canopy. In addition, the two components altered by the presence of plant canopies (i.e. light quantity and light quality), are controlled by different photoreceptors, phytochromes and cryptochromes (see Sections II and IV), and consequently the molecular mechanisms involved might be different (Franklin, 2008; Pierik et al., 2009). The recognition of these two major components is therefore important and hence some caution should be used when making comparisons between the results obtained with different experimental light treatments. In this review, we refer to SAS as the plant responses to the proximity of neighbouring vegetation, i.e. those taking place exclusively by changes in light quality. In that way, perception of an environmental signal indicative of plant proximity induces SAS responses that allow the plant to anticipate the shading, avoiding it by overgrowing neighbouring plants or by flowering to ensure the production of viable seeds for the next generation. Indeed, responding plants grow away from neighbours well before those neighbours diminish their actual interception of light. As we will see in the following section, this anticipation is accomplished by responding to a cue that is an excellent indicator not only of plant proximity (neighbour presence) but also of the shade provided by the vegetation canopy (Smith, 1982). The anticipation of future environmental conditions seems to be a quite extended behaviour in the plant kingdom. Such behaviours do not involve true cognition but define rapid morphological or physiological responses to events, relative to the lifetime of an individual (Karban, 2008). Many deciduous plants living in temperate areas drop leaves in autumn in response to shortening photoperiod as if they were anticipating to cold conditions (normally associated with winter) that have a high probability of damaging leaves and branches and are sub-optimal for photosynthesis (Franklin and Whitelam, 2007). However, in warmer latitudes such as those found in the
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semiarid conditions of the Mediterranean climate, a shortening photoperiod might be indicative of the proximity of the rainy season, when water and temperature are appropriate for plant growth. Therefore, although the so-called long day plants, such as Arabidopsis thaliana or some varieties of tobacco (Nicotiana tabacum), flower in response to lengthening photoperiod associated with the spring, short day plants, such as rice (Oryza sativa), Japanese morning glory (Pharbitis nil) or Kalanchoe, flower in response to shortening photoperiods associated with autumn (Kobayashi and Weigel, 2007). In addition, the responses evoked by the photoperiodic signal may change with the species. For example, tuberization in all potato species (Solanum tuberosum) is affected by photoperiod, but short day conditions are a strict requirement for tuber formation in only some of them (Jackson, 1999; Martinez-Garcia et al., 2001). Similarly, in shade-avoiding plants, the response to plant proximity might vary with the species and the stage of development (see Section V). The mechanisms of the anticipatory responses to plant proximity, i.e. the SAS, will be covered in this review, focusing in the signal itself, the phytochrome photoreceptors involved in the perception of this signal and, finally, the molecular mechanisms behind these complex responses. Specifically, we will pay attention to the components with a role in regulating SAS responses mainly based on the analyses of the response of Arabidopsis hypocotyls and their participation in the complex transcriptional networks that mediate this process.
III. THE LOW RED TO FAR RED RATIO LIGHT: A RELIABLE SIGNAL OF PLANT PROXIMITY The radiation coming from the sun is called daylight (Smith, 1982). In open conditions, i.e. when a plant grows under low plant density and there is no or little vegetation in the vicinity, the daylight spectrum is relatively constant (Fig. 1) (Franklin, 2008; Smith, 1982; Vandenbussche et al., 2005). The range of light we will discuss in here includes the spectrum visible for the human eye (from ~400 nm, blue light, to 700 nm, red light, R) and the far-red (FR) region (from 700 up to 800 nm). Light from the visible region between 400 and 700 nm is also referred as the photosynthetically active radiation (PAR). Because clouds seem to act as nonselective diffusing filters, weather conditions do not substantially alter spectral distribution but do have a very large effect on the fluence rate of daylight (Smith, 1982), i.e. in the total amount of radiant power per area (http://goldbook.iupac.org). By contrast, the spectral distribution of daylight is strongly affected by vegetation. The photosynthetic pigments chlorophylls and carotenoids absorb light over most of the
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Daylight Reflected Filtered 1200
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Fig. 1. Spectral distribution of the photon fluence rate of daylight (R:FR = 1.30), daylight reflected by an Aspidistra leaf (R:FR = 0.71) and daylight filtered through a Aspidistra leaf (R:FR = 0.07). White background indicates the PAR (400–700 nm) and grey background refers to the FR region (700–800 nm) of the spectrum. Spectra were recorded at around 5 pm during August in Barcelona, Spain.
PAR part of the spectrum, although some green is reflected or transmitted through (i.e. passes through) the plant tissues. By contrast, radiation in the FR region (700–800 nm) is poorly absorbed and consequently this light is transmitted through or reflected from vegetation (Fig. 1). As a consequence, to the human eye, which only perceives visible light, plants are green, whereas to a broader sensor (300–800 nm) plants are slightly green and clearly FR-coloured. Indeed, FR sensing is use by satellites as a way to identify vegetation on the earth’s surface (http://earthobservatory.nasa.gov/ Features/MeasuringVegetation or http://mvh.sr.unh.edu/mvhinvestigations/ veg_analysis.htm). Similarly, plants use FR (combined with R) perception for sensing the proximity of surrounding vegetation (see below). For the purposes of analysing the SAS responses, the parameter commonly used to describe the light quality of natural environments is the ratio of photon fluence rate in the R region of the spectrum to that in the FR region (termed R:FR). Photon fluence rate refers to the moles of incident photons per unit of area and time (http://goldbook.iupac.org). This ratio relates directly to the known absorption maxima of the two forms of the photoreceptor phytochromes (see Section IV). The R:FR is precisely defined as the quotient between the photon fluence rate between 655 and 665 nm and the photon fluence rate between 725 and 735 nm (Smith, 1982). As described below, changes in this ratio are a good estimate of proximity perception before the plant is actually
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shaded. The R:FR also undergoes daily reductions at dawn and dusk, periods referred to as twilight (Hughes et al., 1984). The drop in R:FR during twilight seems particularly important at high latitudes (>60˚), where twilight duration is increased during the shortest days around the winter solstice (Linkosalo and Lechowicz, 2006). Nonetheless, analyses of tree bud development in spring indicates that the major cue controlling this response is photoperiodic, whereas twilight reductions in R:FR provide a limited seasonal information (Linkosalo and Lechowicz, 2006). The R:FR of daylight is usually higher than 1 and, as mentioned before, it shows little variation with cloudy conditions or seasons in most latitudes (Franklin, 2008). In the measurements shown in Fig. 1, the R:FR was 1.3. Light transmitted through a plant leaf (plant canopy shade) is affected in both its quantity and its quality by a selective depletion in the light spectrum corresponding to the PAR region (400–700 nm), strongly absorbed by the leaves. By contrast, FR light is not absorbed as much. As a consequence, R:FR under the canopy is greatly reduced mostly by the selected impoverishment in the R component caused by the filtering of light through the leaves. In the measurements shown in Fig. 1, the spectral distribution of the photon fluence rate of daylight filtered through a leaf of the plant Aspidistra reduced the radiation in the 400–700 nm region and it resulted in a R:FR of 0.07. Plant leaves also selectively reflect the FR component towards neighbouring plants without affecting other components of the daylight spectrum, with the exception of green light that is partially reflected, as mentioned above. Consequently, plant proximity affects FR amount without significantly altering the quantity of the PAR spectrum, resulting in a greatly reduced R:FR (in this case by the enrichment of daylight in the FR component reflected by the neighbouring plants). In the example shown in Fig. 1, reflecte light showed a R:FR of 0.71. As indicated in Section II, in this review we refer to the SAS as those responses to plant proximity, which should be clearly distinguished from those in response to reductions in the photon fluence rate in the PAR region (reduction in light quantity) or to the plant canopy shade conditions (that imply reductions in both light quantity and R:FR).
IV. PHOTORECEPTORS INVOLVED IN THE REGULATION OF SAS: THE PHYTOCHROMES In a signalling pathway, the stimulus is perceived by a receptor and transduced by various intermediate molecules to achieve the final responses. In the case of the SAS, the receptors are the R and FR light-absorbing phytochromes, which initiate the signalling cascade when they perceive a reduced R:FR signal.
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The phytochrome photoreceptors exist in two photoconvertible forms, the Pr and Pfr. Phytochromes are synthesized in the cytoplasm in the inactive Rabsorbing Pr form (max of absorbance at 666 nm). In the dark, inactive phytochromes (Pr) remain in the cytoplasm. Upon light perception, the Pr form is rapidly converted into the biologically active FR-absorbing Pfr form (max of absorbance at 730 nm) that exposes a nuclear localization signal and translocates into the nucleus (Nagatani, 2004). FR irradiation can subsequently reconvert Pfr into the Pr form. In addition, Pfr is slowly converted back to Pr in darkness, which is called the dark reversion. The reversible photoconversion between Pr and Pfr upon R or FR absorption allows phytochromes to act as molecular switches. Because the absorption spectra of the Pr and Pfr forms overlap, the relative amounts of R and FR light in the radiation received by the plant are translated into different concentrations of Pr and Pfr forms of the phytochromes, allowing a dynamic equilibrium between these two forms under almost all natural light conditions (Bae and Choi, 2008; Chen et al., 2004; Franklin, 2008). In Arabidopsis, phytochromes are encoded by a small gene family of five members (PHYA to PHYE). The function of the individual phytochromes has been genetically established in the regulation of seedling de-etiolation. Mutant seedlings deficient in phyA do no de-etiolate under continuous FR (FRc) light, indicating that phyA is exclusively responsible for controlling seedling de-etiolation under this condition. Mutant seedlings deficient in phyB partially de-etiolate under continuous R (Rc) light, which indicates that phyB is the major phytochrome in controlling seedling de-etiolaton under Rc (Bae and Choi, 2008; Chen et al., 2004). The most abundant phytochrome in dark-grown seedlings is phyA. Illumination with R light (or polychromatic light of high R:FR) rapidly establishes a high proportion of Pfr, which in the case of phyA (PfrA) is degraded by the ubiquitin/26S proteasome (Seo et al., 2004). In addition to the photolability of the phyA photoactive protein, PHYA expression is rapidly repressed upon light exposure (Canton and Quail, 1999). Thus, phyB, the second most abundant phytochrome takes the lead in the control of seedling morphology in light-grown plants (Bae and Choi, 2008). High R:FR light keeps most of the phyB pool into the active PfrB form that represses hypocotyl elongation and other SAS responses. Following low R:FR light perception, a large pool of phyB is photoconverted to the inactive PrB form, which de-represses SAS responses, easily visualized by the increase of hypocotyl length in Arabidopsis and other plant species. Genetic analyses in Arabidopsis and a few other species have shown that mutants deficient in phyB display long hypocotyls and are early flowering. These phenotypes are very similar to the response of wild-type plants to low R:FR light (Table I). Consequently, phyB mutants have been referred to as displaying constitutive or
TABLE I Summary of Hypocotyl Responses to Simulated Shade or Canopy Shade Displayed by the Arabidopsis Mutants Mentioned in this Review
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saturated SAS responses (Lopez-Juez et al., 1992; Somers et al., 1991). This was interpreted as an indication that phyB is repressing SAS responses under high R:FR light conditions, and it led to conclude that responses to low R:FR are primarily mediated by phyB. Nonetheless, phyB mutant plants still display SAS responses, indicating the participation of additional phytochromes in its regulation (Robson et al., 1993). The expression of PHYA, encoding the phyA photoreceptor itself, showed a rapid up-regulation in response to 1 h of low R:FR light in wild-type and phyB mutant seedlings. However, PHYA expression showed no increase after 24 h of low R:FR treatment relative to that in seedlings grown under white light (W) (Devlin et al., 2003). Nonetheless, although under these conditions the pool of active PfrA is likely reduced, it seems to be abundant enough to repress SAS responses. As a consequence, phyA mutants have been shown to display enhanced responses to low R:FR (Table I), indicating that the role of phyA in moderating the SAS response persists in de-etiolated seedlings and suggests that phyA may antagonize SAS (Devlin et al., 2003; Johnson et al., 1994). The increased hypocotyl elongation observed in phyA mutant seedlings under sustained low R:FR conditions reveals a minor negative role of phyA in regulating the SAS. The repressor activity of phyA over SAS responses is also observed under high R:FR light conditions in transgenic plants overexpressing PHYA (Roig-Villanova et al., 2006). Therefore, although molecularly phyA and phyB appear to have a similar activity in repressing elongation growth and gene expression in response to low R:FR light, its difference in abundance due to the photolability of phyA, particularly under high R:FR light, has led to propose that phyA and phyB have an antagonistic activity in regulating SAS responses. Genetic analyses also showed that phyD and phyE act redundantly with phyB in controlling the SAS. Although phyD or phyE mutants alone show a weak phenotype, the phyB phyD or phyB phyE double mutants show a more elongated phenotype than the single phyB mutant and show an even more attenuated response to a reduction in R:FR (Devlin et al., 1998, 1999). Together, phyB appears to be the major photoreceptor regulating the SAS, with minor roles for phyA, phyD and phyE.
V. HOW AND WHEN: SAS RESPONSES IN DIFFERENT ORGANS AND DEVELOPMENTAL STAGES To experimentally distinguish between effects of light quantity and light quality, each factor should be modified independently in the laboratory. Therefore, to study exclusively the effect of light quality, i.e. to compare
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the effect of high and low R:FR light on a specific response, both light conditions have to provide the same irradiation in the region of the spectrum that provides the PAR (400–700 nm). One accepted approach is to use light generated from fluorescent tubes that provides white light (W) with a high R:FR, and the same source of W supplemented with a lamp producing FR (W þ FR) to decrease R:FR. We will refer to this latter treatment as simulated shade, in contrast with canopy shade treatments that imply reductions in both light quantity and R:FR. Another common practice to study plant proximity is to give a FR pulse of a few minutes at the end of a light period, usually referred to as end-of-day FR (EOD-FR) treatment (Fankhauser and Casal, 2004). In plants grown in light–dark cycles, EOD-FR treatment mimics growth in low R:FR by rapidly decreasing Pfr amounts before the onset of darkness, thereby maintaining a low Pfr pool throughout the dark period. These treatments, although less representative of the natural neighbour signals, are an easy and effective way to induce SAS responses (Fankhauser and Casal, 2004). Because the responses of the plant to the low R:FR signal indicative of plant proximity aim to adapt growth and development to the foreseen new high plant density environments, they might be different depending on the species, the stage of development and/or the organ studied. At the seed stage, the response to low R:FR light involves the inhibition of germination. Germination might take place in response to several environmental conditions, such as a raise in temperature, an increased availability in water and/or light exposure. However, although light is still available under a closed canopy, germination would clearly be disadvantageous for seeds with small reserves; phytochrome-mediated SAS responses are evident at this stage with low R:FR inhibiting germination and imposing secondary dormancy (Smith and Whitelam, 1997). In addition, germination is also affected by the R:FR of the light received by the maternal plant during seed development and maturation, a process in which different phytochromes have been involved (Dechaine et al., 2009). The ecological importance of this maternal effect is likely related with the fact that plants are sessile organisms and seed dispersal is often limited, with most seeds falling relatively close to the maternal plant. Thus, a seedling’s growth environment may frequently be similar to its mother’s, especially where habitat patches are constant between generations and larger than the scale of seed dispersal. Under these conditions, light maternal effects may serve as a form of environmental cuing between generations that enhance offspring performance (Galloway and Etterson, 2007). In summary, perception of the low R:FR signal indicative of plant proximity at both the process of seed maturation and seed germination allow for optimum germination appropriate to environmental light conditions.
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Once the seed has matured and germinated, the presence of neighbouring plants can greatly affect the early stages of seedling development. Hypocotyl elongation is the first (in time) and strongest (in magnitude) response to simulated shade treatments (Fig. 2). In addition, cotyledon and primary leaf longitudinal expansion are also stimulated by simulated shade, but they respond slightly later than hypocotyls to the low R:FR signal. In the case of primary leaves, this is mostly because they do not start to expand until the seedling has reached a certain size and/or age (5- or 6-day-old seedlings in d0
(A)
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Fig. 2. Morphological phenotype of wild-type seedlings growing under W or W þ FR conditions. (A) Seedlings were germinated and grown for 2 days under W (high R:FR) and then either kept in W (white bars) or transferred to W þ FR (grey bars) for up to 6 days. Pictures of representative seedlings of different ages grown under W and W þ FR are shown. Panels are to the same scale. Similar pictures were used to measure hypocotyl (Hyp) length of seedlings of different ages. (B) Cotyledon (Cot) and primary leaf (PL) longitudinal expansion was measured after rolling seedlings flat on a transparent selfadhesive sheet. Pictures of representative seedlings of different ages grown in the indicated conditions are shown. Panels are to the same scale. (C) Length of Hyp, Cot and PL of seedlings of 4-, 6-, 7- and 8-day-old seedlings grown as indicated in (A). At least 15 measurements per treatment were used for each seedling age. Columns represent the mean and bars represent the standard error of the mean (SE) of the data. Asterisks indicate significant differences (p < 0.01) relative to the corresponding W-grown controls.
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our conditions). Petiole elongation is responsible for most of the increase in longitudinal expansion (length) of both cotyledons and primary leaves (Fig. 2B). This is a trait very responsive to low R:FR in leaves formed in all stages of plant development (Djakovic-Petrovic et al., 2007; Lorrain et al., 2008; Tao et al., 2008). Although there are some contrasting results, some authors have also reported that the increase in petiole length in response to simulated shade is usually associated with a reduction in the leaf blade area at later stages of Arabidopsis development (Franklin, 2008; Robson et al., 1993; Tao et al., 2008). Similar results were observed for EOD-FR treatments (Devlin et al., 1996; Johnson et al., 1994). In other species, plants that grow for extended periods under these light conditions also display long stems and an upward reorientation of leaves (leaf hyponasty) (Franklin, 2008). In a rosette plant such as Arabidopsis, however, internodes do not normally become apparent until the bolting of the floral (or cauline) stem, which occurs at the onset of flowering. In the presence of a plant proximity signal, enhancement of cauline stem elongation can be observed, but it is usually a relatively weak response (Botto and Smith, 2002). Leaf hyponasty in combination with stem elongation serves to elevate leaves within the canopy, a response that is likely to enhance light-foraging capacity in dense stands and enable plants to overtop competing vegetation (Ballare, 1999; Mullen et al., 2006; Pierik et al., 2003, 2004b). In the case of rosette plants, such as Arabidopsis, it can also be seen as a physical element of the plant to lean over surrounding plants and help stabilize its taller stature due to the SAS. In Arabidopsis, responses to low R:FR light also include metabolic changes, such as the reduction in leaf chlorophyll and carotenoid content in the seedling (Roig-Villanova et al., 2007). In addition, low R:FR light increases apical dominance, leading to reduced branching (Smith and Whitelam, 1997). In the long term, and once the plant is competent to flower, a persistent reduction in the R:FR of the incident light conditions accelerates flowering (Halliday et al., 1994), a response associated with reduced seed set, truncated fruit development and, as indicated before, a severe reduction in the germination rate of the seeds produced (Dechaine et al., 2009; Smith and Whitelam, 1997). Different plant species display varied SAS responses as a result of its adaptation to their particular environments. For instance, plant with internodes, such as Chenopodium, cowpea (Vigna sinensis), white mustard (Sinapis alba) or tobacco, display a very rapid (within minutes) increase in the elongation growth rate of stems after initiating the simulated shade treatment (Casal and Smith, 1988; Garcia-Martinez et al., 1987; Morgan et al., 1980; Smith, 1982). These alterations are accompanied by changes similar to
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those described for Arabidopsis, such as reduced chlorophyll content, hyponastic leaves, increase in apical dominance (leading to reduced branching in dicots and tillering in grasses) and acceleration of flowering (Casal et al., 1990; Kebrom and Brutnell, 2007; Smith and Whitelam, 1997). Growing under low R:FR light can also affect positively or negatively other plant responses: it has been shown to increase freezing tolerance in Arabidopsis (Franklin and Whitelam, 2007) and to down-regulate defence responses in tobacco (Izaguirre et al., 2006). Overall, the SAS responses involve a global change in the development and metabolism of the plant, directed towards the increase in elongation, flowering induction and a reduced seed production to enhance the probability of a viable next generation of plants in more advantageous light conditions. However, for crop species the SAS could lead to decreased yields if plants invest resources on vegetative growth at the expense of reproductive development. It is generally believed that SAS in crop plants has probably been refined to maximize yield under limited light environments (Kebrom and Brutnell, 2007; Smith, 1982). Fundamental understanding of SAS response pathways might help to further guide breeding programmes towards the creation of varieties that respond to new demands of modern cultivation, such as the use of low-input non-food feedstocks for biofuels or the development of varieties better adapted to a warmer world (Kebrom and Brutnell, 2007; Sawers et al., 2005).
VI. MOLECULAR MECHANISMS IN SAS SIGNALLING Variation in hypocotyl elongation and flowering time in response to low R: FR in over 100 Arabidopsis ecotypes showed wide variation in the extent of these responses between ecotypes, but little correlation between variation in flowering and elongation. For example, several ecotypes displayed greatly attenuated early flowering responses to the simulated shade signal, but exhibited pronounced hypocotyl elongation (Botto and Smith, 2002). This lack of correlation suggested the existence of different signalling pathways downstream from phytochrome perception of plant proximity that operate by independent mechanisms. This possibility is supported by the finding that the early flowering phenotype of phyB mutant plants is temperature dependent, whereas other low R:FR responses, such as petiole elongation, are not affected by changes in ambient temperature (Halliday and Fankhauser, 2003). Because most of what is known regarding the molecular mechanisms in SAS signalling has been established after the analysis of the Arabidopsis hypocotyl elongation in response to simulated shade, in this review we are covering specifically this aspect of the SAS responses.
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In dark-grown seedlings, phyA and phyB are cytosolic, inactive proteins (in the PrA and PrB forms) that migrate to the nucleus upon light activation (when they convert to PfrA and PfrB, respectively) (Quail, 2002). Both Pfr formation and nuclear translocation are necessary for phyB signalling activity (Huq et al., 2003). In the nucleus PHYTOCHROME INTERACTING FACTOR 3 (PIF3), a basic-helix–loop–helix (bHLH) protein, binds preferentially to the Pfr forms of phyA and phyB (Ni et al., 1998). PIF3 simultaneously binds to PfrB and a G-box motif located in the promoter region of several genes, hence having the potential to directly regulate gene expression in a light-dependent manner (Martinez-Garcia et al., 2000). In support of this possibility, PIF3 was shown to bind directly the promoters of light-regulated genes, such as CHS and other genes involved in anthocyanin biosynthesis. However, at least in the case of these promoters, DNA binding seemed to be independent of light conditions (Shin et al., 2007), suggesting the existence of additional mechanisms by which phytochromes regulate PIF3-mediated changes in gene expression in a light-regulated manner (Al-Sady et al., 2008). PIF3 was the founding member of a sub-group of closely related bHLH proteins that act more or less redundantly as regulators of seedling de-etiolation. This sub-group includes several bHLH members able to directly interact preferentially with PfrA and/or PfrB (e.g. PIF1, PIF4), and others that lack the ability to directly interact with the phytochromes, such as LONG HYPOCOTYL IN FAR RED 1 (HFR1) and PIF3-LIKE 1 (PIL1). The latter group has been shown or proposed to heterodimerize with PIFs, potentially modulating the bHLH network activity (Fairchild et al., 2000; Huq and Quail, 2002; Huq et al., 2004; Khanna et al., 2004; Quail, 2002). Further transcriptomic analyses led to the proposal that, during de-etiolation, light might implement the photomorphogenic programme by regulating a complex transcriptional cascade, probably initiated by direct phytochrome regulation of gene expression of a master set of transcriptional regulators via different PIFs (Jiao et al., 2007; Quail, 2002). Phytochrome perception of plant proximity (low R:FR light) initiates rapid and reversible changes in the expression of several dozens of genes (Carabelli et al., 2007; Devlin et al., 2003; Salter et al., 2003). Some studies have established a role of bHLH transcription factors of the PIF family in the SAS responses (Lorrain et al., 2008). Additionally, growth-suppressing proteins of the DELLA family, involved in gibberellin (GA) signal transduction, also appear to participate in the response to low R:FR light (DjakovicPetrovic et al., 2007). Plant proximity perception, like other light treatments, induces massive changes in the plant transcriptome (Jiao et al., 2007).
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The sub-group of early responsive genes to this treatment, which we refer to as PHYTOCHROME RAPIDLY REGULATED (PAR) genes (RoigVillanova et al., 2006), is enriched in members of different families of transcription factors (Devlin et al., 2003; Jiao et al., 2007; Tao et al., 2008). Among the PAR genes functionally analysed, several encode proteins of the HD-Zip class II sub-family of transcription factors, like ARABIDOPSIS THALIANA HOMEOBOX 2/HOMEOBOX FROM ARABIDOPSIS THALIANA 4 (ATHB2/HAT4, hereafter ATHB2), ATHB4, HAT1, HAT2 and HAT3. Another group of PAR genes with a demonstrated role in the regulation of SAS responses, such as HFR1, PIL1, PAR1 and PAR2, are members of the bHLH family of transcriptional regulators. From the dozens of PAR genes identified by transcriptomic analyses, only a few of them have been investigated as putative direct targets of phytochrome signalling during the SAS. Although it is unclear as to what extent the changes in the expression of PAR genes are instrumental for implementing the SAS, it is likely that some of the morphological and physiological photoresponses associated with the SAS are a consequence of the regulation of these genes in the context of a complex transcriptional network controlled by phytochromes. Consequently, we postulated that PAR genes whose expression is directly regulated by phytochrome action, including ATHB2, ATHB4, PIL1 and PAR1, are regulatory components of the SAS responses (Roig-Villanova et al., 2006). That is, that these PAR factors would be the entry points (inputs) for the shade signal perceived by the phytochromes to modulate the pre-existing transcriptional network involved in adapting the pattern of development to plant proximity.
1. The role of PIF and DELLA genes In light-grown seedlings, phyB is the most abundant phytochrome due to the photolability of phyA. Both the Pr and Pfr forms of the phytochromes are present in the nucleus of the plant cells in a R:FR-dependent balance: (i) under high R:FR light, the photoequilibrium is displaced towards the active Pfr forms, and the SAS is suppressed and (ii) under low R:FR light, the photoequilibrium is displaced towards the inactive Pr forms, and probably induce the SAS by affecting the interaction with the whole set of PIF proteins. However, the effect of R:FR light is more complex since the stability of several PIF proteins is also affected by light conditions. Studies on PIF1, PIF3, PIF4 and PIF5 proteins have shown that they accumulate in the dark. When the plant is exposed to R, FR or W light, the interaction of these PIFs with the active form of the phytochromes results in their rapid degradation by the ubiquitin/26S proteasome pathway (Leivar et al., 2008;
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Nozue et al., 2007; Shen et al., 2008). These studies indicate that these PIFs are photolabile proteins. In the case of PIF7, however, light exposure seems to have no effect on its stability, indicating this is a photostable PIF protein (Leivar et al., 2008). Subsequent studies have shown that PIF1, PIF3, PIF4 and PIF5 proteins re-accumulate in the dark during recurring light–dark cycles (Castillon et al., 2007; Nozue et al., 2007). Since low R:FR light also increases the stability of PIF4 and PIF5 (Lorrain et al., 2008), it seems likely that the accumulation of the several photolabile PIF proteins is also affected by the R:FR of the incoming light. Indeed, genetic studies have identified PIF4 and PIF5 as important components in phytochrome signalling to promote SAS responses. As indicated before, PIF4 and PIF5 accumulate to high levels in the dark, are selectively degraded in response to R light, and remain at high levels under simulated shade (low R:FR light) conditions (Lorrain et al., 2008). These authors described that degradation of these transcription factors is preceded by phosphorylation and requires the active phyB binding (APB) domain that mediates their interaction with light-activated phyB. These data suggest that PIF4 and PIF5 are degraded upon interaction with light-activated phyB via the ubiquitin/26S proteasome pathway. The reduced response of pif4 and pif5 mutants to simulated shade indicates a positive role for these two PIF factors in the regulation of the SAS (Table I). In agreement, overexpression of PIF4 or PIF5 results in plants showing a partial constitutive SAS (Table I), which suggests that these two factors promote elongation growth. Together, the SAS might be triggered, at least in part, as a consequence of reduced phytochromemediated degradation of PIF4 and PIF5 (and perhaps other PIFs). Consistent with this idea, the constitutive SAS phenotype of phyB mutants partially reverts in the absence of PIF4 and PIF5 (Lorrain et al., 2008). The interaction between the phytochrome photoreceptors and the PIF proteins is transient and strongly affected by R:FR light conditions, altering the balance between the levels of phytochromes and their growth-promoting partners. As a consequence, after plant proximity perception by the phytochromes, light might implement the corresponding photomorphogenic programme by regulating a pre-existing transcriptional cascade, probably modulated by direct phytochrome regulation of gene expression of a master set of transcriptional regulators via different PIFs. Since the abundance of some PIFs is also affected by low R:FR light, it seems plausible that the master set of transcriptional regulators involved is highly dynamic and can vary temporally (i.e. with the age of the plant), spatially (i.e. with the organ analysed), and with longer exposures to simulated shade. DELLA proteins refer to a group of growth-suppressing proteins that have been originally identified as GA signal transduction components. They
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receive their name because they all contain the amino acid residues DELLA (Achard and Genschik, 2009; Bolle, 2004). These proteins are part of the GRAS family (name derived from the three initially identified members, GAI, RGA and SCARECROW) that possess certain features reminiscent of transcriptional regulatory proteins (Bolle, 2004). In Arabidopsis, the DELLA family is encoded by five gene members: GAI (GA INSENSITIVE), RGA (REPRESSOR OF GA1), RGA-LIKE 1 (RGL1), RGL2 and RGL3. The DELLA proteins are localized in the nucleus where they suppress the expression of GA-responsive genes. In the presence of GA, however, DELLA proteins are targeted for breakdown (Achard and Genschik, 2009). This was recently shown to occur by the binding of the active GAs to its receptors (GID1a, GID1b and GID1c in Arabidopsis), which then interact with an SCF E3 ubiquitin–ligase complex to allow ubiquitination and subsequent DELLA degradation (Achard and Genschik, 2009). DELLA protein stability and GA-mediated DELLA breakdown are affected by several other signals, such as the hormones auxin and ethylene, light and both biotic and abiotic stress conditions (Achard and Genschik, 2009; Swain and Singh, 2005). This has led to the understanding that DELLA proteins may act as molecular integrators of various growth-regulating signalling routes. Genetic analyses using DELLA knockout and gain-of-function mutants and a GFP reporter for the DELLA protein RGA (GFP–RGA) implicated GA and DELLAs in the regulation of SAS responses (Djakovic-Petrovic et al., 2007) (Table I). Enhanced DELLA stability, as in the gai-1 mutant, leads to reduced SAS responses, indicating the importance of DELLA breakdown in allowing these growth responses to occur (Djakovic-Petrovic et al., 2007). However, because SAS responses can also be induced in DELLA knockout mutants, it was concluded that DELLAs cannot account for the full response to low R:FR light and, hence, there are other important SAS regulators that are DELLA independent (Djakovic-Petrovic et al., 2007). The existence of additional SAS regulators is consistent with the concept aforementioned that the SAS responses are implemented by the regulation of pre-existing transcriptional cascades or networks. As such, they are expected to be regulated by a high number of components that modulate the expression of hundreds of genes (Jiao et al., 2007).
2. The role of ATHB and HAT genes The first two genes reported as being rapidly regulated by proximity perception were ATHB2 and ATHB4 (Carabelli et al., 1993). Both of them encode proteins of the HD-Zip class II sub-family of transcription factors characterized by the presence of a DNA-binding homeodomain (HD) and an
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HAT9 HAT22 HAT2
HAT1 ATHB17
ATHB2
HAT14
HAT3 ATHB4
Fig. 3. Phylogenetic tree of the Arabidopsis HD-Zip class II subfamily proteins. Adapted from an available tree of the whole Arabidopsis HD-Zip family (Ariel et al., 2007).
adjacent leucine-zipper (Zip) motif, which mediates protein–dimer formation (Ariel et al., 2007). The Arabidopsis HD-Zip genes have been grouped into four different classes, HD-Zip I to IV, based on sequence similarity criteria and supported by the intron/exon patterns of the genes (Ariel et al., 2007). Interestingly, from the 10 members of the HD-Zip class II (Ciarbelli et al., 2008), a total of five are PAR genes, i.e. their expression is rapidly upregulated after perception of simulated shade: ATHB2, ATHB4, HAT1, HAT2 and HAT3 (Fig. 3). These five members form two sub-groups of likely paralogous genes ATHB2/HAT1/HAT2 and ATHB4/HAT3 (Ciarbelli et al., 2008). Several lines of evidence suggest that some of these HD-Zip class II transcription factors might be SAS signalling components (Table I). Plants overexpressing ATHB2 have longer hypocotyls, smaller cotyledons and leaves and a thinner root system. Consistently, transgenic lines overexpressing antisense ATHB2 (hereafter, anti-ATHB2 lines) exhibited roughly opposite phenotypes (Schena et al., 1993; Steindler et al., 1999). Based on these phenotypes it was suggested that ATHB2 is likely a positive regulator of the SAS responses, at least at the seedling stage (Sessa et al., 2005). In contrast with that described for anti-ATHB2 lines, single ATHB2 loss-offunction mutants do not display a distinctive phenotype under high R:FR light and under greenhouse conditions (our unpublished data). These observations suggest that the expression of genes encoding several members of the HD-Zip class II sub-family might be reduced in the anti-ATHB2
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lines, causing a decrease in the activity not only of ATHB2 but also of other homologous proteins. Seedlings overexpressing HAT1 or HAT2 also display some of the phenotypes described for ATHB2 overexpression: long hypocotyls, smaller cotyledons and leaves and a reduced root system (Ciarbelli et al., 2008; Sawa et al., 2002) (Table I). As for ATHB2, single loss-of-function T-DNA insertional mutants for HAT1 and HAT2 and even hat1 hat2 double mutants did not show any remarkable morphological phenotypes when growing under high R:FR light or under greenhouse conditions, suggesting that HAT1, HAT2 and other close members of the HD-Zip class II sub-family might have redundant functions (Sawa et al., 2002; Sorin et al., 2009). The analyses of overexpression lines suggest that HAT1 and HAT2, like ATHB2, are positive regulators of the SAS responses. To truthfully conclude that these genes are positive regulators of these responses, however, it would be necessary to analyse the phenotype of both gain- and loss-of-function mutant plants growing under high R:FR light and also to analyse their response to simulated shade (low R:FR light). To our knowledge, there are no data available on the response of any of these gain-of-function lines in response to simulated shade, whereas the analyses of the available loss-of-function mutant lines (hat1, hat2 and the hat1 hat2) concluded that only the hat1 hat2 double mutant seedlings displays a slightly reduced hypocotyl response to simulated shade (Sorin et al., 2009), consistent with a role for HAT1 and HAT2 as positive regulators of SAS responses. Another member of the HD-ZIP class II sub-family is ATHB4, whose contribution to the regulation of the SAS has been recently analysed (Table I). Overexpression of ATHB4 (or its increased nuclear activity) is sufficient to alter plant development. At the seedling stage, hypocotyl and cotyledon expansion, root elongation and lateral root development were particularly affected. Single ATHB4 loss-of-function mutants do not display a distinctive phenotype, as observed for all the other HD-Zip class II sub-family genes analysed, again suggesting the existence of genetic redundancy with other members of the sub-family. Phylogenetic analyses pointed to HAT3 as an ATHB4 paralogue (Ariel et al., 2007; Ciarbelli et al., 2008). A HAT3 loss-offunction mutant line was found to display a wild-type phenotype in all the conditions tested. By contrast, athb4 hat3 double mutant plants display a very characteristic phenotype, observable in both seedlings and adult plants (Fig. 4). Although adult athb4 hat3 double mutant plants display strong developmental defects, the seedlings of this line show apparently normal roots and hypocotyls, with only cotyledons showing obvious alterations (Fig. 4). Together, these results clearly indicate a redundant role for ATHB4 and HAT3 as regulators of plant development. When the response
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athb4 hat 3
Fig. 4. Phenotype of representative wild-type (left panels) and athb4 hat3 (right panels) mutant plants are shown. Top and upper panels show the aspect of 3-day-old seedlings and 3-week-old plants, respectively.
to simulated shade was analysed, neither ATHB4 overexpressing nor double athb4 hat3 seedlings responded (as estimated by measuring hypocotyl elongation). Although we cannot rule out that the morphological alterations of the athb4 hat3 mutant cotyledons might also eventually affect SAS seedling responses by indirect mechanisms, these results suggest a role for ATHB4 and HAT3 as regulators of the SAS (Sorin et al., 2009). However, their role is complex, and it is unclear whether they act as positive or negative regulators of these responses. These results reinforce our conclusion that the role of the other members of the HD-Zip class II sub-family in the regulation of the SAS should be revised after the analysis of how overexpression and/or loss-of-function lines respond to simulated shade. Individual overexpression of ATHB2, ATHB4, HAT1 and HAT2 was shown to repress the expression of several genes encoding HD-Zip class II members (Ciarbelli et al., 2008; Ohgishi et al., 2001; Sawa et al., 2002; Sorin et al., 2009), which suggested that at least these four members of the subfamily form a negative regulatory loop in which their homeostasis (levels) is mutually controlled, i.e. high activity of one of these factors down-regulate the expression of the others. Since the expression of these genes is rapidly regulated by simulated shade, phytochrome perception of low R:FR light might rapidly affect the expression of the whole sub-family, which in turn would modulate the expression of at least some of these genes, contributing to the above-mentioned complexity of the role of these transcription factors
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to the control of SAS responses. As a consequence, the similar phenotypes observed in plants with increased activity of some of these factors (ATHB2, ATHB4, HAT1 and HAT2) might result, in part, from the down-regulation of the expression of the whole HD-Zip class II sub-family. The attenuated hypocotyl response to simulated shade displayed by the hat1 hat2 double mutant seedlings is consistent with this possibility. If this is the case, obtaining multiple mutants in several HD-Zip class II genes will be necessary to unveil the specific role of the members of this small sub-family of transcription factors in the regulation of the SAS responses. In summary, independently on these considerations, the genetic and molecular analyses indicate that this small gene sub-family of transcription factors modulates SAS responses.
3. The role of bHLH-encoding PAR genes The first gene of the bHLH class reported as being rapidly regulated by proximity perception and having a role in the SAS was PIL1 (Salter et al., 2003). Its participation in the regulation of the SAS was deduced based on the altered elongation response of mutant hypocotyls to transient exposures (2 h) to simulated shade (Salter et al., 2003). Later on, a negative role for PIL1 was established based on the longer elongation of mutant pil1 hypocotyls in response to sustained treatments (5 days) with simulated shade (Roig-Villanova et al., 2006) (Table I). PIL1 was also involved in the regulation of seedling de-etiolation because pil1 mutants showed hyposensitivity to both Rc and FRc at lower fluence rates (Salter et al., 2003). The expression of PIL2 (also known as PIF6), a close PIL1 homologue, is also rapidly regulated by proximity perception, but its transcript levels rise more slowly (Salter et al., 2003). The function of PIL2 in the SAS regulation has not been reported yet. The negative role of the bHLH-encoding gene HFR1 in the SAS control was initially proposed because of the dramatic increase in elongation responses of mutant hfr1-4 (weak allele) and hfr1-5 (null allele) hypocotyls to canopy shade (Sessa et al., 2005). Later on, its role as a negative SAS regulator was confirmed based on the moderate increase in the hypocotyl elongation response shown by the hfr1-5 mutant to simulated shade (RoigVillanova et al., 2007) (Table I). Previously, HFR1 was genetically identified by different groups because of the hyposensitivity of mutant seedlings specifically under FRc. Although similar to the phyA and phyB interacting factor PIF3 (see below) (Ni et al., 1998), HFR1 did not bind either phyA or phyB on its own. However, HFR1 did bind PIF3, suggesting heterodimerization, and both the HFR1/PIF3 complex and PIF3 homodimer bound
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preferentially to the Pfr form of both phytochromes. Thus, HFR1 may function to modulate phyA signalling via heterodimerization with PIF3 and other PIF factors (Fairchild et al., 2000). Similarly, HFR1 function in the SAS can also take place modulating phyA and/or phyB signalling via heterodimerization with several PIF factors, some of which have been shown to have a positive role in the regulation of the SAS, such as PIF4 and PIF5 (Lorrain et al., 2008). Like some PIF proteins, the stability of HFR1 is also affected by light. In contrast with the photolabile PIFs, HFR1 is a short-lived protein in darkness that is degraded through the ubiquitin/26S proteasome-dependent pathway in a COP1-dependent manner (see the next section). During seedling de-etiolation, light, irrespective of its quality, enhances HFR1 protein accumulation via promoting its stabilization (Yang et al., 2005). We might say, therefore, that HFR1 is photostable but skotolabile. It is unknown, however, whether HFR1 stability is differentially affected by the R/FR ratio of the incoming light. PAR1 and its paralogue PAR2 encode atypical proteins of the bHLH family of transcription factors. The phenotypic analyses of plants with increased or reduced PAR1 and/or PAR2 levels suggested a negative role for these genes in the SAS regulation, indicating that altered levels of PAR1 and/or PAR2 significantly affect plant responsiveness to simulated shade (Table I). Fusions of PAR1 and PAR2 to GFP reporter proteins showed that both are nuclear proteins. To further confirm the nuclear activity of PAR1, transgenic plants constitutively overexpressing a chimera of PAR1 fused to the glucocorticoid receptor (GR) were obtained. The GR domain typically retains a nuclear factor in the cytoplasm in the absence of the synthetic glucocorticoid dexamethasone (DEX). The application of DEX targets the fusion protein to the nucleus. Transgenic seedlings overexpressing PAR1– GR displayed the characteristic dwarf phenotype observed in constitutive PAR1 overexpressors only in the presence of DEX, which supported that PAR1 has to be in the nucleus to show biological activity. Therefore, PAR1 and PAR2 very likely regulate development by modulating gene transcription. In support of this possibility, analysis of global transcript profiles of wild-type and PAR1 overexpressing plants led to the identification of several genes differentially regulated by PAR1 (Roig-Villanova et al., 2007). As mentioned above, HFR1 was shown to have a very strong and clear effect under shade conditions that reduced both R:FR and light quantity, conditions that mimic natural situation when canopy closure occurs. The moderate phenotype shown by the strong hfr1-5 mutant allele under simulated shade is qualitatively and quantitatively similar to that displayed by lines with reduced PAR1, PAR2 and/or PIL1 levels, supporting the
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hypothesis that the control of SAS responses is not exerted by a central player but shared by several factors such as those described in here. B. INTEGRATION OF SAS REGULATORY COMPONENTS INTO TRANSCRIPTIONAL NETWORKS
Although the number of molecular components identified to have a role in the regulation of the SAS is quite limited, in all the cases they are nuclear proteins belonging to different families of transcriptional regulators. Some of these factors have also been shown to participate in the control of gene expression associated with the SAS, implying a function as transcriptional regulators. This is the case for ATHB4, HAT3, HFR1, PAR1 and PAR2 (Roig-Villanova et al., 2007; Sessa et al., 2005; Sorin et al., 2009). In addition, PIF4, PIF5 and some DELLA proteins also encode for nuclear factors with a role in the regulation of transcription (Lorrain et al., 2008; Zentella et al., 2007). Altogether, it is likely that these regulatory components are triggering changes in the subjacent transcriptional network(s) controlling the SAS responses, highlighting the complexity of the regulation of the various SAS responses (Fig. 5). An important task to understand the SAS is to investigate what is the role of these components in the shade-induced transcriptional network modulated by phytochromes, how do they relate to each other (i.e. their hierarchy) and how do they act together to regulate the different responses of the SAS. The complexity of the transcriptional network is illustrated by the high number of components participating in these networks and by the multiple links between them, such as transcriptional relationship. For instance, PIF4 and PIF5 affect the phytochrome-modulated expression of ATHB2, HFR1 and PIL1 after shade perception (Lorrain et al., 2008); at the same time, the bHLH proteins PIL1 and HFR1 have the potential to interact with PIF4 and PIF5, very likely affecting phytochrome signalling initiated after low R:FR light perception, as already shown for HFR1 (Hornitschek et al., 2009). DELLA proteins are nuclear-localized factors that, as GRAS members, have been proposed to act as transcriptional regulators. In agreement with this activity, transcriptomic analysis combined with chromatin immunoprecipitation provided evidence for a role for RGA, one of the five DELLA proteins in Arabidopsis, as a factor regulating directly gene expression (Zentella et al., 2007). DELLA proteins lack a clear DNA-binding domain and attempts to demonstrate DNA-binding ability have failed up to now. However, DELLA proteins physically interact with several members of the PIF family (de Lucas et al., 2008; Feng et al., 2008), in such a way that the interaction inhibits the ability of PIF proteins to bind to, and regulate,
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HAT1
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PIL1
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HFR1
PAR2
SAUR68
PIF4 PIF5
?
DELLAs
HAT2 ?
HAT3
?
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Hypocotyl elongation after low R:FR exposure
Fig. 5. A scheme of the genetic components participating in the regulation of the SAS. Perception of simulated shade triggers the rapid upregulation of the expression of several genes, which encode nuclear proteins (shown in uppercase and enclosed in boxes). Continuous lines represent transcriptional (black) and protein–protein interactions (grey). Dotted lines represent the physiological effect of these components in the hypocotyl elongation after exposure to simulated shade deduced by genetic analyses. Lines with a bar and with a closed arrow represent a negative and positive effect, respectively. White letters enclosed in black boxes refer to proteins whose stability is affected by simulated shade perception. Question marks indicate that the represented interaction might not be direct.
their target promoters, uncovering a mechanisms by which non-DNAbinding DELLA proteins might regulate gene expression. Therefore, DELLA–PIF interaction represents another mechanism by which regulatory components of the SAS might integrate the different molecular signals they receive in the control of the shade-modulated transcriptional network and the associated elongation growth (Fig. 5). In addition to the PIFs, DELLAs and PAR factors, other components with a role in light signalling (initially identified by genetic approaches) might also have a major role in the modulation of shade-induced transcriptional networks. This is the case of COP1 and DET1, two master integrators of light signalling with a role in controlling the stability of several transcription factors that trigger seedling de-etiolation (Jiao et al., 2007). Both factors have also been shown to participate in shade-induced hypocotyl elongation since nonlethal mutations in COP1 and DET1 impair the hypocotyl SAS response (McNellis et al., 1994) (our unpublished observations; see Table I
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and Fig. 5). In addition, there are evidences that they also modulate the shade-induced transcriptional changes. COP1 and DET1 seem to differentially participate in the shade-induced expression of a sub-set of PAR genes, having a strong effect on PIL1 and PAR1 and a minor influence on ATHB2 and ATHB4 (Roig-Villanova et al., 2006). These results suggest that COP1and DET1-mediated proteolysis has an impact in the regulation of the shade-modulated transcriptional network. Therefore, COP1 and DET1 seem to play a role in the ability of light-grown plants to respond to plant proximity, probably by specifically acting at different levels altering the stability of some PIF factors, such as PIF4 and PIF5, and some PAR factors, such as HFR1 (Duek et al., 2004; Lorrain et al., 2008). Finally, there is evidence of a SAS component genetically identified, CONSTITUTIVE SHADE-AVOIDANCE 1 (CSA1), whose gene product is predicted to be located in the cytosol but has a demonstrated role in the shade-modulated transcriptional network (Faigon-Soverna et al., 2006) (Table I). The gene CSA1 encodes a member of the TOLL/INTERLEUKIN RECEPTOR-NUCLEOTIDE BINDING SITE-LEUCINE-RICH REPEAT (TIR-NBS-LRR) gene family, which in plants are known to confer resistance against specific pathogen strains in accordance to the gene-for-gene model (Faigon-Soverna et al., 2006). Molecular and genetic analyses of the csa1 mutant indicate that a part of the mutant phenotype is the result of a dominant-negative effect caused by the expression of a truncated CSA1 protein, completely lacking the LRR and part of the NBS domains, that impairs phytochrome signalling by interfering with the action of related TIR–NBS– LRR proteins. CSA1 somehow down-regulates the expression of ATHB2 and HFR1 (Faigon-Soverna et al., 2006), having the potential of participating by unknown mechanisms in the shade-modulated transcriptional network. Although this type of proteins is predicted to be cytosolic (Gassmann et al., 1999), it is unknown whether CSA1 can also be located in the nucleus of plant cells. Therefore, in order to know if the role of this protein in the modulation of the shade-modulated transcriptional network is direct or indirect, it seems necessary to investigate first the sub-cellular location of this protein.
VII. CROSS TALK OF SAS SIGNALLING WITH OTHER REGULATORY PATHWAYS Since many factors besides SAS signalling regulate seedling responses modulated by simulated shade (i.e. hypocotyl elongation, and cotyledon and primary leaf expansion), it is expected that at least some of these different signalling pathways leading to growth stimulation or repression converge, providing a means to integrate light and other environment information with
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endogenous developmental programmes, such as those controlled by the circadian clock, temperature and phytohormones. A. CIRCADIAN CLOCK
The circadian clock controls daily changes in gene expression, growth, photosynthetic activity and seasonal flowering. Circadian rhythms often take the form of sinusoidal waves that can be described by mathematical terms such as period (duration of one complete rhythmic cycle), phase (the state of a rhythm relative to another reference rhythm, e.g. the day–night cycle) and amplitude (difference between mean value and maximum or minimum of a sinusoidal oscillation). This rhythmic mechanism synchronizes internal signalling processes with external light cues, enabling organisms to separate incompatible metabolic processes and to coordinate phase-sensitive responses so that they occur at a biologically beneficial time of the day or year (season), which seem to confer an adaptive advantage compared with randomly occurring activities (Harmer, 2009; Mas, 2008). Clock components and photoreceptors have an intimate relationship, since light signals transduced by the phytochromes and cryptochromes ensure that the clock is in tune with daily light/dark cycles. This process, known as photoentrainment, is achieved by adjusting the rhythms of the oscillator to match the 24 h solar cycles (Fankhauser and Staiger, 2002). Most aspects of plant growth and development are influenced by the clock. In addition, many signalling pathways are modulated by the clock so that plant sensitivity to stimuli varies across the circadian cycle, a process known as gating. Expression studies of PIL1 and PIL2 revealed that their shade-induced expression was gated by the circadian clock, with minimum increases at subjective dawn for both genes (Salter et al., 2003). Similarly, the shade-induced expression of CBF1, CBF2 and CBF3 genes (see below) was also gated, with the peak of each rhythm occurring 4–8 h after subjective dawn (Franklin and Whitelam, 2007). These results indicate that the activities of some components of the shade-regulated transcriptional network are somehow connected to the circadian clock. In addition, the gating of the expression of shade-responsive genes suggested that physiological responses to low R:FR light may also be gated. Observations that transient (2 h) treatments with simulated shade could elicit a response of the hypocotyl elongation within the following 24 h when seedlings were growing under constant high R:FR light led to the finding that shade-induced hypocotyl elongation was also gated by the circadian clock, with maximum increases at subjective dusk. Interestingly, hypocotyl elongation was even inhibited (rather than induced) following a transient
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simulated shade treatment at subjective dawn (Salter et al., 2003). In accordance, under constant high R:FR light the pattern of hypocotyl elongation is also rhythmic, exhibiting a daily growth arrest spanning subjective dawn and an interval of rapid growth at subjective dusk (Dowson-Day and Millar, 1999). The peaks of hypocotyl elongation coincide with the peaks of shade induction of PIL1 and PIL2 suggesting a role for these genes in the regulation of this response. Indeed, the gated response of the hypocotyl to transient simulated shade was phase-shifted by 6 h in both loss- and gain-of-function PIL1 mutants (Salter et al., 2003) indicating the participation of PIL1 in the regulation of this trait in a complex way, probably in combination with other PIL relatives. In that respect, a role has been proposed for PIF4 and PIF5 as key elements to explain rhythmic growth. During the day, light inactivates these PIF factors by promoting their ubiquitin/26S proteasome-mediated degradation, whereas early in the night the circadian clock does not allow their transcription, leaving a narrow window of a few hours before dawn to grow (Nozue et al., 2007). However, this role has been proposed analysing seedlings growing under short-day photoperiods (8 h light and 16 h darkness cycle), conditions in which hypocotyl growth is also rhythmic, although strikingly different from that seen in continuous light: peak growth occurred at dawn instead of at subjective dusk (Nozue et al., 2007). Since the degradation of the growth-promoting transcription factors PIF4 and PIF5 is induced by light, under long day (16 h light in a 24 h cycle) or constant (high R:FR) light photoperiods, the role of these factors is negligible (Niwa et al., 2009). The rapid (within 15 min) increased stability of PIF4 and PIF5 in response to perception of low R:FR light (Lorrain et al., 2008) might involve the participation of these two PIF factors, together with PIL1 and PIL2, in the gated response of the hypocotyl to transient simulated shade. B. LOW AND HIGH TEMPERATURE
There are several lines of evidence demonstrating the interaction between light perception by phytochromes and temperature in the regulation of some SAS-related responses, such as flowering and germination. However, data on the interaction between phytochrome and temperature in the regulation of the hypocotyl elongation induced by simulated shade are scarce. In a study on the annual weed Abutilon theophrasti, it was shown that hypocotyl elongation in response to low R:FR is increased under high-temperature conditions, indicating that plant responsiveness to low R:FR depends on ambient temperature (Weinig, 2000). Although there are no similar studies in Arabidopsis, exposure of seedlings growing under constant W (high R:FR) to high temperature (28–29˚C) results in an auxin-dependent induction of the
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hypocotyl elongation, compared to seedlings growing at 20–22˚C (Gray et al., 1998; Koini et al., 2009; Stavang et al., 2009). In addition, loss-offunction of SHADE AVOIDANCE 3 (SAV3) (see the next section), that results in shorter hypocotyls when mutants are grown under simulated shade (Table I) and partially suppress the constitutive SAS phenotype of a phyB mutant, is also defective in high-temperature-induced hypocotyl elongation (Tao et al., 2008). These observations suggest that high temperature and low R:FR share regulatory mechanisms in the control of hypocotyl elongation. This possibility led to test the role of PIF4 and PIF5, whose stability is increased by low R:FR light (Lorrain et al., 2008), in mediating plant responses to high temperature. The hypocotyl elongation response was completely abolished in pif4 seedlings transferred to 28–29˚C, whereas an attenuated response was observed in pif5 mutants, suggesting a role for these PIFs in regulating this process, with PIF4 playing an essential role (Koini et al., 2009; Stavang et al., 2009). It is unknown whether the shade-induced hypocotyl elongation in Arabidopsis is affected by high temperature and whether PIF4 and PIF5 also have a major role in regulating this response at high temperature. However, high temperature induces PIF4 expression without apparently increasing the stability of the protein (Stavang et al., 2009), suggesting that the mechanisms by which the increase in ambient temperature and the decrease in the R:FR light enhance the PIF4-mediated hypocotyl elongation are different. A connection between simulated shade and low-temperature treatments has been uncovered by performing transcriptomic analysis of Arabidopsis seedlings grown at two different temperatures (16 and 22˚C) and differentially treated with high or low R:FR for 24 h. These experiments revealed that low R:FR perception activates the expression of genes of the CBF regulon in Arabidopsis in a temperature-dependent manner. The CBF regulon refers to a set of genes whose expression is induced by cold and osmotic stress through the CBF family of AP2 domain transcription factors (Gilmour et al., 2004). As mentioned previously, the induction of CBF1, CBF2 and CBF3 transcription by simulated shade was gated by the circadian clock and it occurred during the peaks of its endogenous rhythm (Franklin and Whitelam, 2007). Transgenic overexpression of CBF genes in Arabidopsis induces elevated expression of the CBF regulon in the absence of a low-temperature stimulus, leading to enhanced freezing tolerance (Gilmour et al., 2004). Similarly, cold acclimation at 4–5˚C induces CBF genes and the CBF regulon, conferring freezing tolerance (Gilmour et al., 2004). The biological significance of the observed interaction between cold- and shade-regulated transcriptional networks was elegantly demonstrated because pretreatments of a few hours with simulated shade of plants grown at 16˚C increased survival rate and reduced
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leaf damage in response to freezing compared to controls plants grown at the same temperature only in high R:FR (with no simulated shade pretreatment). No notable differences in freezing tolerance were produced by simulated shade pre-treatments when plants were grown at 22˚C (Franklin and Whitelam, 2007). Although either decreased ambient temperature or simulated shade alone is unable to protect plants from freezing, the combination of these two signals results in an increased tolerance to protects plants from freezing. This led to suggest that these mechanisms may reflect an adaptation by which plants are able to sense the encroaching winter during autumn, when twilight increases (i.e. the average R:FR decreases) and the temperature starts to drop (Franklin and Whitelam, 2007). In addition to the CBF regulon, transcriptomic analyses of Arabidopsis seedlings grown at 16 and 22˚C and differentially treated with high or low R: FR for 24 h (Franklin and Whitelam, 2007) showed hundreds of genes whose shade-induced expression is affected by the temperature, many of which encode proteins belonging to several families of transcription factors (Fig. 6). Although their function in plant development has not been analysed yet, their role as transcriptional regulators suggests that they might encode key players in the dynamics of the transcriptional networks affected. The extensive connections between transcriptional networks modulated by different external signals, such as simulated shade and cold, represent a potential molecular mechanism to integrate novel combinations of environmental
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Fig. 6. Venn diagrams illustrating the subgroup of differentially expressed genes induced (at least twofold induction) by low R:FR for 24 h of 5-day-old phyA-deficient seedlings germinated and grown at 16 or 22˚C. Based on the available identity of the Affymetrix probes, the microarrays used by the authors contained a total of 22,591 genes (AGI codes). From those 1774 were classified as encoding transcription factors. Brackets indicate the sub-group of expressed genes tentatively identified as encoding transcription. Data are extracted from the microarray data deposited in the National Center for Biotechnology Information Gene Expression Omnibus, GEO Accession code GSE8745 (Franklin and Whitelam, 2007).
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cues and to produce novel responses that confer the adaptive phenotypic plasticity displayed by the plants.
C. HORMONES
All plant hormones that are involved in elongation growth, such as auxins, brassinosteroids (BRs), ethylene and GAs, were potential actors in SAS elongating responses (Vandenbussche et al., 2005). Interactions with the signalling pathways of these hormones have actually been established and will be discussed in this section. The role of jasmonates, another hormone class directly related with defence responses, will be discussed in the next section.
1. Auxins Auxins are known to regulate multiple aspects of plant growth and development. These include cotyledon/leaf expansion, hypocotyl/stem elongation and apical dominance, processes commonly associated with the SAS. Through links to the auxin system, light is able to manipulate plant growth and development in response to the frequent changes in the external environment. In general, light perception imposes a strong influence on the levels, transport and response to auxins (Halliday and Fankhauser, 2003). In the case of plant proximity perception, phytochromes control auxin biosynthesis, transport (distribution through the seedling) and the response to auxin within individual cells. Recently, it was shown that reduction in the levels of active phyB induced by low R:FR light perception produces a rapid (within 1 h) rise in endogenous levels of free indole-3-acetic acid (IAA). This effect involves the action of TAA1 (TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS 1)/ SAV3, since sav3 mutants, that have reduced IAA levels, are unable to mount an elongation response to low R:FR light. TAA1 encodes an auxin biosynthetic enzyme required for the shade-induced rapid rise in the IAA levels and for full induction of SAS responses (Tao et al., 2008). Treatment of wild-type seedlings with the auxin transport inhibitor naphthylphthalamic acid (NPA) suppresses the SAS response of the hypocotyl (Steindler et al., 1999). Furthermore, shoot-derived auxin has been shown to be important for lateral root production, a response that is perturbed by treatment with low R:FR light (Bhalerao et al., 2002; Salisbury et al., 2007). These experiments led to propose that exposure of seedlings to simulated and/or canopy shade produces a reduction of basipetal auxin transport through the central cylinder of the hypocotyl tissues in favour of lateral cell layers in the same organ.
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Because the lateral cell layers are less efficient in draining out auxin from the auxin source, this is likely to result in a transient increase of the auxin pool within these cell layers, enhancing cell elongation in the hypocotyl. In addition, this would result in a reduction in the amount of auxin that reaches the roots (Morelli and Ruberti, 2000, 2002). Together, these experiments involve phytochromes in auxin transport during the regulation of SAS responses. Subjecting seedlings to low R:FR light induces a change in the pattern of expression of GUS in the DR5:GUS line (that contains an artificial auxin reporter gene construct), whose expression is thought to reflect the levels of free auxin (Sabatini et al., 1999; Tao et al., 2008). Although the use of DR5: GUS as a marker of free auxin levels has been recently objected (Petersson et al., 2009), its unquestioned activity as an early auxin-responsive marker within the context of the whole seedling allows to address whether simulated shade alters the response to auxin in different organs and/or within individual cells. Indeed, DR5:GUS seedlings have enhanced GUS expression in the lower third of the hypocotyl and in the cotyledons, and reduced expression in the roots (Salisbury et al., 2007; Tao et al., 2008), indicating that, after plant proximity perception, phytochromes also control the local response to auxin. Several of the genes of the HD-Zip class II sub-family have been implicated in shade signalling (see Section VI). As indicated, plants with elevated levels of ATHB2 display long hypocotyls, reduced cotyledon expansion and a reduced root system (Schena et al., 1993). Auxin-related aspects of the overexpression phenotype, such as lateral root number and hypocotyl length, are restored or abolished following auxin or NPA application, respectively (Steindler et al., 1999). Overexpression of HAT2 also results in auxin-related phenotypes, such as epinastic cotyledons and reduced lateral root production (Sawa et al., 2002). Epinastic cotyledons are typically displayed by auxin-overproducing plants (Boerjan et al., 1995). Finally, overexpression of ATHB4 also increased cotyledon epinasty, particularly under simulated shade conditions. This trait was suppressed when NPA was applied. In addition, plants with increased ATHB4 activity were shown to display a reduced hypocotyl response to 1mM 2,4-D (a synthetic auxin), a dose known to induce hypocotyl elongation, suggestive of a role for this factor in affecting the responsiveness of seedling hypocotyls to auxins (Sorin et al., 2009). Together, these results strongly suggest that the shade-induced expression of these HD-Zip genes may promote SAS responses by affecting auxin levels, transport and/or sensitivity. Therefore, these factors might integrate the shade signal perceived by the phytochromes with some aspects of auxin responsiveness in the modulation of SAS responses.
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2. Brassinosteroids BRs are powerful growth promoters that control SAS-related traits such as hypocotyl/stem elongation and cotyledon/leaf expansion. The implication of BRs in the control of photomorphogenic development was initially suggested by the de-etiolated phenotype of mutants defective in different genes that encode enzymes that function in BR biosynthesis (e.g. DET2, CPD and DWF4) (Li and Chory, 1999). Specifically, the involvement of BRs in the regulation of the SAS was suggested because the hypocotyl of the Arabidopsis BR biosynthesis mutant dwarf1-101 does not elongate when exposed to canopy shade (Luccioni et al., 2002) (Table I). Similarly, the hypocotyl of Arabidopsis BR biosynthesis mutant det2 displays a reduced elongation in response to simulated shade (Fig. 7) (Table I). Another link between simulated shade perception and BRs action was proposed because enhanced expression of BAS1, which encodes a BR-inactivating enzyme, suppresses the long hypocotyl phenotype of phyB mutant seedlings, which display a phenotype reminiscent of a constitutive SAS (Neff et al., 1999). This evidence suggests the importance of an intact BR pathway for the normal
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Fig. 7. Hypocotyl response of det2 seedlings to simulated shade. Length of hypocotyl of 7-day-old seedlings grown as indicated at the top of the image. Briefly, seedlings were germinated and grown for 2 days under W (high R:FR) and then either kept in W (white bars) or transferred to W þ FR (grey bars) for five additional days. At least 15 seedlings per treatment and were used for each genotype. Columns represent the mean and bars represent the standard error of the mean (SE) of the data. Asterisks indicate significant differences (p < 0.01) relative to the corresponding wild-type control.
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display of the SAS responses. However it is unknown whether shade perception induces hypocotyl elongation by affecting BR metabolism and/or levels. Recently, plants with increased activity of the SAS regulator ATHB4 were shown to display an exaggerated hypocotyl response to exogenously applied epibrassinolide (EBL), a commercially available active BR that stimulates hypocotyl elongation. By contrast, hypocotyls from athb4 hat3 double mutant seedlings respond less than wild-type hypocotyls to EBL. Altogether, these results indicate that ATHB4, together with HAT3, have a role as positive regulators of BR-induced hypocotyl elongation and suggests that these two members of the HD-Zip class II sub-family of transcription factors might act as putative integrators of shade perception and BR action, involving the phytochromes in the modulation of SAS responses by controlling BR responsiveness (Sorin et al., 2009). It is currently unknown whether HAT1, HAT2 and ATHB2, the other members of the HD-Zip class II subfamily whose expression is rapidly affected by simulated shade perception, also have a role as integrators of shade and BR responses. Because some of these members of the HD-Zip class II sub-family have been proposed to have an additional role as integrators of shade and auxin responsiveness, it is tempting to propose that they may have a broader role as integrators of at least two different hormone-signalling pathways.
3. Ethylene An important body of evidence also involves the gaseous hormone ethylene in the detection of neighbouring plants, mainly based in the reduced SAS responses of ethylene-insensitive transgenic tobacco plants to neighbours in crowded canopies (Pierik et al., 2003). In addition, application of moderate concentrations of ethylene to wild-type plants induced responses similar to those of the SAS (Pierik et al., 2003). However, when analysing single-grown plants, i.e. those growing under non-crowded conditions, ethyleneinsensitive tobacco transgenic plants responded normally to low R:FR although they were insensitive to reductions in blue light photon fluence rate. These results led to conclude that simulated shade can induce SAS responses independent of ethylene action (Pierik et al., 2004b). Nevertheless, in the same species, simulated shade stimulates ethylene production and some ethylene-induced SAS responses, such as stem and petiole elongation, require intact ethylene sensing (Pierik et al., 2004a). The same authors reported that the growth-promoting effects of ethylene are GA dependent (Pierik et al., 2004a). Although ethylene has been demonstrated to regulate the stability of DELLA proteins (Achard and Genschik, 2009), proposed as molecular integrators of various growth-regulating signalling pathways, and
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genetic analyses suggested the participation of DELLA breakdown in allowing hypocotyl and petiole elongation in response to simulated shade (Djakovic-Petrovic et al., 2007), ethylene action in regulating these two SAS responses act independent of GAs and DELLA proteins, at least in Arabidopsis (Pierik et al., 2009).
4. Gibberellins GAs control multiple aspects of plant development, including germination, hypocotyl elongation, cotyledon/leaf (petiole) expansion and flowering, traits associated with the SAS. Numerous reports in various species (Brassica, cowpea, potato, sorghum and tobacco) indicated that the induction of hypocotyl elongation in response to simulated shade, EOD-FR treatments or a genetic reduction of the phytochrome signalling (phyB mutations) altered the overall metabolism and/or levels of bioactive GAs (Childs et al., 1991; Devlin et al., 1992; Garcia-Martinez and Gil, 2001; MartinezGarcia and Garcia-Martinez, 1992, 1995; Martinez-Garcia et al., 2001; Pierik et al., 2004a). These results led to the hypothesis that proximity perception by the phytochromes alters mainly global GA metabolism and levels to modulate the proper elongation response. By contrast, GA levels do not change after simulated shade or EOD-FR treatment, or in phytochromedeficient mutants in other species (Arabidopsis, cucumber, pea). These findings led to propose that proximity perception by the phytochromes modulate the proper elongation response mainly by altering GA sensitivity in the responding tissues (Lopez-Juez et al., 1995; Reed et al., 1996; Weller et al., 1994). These long-standing and controversial alternative explanations proposed for the interaction of simulated shade perception and GA action (Garcia-Martinez and Gil, 2001), however, are not mutually exclusive. It is likely that, in the regulation of the SAS, light controls stem elongation by altering both GA sensitivity and/or metabolism in different tissues and/or organs, resulting in the observed normal (i.e. wild-type) response. The elongation response of Arabidopsis seedlings can be substituted or mimicked in some cases by auxin, BRs, ethylene or GA application. It is therefore not surprising that many classic studies attempted to implicate alterations of hormone metabolism and/or response in the control of this response by light, in general, and simulated shade, in particular. As mentioned, this has been particularly recurrent in the case of the study of light–GA interaction. A similar proposal can be done for the interaction of light–auxin, light–BRs and light–ethylene in the control of shade-induced hypocotyl elongation. It is also possible that altered hormone response might also affect light sensitivity. This two-way effect between light and
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hormone response has also been previously suggested when analysing the elongation of hypocotyls in response to a combination of increasing doses of brassinolide during the seedling de-etiolation in response to increasing doses of R and FR (Nemhauser, 2008). D. INTEGRATION OF HORMONE- AND SHADE-REGULATED TRANSCRIPTIONAL NETWORKS IN THE SAS CONTROL
Based on the available transcriptomic profiles, it appears that different environmental cues and endogenous signals initiate and/or modulate different transcriptional networks that regulate different although somehow overlapping sets of genes. This is particularly obvious in the case of some hormones (auxins, BRs and GAs) and simulated shade cues (Devlin et al., 2003; Nemhauser et al., 2006; Tao et al., 2008). Indeed, at the molecular level, shade-triggered transcriptional cascades share several components with those controlled by plant hormones that regulate cell division and expansion. The expressions of various auxin-responsive genes (e.g. IAA, GH3-like and SAUR genes), as well as some genes of the PIN family of auxin transport proteins (e.g. PIN3 and PIN7) change rapidly after perception of simulated shade (within 1 h) (Devlin et al., 2003; Tao et al., 2008) or canopy shade (within 4 h) (Carabelli et al., 2007; Sessa et al., 2005). The expression of the BR receptor BRI1 and the BR-inactivating enzyme BAS1 is rapidly up-regulated by a reduction of the R:FR (Devlin et al., 2003; Tao et al., 2008). In addition, the expression of GAI, GA-responsive genes and several genes encoding GA20-oxidase enzymes involved in the GA biosynthetic pathways is also rapidly modulated by shade (Devlin et al., 2003; Tao et al., 2008). The strongest connections between transcriptional networks has been observed between shade and auxin signalling in plants exposed to low R:FR light. In that case, transcript abundance of numerous auxinresponsive genes increases rapidly in response to low R:FR light (within 1 h) (Tao et al., 2008). One day (24 h) after initiating the low R:FR treatment, transcript abundance of numerous auxin-, BR- and GA-responsive and auxin-, BR- and GA-related genes is maintained (Franklin and Whitelam, 2007). Auxins and BRs have been shown to act synergistically in the regulation of hypocotyl elongation in light-grown seedlings (Vert et al., 2008). In addition, auxin- and BR-signalling pathways converge at the level of transcriptional regulation of target genes with common regulatory elements (Nemhauser et al., 2004). In that way, auxins and BRs induce the expression of several common target genes, such as SAUR15 and SAUR68. The expression of these two SAUR genes is also rapidly and transiently induced after perception of simulated shade (Roig-Villanova et al., 2007).
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In summary, these complex networks contain a high number of components (transcriptional regulators and genes regulated), some of which might be interconnected because of direct protein–protein and/or transcriptional interactions between them. The interaction between these different networks might take place, at least, by the following mechanisms: (i) some transcriptional regulators are shared, (ii) the expression of some components in one transcriptional network is modulated by transcriptional regulators participating in the other and/or (iii) some target genes might be regulated independently by two different network components. The atypical bHLH transcription factors PAR1 and PAR2 are nuclear proteins that need to be in the nucleus to have biological activity, consistent with a role for these atypical bHLH factors as transcriptional regulators. In vivo, PAR1 and PAR2 repress the transcription of a sub-set of auxinregulated genes, including SAUR15 and SAUR68, but not HAT2, also known to be auxin-induced (Roig-Villanova et al., 2007; Sawa et al., 2002). The large proportion of genes mis-regulated by PAR1 overexpression that are also differentially expressed in wild-type seedlings upon treatment with auxin and/or BR hormones suggest a role for PAR1 (and likely PAR2) in integrating light and hormone transcriptional networks during the SAS. To address whether the negative role of PAR1 on SAUR gene expression was an early (direct) or late (indirect) effect on transcription, the expression of SAUR15 and SAUR68 was analysed in plants expressing PAR1 fused to the glucocorticoid receptor (GR) domain. The application of DEX in transgenic plants overexpressing PAR1–GR reduced the expression of these two SAUR genes 4 h after PAR1–GR was induced to translocate to the nucleus, indicating that the repressor effect of PAR1 on SAUR expression was early. The early repression of SAUR15 and SAUR68 by PAR1-GR was also detected in the presence of the protein synthesis inhibitor cycloheximide, demonstrating that the repressor effect of PAR1 is translation independent and, therefore, very likely direct (Fig. 5). These observations suggest that PAR1 and PAR2 provide a mechanism by which light can rapidly modulate (reduce) responsiveness to auxin (RoigVillanova et al., 2007). Another PAR gene belonging to the bHLH family of transcription factors with a role in the regulation of the SAS responses is HFR1 (Roig-Villanova et al., 2007; Sessa et al., 2005), which has been shown to control the expression of a group of genes that encodes hormonerelated factors under prolonged canopy shade (low light intensity and R:FR light) (Sessa et al., 2005). Although it is unknown whether HFR1 affects directly the expression of the genes encoding hormone-related factors, it can also provide additional mechanisms by which shade perception can modulate responsiveness to auxin.
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The expression of several auxin-induced genes, such as SAUR15, SAUR68, HAT2 and IAA1, is also regulated by the HD-ZIP class II factors ATHB4 and HAT3, two regulators of the SAS responses with proposed roles as integrators of shade perception and hormone action (Sorin et al., 2009) (Fig. 5). As described for HFR1, it is not known whether the effect of ATHB4 on the expression of these auxin-induced genes is late or early, indirect or direct. Finally, the expression of HAT2, a member of this HD-Zip class II subfamily, is strongly induced not only by low R:FR light but also by auxin application (Sawa et al., 2002). Since the expression of genes of this subfamily forms a small transcriptional network in which their homeostasis is mutually controlled (Ciarbelli et al., 2008; Ohgishi et al., 2001; Sawa et al., 2002; Sorin et al., 2009), these observations provide an additional potential mechanism by which a sub-set of HD-Zip transcription factors participate in the complex integration of shade and auxin signalling. In summary, phytochrome perception of low R:FR light conditions strongly induces the so-called PAR genes, that mostly encode nuclear proteins. Some are members of the bHLH family, the HD-Zip class II subfamily of transcription factors (Roig-Villanova et al., 2006, 2007; Salter et al., 2003; Sessa et al., 2005) or the AP2 domain transcription factors (CBF1, CBF2 and CBF3) (Franklin and Whitelam, 2007). There are still hundreds of functionally uncharacterized PAR genes encoding putative transcriptional regulators from most of the known families of transcription factors (Tao et al., 2008). It is therefore expected that they might be important players in the dynamics of the transcriptional networks affected. Their functional characterization might uncover additional roles as integrators of the exogenous (shade) and endogenous (hormonal) cues that modulate their expression and/or activity. E. PLANT DEFENCE
Physiological experiments have suggested potential points of interaction and cross-talk between the mechanisms involved in neighbour detection and defence signalling. From an ecological perspective, competition and herbivore defence are often seen as antagonistic selective forces for plants (Ballare, 2009): resource allocation to competition can limit investment in defence, thereby increasing vulnerability to herbivores and, reciprocally, allocation to defence can reduce competitive ability against neighbouring plants. This allocation compromise between growth and defence is known as the ‘dilemma’of plants (Ballare, 2009; Herms and Mattoson, 1992; Howe and Jander, 2008). In relation with the molecular mechanisms behind this dilemma, after recognition of an herbivore attack by unknown receptors plant defence
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pathways are activated, with the jasmonates having a central role in this response (Howe and Jander, 2008). Jasmonates refer to the plant hormones jasmonic acid and related signalling components. Their implication in plant defence is based in studies that show that both biosynthetic and signalling jasmonate mutants are compromised in resistance to a wide range of arthropod herbivores in bioassays (Howe and Jander, 2008). Following attack by herbivores, jasmonate production is rapidly increased, a signal that regulates the expression the majority of genes involved in plant defence against these plant eaters (Reymond et al., 2000). In addition to this role in plant defence, jasmonates inhibit cell division and elongation (Yan et al., 2007; Zhang and Turner, 2008). This dual role of jasmonates suggest that they are important in managing the ‘dilemma’of plants to grow or defend when confronted simultaneously with the competition of other plants and the attack by herbivores (Ballare, 2009; Howe and Jander, 2008). In that respect, the observations that pest incidence increases with crop density and activation of the defence programme has a penalty in terms of growth or competitive ability has lead to address whether this negative correlation between defence and plant competition is a simple consequence of resource limitation. Recent studies using South American wild tobacco (Nicotiana longiflora) and Arabidopsis indicate that plants treated with simulated shade support increased insect growth. In these experiments it was also shown that simulated shade treatment altered the expression of several defence-related genes and eliminated the plant phenolic response induced by herbivore attack (Izaguirre et al., 2006; Moreno et al., 2009). This observations lead to conclude that light signals of plant proximity (low R:FR) trigger a down-regulation of defence mechanisms. Interestingly, by using the sav3 mutant of Arabidopsis, that fails to elicit SAS responses because it is impaired in auxin biosynthesis (Tao et al., 2008), it was shown that the effect of simulated shade as a repressor of defence is not an unavoidable consequence of the diversion of resources to SAS responses (Moreno et al., 2009). Thus, when confronted simultaneously with the competition of other plants and the attack by herbivores, the plant’s allocation priorities appear to be placed on outgrowing neighbour competitors to maintain light capture rather than on reducing resource losses caused by herbivore attack (Ballare, 2009). The mechanism behind this strategy is starting to be elucidated and it involves a reduction in the sensitivity to jasmonates. Indeed, changes in gene expression of some, but not all, components of the jasmonate-signalling cascade are impaired by simulated shade treatment. This is the case of ETHYLENE RESPONSE FACTOR 1 (ERF1), a member of a family of integrators of diverse signals that modulate plant defence (Brown et al.,
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2003; Lorenzo and Solano, 2005; Lorenzo et al., 2003), and some JASMONATE ZIM-DOMAIN (JAZ) members, which are key repressors of jasmonate responses associated with defence induction and growth retardation (Chico et al., 2008; Chini et al., 2007; Yan et al., 2007). Despite these advances, the molecular components involved in this cross-talk between plant proximity perception and defence are largely unknown.
VIII. SPATIAL AND TEMPORAL ASPECTS OF THE SAS SIGNALLING Much of our understanding of the molecular components involved in the regulation of the SAS comes mainly from the analyses of whole seedling responses in Arabidopsis. However, models describing the action of phytochromes, its interacting partners and their effect on gene expression are usually restricted to single cells. Although the obvious fact that these regulatory and/or transcriptional networks are in the multicellular context of a plant (where not all the cells are expressing the same genes at the same time) has received relatively little attention in the past, it has been recently addressed in the context of the phytochrome-mediated photomorphogenesis (Bou-Torrent et al., 2008b; Josse et al., 2008; Montgomery, 2008). In here we will focus on the SAS responses. In the context of a whole plant, shade signalling involves at least two steps: (i) intracellular signalling, a cellautonomous step triggered by the phytochrome molecules within the cell upon perception of the low R:FR signal and (ii) intercellular signalling, when the measurable changes in growth and development occur in a place that is distinct from the cells or the tissues that received the light signal. The distinction of two types of spatial phytochrome signalling implies that the sites of light perception and action might be physically separated in the plant, and highlights the importance of long-distance communication to fully understand how plant growth and development is regulated by light during the SAS. In white mustard, epicotyl elongation is induced after irradiating the whole seedling with simulated shade or EOD-FR treatments. However, localized irradiation of only the primary leaves also results in growth of the epicotyl. These experiments suggested the existence of diffusible signals generated by the shade stimulus in irradiated cells (primary leaves, site of light perception) and transmitted to distant responsive cells within a different organ, the stem (site of light action) (Casal and Smith, 1988; Morgan et al., 1980). Consistently, detaching the cotyledons of Arabidopsis seedlings abolishes the hypocotyl elongation response induced by simulated shade treatments, indicating the importance of the cotyledons for this response
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(our unpublished results). This separation between the site of light perception and action, however, is not conserved in the entire plant kingdom because there are examples in other plant species in which localized EOD-FR treatment of leaves does not stimulate stem elongation. In this latter case, detaching the leaves also affects the intensity of the stem response to the EOD-FR treatments, suggesting that these organs also provide some diffusible signals that participate in the stem elongation (Garcia-Martinez et al., 1987). Some data about the nature of this diffusible signal come from experiments performed with transgenic Arabidopsis lines that showed GUS activity specifically in the hypocotyl after EOD-FR treatments. When these lines were used for localized irradiation of the cotyledons, GUS activity was clearly detected in the hypocotyls (Tanaka et al., 2002). Furthermore, application of the auxin transport inhibitor NPA reduced the EOD-FRdependent GUS activity in the hypocotyl, suggesting the involvement of auxin in the transmission of a shade-produced signal originated in the cotyledons (Tanaka et al., 2002). Consistently, simulated shade treatments of the auxin-responsive DR5:GUS marker line rapidly induces GUS activity in the cotyledons (Tao et al., 2008). Spatial and temporal expression analyses led to conclude that phytochromes are found in all tissues analysed and that most plant cells contain functional phytochromes (Sharrock and Mathews, 2006). In the context of a whole plant, the transcriptional networks modulated by the perception of low R:FR by the phytochromes can therefore be affected by the spatial and temporal distribution of the different factors that closely work with the phytochromes to regulate gene expression, e.g. PIF factors. Indeed, data extracted from transcriptomic profiles in roots, hypocotyls and cotyledons of Arabidopsis seedlings showed that the expression patterns of several PIF genes and a few early target genes of phytochrome action during the SAS differ in light-grown seedlings, supporting the hypothesis that phytochromemodulated transcriptional networks vary among the different organs of a seedling (Bou-Torrent et al., 2008b; Ma et al., 2005). In the case of HD-Zip transcription factors with a role in SAS responses, transgenic plants expressing a reporter gene driven by the 1 kb promoter region of ATHB2 and ATHB4 (lines PATHB2:GFP-GUS and PATHB4:GFP-GUS, respectively) showed a spatially restricted and different pattern of expression for the two promoters analysed (Fig. 8). Since the expression of these two genes is rapidly up-regulated by simulated shade, these observations support the postulate that upon low R:FR light perception, the transcriptional networks involved in SAS rapidly diverge in a cell- or organ-specific manner, eventually resulting in different organ responses to the same shade stimulus (Bou-Torrent et al., 2008b).
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Fig. 8. Histochemical analysis of transgenic lines PATHB2:GFP-GUS and PATHB4: GFP-GUS. X-gluc staining of 7-day-old light-grown seedlings expressing GFP-GUS under the control of 1 kb of the promoter region of ATHB2 (lines PATHB2:GFP-GUS) or ATHB4 (lines PATHB4:GFP-GUS). Two independent transgenic events are shown for each promoter construct.
IX. CONCLUDING REMARKS: FROM MASTER GENES TO REGULATORY MODULES OF THE SAS RESPONSES A recurrent goal in the field is to identify what genes are actual master regulators of the SAS morphological responses. For this purpose, functional analyses of some of the hundreds of PAR genes described by different laboratories (Devlin et al., 2003; Franklin and Whitelam, 2007; Salter et al., 2003; Tao et al., 2008) has been an ongoing strategy (summarized in Table I). With this goal in mind, HFR1 was reported as a master regulator of SAS (Sessa et al., 2005). However, its master (central) role in the regulation of the SAS has been recently challenged because of the shade conditions used by these authors. Thus, the role of hfr1 mutant seedlings is similar in strength to that shown for PAR1, PAR2 or PIL1 group under the same conditions (RoigVillanova et al., 2006, 2007). Mutants deficient in PIF4 and PIF5 showed a significantly reduced response to simulated shade, indicative of a role for these factors as regulators of the SAS (Lorrain et al., 2008). However, they are still able to clearly respond to simulated shade (Lorrain et al., 2008). In general, mutants deficient in single PIF or PAR genes show, in the best cases, mild defects on the studied traits. When genetically analysing the role of early components of the transcriptional network initiated by the phytochromes during seedling de-etiolation, similar mild effects were reported in single mutants (Khanna et al., 2006). Together, it appears that simulated shade modulates the expression levels of a wide diversity of partially redundant (rather than master) positive and negative regulators of SAS. The distinct phenotypic effects on plant development observed after overexpression of PIF and PAR genes suggests that these factors might be organized into
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regulatory modules controlling distinct circuits of the shade-regulated transcriptional network involved in implementing different SAS responses (BouTorrent et al., 2008a). Several other observations, indicative of the independent regulation of some SAS traits and of the expression of sets of PAR genes, are consistent with and suggestive of the existence of regulatory modules: (i) low R:FR signals can activate the CBF regulon specifically at 16˚C but not at 22˚C, without affecting the shade-controlled regulation of other PAR genes (Franklin and Whitelam, 2007), (ii) the expression of the sub-set of PAR genes belonging to the HD-Zip class II (such as ATHB2, ATHB4 HAT1, HAT2 and HAT3) is directly interconnected (Ohgishi et al., 2001; Sawa et al., 2002; Sorin et al., 2009), (iii) COP1 and DET1 have a differential effect on sub-sets of direct and primary target genes of phytochrome action (RoigVillanova et al., 2006) and (iv) discrete pathways control different components of the SAS responses, such as hypocotyl elongation, petiole elongation and flowering induction (Botto and Smith, 2002; Halliday and Fankhauser, 2003). The identification of these regulatory modules appears therefore as fundamental and basic to understand how the complex phytochromemodulated transcriptional web(s) controls SAS responses. The shade-induced transcriptional network is dynamic and affected by both endogenous (e.g. genotype, developmental stage) and exogenous (e. g. temperature, circadian time) factors, providing a molecular basis for the complexity of the reported SAS responses. For instance, increased freezing tolerance has been reported as a SAS response specifically in plants grown at low temperature (Franklin and Whitelam, 2007). Finally, it is important to keep in mind that these responses take place in the spatial context of a plant, involving the integration of local and distant signals to achieve a coordinated response among organs (Bou-Torrent et al., 2008b; Montgomery, 2008; Salisbury et al., 2007).
ACKNOWLEDGEMENTS The authors thank M. Rodrı´guez-Concepcio´n for his comments on the manuscript. Financial support of JB-T and MS-M came from the CSIC (JAEdoc and JAEpre Programmes, respectively). AG and MG received predoctoral fellowships from FPU and FPI programmes, respectively, of the Spanish Ministry of Science and Innovation (MICINN). NC-E received a predoctoral fellowship from the Gobierno de Chile. Our research is supported by grants from the Generalitat de Catalunya (Xarxa de Refere`ncia en Biotecnologia and 2009SGR-697) and MICINN–FEDER (BIO2008-00169 and CSD2007-00036).
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Responses of Halophytes to Environmental Stresses with Special Emphasis to Salinity
KSOURI RIADH,* MEGDICHE WIDED,* KOYRO HANS-WERNER† AND ABDELLY CHEDLY*,1 *
Laboratoire d’Adaptation des Plantes aux Stress Abiotiques, Centre de Biotechnologie a` la Technopole de Borj-Ce´dria (CBBC), BP 901, 2050 Hammam-lif, Tunisia † Institute for Plant Ecology, Justus-Liebig University, Giessen, Heinrich-Buff-Ring 26-32, D-35392 Giessen, Germany
I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Molecular Regulation of Ion Homeostasis and Involvement of Intermediary Signalling Components . . . . . . . . . . . . . . . . . . . . . B. Compatible Metabolites that can Prevent Detrimental Changes . C. Anti-Oxidative Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. The Involvement of Plant Hormones in the Transduction of the Stress Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Conclusions and Future Perspectives . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Corresponding author: E-mail:
[email protected]
Advances in Botanical Research, Vol. 53 Copyright 2010, Elsevier Ltd. All rights reserved.
0065-2296/09 $35.00 DOI: 10.1016/S0065-2296(10)53004-0
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ABSTRACT The devastating consequences of desertification and water scarcity can be seen and felt all over the world. About 3.6 billion of the world’s 5.2 billion hectares of dryland used for agriculture have suffered erosion, soil degradation and salination. Desertification can also have a serious impact far beyond the borders of directly affected countries, can hinder efforts for sustainable development and introduces new threats to human health, ecosystems and national economies. Forced migration and potential for inter-state conflict add to the urgency of this global problem. Therefore, solutions are desperately needed, such as the improvement of drought and salinity tolerance of crops. A more recent idea is to take advantage of the genetic potential of crops still have (Glenn et al., 1998). This idea requires to first identify accessions of a crop species showing enhanced drought and/or salt tolerance. Another promising approach under discussion is to use (xero-) halophytes instead of glycophytic crops or to transfer genes from halophytes to regular crops to enhance their salt resistance. Halophytes grow in a wide variety of saline habitats, from coastal regions, salt marshes and mudflats to inland deserts, salt flats and steppes (El Shaer, 2003). Halophytes are introduced already at many sites to valorize highly salinized zones and non-conventional water resources such as waste water. However, the sustainable use of these plants depends under osmotic and ionic stress on the ability to exhibit a wide range of responses at molecular, cellular and whole plant levels. This includes the synthesis of compatibles solutes/ osmolytes, specific proteins and radical scavenging mechanisms, ion uptake and compartmentation of injurious ions. Acclimation of these plants to salinity depends also upon activation of cascades of molecular networks involved to stress sensing, signal transduction and the expression of specific stress-related gene and metabolites. The product of these genes may participate in the generation of regulatory molecules such as plant hormones. The deeper understanding and replicability of the physiological and biochemical basis of drought and salt resistance can provide a basis for the cultivation of suitable plants in regions threatened by desertification and water scarcity sustainable culture conditions. Even the drylands could offer tangible economic and ecological opportunities. The aim of this chapter is to screen the responses of halophytes with regard to drought and salt resistance to identify early indicators allowing successful breeding (Koyro et al., 2009).
I. INTRODUCTION Abiotic stresses such as drought and salinity are serious threats to agriculture and the natural status of the environment. They are recurring features of nearly all the world’s climatic regions since various critical environmental threats with global implications have linkages to water crises (Gleick, 1994, 1998). These threats are collaterally catalysed by global warming and population growth. In the face of a global scarcity of water resources and the increased salinization of soil and water, abiotic stress is the primary reason of crop loss worldwide, reducing average yields for most major crop plants by more than 50% (Bray et al., 2000; Wang et al., 2003) and will soon become even more severe as desertification covers progressively more of the world’s terrestrial area (Vinocur and Altman, 2005).
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A promising approach under discussion, to avoid or reduce crop losses because of drought or salinity, is to use (xero-) halophytic instead of glycophytic crops (Koyro and Lieth, 2008). Plants are classified as glycophytes or halophytes according to their capacity to grow on high salinities (Sairam and Tyagi, 2004). In fact, halophytes are remarkable plants that tolerate salt concentrations even above sea water salinity (Flowers and Colmer, 2008; Koyro and Lieth, 2008; Ksouri et al., 2008). The viability of plants in saline habitats depends on their ability to cope with several major constraints as there are (i) water deficit, (ii) restriction of CO2 uptake, (iii) ion toxicity and (iv) nutrient imbalance. In addition is the salinity tolerance of halophytic plants in most cases multigenic; it comprises a wide range of morphological, physiological and biochemical mechanisms on whole plant, tissue and cellular/molecular levels (Ashraf and Harris, 2004; Koyro et al., 2009; Wang et al., 2003). Only rarely a single parameter is of major importance for the ability to survive at high NaCl salinity. A comprehensive study with the analysis of at least a combination of several parameters is a necessity to get a survey about the mechanisms which in the end lead to the salinity tolerance of individual species. The absence of such a coordinated response can lead (ionic stress and osmotic stress) to the disruption of homeostasis and compartmentation in the cell and to secondary stresses such as nutritional imbalances and oxidative stress (Zhu, 2002). The accumulation of toxic ions such as Naþ and Cl in the cells, can adversely affect cell membrane integrity, enzyme activities, nutrient acquisition (nutrient imbalance) and the function of the photosynthetic apparatus (Geissler et al., 2009; Tester and Davenport, 2003). Although, Naþ represents the major ion causing toxicity related with high salinity, some plant species are also sensitive to chloride, the major anion found in saline soils. High soil concentrations of Naþ and/or Cl disturb the osmotic balance in the plant (‘physiological drought’) and affect the uptake of water (Tu¨rkan and Demiral, 2009). The resulting osmotic stress leads to stomatal closure, a reduced rate of photosynthesis and a reduction in plant growth partly caused by the decrease in carbon assimilation, an increase of energy metabolism but also as a result of direct inhibition of cell division and expansion (Koyro et al., 1993; Megdiche et al., 2008; Munns, 2002). A secondary effect of high salinity is the production of reactive oxygen species (ROS) that are highly destructive to lipids, nucleic acids and proteins (Geissler et al., 2010; Kant et al., 2006; Tu¨rkan and Demiral, 2009). Understanding the mechanisms of plant salt tolerance will help to breed or genetically engineer salt-resistant crops. Therefore, studies on the mechanisms by which halophytes respond or adapt to salt stress are crucial for their sustainable use. Halophytes have evolved several physiological, biochemical
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and molecular mechanisms to cope with the detrimental effects of salt stress such as (i) selective accumulation or exclusions of ions, (ii) control of ion uptake by roots and transport into leaves, (iii) prevent accumulation of Naþ (and Cl) in the cytoplasm by vacuolar compartmentation of Naþ (and Cl) for lowering cell water potential and sustaining water absorption from the soil, (iv) synthesis and accumulation of non-toxic (compatible) osmolytes in the cytosol including quaternary ammonium compounds (glycinebetaine (GB)), amino acids (proline (Pro)), sugars and polyols, (v) change in photosynthetic pathway (C3, C4 and CAM (crassulacean acid metabolism)), (vi) induction of anti-oxidative system for ROS elimination by anti-oxidant compounds and by ROS-scavenging enzymes and (viii) stimulation of phytohormones such as abscisic acid (ABA) and jasmonic acid (JA) (Kant et al., 2006; Koyro et al., 2009; Parida and Das, 2005). The physiological response to salinity bases on changes in gene expression. These genes encode proteins involved in numerous biological processes as well as a large number of proteins of unknown function. Importantly, many of the affected genes such as transcription factors and kinases may function in salt stress-responsive regulatory circuits (Kant et al., 2006; Kreps et al., 2002). Indeed, it has been hypothesized that differences in salt tolerance mechanisms between close related species such as the salt-sensitive glycophyte Arabidopsis thaliana and the salt-tolerant halophyte Thellungiella halophila result from changes in the regulation of the same basic set of genes involved in salt resistance (Inan et al., 2004). Numerous salt stress-responsive genes that are involved in signal transduction, transcriptional regulation and conferment of stress tolerance have been isolated (Chinnusamy et al., 2006). Based on the identification of these genes, progress has been achieved for the improvement of plant salt stress tolerance by genetic manipulation (Zhang et al., 2006). These genes include three major categories: (i) those that are involved in signalling cascades and in transcriptional control, such as SOS pathway, MyC and MAP kinases, phospholipases, and transcriptional factors such as HSF and ABF/ABAE family, (ii) those that function directly in the protection of membranes and proteins, such as Hsps and chaperones, LEA proteins, osmoprotectants and free radical scavengers and (iii) those that are involved in water and ion uptake and transport such as aquaporins and ion transporters (Wang et al., 2003). Indeed, it has been hypothesized that differences in salt resistance mechanisms between salt-sensitive glycophytes, and salt-tolerant halophytes (such as A. thaliana and T. halophila) result from changes in the regulation of the same basic set of genes (Kant et al., 2006; Xiong and Zhu, 2002a; Zhu, 2000, 2001a). This review uncovers the individual biochemical and molecular mechanisms for salt resistance. This includes the perception of salt stress, signalling
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pathways to ensure ion and osmotic homeostasis, the responses at the antioxidative level and recent advances to provide better understanding of the phenomenon of salt resistance. A. MOLECULAR REGULATION OF ION HOMEOSTASIS AND INVOLVEMENT OF INTERMEDIARY SIGNALLING COMPONENTS
Plants, whether glycophytes and halophytes, cannot tolerate high concentrations of Naþ or Cl in the cytoplasm because enzymes (proteins) can be easily precipitated by these ions. Therefore under saline conditions, they either store the excessive salts into the vacuole of metabolic active tissues such as the mesophyll or compartmentalize the ions in not photosynthetic active tissues, or even in adult tissues to facilitate the metabolic functions (Iyengar and Reddy, 1996; Parida and Das, 2005; Zhu, 2003). In fact, the control of intracellular Naþ content is vital for plants coping with salinity, and is partly accomplished by excluding and/or compartmentalizing Naþ, thereby preventing its excessive cytosolic build-up (Apse and Blumwald, 2002). These both processes are mediated by Naþ/Hþ antiporters, which use the pH gradient generated by the plasma membrane and vacuolar Hþ-ATPases (localized at the plasmalemma and the tonoplast, respectively), to actively transport Naþ against its electrochemical gradient towards the vacuole and/or through the plasma membrane (Zhu et al., 2001a, b). However, Naþ/Hþ antiporter activity can increase (e.g. in tomato roots: Wilson and Shannon, 1995; in sun flower roots: Ballesteros et al., 1997), in salt-tolerant species (such as Plantago maritima), as much or even more as in salt-sensitive species (such as P. media) (Staal et al., 1991). Recently, Debez et al. (2004, 2006) showed that the vacuolar Hþ-ATPase activity of leaves of Cakile maritima was significantly enhanced up to 300 mM NaCl salinity (239% of the control value), while decreasing at higher salinities. In contrast, plasma membrane Hþ-ATPase activity increased significantly (on an average by 80%) in plants exposed to 300–500 mM NaCl. An increasing of ATPase activities was also observed in the glycophyte Sorghum and several halophytes such as Spartina townsendii, Aster tripolium and Sesuvium portulacastrum (Koyro et al., 1993; Ramani et al., 2006). The salinity tolerance was also positively correlated with elevated levels of AtNHX1 transcript, and with protein and vacuolar Naþ/Hþ antiporter activity in Arabidopsis (Apse et al., 1999). Although salt-stress sensors remain elusive, some of the intermediary signalling components have been identified. The so called SOS (salt overly sensitive) stress-signalling pathway is involved in the regulation of the ion (Naþ and Kþ) homeostasis (Hasegawa et al., 2000; Sanders, 2000). This signalling pathway functionally resembles the yeast calcineurin cascade that
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controls Naþ influx and efflux across the plasma membrane (Bressan et al., 1998). In plants, the input of SOS is due to excessive intracellular or extracellular Naþ, which somehow triggers a cytoplasmic Ca2þ signal (Zhu, 2001b). The output is changes of the expression and activity of ion transporters such as Naþ, Kþ and Hþ (Sairam and Tyagi, 2004). Liu and Zhu (1998) screened several mutants to identify genes providing salt resistance. They characterized SOS genes through positional cloning. The SOS pathway comprises three keys components: SOS3, acting as Ca2þ sensor (Liu and Zhu, 1998); SOS2, a serine/threonine protein kinase (Liu et al., 2000) and SOS1, a plasma membrane Naþ/Hþ antiporter (Shi et al., 2000). In the SOS pathway, salt stress elicits transiently the intracellular Ca2þconcentration, which activates the Ca2þ binding protein SOS3 (Ishitani et al., 2000). SOS3 interacts with the SOS2 protein kinase (Halfter et al., 2000; Liu et al., 2000) and this complex regulates downstream effectors such as the plasma membrane-localized Naþ/Hþ antiporter SOS1 and other ion transporters (Zhu, 2000). Thus, Naþ/Hþ antiport activity of SOS1 results in efflux of excess Naþ ions and thus contributes to Naþ ion homeostasis (Halfter et al., 2000; Ishitani et al., 2000; Liu et al., 2000; Quintero et al., 2002). SOS1 has also been reported to protect plasma membrane Kþ transport (Shabala et al., 2005). Under salt stress, SOS3/SOS2 protein kinase complex appears to down-regulate the activity of AtHKT1 (low-affinity Naþ transporter) on gene level (Mahajan and Tuteja, 2005) which mediates Naþ entry into the root cells of Arabidopsis (Uozumi et al., 2000; Zhu, 2002). However, the effectivity of SOS3 depends on Ca2þ and N-myristoylation (Ishitani et al., 2000). SOS2 is shown to interact with vacuolar Naþ/Hþ antiporter (NHX) influencing its Naþ/Hþ exchange activity, resulting in sequestration of excess Naþ ions into vacuolar compartment and thus further contributing ion homeostasis (Qiu et al., 2002). In addition, the importance of SOS2 for the salt tolerance was shown by knockout mutants. Active SOS2 kinase enhanced a Naþ/Hþ exchange activity in purified plasma membrane vesicles from wild type but not in SOS1-1 mutants (Wu et al., 1996). In SOS2-2 and SOS3-1 mutants (Zhu et al., 1998). In these mutants is the plasma membrane Naþ/Hþ exchange activity very low, but can be recovered to near wild-type levels by addition of activated SOS2 in vitro to the membrane vesicle preparations (Qui et al., 2001). The plasma membrane Naþ/Hþ antiporter has a long tail, that is protruded to be on the cytoplasmic side. These long cytoplasmic tails has been proposed to function as sensor of all solutes they move across the membrane (Sairam and Tyagi, 2004). The possibility of SOS1 being both, a transporter and a sensor, cannot be dismissed. Besides being regulated by SOS2, the SOS1 activity may also be regulated by SOS4. SOS4 catalyses the formation of pyridoxal-5-phosphate (PLP), an active
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form of vitamin B and an essential cofactor for many cellular enzymes. It is well known, that PLP and its derivatives regulate the activity of certain ion transporters in animal cells (Shi and Zhu, 2002). Recently, Chung et al. (2008) showed, that NaCl stress-induced Ca2þ spike and thereby caused SOS1 activation and thus apoplastic alkalinization, which consequently activated plasma membrane-bound NADPH oxidase. Chung et al. (2008) suggested, that the ROS generated by NADPH oxidase stabilize SOS1 mRNA, thereby greatly increasing its activity and subsequently NADPH oxidase activity. These data give some helpful hints about the possible role of SOS1 at early signalling step of a signal transduction pathway that is common to several abiotic stresses (Tu¨rkan and Demiral, 2009). Transient increases in cytosolic Ca2þ are perceived by various Ca2þ-binding proteins. In the case of abiotic stress signalling, there is evidence, that Ca2þ-dependent protein kinase (CDPKs) and the SOS family of Ca2þ sensors are very important for the coupling of this universal inorganic signal to specific protein phosphorylation cascades. CDPKs are serine/threonine protein kinases with a C-terminal calmodulin-like domain with up to 4-EF-hand motifs that can directly bind Ca2þ. A number of studies have shown that CDPKs are induced or activated by abiotic stresses suggesting that they may be involved in the signal path (Hwang et al., 2000; Xiong et al., 2002b). In rice, the overexpression of OsCDPK7 resulted in increased osmotic stress tolerance (Saijo et al., 2000). Regarding the role of CDPK in stress signal transduction, there is uncertainness how it might be connected to other signalling molecules. In animals and yeast is a lack of a clear connection between Ca2þ binding protein/calmodulin and MAPK pathways. In fungi, Patharkar and Cushman (2000) obtained a CDPK-interacting protein (CSP1) from a yeast two-hybrid screen. CSP1 is a two-component pseudo-response regulator protein that could serve as a transcriptional activator, suggesting a potential role for CDPK in directly shuttling information to the nucleus to activate gene expression. In addition to Ca2þ-regulated protein kinase pathways, plants and fungi also use other phospho-protein modules for abiotic stress signalling. In yeast, the HOG1 MAPK (mitogen-activated protein kinase) pathway is activated in response to hyperosmolarity and is responsible for increased production of osmolytes (Xiong et al., 2002b). A number of studies showed that several MAPK components are activated or their gene expression induced by salt and other stresses (Xiong and Zhu, 2001, 2002b). ROS are important for the MAPK pathways, activated by receptors/sensors such as protein tyrosine kinases, G-protein-coupled receptor and two-component histidine kinases. Abiotic stress such as salinity and drought induce the accumulation of ROS such as hydrogen peroxide. Exogenous H2O2 or
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ABA-induced H2O2 activates Ca2þ channels in guard cells and mediates stomatal closure (Pei et al., 2000). In Arabidopsis, it was shown that H2O2 activates the MAP kinase ANP1 and is involved in the regulation of gene expression in plants (GST6, HSP182 and GH3). Interestingly, tobacco plants over-expressing the tobacco ANP1 orthologue NPK1, exhibit an increased tolerance to heat shock, freezing and salt stress. However, the expression of the stress-responsive gene RD29A is not affected by either H2O2 or the activated MAPK cascade (Kovtun et al., 2000), suggesting that the pathways for the regulation of DRE/CRT genes (Shinozaki and Yamaguchi-Shinozaki, 2000) are activated through different mechanisms (Xiong and Zhu, 2002c). Similarly, the osmotic stress activated MAPK pathways react to other stresses such as UV and g irradiation, cytokines, certain mitogens and oxidative stress. These pathways play a major role in apoptosis, cytokine production, transcriptional regulation and cytoskeletal reorganization (Obata et al., 2000). B. COMPATIBLE METABOLITES THAT CAN PREVENT DETRIMENTAL CHANGES
Severe osmotic stress causes detrimental changes in cellular components. The best characterized biochemical response of plant cells to osmotic stress is the accumulation of high concentrations of either organic ions or other low-molecular-weight organic solutes termed compatible solutes because they do not interfere with essential metabolic (enzymatic) reactions. Organic solutes play a crucial role in higher plants grown under saline conditions. However, their relative contribution varies among species, cultivars and even between different compartments within the same plant (Ashraf and Harris, 2004). A wide range of metabolites, that can prevent these detrimental changes, have been identified, including mono- ,di-, oligo- or polysaccharides (glucose, fructose, sucrose, trehalose, raffinose and fructans), sugar alcohols (mannitol, glycerol and methylated inositols), quaternary amino acid derivatives (Pro, GB, b-alaninebetaine and prolinebetaine), tertiary amines (1,4,5,6-tetrahydro-2-mehyl-4-carboxyl pyrimidine) and sulphonium compounds (choline-O-sulphate, dimethylsulphoniopropionate) have been suggested to accomplish this function in halophytes (Flowers and Colmer, 2008; Vinocur and Altman, 2005). The primary function of compatible solutes is to reduce the water potential, to maintain turgescent cells and to ensure balanced water relations (Wang et al., 2003). In addition, high concentration of compatible solutes exist primarily in the cytosol, to balance the low water potentials achieved by high apoplasmic and vacuolar Naþ and Cl concentration (Tu¨rkan and Demiral, 2009). Recent studies indicate that compatible osmolytes also protect sub-cellular structures, and
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mitigate oxidative damage caused by free radicals produced in response to salt stress (Slama et al., 2008; Smirnoff and Cumbes, 1989). In many halophytes, organic osmolytes such as Pro or GB accumulate at suitably high concentrations to create osmotic potentials even below 0.1 MPa. However, the concentrations of compatible solutes that accumulate are not so high in glycophytes to generate osmotic pressure (Tu¨rkan and Demiral, 2009). This difference between halophytes and glycophytes can be used as an early indicator for salt resistance. Therefore, in the next chapters, the most important compatible solutes are described in detail.
1. Betaines The quaternary ammonium compounds that function as effective compatible osmolytes in plants subject to salt stress are GB, b-alaninebetaine, prolinebetaine, choline O-sulphate, hydroxyprolinebetaine, and pipecolatebetaine (Ashraf and Harris, 2004). GB occurs most abundantly in response to a variety of abiotic stress conditions by numerous organisms including bacteria, cyanobacteria, algae, fungi, animals and many plant families such as Chenopodiaceae and Gramineae (Tu¨rkan and Demiral, 2009). This metabolite is mainly located in chloroplasts and plays a vital role in the stroma adjustment and protection of thylakoid membranes, thereby maintaining the photosynthetic activity (Jagendorf and Takabe, 2001). GB protects the photosystem II (PS-II) complex at high salinity (Murata et al., 1992) and at extreme temperatures or pH (Mohanty et al., 1993). GB also protects membranes against heat-induced destabilization and enzymes, such as RUBISCO, against osmotic stress (Ma¨kela¨ et al., 2000). In higher plants, GB is synthesized from serine via ethanolamine, choline by two-step oxidation reactions that were catalysed by choline monooxygenase and betaine aldehyde dehydrogenase , respectively (Russell et al., 1998). The insertion of serine and glycine can be taken as an indicator for the close relationship of the photorespiration (peroxisomes) to the synthesis of GB. Besides this, recently a biosynthetic pathway of GB from glycine with the involvement of two N-methyl transferase enzymes has been reported (Waditee et al., 2005). Highly tolerant genus such as Spartina and Distichlis accumulated the highest levels, moderately tolerant species intermediate levels and sensitive species hardly any GB (Rhodes Hanson, 1993). Genetic evidence that GB improves salinity tolerance has been obtained for many important agronomical crops such as tobacco, tomato, potato, barley, maize and rice. These listed plants have long been a potential target for engineering GB biosynthesis pathway and thus for resistance against different environmental stress conditions (Sairam and Tyagi, 2004; Tu¨rkan and Demiral, 2009). The
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importance of N-methyltransferase for the stress tolerance could be also shown for Arabidopsis. Genetically modified plants of this genus accumulated betaine to significant levels at different environmental stress conditions and hence improved seed yield (Waditee et al., 2005). A moderate stress tolerance was noted in some transgenic lines based on relative shoot growth in response to salinity, drought and freezing. Huang et al (2000) reported metabolic limitation in betaine production in transgenic plants. In fact, A. thaliana, Brassica napus and Nicotiana tabacum were transformed with bacterial choline oxidase cDNA, the levels of GB were only between 5 and 10% of the levels found in natural betaine producers. Beyond this, choline-fed transgenic plants synthesized substantially more GB. This result was taken as a hint, that these plants require a distinct endogenous amount of choline to synthesize an adequate amount of GB (Sairam and Tyagi, 2004). The protective effect of GB at salinity or drought could also be demonstrated by exogenous application at rice seedlings, soybean and common beans (Ashraf and Foolad, 2007; Demiral and Tu¨rkan 2006). GB pretreatment also alleviated salinity-induced peroxidation (oxidative damage) of lipid membranes of rice cultivars. Besides rice, the correlation between protective effect of GB and anti-oxidative defence system has been observed in chilling-stressed tomato (Park et al., 2006), drought- or salt-stressed wheat (Raza et al., 2007) and salt-stressed suspension cultured tobacco BY2 cells (Hoque et al., 2007).
2. Amino acids and amides It has been reported that amino acids (such as alanine, arginine, glycine, serine, leucine and valine, the non-protein amino acids citrulline and ornithine (Orn)), together with the imino acid Pro, and the amides such as glutamine and asparagine are accumulated in higher plants under salinity stress (Dubey, 1997; Mansour, 2000). Pro is known to occur widely in higher plants and can be accumulated in considerable amounts in response to salt stress and water deficit (Ali et al., 1999; Kavi Kishore et al., 2005). The Pro concentration is metabolically controlled. This imino acid is synthesized in plastids and cytoplasm while degraded to L-glutamate (Glu) in mitochondria. There are two different precursors of Pro in plants; Glu and Orn. Pro is synthesized from Glu via glutamic-g-semialdehyde (GSA) and D1-pyrroline5-carboxylate (P5C). P5C synthase (P5CS) catalyses the conversion of Glu to P5C, followed by P5C reductase (P5CR), which reduces P5C to Pro (Ashraf and Foolad, 2007). The other precursor for Pro biosynthesis is Orn, which is transaminated to P5C by a mitochondrial Orn-g-aminotransferase (OAT)
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enzyme (Verbruggen and Hermans, 2008). In the reverse reaction, Pro is metabolized to Glu in a feedback manner, via P5C and GSA with the aid of Pro dehydrogenase followed by P5C dehydrogenase (P5CDH) (Wang et al., 2003). The contribution of Glu and Orn pathways to stress-induced Pro synthesis differs between species and it has been shown that stress-tolerant plants are able to accumulate Pro in higher concentrations than stress-sensitive plants. Slama et al. (2008) showed a positive correlation between Pro accumulation and tolerance to salt, drought and the combined effects of these stresses. Osmotic stress (particularly mannitol stress) led to a considerable increase of the Pro concentration in the obligatory halophyte Sesuvium portulacastrum, while the contents in soluble sugars contents and in Naþ remained unchanged. In drought-stressed plants, the concentration of Kþ, Naþ, Cl and Pro, as well as ornithine-d-aminotransferase (d-OAT) activity increased significantly. Inversely, Pro dehydrogenase activity was impaired. The re-watering lead to a recover of these parameters at values close to those of plants permanently irrigated with 100% of field capacity. The presence of NaCl and mannitol in the culture medium (ionic and osmotic stress) lead to a significant increase of the Naþ and Pro concentration in the leaves, but it had no effect on leaf soluble sugar content. Slama et al. (2007a, b) assumed that the ability of NaCl to improve plant performance under mannitolinduced water stress is caused by an improved osmotic adjustment through Naþ and Pro accumulation, which is coupled with the maintenance of the photosynthetic activity . Similarly, salt-tolerant alfalfa plants doubled under salt stress the Pro concentration in the root rapidly and significantly more as salt sensitive ones (Petrusa and Winicov, 1997). In addition to its role as an osmolyte for osmotic adjustment, Pro contributes to stabilizing sub-cellular structures (membranes and proteins), scavenging free radicals and buffering cellular redox potential under stress conditions. Pro is also involved in alleviation of cytoplasmic acidosis and sustaining NADPþ/NADPH ratios at required levels for metabolism (Hare and Cress, 1997) and thus supporting redox cycling (Babiychuk et al., 1995). Transgenic approaches proved an enhancement of the plant stress tolerance via over-production of Pro. For instance, transgenic tobacco (N. tabacum), over-expressing the p5cs gene that encodes P5CS, produced 10- to 18-fold more Pro and exhibited better tolerance under salt stress (Kishor et al., 1995). In Aradidopsis, the overexpression of an antisense Pro dehydrogenase cDNA resulted in an increased accumulation of Pro and a constitutive tolerance to freezing and a higher salt tolerance (Nanjo et al., 2003). Similarly, Borsani et al. (2005) reported that the Arabidopsis P5CDH (D1-pyrroline-5-carboxylate dehydrogenase) and SRO5, an overlapping gene of unknown function in the
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antisense orientation, produced two types of siRNAs, 24-nt siRNA and 21-nt siRNA. In fact, they compared the levels of salt stress-induced Pro accumulation in various mutant plants (dcl2, sgs3, rdr6 and nrpd1a) which lacked SRO5-P5CDH nat-siRNAs and cleavage of the P5CDH transcript, Pro accumulation was not significantly induced by salt stress or was induced to a lesser extent than in the corresponding wild type. This result is consistent with their inability to down regulate P5CDH under stress, thereby causing a continued Pro catabolism and reduced Pro accumulation. In contrast, the dcl1 and rdr2 mutants, which were able to degrade P5CDH mRNA, had the same Pro level as the wild type under salt stress. The wild-type level of Pro accumulation in dcl1 indicates that although the 21-nt P5CDH nat-siRNAs were not produced, the 24-nt SRO5-P5CDH nat-siRNA alone was sufficient to cause the down-regulation of P5CDH (Fig. 1). Salt stress
SRO5
Salt stress tolerance P5C
ROS
P5CDH
Proline
SRO5 P5CDH Salt induced
Constitutive P5CDH mRNA P5CDH mRNA degradation
SRO5 mRNA
DCL2 RDR6 SGS3 SDE4
24-nt nat-siRNA formation and initial cleavage RISC
DCL1
dsRNA and 21-nt nat-siRNA formation
Fig. 1. Diagram of phased processing of SRO5-P5CDH nat-siRNAs and its role in a salt-stress regulatory loop (Borsani et al., 2005).
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An alternative approach to improve plant stress tolerance is the exogenous application of Pro. This can lead to either osmoprotection or cryoprotection. For example, in various plant species growing under salt stress, among them the halophyte Allenrolfea occidentalis, exogenous application of Pro led to a higher osmoprotection and an increased growth (Yancey, 1994).
3. Sugars and sugar alcohols Several studies have been attempted to relate the magnitude of changes in soluble carbohydrate to salinity tolerance. Parida and Das (2005) found out that carbohydrates such as sugars (glucose, fructose, sucrose and fructans) and starch are accumulated under salt stress. Furthermore, Megdiche et al. (2007) proved that C. maritima plants accumulates high amounts of total soluble carbohydrates and Pro at high salinity (400 mM NaCl). The major functions of sugars and sugar alcohols are osmoprotection, osmotic adjustment, carbon storage and radical scavenging (Messedi et al., 2006). There is a difference between starch and sugar accumulation in short- and long-term recation (Silva and Arrabaca, 2004). In short-term water stress experiments, a decrease in sucrose and starch content was observed for Setaria sphacelata, a naturally adapted C4 grass while in long-term experiments, a higher amount of soluble sugars and a lower amount of starch were found. Silva and Arrabaca (2004) assumed that the shift of metabolism towards sucrose might occur because starch synthesis and degradation are more affected than sucrose synthesis. Trehalose, a rare, non-reducing sugar, is present in several bacteria and fungi and in some desiccation-tolerant higher plants (Vinocur and Altman, 2005). Under various abiotic stresses the disaccharide treahalose accumulates in many organisms as osmolyte and osmoprotectant, protects membranes and proteins in cells and reduces aggregation of denatured proteins (Ashraf and Harris, 2004). In the transgenic plants lead a comparatively moderate increase in trehalose levels to a higher photosynthetic rate and to a decrease in photooxidative damage during stress. Trehalose is thought to protect biological molecules from environmental stress (such as desiccation-induced damage), as suggested by its reversible water-absorption capacity (Penna, 2003). It was shown, that the contents of reducing and non-reducing sugars and the activity of sucrose phosphate synthase increase under salt stress, whereas starch phosphorylase activity decreases (Dubey and Singh, 1999). In general, the sugar alcohols are divided in acyclic (e.g. mannitol) and cyclic (e.g. pinitol) polyols. Polyols can make up a considerable percentage of all assimilated CO2 and can have several functions such as compatible solutes, low-molecular-weight chaperones and scavengers of stress-induced
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oxygen radicals (Bohnert et al., 1995). Polyols act in two indistinguishable ways: osmotic adjustment and osmoprotection (Parida and Das, 2005). In osmotic adjustment, they act as osmolytes facilitate the retention of water in the cytoplasm and enable the sequestration of sodium into the vacuole or apoplast (cell wall). These osmolytes protect cellular structures by interacting with membranes, protein complexes or enzymes. For instance, mannitol, a sugar alcohol that accumulates upon salt and water stress can alleviate abiotic stress. Transgenic wheat, expressing the mannitol-1-phosphatase dehydrogenase gene (mtlD) of Escherichia coli, was significantly more tolerant to water and salt stress (Abebe et al., 2003). Consequently, the transgenic wheat plants showed an increase in biomass, plant height and number of secondary stems (tillers). The cyclic sugar alcohols, pinitol and ononitol were accumulated in tolerant species such as the facultative halophyte Mesembryanthemum crystallinum when exposed to salinity or water deficit (Bohnert and Jensen, 1996). Pinitol can be synthesized from myoinositol by the sequential catalysis of inositolmethyl transferase and ononitol epimerase. An inositol methyl transferase (Imt) cDNA was isolated from transcripts NaCl induced in M. crystallinum (Vernon and Bohnert, 1992) and transgenic tobacco for Imt has been obtained (Vernon et al., 1993). C. ANTI-OXIDATIVE RESPONSES
Ionic and/or osmotic stresses imposed by high salinity on plants may create secondary stresses such as the accumulation of toxic or unwanted compounds, disturbance in cellular metabolism and nutritional disorders. In fact, environmental conditions (salinity, drought, heat/cold, light and other hostile conditions) may trigger in plants an oxidative stress, generating the formation of ROS (Jaleel et al., 2009). During salinity-induced oxidative stress, availability of atmospheric CO2 is reduced because of increased stomatal closure and in consequence the consumption of NADPH by the Calvin Cycle (Hernandez et al., 1999). When ferrodoxine is over-reduced during photosynthetic electron transfer, electrons may be transferred from PS-I to oxygen to form superoxide radicals (O2•) by the process called Mehler reaction, which initiates chain reactions that produce other harmful oxygen radicals such as singlet oxygen, superoxide anion, hydrogen peroxide and hydroxyl radical (Hsu and Kao, 2003). These cytotoxic ROS are continuously generated during normal metabolic processes in the mitochondria, peroxisomes and cytoplasm and they can damage lipids, proteins and nucleic acids when they are formed in excess (McCord, 2000). Being stationary through their life, most land plants are vulnerable to oxidative damage caused by environmental factors (Hippeli and Elstner, 1996). There is a constant need for efficient mechanisms to mitigate the
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oxidative damage initiated by ROS. Regulation of the number of chloroplasts, chlorophyll content, antennae constitutes and formation of reflective surfaces are some of the adaptive responses to sundry light conditions in order to keep ROS generation at minimal level. Besides this, plants have developed a subtile defence system by enzymatic and/or non-enzymatic anti-oxidants that includes hydrophilic (ascorbic acid (ASC), glutathione (GSH) and phenolics), hydrophobic (a-tocopherol and carotenoids) anti-oxidants and enzymes such as superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), monodehydroascorbate reductase (MDHAR), dehydroascorbate reductase and glutathione reductase (GR) (Dietz et al., 2006; Jaleel et al., 2009; Miller et al., 2009). The harmonized activities of multiple forms of these enzymes in different sub-cellular compartments achieve a balance between the rate of formation and removal of ROS, and sustain H2O2 to act as a signal. It is now widely accepted that the degree of oxidative cellular damage in plants exposed to abiotic stress is considerably controlled by the capacity of the anti-oxidative system (Ben Amor et al., 2005, 2006; Ben Hamed et al., 2007; Tu¨rkan et al., 2005). A correlation between anti-oxidant capacity (such as ROS scavenging) and salinity tolerance has been reported for several halophytic species such as Crithmum maritimum, C. maritima and Plantago genus (Ben Amor et al., 2005, 2006; Sekmen et al., 2007) and glycophytes such as cotton (Gossett et al., 1994), citrus (Gueta-Dahan et al., 1997), rice (Demiral and Tu¨rkan 2004, 2005), wheat (Meneguzzo et al., 1999), pea (Herna´ndez et al., 2000), sugar beet (Bor et al., 2003), wild tomato (Koca et al., 2006), aster (Geissler et al., 2009) and sesame (Koca et al., 2007). An enhanced activity of anti-oxidant enzymes such as SOD, CAT and peroxidases (PODs) especially in shoots can even improve the plant growth at moderate salt levels (50 mM NaCl) as shown by Ben Amor et al. (2005). This was related to enhanced activities of anti-oxidant enzymes such as SOD, CAT and PODs especially in shoots. Mn-SOD is generally found in mitochondria, Fe-SOD in chloroplasts and CuZn-SOD in chloroplasts and/or in cytosol (Asada, 1994). These data suggest that cytosolic compartments such as the mitochondrium, the peroxisomes and chloroplasts are crucial in the protection of tissues against superoxide formation when plants deal with moderate salinity (50 mM NaCl). In C. maritima accessions (Jerba and Tabarka) subjected to 0, 100 and 400 mM NaCl, Ben Amor et al. (2006) showed that intra-specific variability in salt response behaved differently. In fact, Jerba displayed a typical halophytic response, with enhanced biomass production in plants grown at 100 mM NaCl, while Tabarka was sensitive to the NaCl concentration. The better protection in Jerba against oxidative damage might be the outcome of the involvement of more efficient anti-oxidative systems. In fact, under NaCl stress, an up-regulation of anti-oxidative enzymes characterized the response of the salt-tolerant Jerba. These enzyme activities included SOD, POD and those of the
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ASC–GSH regenerating cycle. The relative salt tolerance of Jerba was associated with high antioxidant enzyme activities and GSH content, together with low MDA content, electrolyte leakage and H2O2 concentration. Moreover, in the same C. maritima Tunisian accessions, Ksouri et al. (2007) analysed the relationship between plant growth, leaf polyphenol content and anti-oxidant activity. Results showed that Jerba and Tabarka accessions differed in their growth response to salinity level, but displayed a unique relation of this response with MDA content, radical scavenging activity and polyphenol accumulation. Furthermore, transgenic plants over-expressing ROS-scavenging enzymes, such as SOD (Alscher et al., 2002), APX (Wang et al., 1999), GR (Foyer et al., 1995) and glutathione peroxidase (GPX) (Roxas et al., 2000) showed enhanced tolerance to osmotic, temperature, photo-inhibition and oxidative stresses. Tobacco plants over-expressing SOD featured an enhanced protection against high light and low-temperature-induced oxidative stress and maintained balanced photosynthetic rates under stress conditions (Gupta et al., 1993). The over-expressing of APX in the chloroplasts of transgenic tobacco plants or Arabidopsis mutants led to an enhanced tolerance to salinity, drought polyethylene glycol (PEG) and water stresses (Badawi et al., 2004; Miller et al., 2007). The over-expressing of glutathione-S-transferase led constitutively to an improved protection against both cold and salt stress (Roxas et al., 1997). Likewise, double-antisense (dAS) plants in tobacco, lacking cytosolic APX1 and peroxisomal CAT1, have previously been reported to show more tolerance to light stress in comparison to plants with suppressed expression of APX1 or CAT1 (Rizhsky et al., 2002). The enhanced resistance of dAS plants to oxidative stress has been correlated with suppressed photosynthetic activity, the induction of metabolic genes belonging to the pentose phosphate pathway, the induction of MDHAR and the induction of IMMUTANS, a chloroplastic homologue of mitochondrial alternative oxidase (Rizhsky et al., 2002). These results support the hypothesis, that signals are generated in different cellular compartments cells lacking distinct ROS-scavenging enzymes to enhance stress tolerance (Tu¨rkan and Demiral, 2009). D. THE INVOLVEMENT OF PLANT HORMONES IN THE TRANSDUCTION OF THE STRESS SIGNAL
Plants produce increased amounts of hormones such as ABA and ethylene during biotic and abiotic environmental stress. In addition, salicylic acid (SA) and JA may be involved in some parts of stress response (Parida and Das, 2005; Xiong et al., 2002b). The interaction of theses hormones can be important for the plant stress tolerance: A distinct supply of ethylene enhances ABA effects in seeds (Gazzarrini and McCourt, 2001) but counteracts ABA effects in vegetative tissues under drought stress (Spollen et al., 2000).
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1. Abscisic acid ABA is synthesized from a C40 precursor b-carotene via the oxidative cleavage of neoxanthin and a two-step conversion of xanthoxin to ABA via ABA-aldehyde. Increased ABA levels under drought and salt stress are mainly achieved by the induction of genes coding for enzymes that catalyse ABA biosynthetic reactions. Stress activation of ABA biosynthetic genes is probably mediated by a Ca2þ-dependent cascade, i.e. zeaxanthin epoxidase (ZEP), 9-cis-epoxycarotenoid dioxygenase (NCED), ABAaldehyde oxydase 3 (AAO3) and molybdenum cofactor sulphurase (MCSU) (Xiong and Zhu, 2003; Xiong et al., 2002a). The ABA-inducible genes are predicted to play an important role in the mechanism of salt tolerance in rice (Gupta et al., 1998). The increase of Ca2þ uptake is associated with the rise of ABA under salt stress and thus contributes to membrane integrity maintenance, which enables plants to regulate uptake and transport of ions under high levels of external (Chen et al., 2001). ABA can alleviate the inhibitory effect of NaCl on photosynthesis, growth and translocation of assimilates (Popova et al., 1995) and promotes the switch from C3 to CAM in M. crystallinum under salt stress (Thomas et al., 1992). Noaman et al. (2002) reported that the salt tolerance of the facultative halophytic species Lophopyrum elongatum and the closely related but less salt-tolerant wheat Triticum aestivum L. is enhanced when plants are enabled to gradually acclimate to salt. This acclimation to salt stress is regulated by ABA. A pre-treatment with this phytohormone can substitute the acclimation and can increase the tolerance to a salt shock (Parida and Das, 2005). Since the level of the plant hormone ABA increases with salt, drought and cold stress, it has been postulated to play a central role in the stress response besides seed production. Exogenous ABA can activate transcription of many of the genes induced by salt/drought stress, while other salt/droughtinducible genes are not activated by ABA, suggesting both ABA-dependent and ABA-independent signalling pathways (Bray, 1997; Sairam and Tyagi, 2004; Shinozaki and Yamaguchi-Shinozaki, 2000). The role of ABA in osmotic stress signal transduction was previously addressed by studying the stress induction of several genes in the Arabidopsis ABA-deficient mutant aba1-1 and dominant ABA-insensitive mutants abi1-1 and abi2-1. In summary, it was concluded, that low-temperature-regulated gene expression was relatively independent of ABA, osmotic-stress-regulated genes could be activated through both ABA-dependent and ABA-independent pathways (Shinozaki and Yamaguchi-Shinozaki, 2000; Xiong et al., 2002b). In another study with rice varieties differing in salinity tolerance, ABA-responsive proteins were examined. Classic non-dwarf donors showed a greater ABA
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induction in response to osmotic shock and a greater range of ABA-induced proteins than a salt-sensitive variety (Moons et al., 1995). 2. Jasmonic acid Recently Hilda et al., (2003) has demonstrated that salt-tolerant Lycopersicon cultivars contain higher levels of jasmonate (JA) than salt sensitive ones. JA is considered to mediate signalling, such as defence responses and flowering and senescence (Parida and Das, 2005). The signal transduction of JA is widely unknown. It is presumed that JA interacts with receptors in the cell that activate a signalling pathway resulting in changes in transcription, translation and other responses (Creelman and Mullet, 1997; Sairam and Tyagi, 2004). 3. Salicylic acid SA is an endogenous regulator of phenolic nature, which plays a role in the defence mechanisms against biotic and abiotic constraints and participates in the regulation of physiological processes in plants (Shakirova et al., 2003). This substance has the ability to induce systemic acquired resistance to different pathogen in plants as well as SA is also considered to be plant signalling molecule that play a key role in plant growth, development and defence responses under stress conditions (Misra and Saxena, 2009). Actually, a great interest was accorded to the ability of SA to induce a protective effect on plants under stress. First experimental indicates an increased resistance of wheat seedling to salinity and water deficit after supply of additional SA (Arfan et al., 2007; Bezrukova et al., 2001). Furthermore, SA seems to ameliorate the negative effects of low temperature in maize (Janda et al., 1999) as well as the damaging effects of heavy metal in rice plant (Mishra and Choudhuri, 1999). E. CONCLUSIONS AND FUTURE PERSPECTIVES
Halophytes show a diversity of growth responses to increasing salinity, ranging from inhibition up to dramatic stimulation. All halophytes display a common need to regulate cellular Naþ, Cl and Kþ concentrations as they adjust to the external water potential. The Naþ and Cl is stored mainly in vacuoles, which forces the production of a range of compatible solutes in the cytoplasm. However, the management of the homeostasis requires further investigation. Neither the co-ordinated network of gene regulation nor sensors or signalling cascades are actually known for most species (Flowers and Colmer, 2008). T. halophila, has been promulgated as a valuable model halophyte, owing to its genetic proximity to Arabidopsis and salt tolerance (Flowers and
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Colmer, 2008; Inan et al., 2004). Although the salt tolerance of T. halophila is clearly superior to that of Arabidopsis, and the genetic tools available will expedite research on this species, its relevance to other halophytes needs to be determined. Other species such as M. crystallinum or Aster tripolium are already well established as a model plants in halophyte research (Bohnert and Cushman, 2000; Geissler, 2009). These species tolerate high concentrations of salt and accumulate inositol, its derivatives, Pro or GB. It is more than likely that halophytes use in many cases the same range of transporters and regulatory networks as glycophytes, but with different set points; less likely is that they may use novel transporters and regulatory networks. Ultimately, the functional determination of all genes that participate in stress adaptation or tolerance reactions are expected to provide an integrated understanding of the biochemical and physiological basis of stress responses in plants (Fig. 2). Armed with such information from established models, it will be possible to comprehend and to optimize tolerance traits for improved crop productivity. Signalling types
I
II
III Ionic stress
Osmotic stress
Sensors Secondary signalling molecules
Ca2+ MAPKKK
Phosphoprotein cascade
2nd SM
2nd SM
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Transcription factor
(Transcription factor)
Antioxioxidants osmolytes?
LEA-like protein
Ion transporters
Protection, damage repair and cell cycle
Protection and damage repair
Ion homeostasis
MAPKK MAPK Transcription factor
Output
Function
Fig. 2. Major types of signalling for plants during drought and salt stress. Representative cascades, output and biological functions are shown. Type I signalling involves the generation of ROS scavenging and anti-oxidant compounds as well as osmolytes. The involvement of MAPK pathway in the production of osmolytes in plants has not been demonstrated experimentally. Type II signalling involves the production of stress-responsive proteins mostly of undefined functions. Type III signalling involves the SOS pathway which is specific to ionic stress. Signalling events for homologs of SOS3 (SCaBP) and SOS2 (PKS) are tentatively grouped with SOS3 and SOS2. Primary sensors are shown to be localized in the membrane. Receptors for secondary signalling molecules (2nd SM) are not shown (Xiong et al., 2002b).
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ACKNOWLEDGEMENTS This work was supported by the Tunisian Ministry of Higher Education, Research and Technology (LR02CB02) and by the Tunisian-French ‘Comite´ Mixte de Coope´ration Universitaire’ (CMCU) network # 08G0917.
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Plant Nematode Interaction: A Sophisticated Dialogue
PIERRE ABAD*,†,‡,1 AND VALERIE M. WILLIAMSON§ *
INRA, UMR 1301, 400 route des Chappes, F-06903 Sophia-Antipolis, France † CNRS, UMR 6243, 400 route des Chappes, F-06903 Sophia-Antipolis, France ‡ UNSA, UMR 1301, 400 route des Chappes, F-06903 Sophia-Antipolis, France § Department of Nematology, University of California, Davis, CA 95616, USA
I. Plant Parasitism in Nematodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. The Parasitic Life Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Adaptation to Plant Parasitism . . . . . . . . . . . . . . . . . . . . . . . . . II. Parasitism Genes in Sedentary Nematodes . . . . . . . . . . . . . . . . . . . . A. Effectors Involved in Plant Cell Penetration and Nematode Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Effectors Involved in Plant Defence Suppression . . . . . . . . . . . . C. Effectors Involved in Induction of the Nematode Feeding Site . . III. Key Plant Functions Manipulated During Nematode Infection. . . . . IV. Natural Plant Resistance and Nematode Virulence . . . . . . . . . . . . . . A. Non-Host, Resistant Host, Tolerant Host . . . . . . . . . . . . . . . . . B. Inheritance of Nematode Resistance . . . . . . . . . . . . . . . . . . . . . C. Resistance Phenotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
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Corresponding author: E-mail:
[email protected]
Advances in Botanical Research, Vol. 53 Copyright 2010, Elsevier Ltd. All rights reserved.
0065-2296/09 $35.00 DOI: 10.1016/S0065-2296(10)53005-2
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D. Cloning and Characterization of Nematode Resistance Genes: What Have We Learned? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Genetic Diversity and Virulence Development in the Nematode. V. Genomic Analysis of RKN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Genome Structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. The Secretome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. The RKN Genomes Shed Light on Nematode Diversity . . . . . . D. Developmental Pathways Conserved in Nematodes . . . . . . . . . . VI. Novel Strategies for Controlling Plant-Parasitic Nematodes . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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ABSTRACT Research on nematode parasitism tackles fundamental questions in plant development and host–parasite interaction. The plant-parasitic cyst and root-knot nematodes (RKNs) have evolved sophisticated strategies for exploiting plants with high impacts in agriculture worldwide. We review here recent knowledge acquired on putative parasitism genes and on their roles in the formation of permanent feeding sites within the host plant roots to ensure nematode survival. One of the most intriguing questions is how these nematodes are able to modulate or circumvent the host defence system. We then also discuss the mechanisms underlying the co-evolution between host plant resistance and nematode virulence. Finally, we present a brief overview of the status of genomic researches in RKNs. Their impacts in providing the development of environmentally sustainable new control strategies and fundamental clues as to the evolution and biology in plant-parasitic nematodes (PPNs) are illustrated.
I. PLANT PARASITISM IN NEMATODES Nematodes are roundworms, small metazoans with a simple body plan. They are among the most abundant groups of animals on Earth, due to their ability to adapt to hostile and changing environmental conditions. The phylum Nematoda already contains about 25,000 described species, but it is thought that as many as half a million currently unknown species belonging to this phylum have yet to be discovered. If this estimate is accurate, it would make roundworms second only to arthropods in terms of their diversity. Nematodes would account for an estimated four of every five animals in the world, in terms of the numbers of individuals (Wylie et al., 2004). The majority of nematode species are free-living, but others are predatory, or parasitic. The control of parasitic nematodes is a major challenge in human health and agriculture. Plant-parasitic nematodes (PPNs) are almost microscopic and virtually invisible to the naked eye when in the soil. A few nematode species feed on aerial parts of the plant, such as leaves, stems,
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flowers and seeds, but most feed on underground parts of plants, including roots, bulbs and tubers. It is often difficult to assess nematode damage to plants because of this underground, ‘hidden’ feeding activity. Most PPNs feed on root tissues, damaging the host principally by inhibiting root growth and disrupting vascular structure, thereby impairing water uptake, and by promoting microbial infections through wound sites or by serving as vectors for pathogenic viruses (Wyss, 1997). Nematicides, the use of resistant crop varieties and cropping practices are currently the most important and reliable means of controlling nematodes. However, most nematicides are non-specific, notoriously toxic and pose a threat to the soil ecosystem, ground water and human health. The use of agrochemicals is therefore increasingly restricted and is steadily decreasing. For example, methyl bromide, previously a commonly used nematicide, is now prohibited in Europe by EU regulations, making it impossible to establish a viable crop in some situations and rendering studies on nematode pathogenicity factors and natural plant defence strategies increasingly relevant for the development of safe, sustainable cropping systems. Plant parasitism occurs in two classes of the phylum Nematoda: Adenophorea and Secernentea. Adenophorean parasites are confined to the Longidoridae and Trichodoridae families of order Dorylaimida. These nematodes are exclusively migratory ectoparasites on plant roots and include several economically important vectors of soil-borne viruses. All Secernentean plant parasites belong to the suborder Tylenchida (Blaxter et al., 1998). Their parasitic habits may have evolved from fungus-feeding ancestors (Jasmer et al., 2003; Luc, 1987). Some species have adopted a ‘hit-and-run’ strategy, remaining migratory throughout their life cycle in the plant root, as exemplified by the genera Pratylenchus and Radopholus. An increase in the complexity of host–parasite interactions is associated with increase in the ability of parasites to regulate host plant genes in their favour. More specialized PPNs, such as sedentary endoparasitic nematodes, after an initial migratory phase, adopt a sedentary lifestyle involving the transformation of plant cells into complex feeding structures. Some of these nematodes have a narrow host range (e.g., cyst nematodes of the genera Heterodera and Globodera) whereas others, such as many root-knot nematodes (RKNs, Meloidogyne spp.), are able to reproduce on hundreds of unrelated host plant species. Most studies on PPNs have focused on these two groups of nematodes and the molecular basis of feeding structure establishment as they are generally considered to be the most damaging nematodes for crops. Recently, plant nematology is entering an era of comparative and functional genomics that should open new routes to effective and safe plant parasite control. In this chapter, we briefly describe plant parasitism in nematodes. We focus mainly on data
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collected from the two most damaging groups and summarise recent findings on parasitism genes in sedentary nematodes. This chapter also includes molecular data concerning key plant functions manipulated during nematode infection and natural plant resistance in relation to nematode virulence. In the last part of the chapter we will focus on the recent work on genomic research which may be crucial in developing novel strategies for controlling PPNs. A. THE PARASITIC LIFE CYCLE
Cyst nematodes and RKNs are thought to have evolved the sedentary lifestyle independently (Baldwin et al., 2004; Holterman et al, 2009). However, they have many features in common, including a simple life cycle generally taking 3–6 weeks, depending on the species and environmental conditions (Fig. 1). Both have four juvenile stages in addition to egg-laying adult female; these stages are separated by moults, during which the cuticle is replaced. These PPNs spend most of their active lives within plant roots, feeding on greatly modified host cells. The second-stage juvenile (J2) is the only infective stage. It burrows into the host root close to the growing tip. The tiny J2 (400 mm long and 15 mm wide) differ in their host invasion strategies and in the permanent feeding site that they establish. Cyst nematodes migrate through cells, causing the extensive necrosis of host cells between the point of entry and the vascular cylinder, where they induce their feeding site. By contrast, RKNs follow a complex migration pathway, causing no obvious damage. The J2 enters the root elongation zone and migrates intercellularly towards the root tip. Once it reaches the root
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Fig. 1. Sycytium (A) and gall (B) sections of Arabidopsis roots 10 days post infection with Heterodera schachtii and Meloidogyne incognita, respectively. G, giant cell; N, nematode; S, synticium. Scale bar: 100 mm.
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meristem, it turns round and migrates back up into the differentiating vascular cylinder. On arrival in the zone of protoxylem development, it induces the differentiation of a few selected host cells into feeding cells. Once they have established their feeding sites, cyst and root-knot J2 become sedentary and then undergo three moults to mature into adults. Sexual dimorphism with rotund females and vermiform males is associated with the sedentary lifestyle. The female is always sedentary, whereas the male becomes vermiform and motile again during the third moult, then leaves the root. Sex is determined by environmental conditions, with the frequency of males increased in conditions of crowding or poor nutrition (Triantaphyllou, 1985). The sedentary period is characterized by a feeding process in which secretions from the oesophageal gland cells of the nematode are injected into root cells via the stylet, inducing a transformation of the root cells into enlarged, multinucleate and active cells supplying the nutritional requirements of the nematode. At the end of the cycle, the female produces eggs. Cyst nematodes reproduce exclusively sexually, with eggs produced only after fertilization by the mobile, vermiform males. Sexual reproduction also occurs in some Meloidogyne species, but the most important tropical pathogens, including M. incognita, M. javanica and M. arenaria, reproduce via mitotic parthenogenesis (Castagnone-Sereno, 2006). When a female cyst nematode dies, her body wall forms a protective enclosure for the eggs. By contrast, RKN eggs are released onto the root surface in a protective, gelatinous matrix. In addition, cyst nematodes remain dormant with the cyst, enabling them to persist for up to 20 years without a host. By contrast, RKNs do not persist for extended periods without a host. The first-stage juvenile (J1) moults within the egg to produce the J2, which hatches under favourable conditions. The feeding sites produced in the host by the two types of nematodes play similar roles in sustaining nematode development, but differ profoundly in their ontogeny, although in both cases involving major modifications to plant gene expression. Cyst nematodes select a single cell, often in the vascular parenchyma, to induce a feeding cell, generally known as the initial syncytium cell. This cell displays active metabolism, with the cytoplasm expanding and becoming dense. The partial breakdown of the cell walls surrounding the cell initially selected, followed by fusion of the protoplasts of the adjacent cells, results in a progressively larger syncytium potentially incorporating hundreds of cells. Mature syncytia have enlarged nuclei with large nucleoli and a dense cytoplasm, and display pronounced cytoplasmic streaming (Lilley et al. 2005). By contrast, RKNs induce a feeding site composed of five to seven enlarged cells, known as giant cells. The first sign of giant-cell induction is the formation of binucleate cells. Additional
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mitoses without cell division result in the generation of multinucleate, hypertrophied cells with a dense cytoplasm and a very high level of metabolic activity. Giant cells may be up to 100 times as large as normal root vascular parenchyma cells and may contain up to 100 nuclei (Abad et al., 2003). The plant cells surrounding the nematode and its feeding site become hypertrophic and hyperplastic, resulting in gall formation. Solute uptake from the vascular system is enhanced by the development, by cell walls in syncytia and giant cells, of numerous ingrowths in contact with the xylem, and the constant withdrawal of cytoplasm by the nematodes converts the feeding cells into metabolic sinks for the host plant (Fig. 2). B. ADAPTATION TO PLANT PARASITISM
Secretions from sedentary nematodes are thought to orchestrate changes in host gene expression leading to the development of the feeding site (Gheysen and Fenoll, 2002; Jammes et al., 2005; Puthoff et al., 2003). These secretions may originate from several sources, including the cuticle, the amphids, the rectal glands, the excretory pore, and the oesophageal glands. In this last (A) Root-knot nematode life cycle
(B) Cyst nematode life cycle
Eggs hatch
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adult female
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Fig. 2. Life cycles of (A) root-knot nematodes and (B) cyst nematodes (adapted from Williamson and Gleason, 2003).
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Fig. 3. Nematode adaptation to plant parasitism. (A) Schematical representation of the anterior region of a J2 sedentary endoparasitic nematode (from Vanholme et al., 2004). (B) Esterase activity from Meloidogyne incognita amphids. (C) Feeding tube induced by the PPN Rotylenchulus reniformis in a cotton root cell (from Rebois, 1980). (D) Esophageal secretions from anterior end of J2 stage (stained with coomassie blue as a dye) after in vitro induction with resorcinol. S, stylet; PM, plasma membrane; FT, feeding tube; CW, cell wall.
case the secretions are released via the stylet, a specialized oral aperture characteristic of PPNs. The host cells modified for feeding are located in front of the nematode (at the anterior end), so the amphids and stylet appear to be the most likely organs responsible for secreting molecules directly involved in adaptation to plant parasitism (Hussey, 1989) (Fig. 3A). The amphids are a pair of chemosensory organs of the nematodes (Fig. 3B). In PPN, they may be involved in the complex recognition mechanisms guiding larval behaviour within the soil and the root (Piotte et al., 1999; Stewart et al., 1993). The stylet is a protractile, hollow, needle-like structure at the anterior end of the nematode, which is used to perforate the cell wall (Fig. 3A). It contains a central channel through which salivary secretions are released into the root cells to create an elaborate feeding site. The nematode inserts its stylet through the cell wall, without perforating the plasma membrane, which invaginates itself around the stylet. The nematode subsequently withdraws nutrients from its feeding cells during repeated rounds of ingestion, via a tiny opening in the plasma membrane localized to the area in which the stylet lumen is found. As the diameter of the stylet opening does
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not exceed 0.1 mm, cellular organelles, such as the mitochondria and plastids, cannot be taken up. During feeding, feeding tubes appear in the cytoplasm of the feeding cells. The presence of numerous feeding tubes in the feeding cell cytoplasm suggests that a new tube is formed with each insertion of the stylet into the cell. These feeding tubes are remarkable structures, with unique cell biological features. Their composition is largely unknown and it is thought that they are derived from plant cell components and assembled in response to molecules secreted by the nematode. An ultrastructural study of the feeding tubes induced by M. incognita in giant cells has shown these tubes to be crystalline in nature and surrounded by a complex system of membranes consisting of smooth endoplasmic reticulum in contact with the tube itself and rough endoplasmic reticulum at its ends (Hussey and Mims, 1991). These tubes may act as molecular sieves, allowing the extraction of only low-molecular-weight compounds to enable the developing nematode to feed without killing the feeding cell and avoiding obstruction of the stylet opening (Fig. 3C). Salivary secretions are produced from three oesophageal glands – a dorsal and two subventral glands – and are released into root tissues during different stages of parasitism (Fig. 3D). These glands are hypertrophied and unicellular, and have cytoplasmic extensions ending in a secretion bulb, the ampulla, connected to the oesophagus by an elaborate valve. The contents of the secretion vesicles produced by the dorsal gland are excreted from the ampulla into the esophagus, whereas the two subventral glands discharge their secretion vesicles immediately into the metacorpus (Fig. 3A). A model for the potential modes of action of salivary and amphidial secretions in the formation of feeding sites has been proposed where secretions may be either directly injected into the cytoplasm via the stylet or released into the apoplasm to interact with receptors present on the plasma membrane surface through an activation cascade. Nuclear localization signals have been identified in the sequences of these putatively secreted proteins, suggesting a direct effect on the nuclei of the feeding cells (Davis et al., 2004).
II. PARASITISM GENES IN SEDENTARY NEMATODES Most studies on the secretions of PPNs have focused on those produced by the dorsal and subventral oesophageal gland cells. This approach is based on the assumption that these secretions are involved in establishment and maintenance of the nematode feeding site (Davis et al., 2004; Neveu et al., 2003). Salivary secretions are also thought to be involved in plant cell wall degradation during root invasion and in protection against plant defence
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responses. Analyses of the proteins and transcripts present in salivary secretions have indicated that a large number of different proteins (up to 400) may be secreted through the stylet during parasitism (Bellafiore et al., 2008; Huang et al., 2003; Jaubert et al., 2002b). Salivary secretion proteins were initially identified with specific antibodies raised against salivary secretions or oesophageal glands (Hussey and Mims, 1990). Various monoclonal antibodies recognising amphids, oesophageal glands and/or salivary secretions specifically have been obtained (Davis et al., 1994; Rosso et al., 1996). These antibodies have been used to determine the subcellular distribution and pattern of expression of the secreted proteins during parasitism, to purify these proteins from salivary glands and to identify the genes encoding them (Ray et al., 1994). The first two b-1,4-endoglucanases identified in metazoans were characterized in subventral glands and salivary secretions from J2 of the cyst nematodes Globodera rostochiensis and Heterodera glycines (Smant et al., 1998). A glycoprotein (gp32) present specifically in the amphids of cyst nematode J2 was identified (Stewart et al., 1993). Incubation of larvae with the antigp32 antibody was shown to disrupt their migration towards tomato roots in vitro, suggesting that this glycoprotein may be involved in chemical signal detection. In parallel, studies based on the PCR amplification of ‘candidate genes’ encoding proteins playing a role in nematode parasitism identified several genes with predicted functions corresponding to secreted molecules and demonstrated a need for the targeting of several cellular processes for successful manipulation of the host response (Davis et al., 2000). A. EFFECTORS INVOLVED IN PLANT CELL PENETRATION AND NEMATODE MIGRATION
The plant cell wall constitutes a barrier against micro-organisms. It consists principally of carbohydrate polymers, such as cellulose, hemicellulose and pectin, wall proteins and, possibly, phenolic compounds. Like bacteria and fungi, PPNs have acquired enzyme systems for the degradation of plant cell walls. The hydrolysis of cellulose by PPN is catalyzed by b-1,4-endoglucanases. The expression of genes encoding such enzymes has been reported in both cyst nematodes and RKNs (Ledger et al., 2006; Rosso et al., 1999; Smant et al., 1998). These genes were shown to be transcribed and transduced only in the subventral glands of nematodes from the J2 within the egg until sedentary J3 in plants (de Boer et al., 1999). These enzymes bind cellulose and degrade carboxymethylcellulose, suggesting that they may act directly or indirectly on cellulose. The
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degradation of hemicellulose polymers by RKN has been demonstrated by the characterization of an endo-1,4-b-xylanase (Mitreva-Dautova et al., 2006). Finally, pectate lyases and polygalacturonase enzymes responsible for pectin breakdown have been identified in PPN. The secretion of a pectate lyase into the plant by parasitic J2 has been demonstrated during RKN infections, consistent with the injection of this enzyme via the stylet to facilitate the penetration of root tissues and the intercellular migration of J2 within these tissues (Doyle and Lambert, 2002). Based on their gland cell-specific expression and the presence of predicted secretion signal peptides, glucanases secreted by PPN are also thought to play an active role in nematode migration through root tissues (Doyle and Lambert, 2002; Huang et al., 2005a; Jaubert et al., 2002a). The similarities between these genes in PPNs and bacteria suggest their probable acquisition by horizontal gene transfer from bacteria (Jaubert et al., 2002a; Ledger et al., 2006; Scholl and Bird, 2005). B. EFFECTORS INVOLVED IN PLANT DEFENCE SUPPRESSION
A brief, low-amplitude oxidative burst is observed at the onset of RKN parasitism, but host defences are subsequently suppressed (Jammes et al., 2005; Melillo et al., 2006). This suppression may result from the active modulation of plant defences by the nematode. Effectors synthesized in the oesophageal glands and potentially responsible for regulating plant defence responses have been identified. For example, it has been suggested that an oesophageal gland-specific chorismate mutase plays an active role in suppressing plant defences (Doyle and Lambert, 2003; Huang et al., 2005b). Plant chorismate mutases are key enzymes in the shikimic acid pathway directing the synthesis of cellular aromatic amino acids and several secondary metabolites, including phytohormones and plant defence compounds. The release of the nematode chorismate mutase into the plant cell cytoplasm would decrease the synthesis of flavonoids, salicylic acid, phytoalexins and auxin. A second RKN effector potentially able to modulate plant defences is calreticulin, which accumulates in the walls of giant cells. Calreticulin was the first molecule shown to be secreted into the feeding site via the nematode stylet (Jaubert et al., 2005). The function of nematode calreticulin in the modulation of plant responses remains unclear, but calreticulins secreted by eukaryotic animal parasites have been identified as key modulators of host immune defences (Kasper et al., 2001; Suchitra and Joshi, 2005). RKN calreticulin may also be involved in calcium signalling and cell cycle regulation (Ghiran et al., 2003) during giant cell formation.
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C. EFFECTORS INVOLVED IN INDUCTION OF THE NEMATODE FEEDING SITE
Secreted calreticulin has been observed to accumulate at the giant cell wall, but nematode effectors are thought to be injected into the cytoplasm, diffusing thereafter into the enlarged giant cells. However, direct evidence for a nematode gene in plant cytoplasm has not yet been obtained. The identification of effectors known to act intracellularly (Jaubert et al., 2004) and the recent demonstration of physical interaction between a secreted factor from RKN and plant transcription factors (Huang et al., 2006a) are consistent with RKN secretions being active in the plant cell cytoplasm. It remains a major technical challenge to determine whether salivary secretions are injected into the cytoplasm and to identify their role in the induction of giant cell formation, because these secretions are present in such small amounts in the plant. The soybean cyst nematode and potato cyst nematode (G. rostochiensis) encode small secreted proteins with similarity to CLAVATA/ESR (CLE)-related peptides (Gao et al., 2003; Lu et al., 2009). In plants, CLE peptides have diverse roles in plant growth and development. In nematodes, they are secreted by the dorsal gland, suggesting a role in parasitism. Overexpression of the nematode genes in Arabidopsis can rescue cle mutants and produce phenotypes supporting such a role. However, the first candidate RKN effectors involved in the induction of giant cells are emerging. RKNs have been shown to produce auxin conjugates and cytokinins that may interfere with the hormone balance of the plant cell (De Meutter et al., 2003, 2005), but the role of these nematode products in the plant remains unknown. A 14-3-3 zeta isoform produced in the oesophageal glands of infective juveniles has been identified in purified salivary secretions (Jaubert et al., 2004). In eukaryotic cells, 14-3-3 proteins play a crucial role in modulating the signalling events involved in responses to environmental stimuli, progress through the cell cycle and programmed cell death (Takihara et al., 2000). In plants, a 14-3-3 protein regulates the plasma membrane Hþ-ATPase in response to fungal infection, and a similar role for such proteins has been suggested in nodule development (Wienkoop and Saalbach, 2003). It has also been suggested that secreted nematode peptide signalling molecules are involved in feeding cell formation. A 13-amino acid peptide, 16D10, secreted by the oesophageal glands of parasitic J2, has been shown to stimulate root growth and the generation of extensive lateral roots in tobacco hairy roots. 16D10 has been shown to interact with two putative plant SCARECROW-like transcription factors in planta (Huang et al., 2006a). The role of the identified transcription factors in plant cell development remains unknown, but this work provided the first report of the isolation of plant targets of a nematode effector.
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Many additional effectors have been identified for which no predicted function could be attributed on the basis of sequence similarity. These ‘pioneer’ genes highlight the current lack of knowledge about the effectors of eukaryotic plant pathogens (Jones and Dangl, 2006) and the need to develop genomics for the analysis of nematode functions involved in parasitism.
III. KEY PLANT FUNCTIONS MANIPULATED DURING NEMATODE INFECTION Nematode feeding cells act as transfer cells for the accumulation of nutrients and solutes from the vascular tissues, supplying them to the developing nematodes. RKNs induce the development of five to seven feeding cells, known as giant cells. By contrast, cyst nematodes select a single cell for the induction of a syncytium. In both cases, high levels of metabolic activity and the constant withdrawal of cytoplasm by the nematodes convert the feeding cells into metabolic sinks for the host plant. Feeding cell formation is a complex process associated with major changes in plant gene expression, and substantial differences are observed between these two types of feeding cells (Caillaud et al., 2008a; Gheysen and Fenoll, 2002; Hammes et al., 2005; Jammes et al., 2005; Puthoff et al., 2003; Williamson and Gleason, 2003). Syncytia are symplastically connected to newly formed phloem elements, whereas giant cells are symplastically isolated but are embedded in a tissue that consists exclusively of sieve elements with novel nucleated cells (Hoth et al., 2008). The study of plant–nematode interactions has progressed considerably in recent years, switching from descriptive steps to more molecular approaches. Differential display (Hermsmeier et al., 2000; Vercauteren et al., 2001), promoter–reporter gene fusions (Goddijn et al., 1993; Goverse et al., 1998; Mazarei et al., 2003; Niebel et al., 1996; Puzio et al., 2000), promoter-trap strategies (Barthels et al., 1997; Caillaud et al., 2008b; Favery et al., 1998, 2004), RNA blotting, protein immunolocalization, in situ hybridization (Goellner et al., 2001; Niebel et al., 1993) and differential library screening (Gurr et al., 1991) have been used to study the plant response to RKN and cyst nematode parasitism. Use of the model plant Arabidopsis thaliana, for which efficient genetic tools have been developed and genomic data are available, has made it possible to study nematode feeding site formation in detail and to carry out molecular studies. However, the use of crop plants, such as tomato, alfalfa, tobacco and soybean, is becoming increasingly prevalent with the accumulation of genomic data for these species
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(Alkharouf et al., 2006; Bar-Orl et al., 2005). The recent development of DNA array technology has led to a significant increase in the number of plant genes known to be responsive to nematodes. These genome analyses provide an important overview and have shown that similar numbers of genes are up- and down-regulated and that gene down-regulation may be essential for correct feeding site formation (Jammes et al. 2005). There is evidence to suggest that the process of nematode feeding cell formation involves changes in basic programmes of plant development, with the dedifferentiation of normal cells to generate a highly metabolically active cell type altered in cell cycle, hormone regulation, cell wall architecture and cytoskeleton (Bird and Koltai, 2000; Caillaud et al., 2008b; Davis and Mitchum, 2005; de Almeida Engler et al., 2004; Favery et al., 2004; Goellner et al., 2001). During a compatible interaction, RKNs and cyst nematodes can cause damage or stress host cells between penetration and the establishment of a feeding site, due to their migration through or between cells. Plant defence responses are induced during the infestation process. Plant cells respond to nematode attack by producing superoxide (O2–) and its dismutation product, hydrogen peroxide (H2O2), both of which are toxic to the parasite. A burst of H2O2 occurs during compatible interactions between tomato (Solanum lycopersicum) and M. incognita, in cells surrounding the migrating nematode and in the recently differentiated feeding cells (Melillo et al., 2006). A genome-wide overview of gene expression during plant–nematode interaction has shown that the suppression of plant defences is associated with nematode feeding site development. Thus, 70% of the nematoderegulated genes involved in defence pathways were found to be repressed locally in A. thaliana during RKN infection (Jammes et al., 2005). Plant defences are suppressed through effects on resistance genes and signalling pathways. Successful cyst nematode parasitism of A. thaliana may involve a suppression of the local salicylic acid (SA) signalling pathway in roots (Wubben et al., 2008), whereas ethylene signal transduction increases the susceptibility of the plant to cyst nematodes (Wubben et al., 2001, 2008). Host genes induced early in the formation of feeding cells include several cell cycle genes, and both types of feeding cells subsequently undergo multiple rounds of shortened cell cycles leading to genome amplification. However, the cytological changes are different in these two types of feeding cells. Giant cell progenitors undergo repeated mitosis without cell division, whereas syncytia are formed through repeated S-phases in the absence of mitosis, resulting in endoreduplicated nuclei (Gheysen and Fenoll, 2002). Indeed, the prevention of DNA synthesis or cell cycle blockade with chemical inhibitors after nematode infection significantly inhibits feeding cell progression (de Almeida Engler et al., 1999). The molecular mechanisms
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underlying cell cycle events in syncytia and giants cells have been explored in detail and a few core cell cycle genes in A. thaliana have been shown to be upregulated within the first few hours of nematode feeding cell induction. Early transcriptional activation of cell cycle markers, such as cyclin-dependent kinases (CDC2a and CDC2b) and mitotic cyclins (CYC1At and CYCA2;1), has been observed in feeding cells (de Almeida Engler et al., 1999; Niebel et al., 1996). In addition, genes involved in endoreduplication, such as CCS52, have been shown to be induced in giant cells and syncytia (Favery et al., 2002; Koltai et al., 2001). Nematodes induce rearrangements of the host cytoskeleton during the infection process (de Almeida Engler et al., 2004). Tubulin and actin genes are up-regulated in nematode feeding sites (de Almeida Engler et al., 2004). The formation of a complex network of actin filaments and bundles within the cytoplasm of giant cells suggests a role for the actin cytoskeleton in feeding cell development. The effects of inhibitors, such as oryzalin, which destabilizes microtubules, have been tested at the time of interaction between RKN and A. thaliana. Simple exposure to this agent 3 days after inoculation decreases the number of nuclei per giant cell and inhibits nematode development. Wiggers et al. (2002) quantified the effect of microtubule depolymerization, by treating giant cells with colchicine and assessing the effect of this drug on nematode development. This study showed that a minimum number of nuclei per giant cell were required for completion of nematode development and for feeding site maintenance. The activation of a gene encoding a membraneanchored formin (AtFH6), which is involved in actin nucleation, in giant cells (Favery et al., 2004) supports the hypothesis that the actin cytoskeleton is reorganized during feeding cell development. This gene is activated only during giant cell formation and the uniform distribution of this protein in the membrane indicates that AtFH6 may control the isotropic growth of giant cells by reorganising the actin cytoskeleton. Another cytoskeletal protein essential for plant cell cytokinesis has also been shown to be involved in giant cell formation. Studies involving the inactivation of microtubuleassociated protein 65-3 (MAP65-3) have shown this protein to be essential for giant cell ontogenesis and, therefore, for nematode development. Nematodes carrying mutant MAP65-3 can initiate only the first stages of giant cell formation and cannot complete their development cycle. In giant cells, MAP65-3 is associated with ‘mini’ cellular plates separating the nuclei at the time of their formation. Thus, although the giant cells result from nuclear divisions without cellular division, cytokinesis is initiated and seems to be essential for giant cell formation (Caillaud et al., 2008b). Plant hormones play an important role in successful interactions between plants and nematodes. Evidence has also been obtained to support a role for
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auxin in feeding cell formation. Analyses of mutants and reporter-gene constructs indicate that auxin-responsive genes are induced in the susceptible response to cyst nematodes (Goverse et al., 2000). Similarly, the auxinresponsive GH3 promoter is transiently activated in feeding cells induced during RKN infection (Hutangura et al., 1999). Studies with a synthetic auxin-responsive promoter element, DR5, derived from the soybean GH3 promoter, confirmed early suggestions of a localized increase in auxin concentration in the feeding sites of both RKN and cyst nematode (Karczmarek et al., 2004). However, auxin is not the only plant hormone involved in feeding cell induction and maintenance. The observed up-regulation of a cytokinin-responsive ARR5 (Arabidopsis response regulator) promoter following RKN infection suggests that a spike of cytokinin production is also required for giant cell initiation. Consistent with this hypothesis, the number of galls is decreased by the production of cytokinin oxidase, an enzyme decreasing cytokinin levels in roots (Bird, 2004; Lohar et al., 2004). Mutant and hormone inhibitor analyses have proved invaluable for identification of the ethylene signalling components required for cyst nematode parasitism. Arabidopsis thaliana ethylene-insensitive mutants have been found to be much less susceptible to cyst nematodes than wild-type plants, whereas ethylene-overproducing A. thaliana lines are more susceptible to infection with this nematode (Goverse et al., 2000; Wubben et al., 2001). The RHD1 gene, encoding a UDP-glucose-4-epimerase, seems to be down-regulated by an ethylene signal elicited by nematode infection in cyst nematode-infected roots (Wubben et al., 2004). In general, the ethylene and jasmonate signalling pathways seem to work in synergy. The jasmonate signalling pathway is also required for RKN susceptibility in tomato (Bhattarai et al., 2008). Extensive changes to the cell wall occur during the development of giant cells and syncytia. Plant cell walls consist of layers of cellulose microfibres embedded in a matrix of pectin and hemicellulose. Plant hydrolases may therefore be involved in changes to the wall architecture of feeding cells. Goellner et al. (2001) have shown that cell wall-modifying enzymes of plant origin are involved in feeding cell formation. Several findings from this study confirmed the importance of cell wall enzymes, such as endo-b-1,4-glucanases and pectin acetylesterase, in the response to infection with both RKNs and cyst nematodes (Mitchum et al., 2004; Vercauteren et al., 2002; Wang et al., 2007a; Wieczorek et al., 2008). Up to 25 such cell wall enzymes have been shown to be up-regulated during the formation of syncytia in Arabidopsis. Similar results were obtained for tobacco and tomato crop plants infected with cyst nematodes (Goellner et al., 2001; Karczmarek et al., 2008). The common pattern of cell wall-modifying enzyme regulation in different plants suggests that conserved mechanisms are involved in feeding cell formation and that
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nematodes interact with key plant development processes. However, differential gene expression as a function of feeding cell type has also been observed. For example, the Arabidopsis endo-1,4-b-glucanase CEL1 is active in giant cells but not in syncytia, suggesting that the specific regulation of cell walldegrading enzymes is probably required for the modifications to the cell wall required for the generation of only giant cells (Mitchum et al., 2004). Several additional classes of cell wall-modifying enzymes have been identified in recent microarray analyses as being induced or repressed during feeding cell development. They include cell wall disassembly enzymes, such as b-xylosidases, xyloglucan endotransglycosylases, polygalacturonases and expansins. Expansins are cell wall-loosening proteins that induce extension of the plant cell wall without the hydrolytic breakdown of its major components. In giant cells, seven expansin A genes and two expansin B genes, from the total of 31 Arabidopsis genes encoding the two major classes of expansin, are strongly up-regulated in galls. The specific down-regulation of two expansin-like genes late in giant-cell formation suggests that these proteins may have a particular function in giant-cell formation (Jammes et al., 2005). However, Wieczorek et al. (2006) have demonstrated the expression of several different expansins within developing syncytia. The differential expression of gene family members and their time-co-ordinated regulation suggest that feeding cell formation is a complex process. Further studies are required to determine the role of these enzymes in plant–nematode interactions. Analyses of host cell regulatory sequences and their interacting transcription factors provide information about the signal transduction pathways essential for feeding cell development. Escobar et al. (2003) showed that a short fragment of the promoter of the Hahsp17.7G4 gene, encoding a small heat shock protein involved in embryogenesis and stress response, was specifically expressed in tobacco galls. In addition, point mutations in heat-shock transcription factor binding sites were found to decrease the host response to nematodes significantly. Microarray analyses of both types of feeding cells provide an overview of the transcription factor regulation. In giant cells, 17 of the 21 WRKY genes identified are down-regulated (Jammes et al., 2005). The over-expression of these genes is correlated with stronger plant defences to pathogens consistent with their down-regulation suppressing plant defence responses and generating susceptibility in host plants. A detailed analysis of WRKY23 expression patterns showing the strong expression of this gene in both early giant cells and syncytia (Grunewald et al., 2008). WRKY23 is an auxin-inducible gene and the protein it encodes (WRKT23) acts downstream from the Aux/IAA protein SLR/IAA14 in uninfected plants. Knocking down the expression of WRKY23 results in lower levels of cyst nematode infection. Therefore, despite the implication of auxin in feeding-site formation, the initial
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expression of WRKY23 may be activated by auxin-independent signals early in feeding-cell formation (Grunewald et al., 2008). Moreover, in Medicago truncatula giant cells, PHAN and KNOX, transcription factors required for normal meristem function, are induced; the involvement of these factors in producing changes in phytohormone levels implicates these genes in the regulation of auxin distribution during feeding-cell development (Koltai et al., 2001). Other transcription factors, such as ABI3, also involved in cell differentiation, and ethylene-responsive element-binding protein (EREBP), have been shown to be up-regulated and down-regulated in response to compatible RKN and cyst nematode infections, respectively (De Meutter et al., 2005; Mazarei et al., 2002). Feeding sites have characteristics similar to those of transfer cells, which facilitate nutrient diversion in other parts of the uninfected plant. An increase in transport activity across the plasma membrane is a hallmark of transfer cells, and the feeding site serves as a metabolic sink, providing the nematode with nutrients. The strong expression of the Arabidopsis sucrose transporter gene AtSUC2, which is normally expressed in companion cells, suggests that this protein may be involved in initiating or maintaining the metabolic sink activity of syncytia. However, the AtSUC2 gene is not expressed in giant cells, which also are strong nutrient sinks, suggesting the involvement of other genes (Juergensen et al., 2003). Massive water transport is supported by the upregulation of genes encoding water channel proteins, such as aquaporin. These proteins are also involved in osmoregulation and growth control. Upregulation of the promoter of TobRB7, a tobacco gene normally expressed in root tips, showed that this promoter also directs expression in giant cells (Opperman et al., 1994). Antisense constructs of this gene abolished nematode reproduction, suggesting that the expression of this gene is required for successful feeding site development or maintenance (Opperman et al., 1994). In M. truncatula, the NIP NOD26 aquaporin gene is up-regulated in giant cells (Favery et al., 2002). Two independent microarray analyses on Arabidopsis after RKN infection confirmed the importance of transport activity. In a study on the regulation of 25 aquaporins in dissected gall material, 7 aquaporin genes were found to be down-regulated, including the AtTIP1.1 and AtPIP1.5 genes and genes encoding tonoplast and plasma membrane intrinsic proteins (PIPs), whereas two PIPs and one Nod26-like intrinsic protein are specifically activated in galls (Jammes et al. 2005). In whole RKN-infected roots, 50 of the 635 transporter genes examined were found to be up- or down-regulated and strong expression of two genes encoding amino acid transporters, AtAAP6 and AtCAT6, was observed in giant cells (Hammes et al., 2005, 2006). Recently, analyses based on plants expressing soluble or membrane-anchored green fluorescent protein in the phloem demonstrated that nutrient loading
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into syncytia occurs symplastically via plasmodesmata, whereas apoplastic loading is fundamental for giant cells (Hoth et al. 2008). Feeding cells are metabolically hyperactive indicating that a key action of nematodes is the metabolism reprogramming of selected cells. The oxidative pentose phosphate pathway (OPPP) produces the NADPH required for many biosynthetic reactions and generates carbohydrate intermediates for the synthesis of nucleotides and cell wall polymers. Glucose-6-phosphate dehydrogenase (G6PDH) is the first enzyme of the OPPP pathway and high levels of activity for this enzyme have been detected on histochemical preparations of nematode feeding cells (Endo and Veech, 1969). Further evidence of an important role for the OPPP pathway has been provided by identification of the RPE gene, which encodes ribulose-phosphateepimerase, in giant cells in a promoter-trap strategy (Favery et al., 1998). RPE catalyzes the reversible interconversion of ribulose-5-phosphate and xylulose-5-phosphate. An analysis of homozygous rpe mutants showed this enzyme to be essential for the early steps of giant cell formation. Homozygous rpe mutants result in a lethal phenotype, but these mutants can be rescued in dwarf plants grown on sucrose-supplemented medium While RPE is necessary for giant cell formation, it is not required for syncytium formation (Favery et al., 1998). This difference may be due to giant cell formation requiring more energy than syncytium formation. In addition, phosphoglycerate mutase/bisphosphoglycerate (PGM/bPG) mutase enzymes have been implicated in the early stages of formation of both giant cells and syncytia in Arabidopsis (Mazarei et al., 2003). The increase in the activity of these enzymes during feeding cell development confirms the importance of the OPPP in the formation of the metabolic sink for nematodes, as PGM is a key enzyme catalyzing the reversible interconversion of 3-phosphoglycerate and 2-phosphoglycerate during sugar metabolism by glycolysis. Ithal et al. (2007) recently showed, in a study of coupling laser capture microscopy technology with an exhaustive set of soybean genes, that all the genes encoding proteins involved in glycolysis and the OPPP are up-regulated in syncytia.
IV. NATURAL PLANT RESISTANCE AND NEMATODE VIRULENCE A. NON-HOST, RESISTANT HOST, TOLERANT HOST
PPNs can infect thousands of plant species. Nematode species differ dramatically in host range, and there are major differences in the suitability of particular plant species and varieties as a host for each nematode. While
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many of the best-studied RKNs, that is, those species that are globally distributed and cause major economic damage, have very broad host ranges, some are limited to one or a few hosts (Sasser, 1977; Trudgill and Blok, 2001). In contrast, cyst nematodes usually have narrower host ranges. For example, the highly damaging potato cyst nematode species G. rostochiensis and G. pallida reproduce mainly on potato and related Solanaceous species, and soybean cyst nematodes, while they cause widespread damage on soybean, are mostly limited to this crop (Lilley et al., 2005; Niblack et al., 2006). Non-hosts are plant species on which a particular phytoparasitic nematode species or isolates does not reproduce, but in general the host features that are responsible for this are unknown. These hosts may lack functions required to initiate and maintain a feeding site sufficient for completion of life cycle. The plant machinery may be unresponsive to signals that are produced by the nematode to initiate feeding sites. The broad host range of RKNs suggests that the factors needed for successful host status are fundamental to most plants. The more narrow host range of cyst nematodes suggests that there may be host-specific factors that contribute to the ability to initiate and maintain host feeding sites. Alternatively, the plant may not produce signals that attract nematode or direct it to its feeding site. Compounds toxic or repellent to the nematode could be produced. Roots of different species differ dramatically in their ability to attract individual species of PPNs (Liu and Williamson, 2006; Perry, 1996; Spence et al., 2008). However, the correlation between attraction and host suitability is not perfect, and little is known about the plant signals that modulate nematode behaviour. For some cyst nematode species, hatching of eggs requires host exudates that contain ‘hatching factors’ (Lilley et al., 2005). These likely also play a role in host range. Discrete differences within many host species in the ability to support reproduction of particular nematode species or isolates segregate genetically. Plant varieties that support low or no nematode reproduction are defined as resistant (Cook and Evans, 1987; Roberts 2002, 2004). Incorporation of genes that confer resistance is a goal of many breeding programmes for which particular nematodes are damaging as it has the potential to reduce the need for toxic chemicals to control of these pests. However, as discussed below, incorporation and maintenance of resistance can be challenging. Host tolerance, defined as lack of symptoms even when infestation of nematodes is heavy, is often an additional consideration in breeding programmes. While in most cases resistance and tolerance are correlated, some varieties are highly tolerant and highly susceptible (Roberts, 1992; Trudgill, 1991). This combination, though useful in some applications, can result in high population levels of phytoparasitic nematodes. In other cases, partial
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resistance can be accompanied by low tolerance so that severe symptoms are apparent even though reproduction of the nematode is limited. Host tolerance and its relationship to resistance are poorly understood, which is indicative of how little we know about the dynamics of plant/nematode parasitism. As an example, galling, though an easily visible symptom of RKN infection, is a poor measure of susceptibility, resistance or tolerance (Garcia et al., 1996; Roberts et al., 2008). B. INHERITANCE OF NEMATODE RESISTANCE
Genetic analysis of the inheritance of resistance to nematodes has been investigated widely for cyst nematode and RKN (e.g., Cook and Evans, 1987; Roberts, 1992; Williamson and Kumar, 2006). Genetic loci contributing to resistance have been characterized, and some of the major genes for nematode resistance have been cloned (see below). Often resistance was initially identified in wild relatives of cultivated crops, then introgressed using various breeding procedures. In many cases while the resistance and its inheritance have been assessed in wild species, the trait has not yet been introgressed into cultivated varieties. For example, even though at least nine resistance genes for RKN have been identified in tomato relatives, so far only one, Mi-1, is widely available in commercial varieties. Resistance can be inherited as a single major gene or as combinations of two or more genes or quantitative trait loci (QTLs) that can be dominant, recessive or additive. QTLs may each correspond to different aspects of the defence response as for G. pallida resistance from S. sparsipilum (Caromel et al., 2005). The resistance phenotype can be strong, allowing very little or no nematode reproduction, or partial, showing reduced numbers of progeny. Some combinations of QTLs can display epistatic interactions such that segregating progeny can include transgressive resistance (Wang et al., 2007b). Resistance is often limited to particular species or isolates of nematode and multiple, different resistance traits can be segregating in a host, making analysis difficult. For example, even though many resistance sources for soybean cyst nematode have been identified including diverse major and minor QTLs with various specificities, it has been an ongoing challenge to develop durably resistant soybean lines as there is considerable genetic diversity within soybean cyst nematode lines and rapid adaptation to resistance is an ongoing challenge (Arelli et al, 2009; Niblack et al., 2006). The well-studied resistance gene Mi-1, which confers resistance against RKNs in tomato, can be used as a basis for comparison to other nematode resistance genes (Williamson, 1998). Nematode resistance was discovered in the wild tomato relative Lycopersicon peruvianum (now classified as
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S. peruvianum) in the 1930s and was introgressed into cultivated tomato by embryo rescue. Mi-1 confers resistance against three of the most widely damaging RKN species, M. incognita for which it is named, as well as M. javanica and M. arenaria. However, populations of these three species against which Mi-1 is not effective have been identified (Jacquet et al., 2005; Williamson and Kumar, 2006), and the gene does not confer resistance against M. hapla or M. enterolobii (synonymous with M. mayaguensis) (Brito et al., 2004; Liu and Williamson, 2006). Interestingly, the gene Mi-1 confers resistance to some, but not all, isolates of potato aphid and whitefly (Nombela et al., 2003; Rossi et al., 1998; Vos et al., 1998). The effectiveness of resistance in tomato with Mi-1 has been reported to differ depending on the genetic background, suggesting that additional genes modulate the phenotype (Jacquet et al., 2005; Lopez-Perez et al., 2006). The breadth of resistance conferred by nematode resistance genes differs considerably from gene to gene. The tomato gene Hero A, like Mi-1, has a relatively broad spectrum of resistance and confers resistance to several pathotypes of two cyst nematode species (Ernst et al., 2002), and the potato gene Grp1 confers broad-spectrum resistance to both G. pallida and G. rostochiensis (Finkers-Tomczak et al., 2009). However, the potato genes Gpa2 and Gro1-4 confer resistance to only a small subset of cyst nematode isolates. Inheritance of resistance to cyst nematode in soybean has been particularly complex. Resistance has been identified in several soybean introductions and is mediated by combinations of recessive (rgh) and dominant (Rgh) genes. The two major QTLs (rhg1 and Rhg4) have been best characterized, but germplasm with these genes can differ in resistance to particular nematode populations, indicating that additional genes affecting resistance and epistatic inheritance are present (Wu et al., 2009). C. RESISTANCE PHENOTYPES
The timing and phenotype of nematode resistance varies considerably depending on the gene. Again, Mi-1 is a well-studied example for comparison. Detailed microscopic observations have been carried out to investigate the mechanism of resistance to nematodes mediated by tomato with Mi-1. Nematodes are equally attracted to roots of tomato with and without Mi-1 (Wang et al., 2009). The J2 enter the roots of resistant plants at similar rates to susceptible plants, but do not continue to accumulate and do not initiate a feeding site. Instead a brown, necrotic region resembling the classical hypersensitive response (HR) commonly associated with host resistance can often be seen close to the nematode inside the root (Ho et al., 1992; Melillo et al., 2006). Mi-mediated nematode resistance is temperature sensitive, and full
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resistance requires soil temperatures below 28 ˚C. Temperature shift experiments have helped to establish that resistance is triggered during the first 24 h after nematode invasion (Dropkin et al. 1969). After this time period, resistance is no longer triggered even when roots are shifted from the restrictive to the permissive temperature. The timing and location of the plant cell death suggests that the nematode’s attempt to initiate the feeding site may activate the host’s defence response. The role of the cell death in determination of resistance is unclear though death of feeding cells would seem logical as it could block the development of the feeding cells. However, localized cell death has been observed for several other nematode-resistant interactions in addition to that mediated by Mi-1 (Anthony et al., 2005; Pegard et al., 2005). While for some resistance genes such as Mi-1 resistance is rapid and nearly absolute, for other R-genes resistance is significant, though reduced numbers of progeny are produced and symptoms such as galling are still apparent. For example, for the cowpea gene Rk, the nematodes establish a functional feeding site and develop to the J2 or J4 stage, but the feeding site becomes vacuolated and collapses (Das et al., 2008). Comparison of pepper with various RKN resistance genes (Me genes) revealed that multiple mechanisms can occur (Bleve-Zacheo et al., 1998; DjianCaporalino et al., 2007; Pegard et al., 2005). Resistance to RKNs was associated with unidentified factors including physical barriers that limited nematode penetration as well as with post-penetration biochemical responses, including classical HR. The timing of the resistance response and the mechanism of resistance varied with plant genotype, resistance gene and RKN species. Effectively, the pepper genes Me3, Me1 and Me7 induced an immediate cellular necrosis in the root epidermis adjacent to the J2 in response to M. incognita as reported with the Mi-1 gene in resistant tomato plants. The same genes induced necrosis in the intermediate cortex 1 or 2 days after inoculation when infected with M. javanica. Small giant cells with few gathered nuclei began to develop in pepper lines carrying Me1 gene after M. incognita inoculation and Me3 gene after M. arenaria inoculation, but necrosis was not evident until 5 days after inoculation and females were unable to reproduce. A similar degradation of the feeding site was described for resistant lines of other plants, such as sugar beet, oilseed radish, potato and soybean (Bleve-Zacheo et al., 1998; Endo, 1991; Rice et al., 1985; Wyss et al., 1992; Yu and Steele, 1981). Histological analysis, UV light observations and High Performance Liquid Chromatography (HPLC) analysis of non-infected and infected pepper roots realized by Pegard et al. (2005) also showed that phenolic compounds, especially chlorogenic acid, may be involved in pepper resistance to RKNs. With some cyst nematode resistance genes, including Hero A and H1, nematode development occurs on the host but is characterised by reduced
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reproduction and a high ratio of male to female nematodes (Rice et al., 1985; Sobczak et al., 2005). As for several of the RKN resistance genes discussed above, a feeding site is initiated, but develops poorly and atrophies; the nematodes that do develop are mostly males. For many cyst nematode and RKN species, the sex of the developing nematode is environmentally determined with males found in excess during conditions of crowding and poor nutrition. Thus, the high male ratios in resistant lines likely reflect the poor quality of the feeding cell and limited nutritional available for the developing individual. D. CLONING AND CHARACTERIZATION OF NEMATODE RESISTANCE GENES: WHAT HAVE WE LEARNED?
Several nematode resistance genes have now been cloned (Williamson and Kumar, 2006). Most (including Mi-1, Hero A, Gpa2, Gro1-4) encode nucleotide binding–leucine-rich repeat (NB–LRR) proteins, a large family of plant proteins characterized by the presence of a conserved motif that includes an NB domain and an LRR region near the carboxy terminus. However, the first nematode resistance gene to be cloned, Hs1pro-1 from sugar beet, which confers resistance against sugar beet cyst nematode (Cai et al., 1997), is an exception. The encoded protein does not have obvious similarities to other plant genes. Plants carry a large repertoire of NB–LRR genes, many of which are central components of the active host defence system that protects plants from specific pathogens including viruses, bacteria and fungi (Jones and Dangl, 2006). The encoded NB–LRR proteins are thought to serve as surveillance molecules that recognize the presence of the pathogen, then initiate a rapid defence response that results in resistance to that pathogen. Because this response requires the presence of both a specific gene in the pathogen (the avirulence or Avr gene) and the corresponding gene (R-gene) in the host, it has historically been referred to as ‘gene-for-gene’ resistance (Dangl and McDowell, 2006; Flor, 1971). LRR motifs are typically involved in protein– protein interactions and thus may have a role in pathogen recognition and signal transduction (Belkhadir et al., 2004). The central NB–ARC domain is present in a large class of proteins that undergo conformational changes leading to signaling in response to perceiving a specific elicictor. The N-termini of plant NB–LRR proteins are variable and subgroups can carry specific motifs such as coiled coil (CC) or TIR (toll-interferon receptor) domains. Proteins Mi-1 and Hero A both have an extended N-terminal region compared to most other R-proteins. Gpa2 has a very short N-terminus, and Gro1-4 is characterized by a TIR-like N-terminus (Paal et al., 2004).
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NB-LRR genes frequently occur in clusters in plant genomes, possibly contributing to evolution of specificities (Hulbert et al., 2001). These clusters can contain resistance to diverse pathogens. For example, the nematode resistance genes Gpa2 and Rx1, a gene that confers resistance against potato virus X, encode proteins 88% identical in amino sequence and map in the same cluster of four homologous genes on chromosome 12 in potato (van der Vossen, et al., 2002). Mi-1.2 is one of seven highly homologous copies clustered on the short arm of chromosome 6 in resistant tomato and is the only copy known to confer nematode resistance (Seah et al., 2007). The same number of homologs is present in the corresponding region of susceptible tomato. DNA sequence identity between these homologs ranges from 93% to 97%. The highest similarity of Mi-1.2 to another gene whose function is known is to the gene Rpi-blb2, which maps to the corresponding region of the genome in S. bulbocastanum and which confers broad resistance against the oomycete Phtophtora infestans (van der Vossen et al., 2005). Interestingly, genes for resistance to alfalfa mosaic virus, Gemini viruses, bacterial pathogens and the fungus Oidium neolycopersici also map to the Mi-1 region of chromosome 6 in other Solanum species (Bai et al., 2005; Gebhardt and Valkonen, 2001; Parella et al., 2004; Thoquet et al., 1996; Zamir et al., 1994). How NB-LRR proteins recognize specific pathogens and how the responses result in appropriate defences against these pathogens are areas of intense investigation (Bent and Mackey, 2007; Jones and Dangl, 2006). The encoded proteins are thought to act as surveillance molecules that recognize the presence of the pathogen either by direct recognition of the product of a pathogen molecule or though recognition of changes in the host mediated by pathogen genes (Belkhadir et al., 2004). Domain swaps between the Mi-1 gene and some of its homologs as well as analysis of additional in vitro mutations resulted in loss of function and constitutively active forms. These and other analyses led to the development of a model in which intramolecular interactions hold the Mi-1 protein in an inactive conformation (Hwang and Williamson, 2003; Hwang et al., 2000). Recognition of the presence of the nematode results in conformational changes in the protein that signal an effective defence response. Binding and hydrolysis of nucleotide triphosphate modulates this conformational change in Mi-1 and some other R-proteins (van Ooijen et al., 2007). As with R-gene-mediated resistance against other species, Mi-1 resistance is characterized by an enhanced production of reactive oxygen species and major changes in expression of various defence genes (Glazebrook, 2005; Melillo et al., 2006; Molinari, 2001). A localized necrosis or HR is widely observed, but its role in resistance is debatable and may depend on the pathogen targeted. Defence against biotrophic pathogens is generally
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characterized by a requirement for salicylic acid. Mi-1-mediated nematode resistance also requires presence of salicylic acid as well as the plant genes Hsp90 and Sgt1, which are required for R-gene-mediated resistance against other pathogens (Bhattarai et al., 2007; Branch et al., 2004). A glycosyltransferase is increased in expression in tomato and also has a demonstrated role in Mi-1-mediated resistance (Schaff et al., 2007). Mutations in an unlinked locus called Rme1 result in loss of Mi-1-mediated resistance to nematodes, aphids and whiteflies but not to other pathogens, supporting a specific role for this gene in recognition of the presence of the nematode or in the defence response mediated by Mi-1 (Martinez de Ilarduya et al., 2001). E. GENETIC DIVERSITY AND VIRULENCE DEVELOPMENT IN THE NEMATODE
Nematode isolates of a particular species can differ in the range of host species on which they can reproduce and in their ability to reproduce on hosts carrying specific resistance genes. Nematode virulence, defined as the ability to reproduce on hosts with a specific resistance gene, has been observed for many plant resistance genes. For other pathogens, virulence has, in many cases, been shown to be due to loss or alteration of the pathogen Avr gene, which is responsible for the host R-gene’s ability to recognize the pathogen (Bent and Mackey, 2007). There is strong genetic evidence that a single genetic locus in the potato cyst nematode modulates recognition of the nematode mediated by the H1 resistance gene in potato. In this case, controlled genetic crosses using inbred populations of the potato cyst nematode yielded progeny segregation patterns indicating that a single gene mediated the ability to reproduce on potato with the H1 gene (Janssen et al., 1991). Similarly, crosses between inbred lines of the soybean cyst nematode identified segregation in progeny of both dominant and recessive traits that control the ability of the nematode to reproduce on nematoderesistant soybean (Dong and Opperman, 1997). However, for both of these systems, progress towards identification of pathogen virulence genes has been hampered by the cyst nematode’s obligately outcrossing reproductive mechanism, which limits the ability to obtain sufficient uniform genetic material for molecular analysis. In addition to differences in genes recognized by R-genes, virulence factors are likely to include other genes that improve the ability of the nematode to reproduce on specific hosts. For example, in H. glycines, certain forms of the candidate virulence protein chorismate mutase, an enzyme capable of altering production of defence compounds by the host, are correlated with virulence in inbred lines (Bekal et al., 2003). This correlation suggests that certain forms of chorismate mutase may be virulence factors.
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Genetic evidence also supports the simple inheritance of virulence in the RKN M. hapla. This species reproduces by facultative meiotic parthenogenesis, that is, when males are present, cross-fertilization occurs. However, when no males are present, the sister products of a meiosis can rejoin to form a diploid (Liu et al., 2007; Triantaphyllou, 1985). Analyses of progeny from controlled crosses between an M. hapla strain that is virulent and the one that is avirulent on common bean cultivar NemaSnap were consistent with virulence segregating as a single, recessive trait (Chen and Roberts, 2003). However, molecular markers were not used in this cross, limiting further analysis. A cross of two other M. hapla strains was monitored using polymorphic DNA markers to develop a genetic map and to produce inbred F2 lines for further analysis (Liu et al., 2007; Opperman et al., 2008). Coupled with the availability of the genome sequence, this resource should allow fine mapping of genetics traits contributing to virulence and host range and eventually facilitate the identification and cloning of such traits. The highly damaging RKN species M. incognita, M. javanica and M. arenaria reproduce asexually by mitotic reproduction. With this reproductive strategy progeny should be clones of their mother. However, there are differences among isolates in the ability to reproduce on specific hosts. For example, M. incognita strains have been divided into four ‘host races’ depending on whether or not they can reproduce on cotton or tobacco NC95 (Sasser et al. 1983). In addition some isolates of each of these three species can reproduce on tomato with Mi-1 (Trudgill and Blok, 2001). In some cases, Mi-1-virulent nematodes have been isolated even where tomato has not been previously grown. Resistant strains have also been selected from inbred cultures under greenhouse conditions (Bost and Triantaphyllou, 1987; Eddaoudi et al., 1997; Kaloshian et al., 1996). DNA polymorphism searches between pairs of nearly isogenic strains that are virulent and avirulent on Mi-1 have been carried out in an attempt to elucidate the molecular basis for the acquisition of virulence in two independent studies (Gleason et al., 2008; Semblat et al, 2001). In both cases, the strains were determined to be nearly identical, but with a sequence missing in the virulent strain, consistent with gene-for-gene model in which loss of a pathogen product leads to the inability of the host resistance gene to detect the pathogen. In one case for M. incognita, the missing sequence corresponded to an amphid-expressed gene called map-1 (Semblat, et al., 2001). In another case the missing sequence was part of an M. javanica gene called Cg-1 that corresponds to a small transcript that does not encode a significant protein. Silencing of Cg-1 in avirulent M. javanica resulted in acquisition of virulence, suggesting
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that this gene product is required in the nematode for the function of Mimediated resistance in the plant. However, the role of either map-1 or Cg1 in Mi-mediated recognition has not been determined. Relatively rapid virulence selection has been demonstrated with M. incognita on cowpea with the resistance gene Rk (Petrillo and Roberts, 2005). Some, but not all, of the resulting virulent lineages displayed reduced fecundity on cowpea, indicating a trade-off between reproductive fitness and virulence. This is consistent with finding in other systems that avirulence genes function as pathogenicity factors and that overcoming resistance thus carries a cost to the pathogen (Bent and Mackey, 2007). In some cases, there appears to be an adverse cost of fitness for gain of virulence to Mi-1 in RKNs (Castagnone-Sereno et al., 2007; Roberts, 1995), suggesting that here, too, the avirulence gene may correspond to a pathogenicity factor.
V. GENOMIC ANALYSIS OF RKN The last 2 years represent a milestone in PPN genomics with the first two complete genomes of PPNs being obtained, both from RKN species: M. incognita and M. hapla (Abad et al., 2008; Opperman et al., 2008). The M. hapla genome sequencing project was co-ordinated by the Center for Biology of Nematode Parasitism in Raleigh North State Carolina University (NSCU) (USA), whereas the M. incognita genome was sequenced in France under the initiative of the Nematology group at Institut National de la Recherche Agronomique (INRA) Sophia Antipolis, in close collaboration with the Ge´noscope at Evry (the French national sequencing centre), and the Bioinformatic platform at INRA Toulouse. For both the M. incognita and M. hapla sequencing projects, random clones were end-sequenced from small-insert (2–4 kb) and medium-sized-insert (6–8 kb) plasmid libraries and fosmid or BAC libraries. These two genome sequencing projects were based on a projected 10–12 coverage of the genome. For the M. incognita genome project, the automatically annotated protein-coding genes were manually annotated by a consortium of 27 laboratories. Each laboratory focused on a particular process or gene family relevant to various aspects of M. incognita biology. Patterns of presence/absence and expansion/reduction with respect to Caenorhabditis elegans and other nematodes (and other species, when appropriate) were examined. A. GENOME STRUCTURE
Substantial differences in genome organization have been found between M. hapla and M. incognita. M. hapla VW9 is diploid, with 16 chromosomes.
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The assembled scaffolds result in a 10.4 coverage of about ~54 Mb, spanning more than 99% of the genome. Moderately repetitive DNA accounts for a relatively small percentage of the sequences present, with 12% of the genome consisting primarily of low-complexity sequences. This genome is therefore one of the smallest metazoan genomes characterized to date. The M. incognita genome seems to be more complex. The assembled sequence reads give a total coverage of 86 Mb, almost twice the estimated size of 47–51 Mb per haploid genome (Leroy et al., 2003; Pableo and Triantaphyllou, 1989). This result is consistent with the fixation of heterozygosity in M. incognita. Indeed, most of the genome is present as homologous but diverged segment pairs, with a mean sequence divergence of 7–8% between the aligned regions. This value is among the highest recorded for a sequenced heterozygous organism. Triplicated genomic regions have also been found, with rearrangements in the order of gene sequences. There are two possible reasons for this. The strictly parthenogenetic lifestyle of M. incognita, with no meiotic recombination, may allow alleles to diverge considerably, as suggested for bdelloid rotifers (Mark Welch et al., 2004). Alternatively, the considerable divergence between alleles may be due to recent interspecies hybridization events. Comparisons of Internal Transcribed Spacer (ITS) and mitochondrial sequences of M. incognita and related species strongly support that these species are derived from hybridization events between closely related females and more diverse male paternal lineages (Hugall et al., 1997, 1999). More recent work by Lunt (2008) using single-copy nuclear genes also showed that these sequences did not group phylogenetically according to recognized species, further supporting reticulate origins. In contrast, M. hapla strains when maintained under conditions favourable for asexual reproduction approach homozygosity (Liu et al., 2007). The overall G þ C content of M. incognita (31.4%) is similar to that of other sequenced nematode genomes, such as those of C. elegans, C. briggsae and Brugia malayi, whereas M. hapla has a significantly lower G þ C content, at 27.4%. Satellite DNA families were found in the two RKN genomes, together with rRNA sequences (16S-5.8S-28S), which were organized into clusters. Repetitive/transposable elements (TEs) are more abundant (36%) in the M. incognito genome than in the M. hapla and C. elegans genomes. In both the M. hapla and M. incognita genomes, no DNA attributable to bacterial endosymbiont genomes was identified. There is also a major difference in gene content between these two RKN genomes. The M. incognita genome is thought to contain 19,212 genes, whereas the M. hapla genome appears to carry 14,420 genes, but this substantial difference in gene number may be accounted for by divergent alleles of the M. incognita genome described above. It is tempting to correlate the larger gene set in M. incognita with its strictly mitotic parthenogenetic mode of reproduction, favouring the
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maintenance of functional divergent ancient alleles and duplications. Gene density in M. incognita is very similar to that in C. elegans, whereas that in M. hapla is greater, probably contributing to the small size of the M. hapla genome.
B. THE SECRETOME
As described above, secreted proteins probably play an important role in nematode–plant interactions. A preliminary study indicated that the M. incognita and M. hapla genomes encode more than 3000 and 1534 proteins, respectively. This difference could be explained by the duplicated structure of the M. incognita genome. Half the M. incognita proteins are species specific and two-thirds are pioneer genes. A careful analysis of the secreted proteins of M. hapla showed that 360 of these proteins displayed no significant match to a protein from C. elegans. An interesting feature of PPN underlying their success as plant parasites is the secretion of plant cell wall-modifying (PCWM) enzymes. These proteins appear to play multiple roles in cell wall modification during plant parasitism. One of the most remarkable findings to emerge from genomic studies has been the identification of an extensive set of PCWM, carbohydrate-active enzymes (CAZymes) with no equivalent in any animal studied to date. A few such enzymes have been identified in previous studies of PPNs and in two insect species (Caillaud et al., 2008a; Davis et al., 2004; Wei and Brent, 2006), but these enzymes are otherwise absent from all other metazoans studied to date. In M. incognita, this set of CAZymes comprises 61 proteins completely absent from C. elegans and Drosophila melanogaster, including 21 cellulases and 6 xylanases from the GH5 family, 2 polygalacturonases from the GH28 family and 30 pectate lyases from the PL3 family (Table I). All these enzymes were also reported to be present in the genome of M. hapla, albeit at lower abundance, probably due to the duplicated TABLE I Plant Cell Wall-Degrading and Modifying CAZymes Substrate Family M. incognita C. elegans D. melanogaster a b
Cellulose GH5 (eng) 21 0 0
Xylan a
GH5 (xyl) 6 0 0
Arabinan GH43 2 0 0
Pectin GH28 2 0 0
PL3 30 0 0
Other EXPN 20 0 0
Total
a,b
81 0 0
Cellulose-binding modules of family CBM2 (bacterial type) are found appended to these proteins. EXPN: Expansin modules (modification of plant cell wall).
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structure of the M. incognita genome. Phylogenetic analyses of the genes encoding these enzymes led to the identification of bacterial homologues, and the acquisition of these enzymes by horizontal gene transfer from bacteria is thus thought to be the most likely explanation for their presence. Genes encoding two additional PCWM arabinases (GH43 family) and two invertases (GH32 family) not previously identified in metazoans were found in the genomes of M. incognita and M. hapla. If the arabinase activity of their products is confirmed experimentally, these enzymes can be added to the novel and diverse PCWM arsenal of RKN. Invertases catalyze the conversion of sucrose, an abundant disaccharide in plants, into glucose and fructose, suggesting a possible role (assuming that their activity is confirmed) for these enzymes in converting the sugar circulating in plant hosts into a suitable carbon source for RKN. In addition to the PCWM– CAZymes, 20 candidate expansins have been identified in M. incognita. The precise biochemical function of these proteins remains unknown, but it has been shown that PPNs (Qin et al., 2004; Roze et al., 2008) produce expansinlike molecules that may disrupt non-covalent bonds, loosening the plant cell wall and making its components more accessible to PCWM enzymes (Cosgrove, 2000). Examination of the genome of M. incognita revealed genes encoding such enzymes to be unexpectedly abundant, highlighting the high degree of specialization of this organism as a plant pathogen. In addition to PCM-related CAZymes, members of the family of secreted chorismate mutases were identified in the RKN genomes (Huang et al., 2005b; Lambert et al., 1999). These enzymes closely resemble bacterial enzymes, again suggesting that horizontal gene transfer events have shaped the evolution of plant parasitism within RKNs. C. THE RKN GENOMES SHED LIGHT ON NEMATODE DIVERSITY
C. elegans is a proven model animal, but is it a good model for parasitic nematodes? The analysis of these two first PPN genomes provides some insights into this issue. The genomes of M. hapla and M. incognita differ in many important ways, but they also have a number of characteristics in common. For example, both species carry substantially fewer G-proteincoupled receptors (147 and 108 in M. hapla and M. incognita, respectively) than does the free-living nematode C. elegans (1280 genes). Collagens are ubiquitous structural proteins with essential functions, as demonstrated by the range of defects observed following the mutation of individual collagen genes. Cuticle collagen genes form an abundant gene family in C. elegans, with over 180 members, grouped into six subfamilies on the basis of sequence similarities (Page and Johnstone, 2007). The RKN genomes
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contain fewer cuticle collagen genes, with 122 such genes in M. incognita and 81 in M. hapla. Neuropeptide diversity is particularly high in the phylum Nematoda. The identified neuropeptide complement of M. incognita falls somewhat short of that for C. elegans, but includes new peptides with functions potentially specific to the plant-parasitic lifestyle of Meloidogyne. The nuclear receptor (NR) superfamily is relevant to many aspects of physiology, as NRs are involved in the regulation of gene expression. The evolutionary history of nematode NRs is known to be complex. Many of these receptors, despite being of major physiological importance in other animals, have been lost from C. elegans. In M. hapla, only 25% of the NR genes identified in C. elegans were detected. In M. incognita, the situation is more complex. The 92 predicted NRs identified included clear orthologues of some known nematode NRs. Furthermore, M. incognita contained a larger number of supplementary NRs. These findings suggest that multiple duplication events took place both before and after the separation of the C. elegans and Meloidogyne lineages. While antioxidant enzymes protect the parasite against cytotoxic oxygen radicals from the host, the number of genes encoding antioxidant enzymes of the glutathione S-transferase (GST) and cytochrome P450 (CYP) families in M. incognita was only onethird that in C. elegans. A similar decrease in the number of these genes has been reported for the animal parasitic nematode B. malayi. The parasitic nature of M. incognita may be partly responsible for the smaller number of genes involved in the response to pathogens in this nematode than in C. elegans, as it spends much of its life in a privileged environment, protected from diverse exogenous stresses by the plant tissues. Overall, the data from this preliminary comparative analysis of important traits in nematode physiology suggest that the model species C. elegans is not representative of total genomic diversity in the phylum Nematoda, particularly as concerns parasitic species. D. DEVELOPMENTAL PATHWAYS CONSERVED IN NEMATODES
The pathway of genes responsible for sex determination in C. elegans has been studied in detail and is linked to the dosage compensation pathway (Zarkower, 2006). Despite the differences in their modes of reproduction, M. incognita and M. hapla homologues of these genes (at least one component of each stage of the sex determination cascade) have been identified. These homologues include genes from the dosage compensation pathway, the sex determination pathway itself and many downstream genes, including genes repressing maleness-promoting genes and controlling male differentiation and behaviour. However, genes acting upstream in the pathway were
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not detected, suggesting a divergence between the RKN species and C. elegans in terms of the signals triggering these sex determination pathways. As RNAi can be induced in different RKN species (Lilley et al., 2007; Rosso et al., 2009), components of the RNAi pathway in the M. incognita and M. hapla genomes are expected to be present. Many components of the RNAi pathway were indeed found in these two genomes, but the red4 gene was absent, as in the animal parasitic nematode Haemonchus contortus (Zawadzki et al., 2006). In addition, as reported for B. malayi and H. contortus, homologues of sid-1, sid-2, rsd-2 and rsd-6 involved in systemic RNAi and the spread of double-stranded RNA (dsRNA) to surrounding cells were also absent. Novel or poorly conserved spreading factors may therefore be responsible for the systemic RNAi reported in M. incognita (Rosso et al., 2009). One of the most important general conclusions of this genomic analysis concerns the small size of the M. hapla genome size, associated with substantial gene losses with respect to the model species C. elegans. Consistent with the decrease in genome size observed in B. malayi, this gene loss seems to be clearly related to parasitic lifestyle. However, this gene loss is not typical of the overall trend for the M. incognita genome, in which evolution towards effective haploidy in the absence of sex has tended to lead to the maintenance of divergent functional alleles, probably accounting for the larger number of genes present. For genes involved in the host–parasite interface, this genetic plasticity may account for the extremely wide host range and widespread distribution in agricultural crops of M. incognita and related species. Conversely, M. hapla may be considered a more basal species with a less aggressive survival strategy. More profound comparative analyses of these two genomes, combined with phenotypic analyses of F2 lines of M. hapla and RNAi studies, will shed light on the evolution of Meloidogyne spp. and identify both the basal gene complement and the genes involved in determining host range.
VI. NOVEL STRATEGIES FOR CONTROLLING PLANT-PARASITIC NEMATODES An important long-range goal of basic studies on nematode biology and host interactions is to identify novel control strategies for these often devastating pests. Resistance genes can be very effective for control, but are often not available for a particular host. Attempts to transfer cloned natural resistance genes to new hosts have met with limited success. The tomato gene Mi-1 confers RKN resistance when transferred to eggplant, but not to more
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distantly related hosts (Goggin et al., 2006). The tomato gene Hero A, which confers resistance to G. rostochiensis, disappointingly did not function when transferred to potato in which this nematode is a serious problem (Sobczak et al, 2005). A variety of novel transgenic approaches have also been carried out. Some strategies have expressed transgenically proteins that are detrimental to the nematode (Fuller et al., 2008). Transgenic expression of cysteine proteinase inhibitors (cystatins) has so far shown the most promise. Field trials have indicated that commercially useful resistance can be obtained in potato (Urwin et al., 2001). Promoters limiting expression of cystatin to specific tissues such as the feeding structures also have resistance to both cyst nematode and RKN (Lilley et al., 2004). The expression of specific crystal (Cry) proteins from Bacillus thuringiensis in plants has been widely successful for controlling targeted insects. Genes encoding Cry proteins with toxicity to nematodes have been identified, extensively modified and expressed in tomato roots; results so far suggest that this strategy has promise for control against PPNs (Li et al., 2008). There has been considerable interest in using the strategy of silencing nematode genes by expression in planta of dsRNA corresponding to nematode genes in plants (Gheysen and Vanholme, 2007; Lilley et al., 2007). This approach was stimulated by evidence from several independent groups that genes can be silenced in both RKNs and cyst nematodes by soaking infective J2 in solutions of dsRNA corresponding to that gene (Chen et al., 2005; Rosso et al, 2005; Urwin et al., 2002). Because PPNs normally feed only on the cytoplasm of their feeding cells, a chemical stimulus was often used to facilitate the uptake of the dsRNA from the solution. These experiments stimulated efforts to test whether resistance to nematodes could be produced by in planta production of dsRNA. Yadav et al. (2006) found that expression of dsRNA corresponding to specific RKN genes could confer resistance in tobacco plants. Similarly, reduced reproduction of root-knot species was found in Arabidopsis and of H. glycines on transgenic soybean with other constructs designed to express dsRNA corresponding to specific nematode genes (Huang et al., 2006a; Steeves et al., 2006). Further studies have indicated that often resistance generated by this method is partial and durability of resistance generated using this strategy is yet to be investigated (Sindhu et al. 2009). The now available genome sequences are resources for the identification of pathways unique to nematode development and parasitism that could potentially serve as new targets for nematicides. For example, searching the RNAi experiment repository in WormBase for C. elegans genes for which RNAi led to a lethal phenotype identifies more than 340 M. incognita genes as potential nematode targets for antiparasitic drug design.
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Past experience with pest control has shown that there is not likely to be one simple, permanent cure for these persistent pests. Combining or pyramiding different resistance types is likely to be of value. Importantly, continued studies on the biology of PPNs and their complex interactions will be necessary, but these are fascinating creatures and in many ways it is an exciting adventure.
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Optimization of Nutrition in Soilless Systems: A Review
´ NGELES CALATAYUD1 ELISA GORBE AND A
Department of Horticulture, Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Naquera km. 4.5, 46113 Moncada, Valencia, Spain
I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Nutrient Solution Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Considerations about the Optimum Nutrient Solution Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Measurement of Plant Nutrient Uptake. . . . . . . . . . . . . . . . . . . C. Factors That Regulate Nutrient Uptake by the Plant. . . . . . . . . D. Diagnosis of Plant Stress Caused by Nutrient Solution Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Water Supply. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Considerations about the Optimum Water Supply . . . . . . . . . . . B. Diagnosis of Plant Stress Caused by Water Supply . . . . . . . . . . IV. Electrical Conductivity and pH in the Nutrient Solution . . . . . . . . . . A. Considerations about the Optimum Electrical Conductivity and pH in the Nutrient Solution . . . . . . . . . . . . . . . . . . . . . . . . B. Diagnosis of Plant Stress Caused by Electrical Conductivity and pH in the Nutrient Solution . . . . . . . . . . . . . . . . . . . . . . . . V. Dissolved Oxygen Concentration in the Nutrient Solution . . . . . . . . A. Considerations about the Optimum Oxygen Concentration in the Nutrient Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Diagnosis of Plant Stress Caused by Dissolved Oxygen Concentration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Advances in Botanical Research, Vol. 53 Copyright 2010, Elsevier Ltd. All rights reserved.
0065-2296/09 $35.00 DOI: 10.1016/S0065-2296(10)53006-4
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VI. Nutrient Solution Temperature . . . . . . . . . . . . . . . . . . . . . . . . A. Considerations about the Optimum Nutrient Solution Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Diagnosis of Plant Stress Caused by Nutrient Solution Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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ABSTRACT High yield and product quality of crops grown in soilless systems are only possible if nutrition is optimized. This implies the accurate management of all factors involved in crop nutrition: nutrient solution composition, water supply, nutrient solution temperature, dissolved oxygen concentration, electrical conductivity and pH of the nutrient solution. If any of these factors is under non-optimal conditions, plants may suffer from stress leading to a decline of yields and product qualities. In order to specify the range of optimal conditions of a particular crop, a precise diagnosis of plant stress caused by an incorrect management of any of above mentioned factors is needed. This review analyses, for every factor, the aspects that need to be considered while determining the optimum ranges and the physiological methods that can be used to diagnose plant stress at non-optimal conditions. The most extensively used methods of plant stress assessment include measurements of: photosynthetic activity (leaf gas exchange, chlorophyll fluorescence, pigment content and related enzyme activities), oxidative stress and antioxidant capacity, content and partitioning of several compounds in the plant (carbohydrates, hormones, amino acids and nutrient elements), activity of specific enzymes, plant water relationships and expression of specific genes.
I. INTRODUCTION Continuous cultivation of crops in soil throughout many decades has resulted in poor soil fertility, increase of salinity or infestations by pathogenic organisms. This situation has led to poor yield and quality of crops. Furthermore, some soils in the world are not suitable for plant growth for being poorly textured or shallow, degraded due to erosion or too close to metropolitan areas. Whenever soil conditions are unfavourable, soilless culture can be a solution. Soilless culture is a method of growing plants in any medium other than soil. Many crops are grown in soilless systems (Van Os et al., 2008). Over the last decades, this technique has progressed rapidly in many developed countries (e.g. the Netherlands, Japan and USA) linked to greenhouse building,
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automation and computerization. However, the application of soilless culture for crop production is still limited in many countries such as the Mediterranean due to their lower technological development in agriculture (Olympios, 1999). The use of soilless systems allows the possibility of an exhaustive control of nutrient solution, which permits the optimization of crop nutrition and the improvement of water and nutrient use efficiency. However, these advantages can turn into problems if a good management of the system is not carried out. This is due to the lower buffering capacity of soilless systems compared to soil systems, which involves that quick decisions should be taken when sudden deviations from optimum conditions appear. Therefore, the personnel in charge of soilless systems should be trained in the control of the technique. In addition, these systems imply a higher initial capital investment to the grower. Hence, when cultivating in soilless systems, it is very important to learn to optimize crop nutrition so that advantages greatly exceed disadvantages. The advantages and disadvantages of the soilless systems have been reviewed by Olympios (1999). In this review, a study of the most important abiotic factors that need to be controlled in soilless systems for an optimum management of nutrition is performed. These factors are: nutrient solution composition and concentration, water supply, nutrient solution temperature, dissolved oxygen concentration, electrical conductivity (EC) and pH of the nutrient solution (Table I). An incorrect management of any of these factors can lead to stress in plants (Table II). Therefore, a precise detection of stress is essential in research to identify inadequate management strategies and to develop recommendations to growers about the abovementioned factors with the aim of obtaining the maximum yield and quality of horticultural products. Hence, in this review, a
TABLE I List of Abiotic Factors Affecting the Shoots and Roots Environment of Soilless Crops Abiotic factors (Shoot) Air temperature Light intensity Photoperiod Relative Humidity Environmental pollutants CO2 concentration
Abiotic factors (Root) Nutrient solution composition Water supply Electrical Conductivity Nutrient solution concentration Concentration of injurious ions (Naþ, Cl–) pH Dissolved oxygen concentration Nutrient solution temperature
Although directly affecting the surroundings of certain parts of the plant, these factors influence the whole plant physiology.
TABLE II Possible Stresses Caused by the Different Nutrient Solution Factors Possible stress Factors Nutrient solution composition Water supply Electrical conductivity (nutrient solution concentration) Electrical conductivity (injurious ions) pH Dissolved oxygen concentration Nutrient solution temperature
Low level of the factor Specific nutrient deficiency Nutrient imbalance Water stress General nutrient deficiency – General nutrient deficiency Root damage Hypoxia/Anoxia Chilling stress
High level of the factor Specific nutrient toxicity General nutrient deficiency Osmotic stress Osmotic stress Toxicity Nutrient imbalance General nutrient deficiency (After anoxia) Oxidative stress High temperature stress Hypoxia/anoxia
An inadequate management of any of these factors in cultivation of soilless crops may lead to suboptimum or supra-optimum levels of the factor, which consequently results in plant stress.
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list of the most important methods that can be used to diagnose plant stress due to an incorrect management of each studied factor is shown (Fig. 1), and emphasis is given at why each method may be used to detect stress symptoms. Osmotic adjustment
Water uptake • Root hydraulic conductance
Plant water status
• Aquaporin activity +
• H -ATPhase activity
• Abscisic Acid
• Relative water content
• Cytokinin
Stomatal conductance
Photosynthesis
Hormone synthesis
• Water potential
Transpiration
• Leaf Temperature
• CO2 assimilation • Cholorophyll and carotenoids content
Photoassimilate production
• Light reactions -Chlorophyll fluorescence
• Partitioning among metabolic, structural and storage forms
• Activity of Calvin Cycle enzymes
• Amino acid synthesis and accumulation • Phloem export of photoassimilates
• Ultrastructural changes in chloroplasts
• Carbohydrate accumulation in plant tissues • Carbohydrate partitioning
Oxidative stress • Generation of ROS Root respiration • Lipid peroxidation • Content of antioxidant compounds
Plant growth
• ATP synthesis
• Plant biomass
• Carbohydrates content in the root
• Leaf area • Root mass
• Activity of antioxidant enzymems
• Fermentation processes • Shoot/Root Nutrient uptake
• Senescence
+
• H -ATPase activity • Nitrate Reductase activity • Nutrient content in plant tissues
Fig. 1. Scheme of general (capital letters) and related (lower case letters) plant processes that might be affected by stress due to nutrient solution factors (i.e. nutrient solution composition and concentration, water supply, nutrient solution temperature, dissolved oxygen concentration, electrical conductivity and pH of the nutrient solution). The connection among physiological functions is shown by arrows, and indicates that an alteration of a given function has consequences on associated processes and, often, on every plant process. The measurement of affected plant physiological functions may be a tool to diagnose plant stress caused by non-optimum levels of any of the nutrient solution factors.
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II. NUTRIENT SOLUTION COMPOSITION A. CONSIDERATIONS ABOUT THE OPTIMUM NUTRIENT SOLUTION COMPOSITION
It is essential to have a good knowledge of plant mineral requirements in order to formulate optimum nutrient solutions. The ideal solution would provide the plant with the precise elements for producing the highest yield and/or quality and reduce the susceptibility to biotic and abiotic stresses. The method of formulating optimum nutrient solutions is discussed ahead. However, fertilization is often empirically based. Commercial greenhouse growers generally use high nutrient concentrations in an attempt to maximise crop yield (Rouphael and Colla, 2009), but this relationship is not necessarily straightforward. In general, crop yield responds positively to increasing concentrations up to a level after which further increases often lead to no further improvement of yield (luxury consumption). When concentrations are too high, yields may even decrease (toxicity) (Salisbury and Ross, 1991). Several studies have documented the advantage of using lower concentrations than the standard. Locascio et al. (1992) showed that the quality of chipping potatoes decreased with excessive potassium. Zheng et al. (2005) and Rouphael et al. (2008) proved that nutrient solution concentration used by growers can be reduced by 50% without any adverse effect on biomass and quality parameters in geranium and gerbera, respectively. Dufour and Gue´rin (2005) demonstrated that more than 60% of the nutrients supplied in the cultivation of Anthurium andreanun were lost in the leachate. This results in contamination of groundwater and is no longer permissible. Efforts should be made, from an environmental standpoint, to find and use the less concentrated but optimum nutrient solution possible. High concentrations, though, may be advisable for some crops to achieve high quality of the produce. For example, in tomato, a high proportion of Kþ in the nutrient solution (14.2 meq L–1 vs 3.4 meq L1) increased fruit dry matter, total soluble solids content and lycopene concentration (Fanasca et al., 2006). Gorbe and Calatayud (2009) observed that a dilution of the nutrient solution concentration by 40% with respect to the standard shortened vase life of rose flowers. Finding out the optimum nutrient solution concentration is desirable, but it is important to consider that what actually affects nutrient uptake is not the average solution concentration but the nutrient concentration at the root surface (see Section IIC1). This fact involves an important effect of transpiration rate and fertigation management on nutrients uptake. On the one
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hand, all ions solved in the nutrient solution are transported to the root through mass flow, which is driven by the transpiration rate (Mengel and Kirkby, 2001). Therefore, high transpiration rates will result in high concentration of ions close to the root surface, thus, enabling a higher nutrient uptake. On the other hand, the frequency and duration of irrigation also modify the nutrient concentration at the root surface. In general, increasing the frequency of irrigation reduces the variations in nutrient concentration, thereby increasing their availability to plants (Silber and Bar-Tal, 2008). However, frequent irrigation leads to a regularly wet substrate surface subjected to continuous evaporation, which causes accumulation of nutrients in the top layer that may reduce their availability to the roots (Sonneveld and Voogt, 2009). The impact of fertigation frequency on the uptake of nutritional elements by plants is related to both their mobility and their availability. Actually, the improvement of P and K uptake due to high-frequency irrigation is larger than that of N, this effect being lower as their concentration in nutrient solution increases (Silber and Bar-Tal, 2008). In addition, long irrigation events increase the leaching fraction (Lieth and Oki, 2008), which reduces the availability of nutrients to the roots. In addition to optimizing ion concentration, it is crucial to formulate nutrient solutions with a balanced relationship among the different ions (Can˜amero et al., 2008). Some ions in excess can cause nutrient deficiencies in plants by interfering with the uptake of other ions, which is called ion antagonism. Studies of antagonisms that may occur in soilless culture of horticultural crops have been reviewed by Mengel and Kirkby (2001), Pendias (2001) and Hall (2008). The importance of nutrient balance highlights the limitation of the current way of nutrient management by monitoring EC level, which is unable to distinguish between different nutrients. The source of N should be also taken into account when designing the optimum nutrient solution for a particular crop, since it leads to important effects on plant metabolic processes. Urea is not commonly used in soilless culture, NH4þ and NO3 being the main N sources (Silber and Bar-Tal, 2008). Some crops like rice prefer NH4þ but, in general, NO3 is primarily absorbed (Sa´nchez, 2004). It has been widely reported that NH4þ absorption depresses the uptake of cations and NO3 uptake depresses that of anions (Kirkby and Mengel, 1967). The use of NH4þ is advisable to buffer the increase of pH in the nutrient solution caused by NO3 absorption (see section IVA) and it enhances phosphate absorption (Lewis, 1992). Moreover, NH4þ absorption does not require reduction prior to utilization by the plant, thus resulting in considerable energy saving (Lewis, 1992). However, high concentrations of NH4þ are toxic to most plants, especially at high root temperatures and under high salinity (Silber and Bar-Tal, 2008). For that reason, NH4þ is rapidly
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assimilated in the roots preventing its presence in leaves. This fact results in a higher demand of carbohydrates and oxygen in the roots than NO3 uptake (Silber and Bar-Tal, 2008). Therefore, it is important to determine the threshold levels of NH4þ in the nutrient solution and/or plan strategies to reduce NH4þ toxicity (Ajayi et al., 1970; Koenig and Pan, 1996). The first step to formulating the optimum nutrient solution for a particular crop is to understand the factors that regulate nutrient absorption by the plant and, for that purpose, the measurement of plant absorption under different conditions is needed. B. MEASUREMENT OF PLANT NUTRIENT UPTAKE
Plant nutrient uptake can be determined by measuring nutrient depletion in the root environment and by quantifying nutrient content in plant tissues. 1. Measurement of nutrient depletion in the root environment This method is based on determining the difference in the amount of a certain ion in the root environment throughout a given period of time. This difference is associated with plant nutrient uptake, which can be calculated as [(V1C1)–(V2C2)] (Cabrera et al., 1995). In this equation, V1 and V2 are the volumes (L) of the nutrient solution at time 1 and 2, respectively, and C1 and C2 are the nutrient concentrations (mmol L1) at time 1 and 2, respectively. This method allows a good accuracy of nutrient uptake over time (Kla¨ring, 2001) and the results are comparable to those obtained by destructive longterm N measurements (Barak et al., 1996). However, a correct methodology should be applied to avoid errors in the measurements. Obtaining samples from the root environment is difficult, and samples of the drainage might not represent the composition of the nutrient solution surrounding the roots. In that case, a soilless system with a low inertia should be used (e.g. nutrient film technique and aeroponic system). Moreover, the system should avoid evaporation so that all volume losses can be attributed to water and nutrient uptake. Finally, this method is less accurate when nutrient solution concentration is elevated (Le Bot et al., 1998a) thus, diluted solutions are recommended. 2. Measurement of nutrient content in plant tissues This method is based on measuring nutrient content in plant tissues. Not only can this method provide information about plant uptake, but it can also differentiate between the allocations of this uptake with different parts of the plant. This technique is very useful in crops with a growing period of several
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months, in which nutrient content in their tissues can be easily related to its uptake during a known period of time. However, in other crops such as woody plants in which cultivation lasts for several years, the time when the nutrient content measured in plant tissues was absorbed is more difficult to estimate. Redistribution processes among the different parts of the plant are common in woody plants. For example, in rose plants, endogenous N is redistributed within the plant during each flowering cycle (Cabrera et al., 1995). Therefore, in these cases, the measurement of nutrient content in plant tissues can be carried out by using isotopically labelled fertilizers and tracing the fate and recovery of these nutrients by the crop (Strong, 1995). Nitrogen is the element that has been most widely used as labelled [15]N for being quantitatively the most abundant in plant tissues. [15]NO3 and/or [15]NH4þ fertilizers have been used in several crops (Dong et al., 2001; Gonza´lez-Mas et al., 2007; Quin˜ones et al., 2003). The disadvantage of measuring the nutrient content in plant tissues is that it is a destructive technique and, while using labelled fertilizers, it is expensive and requires qualified personnel. C. FACTORS THAT REGULATE NUTRIENT UPTAKE BY THE PLANT
There are two theories that explain how plants absorb nutrients. 1. First theory: nutrient supply as the only factor driving nutrient uptake This theory assumes that the only factor driving nutrient uptake is nutrient supply. This has been supported by Bugbee (2003), who recommended that adding nutrients to the solution depended on what one wanted the plant to absorb. This theory is based on the proved fact that nutrients transporters are induced by the concentration of their own substrate outside the root (Crawford and Glass, 1998; Glass et al., 2002). Actually, the high degree of specificity that nutrient transporters have for individual ions is comparable with the degree of specificity that enzymes have for a specific substrate (Bassirirad, 2000). Because of this analogy, Epstein and Hagen suggested in 1952 that carrier-mediated ion transport across the root can be described by the Michaelis-Menten kinetics: v¼
Vmax c Km þ c
ð1Þ
where c is the concentration of an individual ion whose uptake rate, v, is controlled by uptake capacity when all available carriers are occupied (Vmax), and by the apparent affinity of the transporters (Km). Although this hypothesis has been mainly proved for low external ion concentrations
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(<1 mM), the correlation between ion uptake and external ion concentration also occurs for the high range (>1 mM) (Devienne-Barret et al., 2000; Kim et al., 2008). Many authors have adapted Epstein and Hagen’s work (1952) to different crops like rose plants (Kim et al., 2008; Massa et al., 2009; Mattson and Lieth, 2007; Silberbush and Lieth, 2004), maize (Caassen and Barber, 1976), cotton (Brouder and Cassman, 1994) or tomato (CardenasNavarro et al., 1999) through the development of mathematical models. According to this theory, the method of designing the optimum solution would be by choosing plants having the best productions in terms of quantity and/or quality and/or having the highest resistance to stresses, and measuring the nutrient content in their tissues throughout the cultivation period. This would result in nutrient absorption curves based on which, optimum nutrient solutions may be formulated. Moreover, based on this theory, routine analysis of nutrient content in the leaf during the cultivation period may be used for corrections of the nutrient solution by comparing with the desired concentrations (Thomas, 1937). Several approaches, such as Compositional Nutrient Diagnosis (CND), Critical Nutrient Level (CNL), Sufficiency Range Approach (SRA) and Deviation from Optimum Percentage (DOP), have been suggested for diagnosing plant nutritional status according to foliar analysis. However, Diagnosis and Recommendation Integrated System (DRIS), which was proposed by Beaufils in 1973, has been considered the most accurate of all (Can˜amero et al., 2008; Rodrı´guez and Rodrı´guez, 2000) and, hence, has been applied to many crops (Rodrı´guez and Rodrı´guez, 2000). For successful use, the DRIS system must have known norms that are associated with the maximum yields for each crop (Foth and Ellis, 1997; Rodrı´guez and Rodrı´guez, 2000). Several comparisons are made among indexes for different elemental ratios against the established norm values (Pierzynski et al., 2005). The yield of the crop is proportional to the sum of all indexes in absolute values (Can˜amero et al., 2008). These indexes are obtained by calculating the mean of several functions in which a given nutrient element is combined with the remaining elements (Walworth and Summer, 1987). Three advantages of using DRIS method are: (i) analyses are independent of plant age and tissue; (ii) nutrients are ranked in order of the most to the least limiting and (iii) nutrient balance is emphasized (Foth and Ellis, 1997; Pierzynski et al., 2005). The error in the first theory is that it assumes that the plant would absorb the same amount of nutrients when keeping the same solution composition although other factors could change. However, it is well known that plant nutrient uptake changes with season (Le Bot et al., 1998a) or with its developmental stage (Cabrera et al., 1995; Kim et al., 2008) among others. Therefore, this approach alone cannot provide optimum nutrient solutions.
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2. Second theory: plants regulate their uptake according to their demand This theory suggests an active role of the plant in nutrient uptake and establishes that plants regulate their uptake according to their demand. If this statement is true, one would have to predict plant nutrient absorption, that is plant demand, to design the optimum nutrient solution. So next the question would be: What does plant demand depend on? In order to answer this question, many authors have developed mechanistic or empirical models that try to predict different crop nutrients uptake from several factors, with or without including nutrient solution concentration (Brun and Chazelle, 1996; Kla¨ring and Cierpinski, 1998; Kla¨ring et al., 1997; Le Bot et al., 1998b; Mankin and Fynn, 1996; Papadopoulos and Liburdi, 1989; Pardossi et al., 2005; Zerche, 2000). Plant nutrient uptake depends on the root surface area and on the uptake properties of this surface. Due to the importance of root surface area on plant nutrient uptake, it may be augmented at low nutrient supply, for example by increasing root hair length (Lambers et al., 2008). In addition, nutrient uptake per unit root surface depends on the transport rate of ions across root membrane, and this is determined by the number of transporters in the membrane and the activity of those transporters (Smith, 2002). Besides, nutrient uptake needs energy to be carried out (Marschner, 1995). Then, any factor that affects directly or indirectly any of those parameters will affect nutrients uptake and will be a candidate for the model. The most interesting from the perspective of optimizing plant nutrition would be developing models that include simple measurable parameters so that they can be implemented in decision support systems for the management of nutrient solution in soilless culture (Carmassi et al., 2005; Marcelis et al., 1998; Massa et al., 2008). Examples of simple measurable parameters related to nutrient uptake are water uptake (Del Amor and Marcelis, 2004; Pardossi et al., 2005) and climatic factors such as radiation (Brun and Chazelle, 1996; Cedergreen and Madsen, 2003; Mankin and Fynn, 1996; Pardossi et al., 2005), vapour pressure deficit (VPD) (Kla¨ring et al., 1997), air temperature (Adams, 1992; Kla¨ring et al., 1997; Pardossi et al., 2005) and nutrient solution temperature (Adams, 1992; Bassirirad, 2000; Bougoul et al., 2000; Brun and Chazelle, 1996; Dong et al., 2001). Problems may arise when there is a mismatch between the output of these models and the real demand of the plant, because of different conditions, that is when using different cultivars or when any kind of stress affects the plant (Grattan and Grieve, 1998; Kla¨ring et al., 1997). This is due to the fact that there are other internal factors that control nutrients uptake (Imsande and Touraine, 1994). Gorbe (2009) tried to reduce over or underestimation of nutrient uptake models of rose plants by including other factors that significantly affect nutrients uptake such as the production of flower shoot
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and some common practices of rose cultivation. However, there is an additional limitation of this theory: Sometimes it might not be advisable to use a nutrient solution that exactly matches plant demand, because the use of high concentrations may occasionally lead to a product of higher quality as explained earlier. A combination of both theories may be advisable for developing useful and realistic mathematical models that include the effect of nutrient supply and the effect of plant demand on nutrient uptake. Once developed and validated, these models would be implemented in computer-controlled systems to manage fertigation in soilless cultivation. Some models have been proved with success, but more research is still needed (Van Os et al., 2008). Mathematical modelling of water and nutrients uptake is one of the most important future tools in optimizing crop nutrition. Alternatively, if ionspecific electrodes could be improved to be more stable, then such modelbased ion uptake predictions may not be needed. This would allow a faster adjustment of nutrient solution concentration (Van Os et al., 2008). D. DIAGNOSIS OF PLANT STRESS CAUSED BY NUTRIENT SOLUTION COMPOSITION
An inadequate management of nutrient solution composition may be a consequence of the use of a too high or a too low concentration of the nutrient solution, or of an imbalanced ion composition. The first situation involves a high EC of the nutrient solution and, thus, a salt stress so this will be discussed ahead. The other two situations lead to a similar problem in plants: nutrient deficiency. One is due to insufficient supply and the other due to ion antagonism, but both cases have similar consequences: a decrease in plant growth. A reduction of plant biomass has been reported under N, P, K, Ca, Mg, S, Cu, Zn or Mn deficiencies (Ding et al., 2008; Fujita et al., 2004; Matcha, 2007; Tewari et al., 2004; Yu and Rengel, 1999; Zhao et al., 2005). It is well known that characteristic visual symptoms of specific nutrient deficiencies may appear in plant tissues. However, most of the classic deficiency symptoms described in textbooks are characteristic of acute deficiencies, that is when a nutrient is suddenly no longer available to a rapidly growing plant. In commercial cultivation in soilless systems, it is more common to find chronic deficiencies, that is the result of an insufficient supply of a nutrient compared to the demand of the plant (Berry, 2006). For chronic deficiencies, visual symptoms are not that clear so other methods have to be used to diagnose nutrient deficiencies. These methods are based on the key roles that nutrients play in plant metabolism because limiting levels of a nutrient affect the metabolic role in which it is involved.
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For example, deficiencies of any of the essential mineral elements may affect photosynthesis (Dietz and Harris, 1997). A decrease in the rate of photosynthesis has been observed under N deficiency (Ciompi et al., 1996; Cruz et al., 2003; Fujita et al., 2004; Huang et al., 2004; Lima et al., 2000; Matcha, 2007; Zhao et al., 2005), P deficiency (Lima et al., 2000) or Mg deficiency (Ding et al., 2008). This has not only been attributed to the lower plant leaf area (Zhao et al., 2005) but also to a decrease of chlorophyll content (Cruz et al., 2003; Zhao et al., 2005), a reduced stomatal and/or mesophyll conductance (Cruz et al., 2003; Natr, 1975; Zhao et al., 2005) and to direct effects on light and dark reactions. There are several cases of specific nutrients deficiency affecting photosynthetic light reactions. Mineral nutrients influence photosynthetic electron flow either for being constituents of the light harvesting complex, or for facilitating electron flow. For review about the specific roles of different nutrients in photosynthetic light reactions see Dietz and Harris (1997) and Cakmak and Engels (1999). This effect can be assessed by the chlorophyll fluorescence (CF) technique. A number of studies have shown that CF parameters are good indicators of nutrient deficiency. For example, Jacob (1995) stated that in P deficient plants, the ability of photosystem II (PSII) pigments to absorb and transfer light energy to the reaction centres is decreased, a phenomenon that is accompanied by an increase in nonphotochemical quenching and linked to an enhanced dissipation of thermal energy, which is also associated with enhanced formation of the xanthophyll pigment zeaxanthin. This is considered a protective response against overexcitation of PSII and destruction of photosynthetic apparatus (DemmingAdams and Adams, 1992). Likewise, N deficiency has been associated with a higher dissipation of the absorbed light energy and the formation of zeaxanthin, paralleled by a decrease in the quantum yield of electron transport that suggests a down-regulation of PSII photochemistry (Cakmak and Engels, 1999; Cruz et al., 2003; Lu and Zhang, 2000). This may occur in order to match the decreased demand in the Calvin cycle (Lu and Zhang, 2000) due to low CO2 influx (i.e. closed stomata) or reduced carboxylation efficiency (Ciompi et al., 1996; Huang et al., 2004). Photoinhibition did occur under different nutrient deficiencies in some studies (Huang et al., 2004; Lima et al., 2000) but not in others (Cruz et al., 2003; Lima et al., 2000; Lu and Zhang, 2000; Sun et al., 1989). One of the most useful indicators of N stress in plants is the ratio of UV excited blue fluorescence to chlorophyll fluorescence (BF/CF) (Cavender-Bares and Bazzaz, 2004). An increase in this ratio in stressed plants is due to an accumulation of phenolic or flavonoid compounds in leaf epidermis. Also, a dual fluorescence emission ratio of red fluorescence to far-red fluorescence excited at 355 and 532 nm was
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found to be strongly positively correlated with chlorophyll content, which decreases with mineral deficiencies (Cavender-Bares and Bazzaz, 2004). Moreover, certain nutrient deficiencies can directly affect the dark reactions through non-stomatal factors. For instance, the rate of CO2 fixation shows a strong positive correlation with leaf N content because the main portion of the leaf N is in RuBisCO, thylakoid proteins and Calvin cycle enzymes (Dietz and Harris, 1997). In addition, Mg is directly involved in the activation of RuBisCO (Dietz and Harris, 1997) and a decrease in the content of RuBisCO has been observed in S-deprived plants (Lunde et al., 2009). Nutrient deficiencies may also affect the fate of the Calvin cycle products. The biosynthesis and degradation of starch and sucrose are affected by nutrient deficiencies (Cakmak and Engels, 1999; Lunde et al., 2009). Most nutrient limitations may result in accumulation of starch in plant tissues (Loescher et al., 1990), although an increase in the sucrose/starch ratio has been observed in N-stressed sunflower plants (Ciompi et al., 1996). Moreover, nutrient deficiencies also affect the synthesis and accumulation of amino acid in plant tissues. Under N-limiting conditions, the levels of proline, asparagine and glutamine may decrease (Lemaıˆtre et al., 2008). In contrast, deficiencies of other nutrients different from N may increase amino acid content (Black, 1993). Although total dry matter production is similarly affected by different nutrient deficiencies, the effects on its partitioning are specific of the nutrient involved (Cakmak and Engels, 1999). For example, K and Mg deficiencies influence phloem export of photosynthates (Cakmak and Engels, 1999), which results in a higher accumulation of sucrose in leaves (Ding et al., 2008) and in a lower accumulation of photosynthates in the sinks such as cereal grains or roots (Cakmak and Engels, 1999). On the other hand, P and N deficiencies stimulate the phloem export of photosynthates. This often results in a reduction of leaf area that decreases the sink strength of the shoots, leading to a preferential allocation of photosynthates to the roots and to reduced shoot/root ratios (Cakmak, 2008; Cakmak and Engels, 1999; Ciompi et al., 1996; Fujita et al., 2004; Matcha, 2007; Zhao et al., 2005). In addition, deficiencies of N, Mg, K and Zn may increase the sensitivity of plants to photooxidative damage (Cakmak, 2008; Cakmak and Engels, 1999; Lu and Zhang, 2000). This higher susceptibility has been associated with an increased accumulation of inactivated PSII reaction centres, a decreased capacity of non-photochemical quenching, and an increase in the fraction of the primary quinone acceptor (QA) in the reduced state (Lu and Zhang, 2000). As a result of the generation of reactive oxygen species (ROS), enhanced lipid peroxidation, accumulation of malondialdehyde
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(MDA) and hydrogen peroxide (H2O2) as well as premature senescence of older parts implying oxidative stress in the plants have been measured under different nutrient deficiencies (Ding et al., 2008; Kiyoshi et al., 1999; Lima et al., 2000; Tewari et al., 2004). Plants have defense mechanisms for protection against ROS, which include low molecular antioxidants and antioxidant enzymes (Kiyoshi et al., 1999). In particular, an increase in the levels of ascorbic acid was observed under N deprivation (Kandlbinder et al., 2004), of ascorbate and glutathione under P starvation (Kandlbinder et al., 2004), of ascorbate under Mg deficiency (Anza and Riga, 2001) and of flavonoids and anthocyanins under S deprivation (Lunde et al., 2009). In addition, stimulation of the activities of antioxidant enzymes, such as superoxide dismutase (SOD), ascorbate peroxidase (APX) and other peroxidases (POX), glutathione reductase (GR), monodehydroascorbate reductase (MDAR) or catalase (CAT) has been observed under limiting supply of N (Polesskaya et al., 2004; Tewari et al., 2004), P (Kandlbinder et al., 2004; Tewari et al., 2004), K (Tewari et al., 2004), Mg (Anza and Riga, 2001; Ding et al., 2008), Ca (Tewari et al., 2004) and S (Kandlbinder et al., 2004; Lunde et al., 2009; Tewari et al., 2004). However, under N deficiency, decreases in SOD (Polesskaya et al., 2004), APX (Kandlbinder et al., 2004) and CAT (Kandlbinder et al., 2004) have been reported, which may be related to a severe deficiency. Micronutrient deficiency (Cu, Zn or Mn) has also been observed to alter the activities of SOD depending on the kind and severity of the deficiency stress (Yu and Rengel, 1999). Besides the effect of nutrient deficiency on the activity of antioxidant enzymes, there are other enzymes, with or without an antioxidant role, which may be affected if that nutrient is a cofactor in the active site of the enzyme. Therefore, the measurement of these enzymes activities may be used as indicators of nutrient deficiencies in plants (Lavon and Goldschmidt, 1999). For example, POX activity, for which Fe is a constituent, has been measured to distinguish iron deficiency from Mn deficiency in citrus (Bar Akiva, 1961). Carbonic anhydrase has been employed to identify Zn deficiency (Barker and Pilbeam, 2007). Ascorbic acid oxidase or cytochrome oxidase activities have been used to identify Cu deficiency (Bar Akiva et al., 1969; Walker and Loneragan, 1981). Mo and Fe deficiencies have been associated with low levels of nitrate reductase (NR) activity (Alcaraz et al., 1986; Shaked and Bar Akiva, 1967). NR activity has been also used for the assessment of N deficiency (Barker and Pilbeam, 2007; Hall et al., 1990; Lemaıˆtre et al., 2008; Oosterhuis and Batea, 1983; Tanaka et al., 1987), glutamate-oxaloacetate aminotransferase for P deficit and pyruvic kinase for K deficiency (Lavon and Goldschmidt, 1999).
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To conclude, diagnosis of nutrient deficiencies can be carried out successfully by measuring the activity or resulting products of certain metabolic functions in which the limiting element is actively involved. This includes: measurements of biomass production and yield, photosynthetic activity, stomatal conductance, chlorophyll content, RuBisCO content or activity, CF, the formation of zeaxanthin, carbohydrates and amino acid content, sucrose/starch ratio, carbohydrates and dry matter partitioning in the plant, shoot/root ratio, lipid peroxidation and ROS species, the amount of antioxidant compounds and the activity of several enzymes.
III. WATER SUPPLY A. CONSIDERATIONS ABOUT THE OPTIMUM WATER SUPPLY
In soilless culture, an accurate and dynamic control of the water supply is needed to meet plant water requirements due to the low water holding capacity of the system (De Boodt and Verdonck, 1972). Optimum water supply should fulfil plant demand and also prevent salt accumulation in the substrate area surrounding the root. However, under conditions of high transpiration (e.g. at midday in summertime), supply of water may be often insufficient leading to temporal water stress in the plant. In order to avoid this, sometimes excess water is supplied. This results in excessive ion lixiviation within the root environment and loss of unabsorbed water, which should be avoided from an environmental standpoint because water is a scarce resource. For review about the environmental impact of irrigation see Stockle (2001). In order to carry out an effective management of irrigation, precise information of water status of the group substrate-plant-environment is needed. Different methods try to approach this objective through measurements in the plant, in the substrate or by means of climatic sensors. An indetail review of these methods is included in Medrano (1999). At present, most soilless systems rely on the measurement of a single sensor, normally a radiometer to determine solar radiation or a tensiometer to determine substrate water potential. When the level of water potential or cumulated radiation reaches a threshold, an irrigation event is activated. A higher level of precision, though, may be obtained through the integration of a more complex model in the irrigation computer control system, which estimates water demand according to several parameters. Many models have been developed with different levels of complexity (Medrano, 1999) but currently, most of them are based on Penman-Monteith equation, which
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include radiation, VPD and leaf area, among other parameters (Monteith and Unsworth, 2007). Due to water scarcity, new irrigation scheduling approaches designed to ensure the optimal use of water have come up. Deficit irrigation and partial root-zone drying are two ways of maximizing water use efficiency for higher yields per unit of irrigation water supplied. The expectation is that any yield reduction will be insignificant compared with water saved (Kirda, 2002). Although certain water stresses might be suffered by plants irrigated through those strategies, sometimes a mild water stress may be advisable for obtaining a high quality of the product. For example, water stress conditions significantly affected xylem anatomy and functioning of two Zinnia elegans cultivars, which resulted in a longer vase life (Twumasi et al., 2004). For fruit quality, solute accumulation is a recognized physiological response to water stress. Accordingly, moderate water stress improved the quality of kiwi (Miller et al., 1998) of “Merlot” grapes and wine (Peterlunger et al., 2005) and of wheat kernel (Ozturk and Aydin, 2004). Withholding irrigation water during certain periods of time may be a useful management tool to manipulate some quality attributes of the produce (Miller et al., 1998), but it is important to study when to apply water stress to avoid a significant yield reduction. B. DIAGNOSIS OF PLANT STRESS CAUSED BY WATER SUPPLY
If water supply is higher than that required by plant, salt may lixiviate from the root environment possibly leading to nutrient deficiencies. In contrast, if water supply is lower than plant demand, plant water status may decrease. When water deficit is very limiting, plants wilt and visual symptoms are clear. However, water supply can be at suboptimum levels while showing no visual symptoms. In that case, several techniques based on the effects of water deficit on plant functions can help evaluate the degree of the stress, which may vary depending on the cultivar and on the extent and duration of water deprivation. The first process that might be affected by a decrease in plant water content is cell expansion (Munns and Tester, 2008). This results in a reduction of leaf expansion and root elongation, leaf expansion being a more sensitive process (Ball et al., 1994) that leads to a decrease in shoot/root ratio (Fageria et al., 2006). Besides leaf expansion, the number and growth rate of branches are reduced and old leaf abscission is stimulated. Therefore, whole plant leaf area decreases (Taiz and Zeiger, 2002, p. 593). The decrease in plant water status may be quantified as a decrease (i.e. more negative) in water potential (Verslues et al., 2006). For review about the measurement of water potential see Taiz and Zeiger (2006).
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Relative water content has been also used as a measure of plant water status. Plants can actively modify their water potential through osmotic adjustment, by which a reduction of osmotic potential may be achieved by increasing the cell concentration of a variety of common solutes (Taiz and Zeiger, 2002, p. 596). Through osmotic adjustment, leaves may maintain turgor during a certain time under water stress. This increases the lifetime of active tissues and extends the period of tissue preparation for drought (drought hardening) (Pugnaire et al., 1999). Under water stress conditions, higher amounts of sugars like sorbitol, manitol, glucose or sucrose (Arji and Arzani, 2008; Chehab et al., 2009; Fredeen et al., 1991; Kameli and Losel, 1993; Wang et al., 1995) and higher levels of proline (Arji and Arzani, 2008; Kameli and Losel, 1993; Prasad et al., 1982; Ramachandra Reddy et al., 2004) have been measured in several crops as a result of osmotic adjustment. The majority of drought-tolerant species have the ability to build up a high content of sugars in dry habitats, whereas drought-sensitive species accumulate far less. Several genes coding for enzymes associated with osmotic adjustment are either up-regulated or down-regulated by water stress (Taiz and Zeiger, 2002, p. 599). In addition, the expression of genes that encode proteins associated with membrane transport including Hþ-ATPases and aquaporins are sensitive to water stress (Galme´s et al., 2007; Taiz and Zeiger, 2002, pp. 599–600). In order to prevent water loss, stomata can actively close when leaves and roots are dehydrating. This is triggered by Abscisic acid (ABA), which accumulates in stressed tissues (Jiang and Zhang, 2002; Ramachandra Reddy et al., 2004; Taiz and Zeiger, 2002, pp. 594–595). Stomatal closure reduces CO2 intake and thereby decreases net photosynthesis (Dejong and Phillips, 1982; Dubey, 1997; Huber et al., 1984; Tezara et al., 2008). In any case, the photosynthetic rate per unit leaf area is affected by water deficit to a lesser extent than leaf area (Taiz and Zeiger, 2002, p. 595). In order to be adjusted to the reduced CO2 assimilation, electron transport rate and photochemical quenching have to be down-regulated (Chaves et al., 2002; Tezara et al., 2008). As a result, a great proportion of incoming light energy has to be dissipated as heat and nonphotochemical quenching increases (Fig. 2) (Calatayud et al., 2006; CavenderBares and Bazzaz, 2004; Tezara et al., 2008). Moderate stress does not induce a decrease in the PSII primary photochemistry as judged by the unchanged Fv/ Fm in several crops (Calatayud et al., 2006; Fracheboud and Leipner, 2003; Tezara et al., 2008). In contrast, the steady-state parameter Fs appears useful in detecting water stress in plants. In well-watered plants, Fs increases with light intensity, but as water stress progresses, it decreases with increasing light intensity (Flexas et al., 2000). Fs/F0 is also an indicator of declining stomatal conductance, CO2 assimilation and generation of non-photochemical quenching during water stress (Flexas et al., 2002).
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105.6%
0.45 0.4
φNPQ
0.35
84.6%
0.3 0.25
100%
0.2 0.15
63.3%
0.1 0.05 0 0
20
40
60
80 100 120 140 160 180 200 220 240 260 280 300 Time (s)
&
Fig. 2. Dark–light induction curve of the quantum yield of regulated energy dissipation in photosystem II (fNPQ), in chrysanthemum flower leaves under four levels of progressive stress. Values are means of n = 5. Leaves were darkened for 15 minutes prior to measurement. Then actinic light (blue light, 200 mmol m2 s1) was switched on and saturating pulses of light were applied at 20 second intervals for 5 minute in order to determine chlorophyll fluorescence parameters. For description of the measuring protocol, see Calatayud et al. (2006). Stress had been caused by the harvest of chrysanthemum flower shoots and their water loss due to postharvest conditions. The number relative to each curve stands for the fresh weight of the flower shoots, in percentage values with respect to their weight at harvest. The first curve ( ) corresponds to 2–3 hour after harvest, when no stress was detected according to fNPQ values. The second curve (&) corresponds to 24 hour after harvest, when the incision of harvest activated the photoprotective mechanisms in the leaf. This is a short-term defense response against stress that cannot be associated with water stress since flower shoot fresh weight was higher than that at harvest. The third curve (D) corresponds to the sixth day after harvest, when the progressive water loss in the flower shoot had affected the photoprotective mechanisms. The steady-state (t = 300 second) value of fNPQ had decreased compared to that of the previous curve. The last curve (*) corresponds to day 11 after harvest, when water stress was severe in the flower shoot and the photoprotective mechanisms were no longer operational. This step may be irreversible, thus leading to flower death. This suggests that under a certain stress, the nonphotochemical mechanisms might be stimulated or depressed, and the severity of the stress is what determines the response.
Under severe water deficit, photosynthetic activity may be affected by non-stomatal factors due to a strong dehydration of mesophyll cells (Fracheboud and Leipner, 2003). Decreased activity of many enzymes of the Calvin Cycle has been reported (Pugnaire et al., 1999), for example a strong decrease in RuBisCO activity in sunflower (Pankovic et al., 1999). This effect may be reversible if water stress is not too severe (Pugnaire et al., 1999). In addition, water stress may lead to ultrastructural changes in chloroplasts (Ackerson and Hebert, 1981; Dubey, 1997), which ultimately impair photosynthesis (Dubey, 1997). Concerning the light reactions, although leaf PSII
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photochemistry has been proved to be very resistant to water-stress conditions (Flexas et al., 2009), it may be completely lost if the stress is severe (Cavender-Bares and Bazzaz, 2004) (Fig. 2). In rose plants under severe water deficit, energy dissipation by non-photochemical quenching, electron transport rate and the fraction of the oxidized state of QA decreased, while non-regulated energy dissipation increased (Calatayud et al., 2006), hence, allowing a higher ROS production. It has been suggested that the weak tolerance of PSII photochemical capacity to severe water stress in desiccation-sensitive plants is related to oxidative stress (Cavender-Bares and Bazzaz, 2004; Flexas et al., 2006). Down-regulation of PSII photochemistry is, hence, needed to prevent the generation of ROS within the chloroplast (Navari-Izzo and Rascio, 1999). Accumulation of ROS or lipid peroxidation has been measured in several crops subjected to water stress (Esfandiari et al., 2007; Jiang and Zhang, 2002; Sairam et al., 1998). Crop species that are tolerant to water stress show reduced membrane damage due to increased synthesis of free radical scavengers (Dubey, 1997). An enhanced activity of GR, CAT, APX, SOD or MDAR (Esfandiari et al., 2007; Jiang and Zhang, 2002; Ramachandra Reddy et al., 2004; Sairam et al., 1998) and an increase in the content of antioxidant compounds such as ascorbic acid (Ramachandra Reddy et al., 2004; Sairam et al., 1998) have been measured in different crops under water stress. For review of oxidative stress under water deficit see Navari-Izzo and Rascio (1999). Translocation of photosyntates may be unaffected until water deficit becomes severe. This relative insensitivity of translocation to mild water stress allows plants to mobilize and use reserves where they are needed (Taiz and Zeiger, 2002, p. 596). Export of assimilates is less affected by water stress than by carbon exchange rates (Huber et al., 1984). The decrease in the export of assimilates, which leads to the accumulation of carbohydrates in the leaves (Pugnaire et al., 1999), may be due to the dependence of phloem transport on turgor pressure (Taiz and Zeiger, 2002, p. 596) and might depend on plant acclimation to water stress. For example, droughtadapted cotton plants exported sucrose whereas non-adapted plants accumulated sucrose at the same leaf water potential (Ackerson, 1981). In addition to what has been said, water stress induces other responses in plants. The decreased transpiration rate under water deficit causes an increase in leaf temperature, which may lead to heat damage under hot conditions. A decrease of respiration has been measured in beans and peppers (Gonza´lez-Meler et al., 1997) and a decrease in ATP production was measured in sunflower (Tezara et al., 2008) and soybean (Ribas-Carbo et al., 2005). Water deficit has an important indirect effect on nutrient
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uptake, which may be as important as its effect on growth (Pugnaire et al., 1999). Maybe because of that, N content in the plant is reduced under water deficit (Dejong and Phillips, 1982; Mahieu et al., 2009) and the activity of NR is also depressed (Correia et al., 2005; Fresneau et al., 2007; Pugnaire et al., 1999). To conclude, diagnosis of water stress in plants can be assessed by different techniques that measure plant processes affected by the loss of turgor. Measurements of plant water potential or relative water content can be used as indicators of plant water status. Measurements of biomass production and yield, leaf area, water uptake, photosynthetic activity, stomatal conductance, ABA accumulation, CF, RuBisCO activity, osmotic adjustment, carbohydrate content and partitioning in the plant, accumulation of several compounds in the leaves for osmotic adjustment (sugars, amino acids and so on), lipid peroxidation and ROS species, the amount of antioxidant compounds, the activity of antioxidant enzymes, the activity of NR, leaf temperature, nutrient uptake, N content, respiration or the expression of genes coding for Hþ-ATPases and aquaporins may give clues to determine the severity of water stress in the plant.
IV. ELECTRICAL CONDUCTIVITY AND pH IN THE NUTRIENT SOLUTION A. CONSIDERATIONS ABOUT THE OPTIMUM ELECTRICAL CONDUCTIVITY AND pH IN THE NUTRIENT SOLUTION
EC is an index of salt concentration that informs about the total amount of salts in a solution. Hence, EC of the nutrient solution is a good indicator of the amount of fertilizer available to the plants in the root zone (Nemali and Van Iersel, 2004). When plants absorb nutrients and water from the solution, the total salt concentration, that is the EC of the solution changes, and measurements of EC level are easy, fast and economic, hence, can be carried out daily by growers. Thus, fertigation management is currently based on the control of EC and pH in order to correct a preset nutrient solution prepared according to previous experience. This is a practical method but it is important to note that EC does not inform about the concentration of specific ions in the solution, hence, this way of managing nutrient solution may lead to nutrient imbalances. The ideal EC range for soilless crops is between 1.5 and 2.5 dS/m. However, the effect of salinity on crops is specific on the species and cultivar (Greenway and Munns, 1980). In general, EC > 2.5 dS/m may lead to salinity
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problems whereas EC < 1.5 dS/m may lead to nutrient deficiencies. In greenhouse culture, the high input of fertilizers is the main cause of the salinity problems (Li, 2000). In addition, a high EC may also be caused by the presence of specific ions such as Naþ and Cl in the solution. In order to avoid salinity problems, growers add fresh water to reduce EC. However, in some regions there is the added problem of having irrigation water of bad quality, that is with high content of Naþ and/or Cl. In that case, the addition of fresh water to the nutrient solution would not alleviate the problem of salinity and the use of cultivars with salinity tolerance may be the solution. Nevertheless, the amount and the frequency of fertigation may be managed in order to avoid salinity problems (Sonneveld and Voogt, 2009). High irrigation frequency and long irrigation events resulting in high leaching fractions may delay the rate of salt accumulation in the root zone, thereby mitigating the deleterious salinity effects (Lieth and Oki, 2008; Savvas et al., 2007). In some cases, though, it may be advisable to use a high EC to improve the quality of the produce. For example, the quality of flavouring and healthpromoting compounds in hydroponically grown tomatoes improves with increasing electrical conductivity in the nutrient solution (De Pascale et al., 2003; Krauss et al., 2007). On the other hand, pH is a measure of the acidity or basicity of a solution and determines the availability of essential elements to plants. pH is an essential parameter to control in soil and soilless system, but in the latter, its correction should be done on daily basis because of the lower buffering capacity of soilless systems (Urrestarazu, 2004). In fertigation, pH should be such that it does not damage plant roots and allows all essential nutrients to be dissolved in the nutrient solution to prevent the formation of precipitates that block the irrigation systems and decrease nutrients availability to plants. The optimum nutrient solution pH depends on the plant but, in general, it ranges between 5.5 and 6.5, in which the maximum number of elements is at their highest availability for plants (Taiz and Zeiger, 2002, p. 79). For review of the management of pH in soilless systems see Urrestarazu (2004). The change of pH in nutrient solutions is mainly related to the uptake of cation and anion species and especially to the uptake of nitrate and ammonium (Mengel and Kirkby, 2001). Three possible transport systems have been ascribed to nitrate uptake (i.e. 1NO3/2Hþ symport, 1NO3/ 2OH antiport and 1NO3/2HCO3 antiport) but, in any case, the result is the alkalinization of the nutrient solution (Touraine, 2004). In contrast, uptake of NH4þ is mainly driven by facilitated diffusion in response to the electropotential difference, and results in a decrease of pH in the nutrient
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solution (Mengel and Kirkby, 2001). Actually, the incorporation of NHþ 4 in the nutrient solution as a source of N (5–10%) has been used as a tool to regulate pH (Adams, 2004). In addition to nutrient uptake, pH may change due to release of protons by nitrification and excretion of protons by roots. Padgett and Leonard (1993) reported that conversion of NH4þ to NO3 by nitrifying organisms is of significant importance in NH4þ-based solutions in soilless systems. Moreover, roots release organic and inorganic compounds into the nutrient solution, thus reducing its pH (Mengel and Kirkby, 2001). For example, protons are pumped out of the plasmalemma of root cells by means of Hþ-ATPase pumps, providing the driving force for nutrient uptake. Plants can also excrete organic acids, which may be related to the nutrient status of plants, mainly to the P status, with the aim of increasing the availability of nutrients to plants (Mengel and Kirkby, 2001). B. DIAGNOSIS OF PLANT STRESS CAUSED BY ELECTRICAL CONDUCTIVITY AND pH IN THE NUTRIENT SOLUTION
The use of solutions with too low EC and the incorrect management of pH may lead to nutrient deficiencies, which have been reviewed earlier. In this section, we will discuss about ways of detecting salinity stress in plants. Depending on whether high EC is due to the use of highly concentrated solutions or due to the use of water with high levels of Naþ and Cl, the responses of plants are twofold: First, the presence of high levels of salts in the soil solution reduces the ability of the plant to take up water, which is referred to as the osmotic or water-deficit effect of salinity. Second, if excessive amounts of injurious ions (e.g. Naþ or Cl) enter the plant in the transpiration stream, there may be injury to cells in the transpiring leaves, which is called the salt-specific or ion-excess effect of salinity (Greenway and Munns, 1980). The osmotic effect of salinity induces metabolic changes in the plant identical to those caused by water stress (Munns, 2002). Specifically, the following effects have been observed in different crops under salinity stress: a decrease of biomass production and growth (Giuffrida et al., 2008; Shani and Ben-Gal, 2005; Soussi et al., 1998; Tavakkoli et al., 2008; Zhao et al., 2007; Zribi et al., 2009); a decrease of leaf area (Giuffrida et al., 2008; Netondo et al., 2004; Taiz and Zeiger, 2002, p. 614; Terry et al., 1983; Zhao et al., 2007); an increase of leaf abscission (Taiz and Zeiger, 2002, p. 614); a decrease of root growth (Rodrı´guez et al., 1997) but to a lesser extent than the reduction in leaf growth (Munns, 2002); a lower shoot/root ratio (Houimli et al., 2008; Meloni et al., 2004); a reduction in stomatal
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conductance (Netondo et al., 2004; Sultana et al., 1999; Terry et al., 1983; Zribi et al., 2009); an accumulation of ABA (He and Cramer, 1996); a decrease in CO2 assimilation (Maricle et al., 2007; Netondo et al., 2004), the effect in photosynthetic rate being less important than the effect in leaf enlargement (Terry et al., 1983); a decrease of water uptake (Giuffrida et al., 2008); a decrease in water potential (De Pascale et al., 2003; Zribi et al., 2009); a decrease in relative water content (Meloni et al., 2004); an increase in osmotic adjustment (De Pascale et al., 2003; Taiz and Zeiger, 2002, p. 612) due to accumulation of glycine betaine (Agastian et al., 2000; Meloni et al., 2004), proline (Agastian et al., 2000; Mattioni et al., 1997; Soussi et al., 1998) or sugars (Agastian et al., 2000; Soussi et al., 1998) among other compounds; down-regulation of photosynthetic electron transport (Netondo et al., 2004); a relative resistance of PSII primary photochemistry (Maricle et al., 2007; Zribi et al., 2009); an increased production of ROS (Cakmak, 2008); a stimulation of antioxidant enzymes such as SOD, APX, MDAR, CAT or GR (Esfandiari et al., 2007; Herna´ndez et al., 2000; Tanaka et al., 1999); a higher synthesis of antioxidant compounds like glutathione, carotenoids and lycopene (De Pascale et al., 2003; Ruiz and Blumwald, 2002); a decrease in RuBisCO activity (Miteva et al., 1992); a change in the ultrastructure of chloroplasts similar to that caused by water stress (Dubey, 1997); a lower translocation of photosynthates leading to an accumulation of carbohydrates in the photosynthesizing leaves (Dubey, 1997); an increase of leaf temperature (Kluitenberg and Biggar, 1992); a decrease of nutrient uptake (Dubey, 1997) and N content (Meloni et al., 2004); a decreased ATP synthesis (Dubey, 1997); a decrease of NR activity (Meloni et al., 2004); a reduced viability of reproductive organs (Munns, 2002); and, finally, a change in gene expression, similar to that caused by water stress (Taiz and Zeiger, 2002, p. 614). Therefore, the same methods can be used for diagnosis of any osmotic effect, either caused by water or by salinity stress. On the other hand, salt-specific effects may result in toxicity, deficiency or changes in mineral balance. First, plant deficiency of several nutrients and nutritional imbalance (i.e. extreme ratios of Naþ/Ca2þ, Naþ/Kþ, Ca2þ/ Mg2þ and Cl/NO3 in plant tissues) may be caused by the higher concentration of Naþ and Cl in the nutrient solution derived from ion antagonism (Grattan and Grieve, 1998). For example, Ca2þ and Kþ deficiencies have been observed under salt stress, which affects membrane integrity (Cramer et al., 1985) and root growth (Munns, 2002). Second, toxicity in plant cells may appear as a consequence of accumulation of Naþ and/or Cl in transpiring leaves. Plants are capable of compartmentalizing these ions in the vacuole up to a certain extent, but if the limit is exceeded, ions
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build up in the cytoplasm and inhibit enzyme activity, or they build up in the cell walls and dehydrate the cell, eventually causing cell death (Munns, 2002). The salt-specific effects of salinity depend on the concentration of salts, the duration of salinity exposure as well as on the plant species. Salt tolerant plants differ from salt-sensitive ones in having a low rate of Naþ and Cl transport to leaves and in the ability to compartmentalize these ions in vacuoles to avoid salt toxicity (Munns, 2002). Therefore, the resistance of salt-tolerant plants to salts is not a consequence of salt-resistant metabolism but of strategies that avoid salt injury (Taiz and Zeiger, 2002, p. 613). The toxicity effects of salts have metabolic consequences. Photosynthesis may be inhibited when high concentrations of Naþ and/or Cl accumulate in chloroplasts (Plaut et al., 1989; Taiz and Zeiger, 2002, p. 613). For example, alterations in the photochemical activity have been observed under salinity in salt sensitive crop species (Muranaka et al., 2002). Accumulation of injurious ions in the cytoplasm inactivates enzymes, inhibits protein synthesis and damages chloroplasts and other cell organelles (Taiz and Zeiger, 2002, pp. 612–613). These effects are more important in older leaves as they have been transpiring the longest, hence, accumulating more ions (Munns, 2002). This results in a progressive loss of the older leaves with time and reduces the photosynthetic leaf area of the plant to a level that cannot sustain growth. The rate at which leaves die becomes the crucial issue determining the survival of the plant (Munns, 2002). Hence, vine mortality has been correlated with the increase in Naþ and Cl content of leaves (Shani and Ben-Gal, 2005). To summarise, plant growth might be reduced by both the osmotic and the salt-specific effect of salinity, sometimes being difficult to determine which of the two effects is responsible for the growth reduction. For that reason, Munns et al. (1995) proposed a two-phase model of salt injury, where growth is initially reduced by osmotic stress and then by salt toxicity. According to the authors, the effect of salinity takes some time to develop and may become obvious over weeks, especially in the more sensitive species (Munns, 2002). This model has been proved in broccoli under salinity stress (Lo´pez-Berenguer et al., 2006). However, it is difficult to assess with confidence the relative importance of the two mechanisms on yield reduction because they overlap (Tavakkoli et al., 2008). In brief, diagnosis of salinity stress in plants can be evaluated by the same techniques used for water stress in addition to the measurement of the concentration of Naþ and Cl content in leaves. Special attention should be paid to the old leaves as they are the target of salt injury.
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V. DISSOLVED OXYGEN CONCENTRATION IN THE NUTRIENT SOLUTION A. CONSIDERATIONS ABOUT THE OPTIMUM OXYGEN CONCENTRATION IN THE NUTRIENT SOLUTION
Oxygen is essential for the functioning of roots, hence, its deficiency is an important concern. Problems with oxygen supply may periodically appear in soil conditions after rains. Also, in soilless systems, water and nutrients are supplied continuously and these wet conditions limit diffusion of oxygen to the root system (Veen, 1988). Oxygen deficiency stress in plants is distinguished by three physiologically different states: transient hypoxia (insufficient supply of oxygen), possible anoxia (complete lack of oxygen) and reoxygenation (Blokhina et al., 2003). An inadequate management of irrigation may lead to temporal hypoxia conditions caused by inadequate aeration in some parts of the root system (Morard and Silvestre, 1996). In contrast, anoxia is rare in soilless culture (Kla¨ring and Zude, 2009; Morard and Silvestre, 1996). In order to avoid oxygen deficiency in the root environment, it is essential to provide the nutrient solution with enough O2. Possibilities for accurate control of root oxygen supply are more easily achieved in soilless cultures than in soil systems (Olympios, 1999). The best oxygenation system of the root environment is the aeroponic system, which allows the roots to grow in air with a plentiful supply of oxygen, hence, no extra mechanism is needed. In liquid systems, aeration might be needed by means of pumps if the solution culture is static. However, in continuous flow solution culture like the nutrient film technique, there is an abundant supply of oxygen to the roots of the plants if the system is well designed. In substrate systems, it is essential to choose a substrate that has a correct distribution of particle size, a low bulk density, a high porosity and a stable structure so that the supply of air to the roots is sufficient (Abad et al., 2004). If more aeration is needed, Urrestarazu and Mazuela (2005) have observed that the addition of potassium peroxide as chemical oxygenation improves water uptake and yield of different vegetables as sweet pepper, melon and cucumber. Also, the application of exogenous nitrate to plants under oxygen deprivation has been observed to improve their survival through the mechanism of “nitrate respiration” (see ahead) (Morard et al., 2004). In addition to the capacity of the substrate to provide the roots with enough aeration, the availability of oxygen in the root environment also depends on O2 consumption by roots and microorganisms (Naasz et al., 2008). O2 consumption increases with increasing nutrient solution
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temperature, root weight and photosynthates concentration in the roots, which leads to an increase in the relative CO2 concentration in the root environment if root aeration is not adequate. The increased CO2 concentration leads to an increase of anaerobic respiration, which continues to release CO2. Therefore, oxygen depletion is linked to the increase in the relative CO2 concentration in the root environment (Morard and Silvestre, 1996). B. DIAGNOSIS OF PLANT STRESS CAUSED BY DISSOLVED OXYGEN CONCENTRATION
An insufficient supply of oxygen to the root has a negative effect in a number of metabolic processes, and its symptoms become visible, that is plants become wilted and defoliated (Morard and Silvestre, 1996), when they are irreversibly damaged (Kla¨ring and Zude, 2009). Growth may be decreased and sometimes impaired under oxygen deficiency (Incrocci et al., 2000; Kogawara et al., 2006; Parelle et al., 2006; Taiz and Zeiger, 2002, p. 616; Wagner and Dreyer, 1997). Leaf growth is restricted (Incrocci et al., 2000; Pezeshki et al., 1996) and older leaves senesce prematurely because of reallocation of phloem mobile nutrients to younger leaves (Taiz and Zeiger, 2002, p. 618), hence, a reduction in plant leaf area. Root growth is limited (Mielke et al., 2003; Pezeshki et al., 1996; Smethurst et al., 2005) even more than shoot growth (Smethurst and Shabala, 2003), which increases the shoot/root ratio (Kla¨ring and Zude, 2009). Therefore, it is very important to detect the stress caused by hypoxia in time to prevent further yield reductions or even plant death (Kla¨ring and Zude, 2009). The effect of oxygen deficiency and subsequent recovery in plant tissues depends on the duration and severity of oxygen deprivation, tolerance of the species or cultivars to oxygen deficiency, age and developmental stage of the plant, type of tissue and light level and ambient temperature (Blokhina et al., 2003; Bragina et al., 2001; Fukao and Bailey-Serres, 2004; Kla¨ring and Zude, 2009; Morard et al., 2000; Smethurst et al., 2005). Therefore, varied and sometimes contradictory plant responses have been recorded. The most immediate effect of the decline of oxygen concentration in the root environment is that root aerobic respiration is seriously restricted (Islam and Macdonald, 2004; Taiz and Zeiger, 2002, p. 616). Pyruvate, the product of glycolysis, is then transformed to lactate, malic acid or mainly ethanol, which represent the main fermentation pathways in plants (Saenger, 2002; Sousa and Sodek, 2002). Fermentation involves a severe reduction of ATP synthesis that affects plant cell metabolism (Bertrand et al., 2003; Morard and Silvestre, 1996). It also leads to the accumulation of toxic compounds like ethanol or acetaldehyde (Kla¨ring and Zude, 2009; Morard
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and Silvestre, 1996; Schmull and Thomas, 2004), but normally to levels that do not injure plant tissues (Lambers et al., 2008). Fermentation causes acidification of cytoplasm that decreases the activity of many enzymes, a possible cause of cell death (Vartapetian and Jackson, 1997). Despite its negative consequences, fermentation seems to ensure root survival under anaerobic conditions and it is very important for stress tolerance (Blokhina et al., 2003; Fukao and Bailey-Serres, 2004; Taiz and Zeiger, 2002, pp. 619– 620). The early induction of the ethanolic fermentation pathway and sugar utilization under hypoxia allows the maintenance of the energy status and, hence, improves anoxia tolerance (Blokhina et al., 2003). Acclimation to anaerobic conditions enhances the expression of genes that encode many of the anaerobic stress proteins, which are mainly related to enzymes of the glycolytic and fermentation pathways (Blokhina et al., 2003; Lambers et al., 2008; Taiz and Zeiger, 2002, p. 620). A high-activity fermentative enzyme alcohol dehydrogenase (ADH) has been measured in many plants, whether tolerant to hypoxia or not (Kogawara et al., 2006; Pezeshki et al., 1996; Weng and Chang, 2004), and it is considered an indicator of hypoxia in plants (Kogawara et al., 2006). The activity of enzyme sucrose synthase is also promoted under hypoxia with the aim of sustaining glycolytic flux (Kla¨ring and Zude, 2009; Parelle et al., 2006). However, an inhibition of the sucrolytic, glycolytic and fermentative enzymes may occur under anoxia (Mustroph and Albrecht, 2003). Fermentation accelerates the use of carbon reserves, so a prolonged period of oxygen deficiency may lead to the exhaustion of substrates (Bertrand et al., 2003). In order to protect root functions, plants tolerant to oxygen deficiency appear capable of sustaining photoassimilate transport to hypoxic roots (Kogawara et al., 2006). However, a reduction in distribution of photosynthates towards the roots has been reported in sensitive plants, which leads to an increased concentration of carbohydrates in the shoots (Islam and Macdonald, 2004; Kogawara et al., 2006) and may lead to feedback inhibition of photosynthesis (Smethurst et al., 2005). Once in the roots, photoassimilates may be partitioned among metabolic, structural and storage processes (Kogawara et al., 2006), the partitioning being metabolically available forms the most advisable to maintain a high energy status, as occurs in highly tolerant species (Kogawara et al., 2006). However, in sensitive species, root hypoxia might increase photoassimilate partitioning into the storage fraction and decrease partitioning to metabolic processes and structural components in roots (Kogawara et al., 2006). As a result of the reduced root biomass (Smethurst et al., 2005) and the decrease of ATP in the roots due to the inhibition of aerobic respiration (Morard and Silvestre, 1996; Morard et al., 2004) and the lower import of
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photosynthates in the roots, the absorption of nutrients may decrease under oxygen deprivation (Smethurst et al., 2005; Taiz and Zeiger, 2002, p. 618; Vartapetian and Jackson, 1997). The depressive effects of oxygen deficiency on uptake have been classified by Morard and Silvestre (1996) in the following order: K > N > P > H2O > Mg-Ca. Potassium uptake is the most sensitive and even efflux has been observed soon after the exposition to oxygen deficiency (Morard et al., 2000). It has been attributed to depolarization of root cell membranes, a direct consequence of Hþ-ATPase inhibition (Morard and Silvestre, 1996). In addition, a low concentration of oxygen in the root environment decreases the selectivity of Kþ/Naþ uptake in favour of Naþ and retards the transport of Kþ to the shoots (Armstrong and Drew, 2002). Smethurst et al. (2005) observed nutrient deficiencies after 20 days of oxygen deficiency in Medicago sativa L. However, irreversible nutritional stress has not been detected in plants under these conditions (Morard and Silvestre, 1996). Stomatal closure has been observed under root oxygen deficiency in many species (Bradford and Hsiao, 1982; Incrocci et al., 2000; Islam and Macdonald, 2004; Jackson and Hall, 1987; Kogawara et al., 2006; Mielke et al., 2003; Pezeshki et al., 1996; Schmull and Thomas, 2004; Weng and Chang, 2004; Yordanova and Popova, 2001; Yordanova et al., 2003) often associated with a high concentration of ABA in their tissues (Incrocci et al., 2000; Jackson and Hall, 1987; Sojka, 1992). This has been mostly attributed to the production of ABA by the older lower leaves that wilt and export their ABA to the younger leaves, where stomata close (Zhang and Zhang, 1994). In addition, roots may stimulate ABA production or reduce cytokinin synthesis (Morard and Silvestre, 1996) under oxygen deficiency. The decrease in stomatal conductance leads to a reduction of transpiration, water uptake and root hydraulic conductance (Islam and Macdonald, 2004; Jackson and Hall, 1987; Morard and Silvestre, 1996; Morard et al., 2000; Nicola´s et al., 2005; Schmull and Thomas, 2004; Smethurst and Shabala, 2003; Vartapetian and Jackson, 1997; Weng and Chang, 2004; Yordanova and Popova, 2001; Yordanova et al., 2003; Yoshida et al., 1996). Unexpectedly, this has no negative consequence on leaf hydration since leaf water potential is unchanged (Bradford and Hsiao, 1982; Incrocci et al., 2000; Taiz and Zeiger, 2002, p. 618; Weng and Chang, 2004) or even increased (Jackson and Hall, 1987). In addition to the effect of stomatal closure on transpiration, it also reduces CO2 intake and, thus, CO2 assimilation (Islam and Macdonald, 2004; Kogawara et al., 2006; Mielke et al., 2003; Mustroph and Albrecht, 2003; Pezeshki et al., 1996; Wagner and Dreyer, 1997). Nevertheless, some species tolerant to oxygen deficiency can sustain photosynthesis under root
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hypoxic conditions (Kogawara et al., 2006). In addition to stomatal closure, other non-stomatal factors may affect photosynthesis. For example, a reduction of RuBisCO content or activity (Panda et al., 2008; Yordanova and Popova, 2001; Yordanova et al., 2003) and a decrease in leaf chlorophyll content (Schlu¨ter and Crawford, 2001; Smethurst and Shabala, 2003; Wagner and Dreyer, 1997; Yordanova and Popova, 2001) have been measured under oxygen deficiency. Also, changes in the profile of carotenoids may occur and, accordingly, Kla¨ring and Zude (2009) suggested that the measurement of leaf diffuse reflectance in the carotenoids absorption bands (at 550 and 455 nm) may provide a sensitive tool of stress diagnosis under these conditions. Photochemistry might also be affected by oxygen deprivation as a consequence of the lower CO2 assimilation rate (Mielke et al., 2003). Downregulation of PSII has been measured by CF as an increase of nonphotochemical quenching (Mielke et al., 2003; Schlu¨ter and Crawford, 2001) usually coupled with a decrease in photochemical quenching (Schlu¨ter and Crawford, 2001). In the long term, though, photochemistry may be affected by direct damage of components and membranes of the photosynthetic apparatus (Yordanova et al., 2003) or even by the nutrient deficiency caused by the impaired nutrient uptake (Smethurst et al., 2005). Then, the capacity for non-photochemical quenching may diminish, which leads to a permanent overexcitation of the thylakoids and enhanced danger of photoinhibitory damage (Schlu¨ter and Crawford, 2001). As a result, a decrease of Fv/Fm has been measured in some species under oxygen deficiency (Panda et al., 2008; Schlu¨ter and Crawford, 2001; Smethurst and Shabala, 2003; Smethurst et al., 2005; Wagner and Dreyer, 1997). Fv/Fm and nonphotochemical quenching have been considered as reliable indicators of tolerance to oxygen deficiency (Smethurst and Shabala, 2003; Smethurst et al., 2005). In addition to the already explained consequences of oxygen deficiency, it also contributes to oxidative stress in plants. An in-depth review of oxidative stress in plants under oxygen deficiency has been made by Blokhina et al. (2003). Generation of ROS can take place in hypoxic tissues as a result of overreduction of redox chains under hypoxia and especially under reoxygenation. Hence, anoxic stress is always accompanied to some extent by oxidative stress (Blokhina et al., 2003). Hydrogen peroxide accumulation under hypoxic conditions has been reported (Yordanova et al., 2003). In order to protect membranes integrity, the antioxidant system is stimulated by oxygen deficiency (Blokhina et al., 2003). For example, an increase in the activities of several antioxidant enzymes like CAT, APX or SOD (Biemelt et al., 1998; Yordanova et al., 2003) or a higher level of antioxidant
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compounds like ascorbate and glutathione (Biemelt et al., 1998) have been measured under oxygen deprivation. After hypoxia and/or anoxia conditions, physiological functions can eventually be recovered (Morard and Silvestre, 1996; Panda et al., 2008; Schlu¨ter and Crawford, 2001; Smethurst et al., 2005), although, sometimes, growth may remain reduced (Smethurst et al., 2005). This recovery may take different times depending on the duration of the stress or the tolerance of the species (Schlu¨ter and Crawford, 2001) and might depend on the preservation of membrane integrity under anoxia (Blokhina et al., 2003). Under reoxygenation, plants suffer not only from weakening by anoxia stress but they also have to endure the formation of ROS (Schlu¨ter and Crawford, 2001). Plants may adapt to the lack of oxygen in the root environment by a mechanism called “nitrate respiration”, where NO3 is reduced in root cells to NO2 by NR and acts as an alternative electron acceptor to O2 (Morard and Silvestre, 1996). This phenomenon has been observed in tomato when, after 12 hour of anoxia, nitrites were detected in the nutrient solution (Morard et al., 2000). An increase of NR activity has been also observed by Alle`gre et al. (2004) and Morard et al. (2004) under oxygen deficiency. Stoimenova et al. (2007) observed that mitochondria isolated from the roots of barley and rice seedlings were capable of oxidizing external nicotinamide adenine dinucleotide (NADH) and nicotinamide adenine dinucleotide phosphate (NADPH) anaerobically in the presence of nitrite. It has been suggested that nitrate reduction actually serves as an intermediate step of a respiratory pathway alternative to glycolytic fermentation: the haemoglobin (Hb)/nitric oxide (NO) cycle. In this cycle, NO produced from nitrate is oxidized back to nitrate in a reaction involving non-symbiotic Hb. The drop in ATP levels seems to stimulate the gene expression of Hb (Parelle et al., 2006), and enhance the activation of NR. The anaerobic ATP synthesis rate may be about 3–5% of the aerobic mitochondrial ATP synthesis rate (Stoimenova et al., 2007). For review see Igamberdiev and Hill (2004) and Igamberdiev et al. (2005). To sum up, in order to carry out a reliable diagnosis of oxygen deficiency in plants, the following techniques can be used: measurements of biomass production and yield, shoot/root ratio, leaf area, root respiration, accumulation of ethanol and acetaldehyde, measurements of lipid peroxidation and ROS species, the amount of antioxidant compounds, photosynthetic activity, chlorophyll content, stomatal conductance, transpiration, water uptake, root hydraulic conductance, ABA accumulation, CF, content, type and partitioning of carbohydrates, leaf diffuse reflectance, nutrient uptake and measurements of the level, gene expression and/or activity of ADH, sucrose synthase, Hb, NR, RuBisCO or antioxidant enzymes.
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VI. NUTRIENT SOLUTION TEMPERATURE A. CONSIDERATIONS ABOUT THE OPTIMUM NUTRIENT SOLUTION TEMPERATURE
Nutrient solution temperatures may reach injuriously high levels during summers, or damaging low levels in winters, which strongly influence growth and survival of the whole plant. This parameter not only depends on solar radiation and aerial temperature but also on the characteristics of the system. In general, soilless systems are exposed to larger daily variations in root temperatures than soil systems (Kafkafi, 2001) but possibilities for accurate control of root temperatures are more easily carried out in soilless cultures than in soils systems (Olympios, 1999), through cooling or heating systems. However, sometimes an excessive energy input is spent to protect the crop from incorrectly established temperature ranges. In order to optimize the use of energy in greenhouse production, it is necessary to know the range of nutrient solution temperatures, specific for each crop cultivar (Kafkafi, 2001), which permits plant growth and promotes high yields. In general terms, root zone temperatures below 18˚C and above 28˚C may seriously impair uptake and root growth, hence, temperatures outside this range should be avoided (Bar-Yosef, 2008). In some cases, though, a higher product quality may be obtained on exposing roots to infra- or supraoptimum temperatures during a short period of time. For example, a treatment of one week of low temperature stress in spinach plants increased the leaf concentrations of quality compounds like sugars, ascorbic acid and Fe2þ, at the same time reduced the leaf concentrations of compounds considered harmful for human health like NO3 and oxalic acid (Hidaka et al., 2008).
B. DIAGNOSIS OF PLANT STRESS CAUSED BY NUTRIENT SOLUTION TEMPERATURE
If the root temperature, significantly affected by the management of nutrient solution temperature, strays from the optimum range, several metabolic processes may be affected. This depends on the actual temperature, the duration of the stress, the physiological stage of the crop, the species and even cultivar (Kafkafi, 2008; Rachmilevitch et al., 2006a; Sanders and Markhart, 2000). In spite of the importance of root temperature to wholeplant responses, relatively little is known in comparison to the effect of air temperature, which has been studied extensively (Rachmilevitch et al., 2006b; Zhang et al., 2007). However, Xu and Huang (2000) suggested that root
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temperature appears to be more critical than air temperature in controlling plant growth. One of the most widely observed symptoms of root temperature stress is that root growth is inhibited and number of roots and root dry weight may decrease. This has been observed in many plants with their roots subjected to supra-optimal (Kafkafi, 2008; Lyons et al., 2007; Rachmilevitch et al., 2006b; Sattelmacher et al., 1990) or infra-optimal temperatures (Ali et al., 1996; Apostol et al., 2007; Bowen, 1970; Franklin et al., 2005; Sanders and Markhart, 2002). Root viability decreases (Rachmilevitch et al., 2006a) and plants may die if the stress is very severe. The cause of the reduced root growth may be due to a reduced import of photosynthates from the shoots (see ahead), but in the case of supra-optimal root temperatures, the cause seems to be mainly related to the enhanced consumption by root respiration rather than to the reduced translocation. Root respiration increases with root temperature (Lyons et al., 2007; Rachmilevitch et al., 2006b; Xu and Huang, 2000). Oxygen is consumed at a high rate and, in addition, oxygen solubility is reduced as temperature increases (Jones, 1997). Accordingly, high root temperature is generally associated to hypoxia stress in soilless systems (Incrocci et al., 2000). Respiration is a major avenue of carbohydrate consumption and may lead to shortage of assimilates when temperatures are too high. Actually, this fact has been proposed to be a primary factor responsible for root growth inhibition and dysfunction at high root temperatures (Kafkafi, 2008; Rachmilevitch et al., 2006b). The down-regulation of plant respiratory rates and the increase of respiratory efficiency by lowering maintenance and ion uptake costs are key factors for plant acclimation to high root temperatures (Lyons et al., 2007; Rachmilevitch et al., 2006b, b). In addition to the effect of root temperature on root growth, it also affects root morphology. Under low root temperatures, roots might be more succulent (Calatayud et al., 2008; Dielman et al., 1998; Kanda et al., 1994), whiter (Calatayud et al., 2008; Dielman et al., 1998), with lower development of lateral roots (Bowen, 1970; Dielman et al., 1998; Sanders and Markhart, 2002) and higher content of unsaturated fatty acids in phospholipids (Kanda et al., 1994). The latter has been associated with tolerance to low root temperatures (Lee et al., 2005a). In contrast, under high root temperatures, roots may be shorter and highly branched (Stout et al., 1997). These differences in root morphology may lead to changes in hydraulic properties and in roots capacity for ion and water uptake. The majority of the studies about the effect of root temperature on water uptake have been carried out under low temperatures, although water uptake may be affected by heat stress as well (Geater et al., 1997; McMichael
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and Burke, 1999). Many studies have reported a decrease in water uptake as root temperatures drop (Abdel-Mawgoud et al., 2005; Calatayud et al., 2008; Cornillon, 1988; Economakis, 1997; Murai-Hatano et al., 2008; Pavel and Fereres, 1998; Sanders and Markhart, 2002). The decrease in water uptake seems to be immediate (Sanders and Markhart, 2002) and has been attributed to higher water viscosity (Abdel-Mawgoud et al., 2005; Affan et al., 2005) and higher root hydraulic resistance (Pavel and Fereres, 1998). A decrease in the permeability of the root cell membranes (Yoshida and Eguchi, 1990) caused by a reduction in the activity of the plasma membrane Hþ-ATPases and linked to changes in the activity (open/closed) of aquaporins (Kafkafi, 2008; Lee et al., 2005b; Murai-Hatano et al., 2008 Radin, 1990; Sanders and Markhart, 2002; Yoshida and Eguchi, 1990) have suggested the causes for the increase in root hydraulic resistance. In addition to water uptake, nutrient uptake is very sensitive to nutrient solution temperature (Xu and Huang, 2006). A restriction of nutrient uptake has been observed under supra-optimal (Rachmilevitch et al., 2006b) or infra-optimal (Ali et al., 1996; Dong et al., 2001; Macduff et al., 1987) temperatures. Actually, crops may suffer from nutrient deficiencies during long cold periods (Sanders and Markhart, 2002). However, in some studies neither any significant effect has been measured (Osmond et al., 1982) nor an increase of nutrient uptake has been determined under low temperatures (Calatayud et al., 2008). This might be dependent on the tolerance of the species and the specific temperature used in the study. Nutrient uptake may be limited by uptake per unit of root or by reduced root growth. The latter may become more significant over the long term (Sanders and Markhart, 2002). Regarding supra-optimal temperatures, the reduction of nutrient uptake per unit of root may be due to the shortage of root assimilates consumed by the enhanced respiration. With regard to the decrease of nutrient uptake per unit of root under low root temperatures, it has been associated with the change in the structure of membrane lipids in roots and with the decrease in the activities of enzymes responsible for nutrient uptake such as Hþ-ATPase (Dong et al., 2001). The uptake of different nutrients may have different sensitivities to root temperature. For example, NO3 absorption appears more sensitive than NH4þ absorption at low root temperatures (Clarkson and Warner, 1979; Kafkafi, 2008; Macduff et al., 1987). This maybe due to the lower energy demand for NH4þ assimilation (Kafkafi, 2008). Another root function that is influenced by root temperature is the synthesis and translocation of hormones like cytokinins, gibberellins and ABA (Ali et al., 1996; McMichael and Burke, 1999; Rachmilevitch et al., 2006b; Singh et al., 2007). A high level of cytokinin in the roots (Kanda et al., 1994)
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has been associated with tolerance to infra-optimal temperatures. Moreover, there is evidence that ABA is involved in cold-temperature signalling (Franklin et al., 2005), and that it may be a means of long-distance rootto-shoot signalling in plants with cooled root systems (Franklin et al., 2005). The reduced water uptake at low root temperatures might decrease leaf water potential and leaf turgor (Radin, 1990; Sanders and Markhart, 2002). Nevertheless, plants can respond to their decreased water status by increasing ABA concentrations in the shoot (Udomprasert et al., 1995; Zhang et al., 2008), which triggers stomatal closure (Apostol et al., 2007; Zhang et al., 2008). The decrease in transpiration caused by stomatal closure has been indirectly determined by measuring leaf temperature (Ahn et al., 1999; Malcolm et al., 2008), which has been suggested as a very sensitive parameter in identifying stress caused by low root temperature (Ahn et al., 1999). In sensitive species, stomata may be slow to respond and water stress may occur, which can result in transient or permanent wilting (Sanders and Markhart, 2002). The closure of stomata results in a decrease in CO2 assimilation rate (Zhang et al., 2008). A decline in photosynthetic rate has been measured under high (Lyons et al., 2007; Rachmilevitch et al., 2006a, b; Xu and Huang, 2000) and low (Apostol et al., 2007; Malcolm et al., 2008) root temperatures. Accordingly, a decrease in the maximum and the effective quantum yield of photochemical efficiency of PSII and the fraction of open PSII reaction centres has been observed at non-optimal temperatures (Rachmilevitch et al., 2006a; Repo et al., 2004; Zhang et al., 2007, 2008). In contrast, the effective quantum yield and the fraction of open PSII reaction centres increased in rose plants with their root exposed at 10˚C (Calatayud et al., 2008). In addition to the closure of stomata, changes in the ultrastructure of cortical cells that may affect the photosynthetic apparatus have been observed under low root temperatures (Lee et al., 2002). The decline in photosynthetic activity results in the reduction of shoot growth, shoot dry weight and/or leaf area under both supra-optimal (Kafkafi, 2008) and infraoptimal root temperatures (Ali et al., 1996; Apostol et al., 2007; Field et al., 2009; Franklin et al., 2005; Malcolm et al., 2008; Sanders and Markhart, 2002; Solfjeld and Johnsen, 2006). In addition, a high root temperature may also accelerate the senescence of aerial parts (Guedira and Paulsen, 2002). The assimilate use in plants is altered by root temperature but differently depending on whether temperatures are above or below the optimum range. Under low temperatures, the leaf content of total non-structural carbohydrates increases (Ali et al., 1996; Repo et al., 2004; Solfjeld and Johnsen, 2006). This been attributed to a lower partitioning of assimilates into structural carbohydrates (Solfjeld and Johnsen, 2006), a delayed loss of
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starch (Repo et al., 2004), a reduction of translocation (phloem loading/ unloading) or a decrease of root sink demand (Sanders and Markhart, 2002). In contrast, some authors (Ali et al., 1996; Calatayud et al., 2007) have measured an increase of carbohydrates in the roots, which has been associated with tolerance to low root temperatures (Kanda et al., 1994). On the other hand, at high root temperatures total non-structural carbohydrates decrease in shoots and roots (Guedira and Paulsen, 2002; Kubota et al., 1987; Xu and Huang, 2000) due to the imbalance between photosynthesis and respiration in which carbon consumption exceeds production (Xu and Huang, 2000). Also, high root temperatures lead to changes in allocation pattern favouring root growth at the expense of shoot growth (Rachmilevitch et al., 2006a). The reduced nutrient uptake under non-optimal root temperatures may lead to a decrease in the leaf concentration of several nutrients (Kafkafi, 2008; Malcolm et al., 2008). Besides nutrient uptake, nutrient partitioning and assimilation are also altered by root temperatures (Sanders and Markhart, 2002). For example, an increase of NR activity has been measured under low root temperatures in leaves (Calatayud et al., 2008) and roots (Sanders and Markhart, 2002), while nitrate assimilation rate seems to decrease under high root temperatures (Rachmilevitch et al., 2006a). Besides, both an increase of ammonium content in leaves (Calatayud et al., 2008) and a decrease of amino acid content (Kubota et al., 1987) have been measured under low root temperatures. These divergences may depend on the species and the specific temperature of the study. The exposure of plant roots to non-optimal temperatures may lead to oxidative stress. Actually, membrane injury has been pointed as the cause of the inhibition of root functions (Sanders and Markhart, 2002). H2O2 (Rhee et al., 2007) and MDA (Zhang et al., 2007) have been detected in plant tissues under non-optimal root temperatures. In order to prevent the accumulation of ROS in root cells, plants may respond to unfavourable root temperatures by increasing their synthesis of ascorbate and glutathione, or the activity of SOD, CAT or APX (Zhang et al., 2007). Plants tolerant to non-optimal root temperatures should be capable of dealing with ROS (Rhee et al., 2007) and preventing injury of the membrane (Rachmilevitch et al., 2006b). To conclude, diagnosis of stress caused by non-optimal root temperatures in plants may be assessed by different techniques: measurements of biomass production and yield, leaf area, shoot/root ratio, root morphology, root respiration, water and nutrient uptake, nutrient content in plant tissues, photosynthetic activity, CF, stomatal conductance, transpiration, root hydraulic resistance, hormone accumulation in roots and shoots,
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carbohydrate content and partitioning in the plant, amino acid and ammonium content in plant tissues, lipid peroxidation and ROS species, the amount of antioxidant compounds, leaf temperature and the activities of several enzymes.
VII. CONCLUSIONS Optimization of nutrition in soilless systems can be achieved by means of an accurate management of all factors involved (i.e. nutrient solution composition and concentration, water supply, nutrient solution temperature, dissolved oxygen concentration, EC and pH of the nutrient solution). If any factor affecting plant nutrition is under non-optimal conditions, plants may suffer from stress (Table II), and yields (quantity and/or quality) may diminish. A precise diagnosis of plant stress caused by these factors is, hence, of great importance so that non-optimal levels of each factor could be determined and strategies for maximum benefits for growers can be planned (Fig. 3). Regarding methods for diagnosis of plant stress, many physiological techniques are available (Fig. 1). These methods are based on the fact that the above-mentioned factors affect the functioning of several plant physiological processes, and changes in these processes may be a sign of stress. The most important plant processes/traits that have been most
Low level of the factor Severe stress
?
?
Mild stress
Activation of defence system Damage
Acclimation
?
Optimum range
Short term Long term
?
Mid stress
High level of the factor Severe stress
Activation of defence system Acclimation
Damage
Fig. 3. General plant response against stress conditions caused by a deviation of a given nutrient solution factor from optimum conditions, due to a high or a low level of the factor. In the short term, a movement away from optimum conditions may activate the defense mechanisms that plants have to overcome the stress. In the long term, the response may depend on the severity of the stress. Under mild stress, plants may acclimate to the new conditions. However, under severe stress, plants may be seriously damaged and may even die. The question marks point to the limits of the different ranges of a given factor. The specific values underlying these question marks should be uncovered by researchers in order to plan precise strategies for growers of soilless systems to achieve the best yields and product qualities. Different values might result when different cultivars, developmental stages or levels of other factors are considered.
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widely measured in this respect are: the photosynthetic activity, the antioxidant capacity and oxidative stress, the content and partitioning of several compounds in the plant (carbohydrates, hormones, amino acids, and nutrient elements), the activity of specific enzymes, plant water relationships and the expression of specific genes. It is important to point out, though, that the effect on these processes may depend on the tolerance of the species or cultivar, the level of the factor and the duration of the stress. For example, in the short term, plants may activate their defense mechanisms against stress. However, in the long term, plants may acclimate to a mild stress or may be seriously damaged if the stress was severe (Fig. 3). The resulting severity of the stress in the plant may be evaluated through measurements of chlorophyll fluorescence and/or oxidative stress. In addition, similar symptoms might be a result of different stresses. In that case, it is important to carry our additional measurements to find out the cause of the stress. For example, measurements of nutrient content in plant tissues to assess possible nutrient deficiencies, water potential and osmotic adjustment for a possible osmotic (water and salinity) stress, Naþ and Cl in the leaves for a possible salinity stress, root respiration and ADH activity for a possible hypoxia/ anoxia, root growth and water uptake for a possible stress due to low solution temperatures, and root growth and root respiration for a possible stress due to high solution temperatures. In conclusion, in order to give a correct diagnosis, it is important to take into account the following aspects: (i) keeping in mind the growing conditions; (ii) analyzing the severity of the stress and (iii) finding out the cause of the stress.
ACKNOWLEDGEMENTS The authors gratefully acknowledge the Ministry of Education and Technology and the European Social Fund by INIA contract for Angeles Calatayud (INIACCAA, DR03-654) and the Instituto Valenciano de Investigaciones Agrarias (predoctoral fellowship, 2005/X3104 for Elisa Gorbe) for their support.
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AUTHOR INDEX
A Abad, M., 218, 230 Abad, P., 147–180, 180 Abdelly, C., 117–136, 137, 140, 143 Abdel-Mawgoud, A. M. R., 226, 230 Abebe, T., 130, 136 Achard, P., 83, 99, 109 Ackerson, R. C., 211, 212, 231 Adam-Blondon, A. F., 38, 53 Adams, P., 203, 205, 215, 231 Affan, F. F. M., 226, 231 Aga, E., 28, 29, 53, 54 Agastian, P., 216, 231 Ahn, S. J., 227, 231 Ajayi, O., 200, 231 Akaffou, D. S., 33, 54 Akaffou, S., 23–53, 58 Alba, R., 44, 54 Albrecht, G., 220, 221, 232, 240 Alcaraz, C. F., 207, 231 Ali, G., 126, 136 Ali, I. A., 225, 226, 227, 228, 231 Alkharouf, N. W., 159, 180 Alle`gre, A., 223, 231 Allis, C. D., 3, 4, 16, 18 Al-Sady, B., 80, 111 Alscher, R. G., 132, 136 Altman, A., 118, 124, 129, 144 Alvarez-Venegas, R., 6, 9, 10, 16, 21 Amasino, R. M., 2, 8, 18 Andrade, A. C., 44, 54 Anthony, F., 168, 180 Anza, M., 207, 231 Apostol, K. G., 225, 227, 231 Arelli, P. R., 166, 180 Arfan, M., 134, 136 Ariel, F. D., 84, 85, 109 Arji, I., 210, 231 Armstrong, W., 221, 231 Arrabaca, M. C., 129, 143 Arzani, K., 210, 231 Asada, K., 131, 136 Ashihara, H., 42, 54 Ashraf, M., 119, 124, 125, 126, 129, 136, 142 Avramova, Z., 4, 9, 16, 17
Ay, N., 13, 17 Aydin, F., 209, 240 B Babiychuk, E., 127, 136 Badawi, G. H., 132, 136 Bae, G., 72, 109 Bailey-Serres, J., 219, 220, 235 Bai, Y., 170, 180 Baldwin, J. G., 150, 180 Ballare, C. L., 78, 103, 104, 109, 111, 113 Ballesteros, E., 121, 136 Ball, R. A., 209, 231 Bar Akiva, A., 207, 231, 242 Barak, P., 200, 232 Barber, S. A., 202, 232 Barker, A. V., 207, 232 Bar-Orl, C., 159, 180 Baroux, C., 2, 8, 17 Barre, P., 33, 34, 35, 54 Barsalobres-Cavallari, C. F., 44, 54 Bar-Tal, A., 199, 200, 243 Barthels, N., 158, 180 Baruah, A., 29, 54 Bar-Yosef, B., 224, 231 Batea, G. C., 207, 240 Baumbusch, L. O., 3, 4, 17 Bazzaz, F. A., 205, 206, 210, 212, 233 Beaufils, E. R., 202, 232 Bekal, S., 171, 180 Belkhadir, Y., 169, 170, 180 Bellafiore, S., 155, 180 Ben Amor, N., 131, 136, 140 Ben Hamed, K., 131, 137 Bender, J., 3, 11, 12, 13, 17, 18 Ben-Gal, A., 215, 217, 242 Bent, A. F., 170, 171, 173, 180 Bernatavichute, Y. V., 3, 13, 15, 17 Bernatzky, R., 26, 28, 54 Berr, A., 10, 17 Berry, W., 204, 232 Berthou, F., 27, 54 Bertrand, B., 219, 220, 232 Bezrukova, M. V., 134, 137 Bhalerao, R. P., 96, 109 247
248
AUTHOR INDEX
Bhattarai, K. K., 161, 171, 181 Biemelt, S., 222, 232 Biggar, J. W., 216, 237 Bird, D. M., 156, 159, 161, 181, 187, 189 Black, C. A., 206, 232 Blanc, G., 12, 17 Blaxter, M. L., 149, 181 Bleve-Zacheo, T., 168, 181 Blok, V. C., 165, 172, 190 Blokhina, O., 218, 219, 220, 222, 223, 232 Blumwald, E., 216, 242 Boerjan, W., 97, 109 Boguski, M. S., 36, 54 Bohnert, H. J., 130, 135, 137, 138, 144 Boisvert, F. M., 14, 17 Bolle, C., 83, 109 Bonierbale, M., 26, 54 Bor, M., 131, 137 Borsani, O., 127, 128, 137 Bost, S. C., 172, 181 Botto, J. F., 78, 79, 108, 109 Bougoul, S., 203, 232 Bou-Torrent, J., 65–108, 109 Bowen, G. D., 225, 232 Bradford, K. J., 221, 232 Bragina, T. V., 219, 232 Branch, C., 171, 181 Bray, E. A., 118, 133, 137 Brent, M. R., 175, 191 Bressan, R. A., 122, 137 Brito, J., 167, 181 Brouder, S. M., 202, 232 Brown, R. L., 104, 109 Brun, R., 203, 232 Brutnell, T. P., 79, 112 Bryngelsson, T., 28, 29, 53, 54 Budiman, M. A., 38, 54 Bugbee, B., 201, 232 Burke, J. J., 226, 239 Bustamante-Porras, J., 30, 41, 44, 54, 55, 57 C Caassen, N., 202, 232 Cabrera, R. I., 200, 201, 202, 232 Cai, D., 169, 181 Caillaud, M. C., 158, 159, 160, 175, 181 Cakmak, I., 205, 206, 216, 232 Calatayud, A., 193–230, 232, 233, 235, 236 Campa, C., 23–53, 55 Can˜amero, M., 199, 202, 233 Canton, F. R., 72, 109 Cao, X., 13, 17
Carabelli, M., 80, 83, 101, 109, 110 Cardenas-Navarro, R., 202, 233 Carmassi, G., 203, 233 Caromel, B., 166, 181 Cartagena, J. A., 9, 17 Carvalho, C. R., 34, 55 Casal, J. J., 76, 78, 79, 105, 109, 110 Cassman, K. G., 202, 232 Castagnone-Sereno, P.,151, 173, 181, 188 Castillon, A., 82, 109 Cavender-Bares, J., 205, 206, 210, 212, 233 Cazzonelli, C. I., 9, 17 Cedergreen, N., 203, 233 Chabrillange, N., 41, 55 Chang, J. C., 220, 221, 245 Chan, S. W., 13, 19 Chaves, M. M., 210, 233 Chazelle, L., 203, 232 Chehab, H., 210, 233 Cheng, Z., 39, 55 Chen, M., 38, 55, 72, 110 Chen, P., 172, 181 Chen, Q., 179, 181 Chen, S., 133, 137 Chevalier, A., 25, 55 Chico, J. M., 105, 110 Childs, K. L., 100, 110 Chini, A., 105, 110 Chinnusamy, V., 120, 137 Choi, G., 72, 109 Chory, J., 98, 112 Choudhuri, M. A., 134, 141 Chuikov, S., 15, 17 Chung, J. S., 123, 137 Ciarbelli, A. R., 84, 85, 86, 103, 110 Cierpinski, W., 203, 237 Ciompi, S., 205, 206, 233 Citterio, E., 11, 17 Clarindo, W. R., 34, 55 Clarkson, D. T., 226, 233 Clifford, M. N., 42, 55 Cokus, S., 3, 17 Colla, G., 198, 242 Colmer, T. D., 119, 124, 134, 138 Combes, M. C., 29, 55 Cook, R., 165, 166, 182 Cornillon, P., 226, 233 Correia, M. J., 213, 233 Cosgrove, D. J., 176, 182 Coste, R., 25, 55 Coulibaly, I., 30, 33, 55 Couture, J. F., 7, 18 Couturon, E., 27, 55
AUTHOR INDEX Cramer, G. R., 216, 233, 236 Crawford, N. M., 201, 233 Crawford, R. M. M., 222, 223, 242 Creelman, R. A., 134, 137 Cress, W. A., 127, 138 Cristancho, M., 47, 55 Cros, J., 25, 34, 56 Crouzillat, D., 23–53, 58, 63 Crozier, A., 42, 54 Cruz, F., 44, 56 Cruz, J. L., 205, 233 Cubry, P., 29, 56 Cumbes, Q. J., 125, 143 Cushman, J. C., 123, 135, 137, 141 D Da Cruz, N. D., 34, 35, 61 Dangl, J. L., 158, 169, 170, 182, 186 Darby, A. C., 45, 56 Dart, S. K., 42, 56 Das, A. B., 120, 121, 129, 130, 132, 133, 134, 141 Das, S., 168, 182 Davenport, R., 119, 143 Davis, E., 155, 182 Davis, E. L., 154, 155, 182 de Almeida Engler, J., 159, 160, 182, 183 Debez, A., 121, 137 de Boer, J. M., 155, 182 De Boodt, M., 208, 234 Dechaine, J. M., 76, 78, 110 Dejong, T. M., 210, 213, 234 de Kochko, A., 23–53, 55, 59, 61, 62 Del Amor, F. M., 203, 234 De Lucas, M., 89, 110 De Maria, C. A. B., 42, 56 De Meutter, J., 157, 163, 182 Demiral, T., 119, 123, 124, 125, 126, 131, 132, 137 Demming-Adams, B., 205, 234 De Nardi, B., 37, 45, 56 De Oliveira, A. C. B., 30, 56 De Pascale, S., 214, 216, 234 Dessalegn, Y., 28, 56 Devienne-Barret, F., 202, 234 Devlin, P. F., 75, 78, 80, 81, 100, 101, 107, 110, 114 Devos, K. M., 26, 56 Dielman, J. A., 225, 234 Dietz, K. J., 205, 206, 234, 237 Ding, Y. C., 204, 205, 206, 207, 234
249
Djakovic-Petrovic, T., 78, 80, 83, 100, 110, 114 Djian-Caporalino, C., 168, 182 Dong, G., 9, 18 Dong, K., 171, 182 Dong, S., 201, 203, 226, 234 Dowson-Day, M. J., 93, 110 Doyle, E. A., 156, 182 Drew, M. C., 221, 231 Dreyer, E., 219, 221, 222, 244 Dropkin, V. H., 168, 182 Dubey, R. S., 126, 129, 138, 210, 211, 212, 216, 234 Duek, P. D., 91, 112 Dufour, L., 198, 234 Dufour, M., 29, 56 E Ebbs, M. L., 12, 13, 18 Economakis, C. D., 226, 234 Eddaoudi, M., 172, 183 Eguchi, H., 226, 245 Ellis, B. G., 202, 235 Elstner, E. F., 130, 139 Endo, B. Y., 164, 168, 183 Engels, C., 205, 206, 232 Epstein, E., 201, 202, 234 Ernst, K., 167, 183 Escobar, C., 162, 183 Esfandiari, E., 212, 216, 234 Etterson, J. R., 76, 111 Evans, K., 165, 166, 182 F Fageria, N. K., 209, 234 Faigon-Soverna, A., 91, 110 Fairchild, C. D., 80, 88, 110 Fanasca, S., 198, 234 Fankhauser, C., 76, 79, 92, 96, 108, 110, 111 Favery, B., 158, 159, 160, 163, 164, 180, 183, 189 Feldman, J. R., 42, 56 Feng, S., 89, 111 Fenoll, C., 152, 158, 183, 192 Fereres, E., 226, 241 Fernandez, D., 44, 56 Ferrao, M. A. G., 28, 56 Finkers-Tomczak, A., 167, 183 Flament, I., 42, 56 Flexas, J., 210, 212, 234, 235 Flor, H. H., 169, 183
250
AUTHOR INDEX
Flowers, T. J., 119, 124, 134, 138 Foolad, M. R., 126, 136 Foth, H. D., 202, 235 Foyer, C. H., 132, 138 Fracheboud, Y., 210, 211, 235 Franklin, J. A., 225, 227, 235 Franklin, K. A., 68, 69, 71, 72, 78, 79, 92, 94, 95, 101, 103, 107, 108, 111, 115 Fredeen, A. L., 210, 235 Fresneau, C., 213, 235 Fujita, K., 204, 205, 206, 235 Fukao, T., 219, 220, 235 Fuller, V. L., 179, 183 Fulton, T. M., 29, 36, 56 Fynn, R. P., 203, 239 G Galloway, L. F., 76, 111 Galme´s, J., 210, 235 Galstyan, A., 65–108, 109, 114 Ganesh, D., 44, 57 Gao, B., 157, 183 Garcia, G. M., 166, 183 Garcia-Martinez, J. L., 78, 100, 106, 111, 113 Gassmann, W., 91, 111 Gazzarrini, S., 132, 138 Geater, C. A., 225, 235 Gebhardt, C., 170, 183 Gehring, M., 8, 18 Geissler, N., 119, 131, 135, 138 Gelbart, W. M., 7, 19 Gendler, K., 4, 18 Gendrel, A. V., 3, 13, 18 Genschik, P., 83, 99, 109 Geromel, C., 43, 44, 57 Gheysen, G., 152, 158, 159, 179, 183, 191 Ghiran, I., 156, 183 Gichuru, E. K., 28, 57 Gil, J., 100, 111 Gilmour, S. J., 94, 111 Giuffrida, F., 215, 216, 235 Glass, A. D. M., 201, 233, 235 Glazebrook, J., 170, 183 Gleason, C. A., 152, 158, 172, 184, 191 Goddijn, O. J. M., 158, 184 Goellner, M., 158, 159, 161, 184 Goggin, F. L., 179, 184 Goldschmidt, E. E., 207, 238 Goldstein, J., 43, 63 Gomez, C., 29, 57 Gonza´lez-Mas, M. C., 201, 235 Gonza´lez-Meler, M. A., 212, 235
Gorbe, E., 193–230, 232, 233, 235, 236 Gossett, D. R., 131, 138 Goverse, A., 158, 161, 184 Grafi, G., 13, 18 Grattan, S. R., 203, 216, 236 Gray, W. M., 94, 111 Greenway, H., 213, 215, 236 Grieve, C. M., 203, 216, 236, 241 Grossniklaus, U., 8, 15, 18 Grunewald, W., 162, 163, 184 Gue´rin, V., 198, 234 Guedira, M., 227, 228, 236 Gueta-Dahan, Y., 131, 138 Gupta, S., 132, 133, 138 Gurr, S. J., 158, 184 Guyot, R., 23–53, 57 H Hagen, C. E., 201, 234 Halfter, U., 122, 138 Hall, D. A., 207, 236 Hall, K. C., 221, 236 Hall, N., 45, 56 Hall, R., 199, 236 Halliday, K. J., 78, 79, 96, 108, 111 Hammes, U. Z., 158, 163, 184 Hamon, P., 23–53, 57 Hanson, A. D., 125, 142 Hare, P. D., 127, 138 Harmer, S. L., 92, 111 Harris, G. C., 205, 206, 234 Harris, P. J. C., 119, 124, 125, 129, 136 Hasegawa, P. M., 121, 138 Hebert, R. R., 211, 231 Hendre, P. S., 29, 57 Hermans, C., 127, 143 Herms, D. A., 103, 111 Hermsmeier, D., 158, 184 Herna´ndez, J. A., 130, 139, 216, 236 Herrera, J. C., 29, 35, 57 Herrera, P. J. C., 28, 57 He, T., 216, 236 He, Y., 2, 8, 18 Hidaka, K., 224, 236 Hilda, P., 134, 139 Hinniger, C., 44, 57 Hippeli, S., 130, 139 Hochedlinger, K., 2, 18 Ho, J.-Y., 167, 184 Holterman, M., 150, 184 Hoque, M. A., 126, 139 Hornitschek, P., 89, 111
AUTHOR INDEX Hoth, S., 158, 164, 184 Houimli, S. I. M., 215, 236 Howe, G. A., 103, 104, 111 Hsiao, T. C., 221, 232 Hsu, S. Y., 130, 139 Huang, B., 224, 225, 226, 227, 228, 245 Huang, G., 155, 156, 157, 179, 184, 185 Huang, G. Z., 156, 176, 185 Huang, J., 126, 139 Huang, Z. A., 205, 236 Huber, S. C., 210, 212, 236 Hugall, A., 174, 185 Hughes, J. E., 71, 111 Hulbert, S. H., 170, 185 Huq, E., 80, 111 Hussey, R. S., 153, 154, 155, 185, 189 Hutangura, P., 161, 185 Hwang, C., 170, 185 Hwang, C. F., 170, 185 Hwang, I., 123, 139 I Igamberdiev, A. U., 223, 236 Imsande, J., 203, 236 Inan, G., 120, 135, 139 Incrocci, L., 219, 221, 225, 236 Ishitani, M., 122, 139 Islam, M., 219, 220, 221, 236 Ithal, N., 164, 185 Izaguirre, M. M., 79, 104, 111 J Jackson, J. P., 3, 11, 12, 13, 18 Jackson, M. B., 220, 221, 236, 244 Jackson, S. D., 69, 112 Jacob, J., 205, 237 Jacob, Y., 10, 11, 15, 18 Jacquet, M., 167, 185 Jagendorf, A. T., 125, 139 Jaleel, A. C., 130, 131, 139 Jammes, F., 152, 156, 158, 159, 162, 163, 185 Janda, T., 134, 139 Jander, G., 103, 104, 111 Janssen, R., 171, 185 Jasencakova, Z., 12, 18 Jasmer, D. P., 149, 185 Jaubert, S., 155, 156, 157, 186 Jensen, R. G., 130, 137 Jenuwein, T., 3, 5, 18, 19 Jiang, D., 8, 19 Jiang, M., 210, 212, 237
251
Jiao, Y., 80, 81, 83, 90, 113 Ji, H. L., 45, 62 Joet, T., 45, 57 Johnsen, Ø., 227, 243 Johnson, E., 75, 78, 112 Johnson, L., 12, 19 Johnson, L. M., 11, 12, 13, 19 Johnstone, I. L., 176, 188 Jones, J. D., 158, 169, 170, 186 Jones, M. R., 39, 40, 57 Jones, R. S., 7, 19 Joshi, P., 156, 190 Josse, E. M., 105, 112 Juergensen, K., 163, 186 K Kafkafi, U., 224, 225, 226, 227, 228, 237 Kaloshian, I., 172, 186 Kameli, A., 210, 237 Kanda, H., 225, 226, 228, 237 Kandlbinder, A., 207, 237 Kant, P., 119, 120, 139 Kao, C. H., 130, 139 Kaplan, N., 2, 19 Karban, R., 68, 112 Karczmarek, A., 161, 186 Karp, P. D., 49, 60 Kasper, G., 156, 186 Katz, A., 8, 19 Kavi Kishore, P. B., 126, 139 Kebrom, T. H., 79, 112 Khanna, R., 80, 107, 112 Kim, W. S., 202, 237 Kinoshita, T., 15, 19 Kirda, C., 209, 237 Kirkby, E. A., 199, 214, 215, 237 Kirschbaum, M. U. F., 67, 112 Kiyoshi, T., 207, 237 Kiyosue, T., 8, 19 Kla¨ring, H. P., 200, 203, 218, 219, 220, 222, 237 Kluitenberg, G. J., 216, 237 Kobayashi, Y., 69, 112 Koca, H., 131, 143 Koenig, R. T., 200, 237 Kogawara, S., 219, 220, 221, 222, 237 Koini, M. A., 94, 112 Koltai, H., 159, 160, 163, 186 Koshiro, Y., 44, 58 Kouskouti, A., 15, 19 Kouzarides, T., 2, 3, 19 Kovtun, Y., 124, 140
252
AUTHOR INDEX
Koyro, H.-W., 117–136, 140 Kraft, E., 11, 19 Krauss, S., 214, 237 Kreps, J. A., 120, 140 Krichevsky, A., 14, 19 Ksouri, R., 117–136, 140 Kubota, N., 228, 237 Kulikova, O., 35, 58 Kumar, A., 166, 167, 169, 191 Ky, C. L., 30, 33, 58 L Lachner, M., 3, 5, 19 Lai, C. W., 38, 58 Lambers, H., 203, 220, 238 Lambert, K. N., 156, 176, 180, 182, 184 Lashermes, P., 33, 35, 40, 50, 58 Laue, K., 9, 19 Lavon, R., 207, 238 Le Bot, J., 200, 202, 203, 238 Lechowicz, M. J., 71, 112 Ledger, T. N., 155, 156, 186 Lee, J. S., 8, 19 Lee, S. H., 225, 226, 227, 238 Leipner, J., 210, 211, 235 Leivar, P., 81, 82, 112 Leloup, V., 42, 58 Lemaıˆtre, T., 206, 207, 238 Leonard, R. T., 215, 241 Lepelley, M., 43, 44, 58 Leroy, S., 174, 186 Leroy, T., 29, 30, 40, 41, 47, 58 Lewis, O. A. M., 199, 238 Li, C. F., 14, 19 Liburdi, N., 203, 241 Lieth, H., 119, 140 Lieth, J. H., 199, 202, 238, 243 Lieth, L. H., 202, 239 Li, J., 98, 112 Lilley, C. E., 151, 165, 186 Lilley, C. J., 178, 179, 186, 187 Lima, J. D., 205, 207, 238 Li, M. G., 29, 58 Lin, C., 35, 37, 38, 58 Linkosalo, T., 71, 112 Lister, R., 3, 20 Liu, J., 122, 140 Liu, Q. L., 165, 167, 172, 174, 187 Li, X. Q., 179, 186 Li, Y. L., 214, 238 Locascio, S. J., 198, 238 Loescher, W. H., 206, 238
Lohar, D. P., 161, 187 Loneragan, J. F., 207, 244 Lopes, F. R., 38, 58 Lo´pez-Berenguer, C., 217, 238 Lopez-Juez, E., 75, 100, 112 Lopez-Perez, J. A., 167, 187 Lorenzo, O., 105, 112 Lorrain, S., 78, 80, 82, 88, 89, 91, 93, 94, 107, 112 Losel, D. M., 210, 237 Lu, C., 205, 206, 238 Luccioni, L. G., 98, 112 Luc, M., 149, 187 Luff, B., 11, 20 Luger, K., 2, 20 Lunde, C., 206, 207, 238 Lunt, D. H., 174, 187 Lu, S.-W., 157, 187 Lyons, E. M., 225, 227, 238 M Macdonald, S., 219, 220, 221, 236 Macduff, J. H., 226, 238 Mackey, D., 170, 171, 173, 180 Madsen, T. V., 203, 233 Mahajan, S., 122, 140 Mahe´, L., 39, 59 Mahesh, V., 30, 43, 44, 58, 59 Mahieu, S., 213, 239 Maillard, L. C., 42, 43, 59 Makarevich, G., 8, 20 Ma¨kela¨, P., 125, 141 Ma, L., 106, 113 Malagnac, F., 11, 12, 13, 20 Malcolm, P., 227, 228, 239 Mankin, K. R., 203, 239 Mansour, M. M. F., 126, 140 Mao, L., 38, 59 Marcelis, L. F. M., 203, 234, 239 Mardis, E. R., 44, 45, 59 Maricle, B. R., 216, 239 Mark Welch, J. L., 174, 187 Markhart, A. H., 224, 225, 226, 227, 228, 242 Marraccini, P., 44, 59 Marracini, P. R., 44, 59 Marschner, H., 203, 239 Martinez de Ilarduya, O., 171, 187 Martı´nez-Garcı´a, J. F., 65–108, 113 Mas, P., 92, 113 Massa, D., 202, 203, 239 Masumbuko, L. I., 28, 59 Matcha, S. S. K., 204, 205, 206, 239
AUTHOR INDEX Mathews, S., 106, 115 Mathieu, O., 11, 15, 20 Mattioni, C., 216, 239 Mattoson, W. J., 103, 111 Mattson, N. S., 202, 239 Maurin, O., 25, 59 Mazarei, M., 158, 163, 164, 187 Mazuela, P., 218, 244 McCarthy, A. A., 42, 44, 59 McCord, J. M., 130, 140 McCouch, S., 29, 60 McCouch, S. R., 29, 59 McCourt, P., 132, 138 McDowell, J. M., 169, 182 McMichael, B. L., 225–226, 239 Mcnellis, T. W., 90, 113 Medrano, E., 208, 239 Megdiche, W., 117–136, 140 Melillo, M. T., 156, 159, 167, 170, 187 Meloni, D. A., 215, 216, 239 Menda, N., 46, 59 Meneguzzo, S., 131, 141 Mengel, K., 199, 214, 215, 239 Messedi, D., 129, 141 Mielke, M. S., 219, 221, 222, 239 Millar, A. J., 93, 110 Miller, G., 132, 141 Miller, S. A., 209, 239 Mims, C. W., 154, 155, 185 Ming, R., 23–53, 59 Mishra, A., 134, 141 Misra, N., 134, 141 Mitchum, M. G., 159, 161, 162, 182, 187 Miteva, T. S., 216, 239 Mitreva, M., 44, 59 Mitreva-Dautova, M., 156, 187 Mizuno, K., 42, 59 Mohanty, P., 125, 141 Molinari, S., 170, 187 Moncada, P., 29, 60 Mondego, J. M. C., 44, 60 Monteith, J. L., 209, 239 Montgomery, B. L., 105, 108, 113 Montoya, G., 37, 60 Moons, A., 134, 141 Morard, P., 218, 219, 220, 221, 223, 240 Morelli, G., 97, 113 Moreno, J. E., 104, 113 Morgan, D. C., 78, 105, 113 Moroldo, M., 38, 60 Moschetto, D., 41, 60 Mozo, T., 38, 60 Mueller, L. A., 23–53, 60
253
Mullen, J. L., 78, 113 Muller, J., 7, 20 Mullet, J. E., 134, 137 Munns, R., 119, 141, 209, 213, 215, 216, 217, 236, 240 Murai-Hatano, M., 226, 240 Muranaka, S., 217, 240 Murata, N., 125, 141 Mustroph, A., 220, 221, 240
N Naasz, R., 218, 240 Nagatani, A., 72, 113 Nanjo, T., 127, 141 Natr, L., 205, 240 Naumann, K., 12, 13, 20 Navari-Izzo, F., 212, 240 N’Diaye, A., 28, 30, 33, 60 Neff, M. M., 98, 113 Nemali, K. S., 213, 240 Nemhauser, J. L., 101, 113 Netondo, G. W., 215, 216, 240 Neveu, C., 154, 188 Ng, D. W., 3, 4, 8, 20 Niblack, T. L., 165, 166, 188 Nicola´s, E., 221, 240 Niebel, A., 158, 160, 188 Niinemets, U., 67, 116 Ni, M., 80, 87, 113 Niu, L., 3, 20 Niwa, Y., 93, 113 Noaman, M. M., 133, 141 Noguchi, K., 67, 114 Noir, S., 39, 40, 60 Noirot, M., 25, 34, 60 Nombela, G., 167, 188 Nozue, K., 82, 93, 114 Nursten, H. E., 42, 56
O Obata, T., 124, 141 Ogawa, M., 42, 60 Ogita, S., 43, 60 Ohgishi, M., 86, 103, 108, 114 Oki, L. O., 199, 214, 238 Olympios, C. M., 195, 218, 224, 240 O’Malley, R. C., 44, 60 Oosterhuis, D. M., 207, 240 Opperman, C. H., 163, 171, 172, 173, 182, 188 Osmond, D. L., 226, 240 Ozturk, A., 209, 240
254
AUTHOR INDEX
P Paal, J., 169, 188 Pableo, E. C., 174, 188 Padgett, P. E., 215, 241 Paillard, M., 28, 30, 60 Paley, S. M., 49, 60 Panda, D., 222, 223, 241 Pankovic, D., 211, 241 Pan, W. L., 200, 237 Papadopoulos, A. P., 203, 241 Pardossi, A., 203, 241 Parella, G., 170, 188 Parelle, J., 219, 220, 223, 241 Parida, A. K., 120, 121, 129, 130, 132, 133, 134, 141 Park, E. J., 126, 141 Patharkar, O. R., 123, 141 Paulsen, G. M., 227, 228, 236 Paux, E., 38, 39, 61 Pavel, E. W., 226, 241 Pearl, H. M., 28, 30, 50, 61 Pegard, A., 168, 188 Pei, Z. M., 124, 142 Pen˜a, P. V., 9, 20 Pendergrast, M., 24, 61 Pendias, H., 199, 241 Penna, S., 129, 142 Pereira, L. F. P., 43, 61 Perry, R. N., 165, 188 Peterlunger, E., 209, 241 Petersson, S. V., 97, 114 Petitot, A. S., 44, 61 Petrillo, M. D., 173, 188 Petrusa, L. M., 127, 142 Pezeshki, S. R., 219, 220, 221, 241 Pfluger, J., 4, 5, 20 Phillips, D. A., 210, 213, 234 Pien, S., 9, 10, 20 Pierik, R., 68, 78, 99, 100, 114 Pierzynski, G. M., 202, 241 Pilbeam, D. J., 207, 232 Pinto-Maglio, C. A. F., 34, 35, 61 Piotte, C., 153, 188 Plath, K., 2, 18 Plaut, Z., 217, 241 Plechakova, O., 46, 48, 61 Poncet, V., 23–53, 61 Pontes, O., 14, 20 Popova, L. P., 133, 142, 221, 222, 245 Prakash, N., 29, 61 Prakash, N. S., 28, 61 Prasad, R., 210, 241 Pre, M., 44, 61
Pugnaire, F. I., 210, 211, 212, 213, 241 Puthoff, D. P., 152, 158, 188 Puzio, P. S., 158, 188 Q Qin, L., 176, 188 Qiu, C., 9, 20 Qiu, Q. S., 122, 142 Quail, P. H., 72, 80, 109, 111, 114 Quin˜ones, A., 201, 241 Quintero, F. J., 122, 142 R Rachmilevitch, S., 224, 225, 226, 227, 228, 241 Radin, J. W., 226, 227, 241 Raina, S. N., 34, 62 Ramachandra Reddy, A., 210, 212, 242 Rao, G. S., 43, 62 Rascio, N., 212, 240 Ray, C., 155, 189 Raynaud, C., 10, 20 Raza, S. H., 126, 142 Rea, S., 6, 7, 20 Rebois, R. V., 153, 189 Reed, J. W., 100, 114 Rengel, Z., 204, 207, 245 Repo, T., 227, 228, 242 Reymond, P., 104, 114 Rhee, J. Y., 228, 242 Rhodes, D., 125, 142 Ribas-Carbo, M., 212, 242 Rice, D. W., 14, 20 Rice, S. L., 168, 169, 189 Richards, E. J., 3, 21 Riga, P., 207, 231 Rizhsky, L., 132, 142 Roberts, P. A., 165, 166, 172, 173, 181, 188, 189 Robson, P., 75, 78, 114 Rodrı´guez, H. G., 215, 242 Rodrı´guez, O., 202, 242 Ross, C. W., 198, 242 Rossi, M., 167, 189 Rosso, M. N., 155, 178, 179, 186, 188, 189 Rova, J. H. E., 25, 62 Roxas, V. P., 132, 142 Roze, E., 176, 189 Ruas, P. M., 28, 62 Ruberti, I., 97, 113 Rudd, S., 36, 62 Ruiz, J. M., 216, 242
AUTHOR INDEX Ruiz, M., 47, 62 Russell, B. L., 125, 142 S Sabatini, S., 97, 114 Saenger, P., 219, 242 Saijo, Y., 123, 142 Sairam, R. K., 119, 122, 125, 126, 133, 134, 142, 212, 242 Saleh, A., 9, 10, 15, 21 Salisbury, F. B., 198, 242 Salisbury, F. J., 96, 97, 108, 114 Salmona, J., 44, 62 Salter, M. G., 80, 87, 92, 93, 103, 107, 115 Sa´nchez, A., 199, 243 Sanders, D., 121, 142 Sanders, P. L., 224, 225, 226, 227, 228, 242 Sasser, J. N., 165, 172, 189 Sattelmacher, B., 225, 242 Savvas, D., 214, 242 Sawa, S., 85, 86, 97, 102, 103, 108, 115 Sawers, R. J., 79, 115 Saxena, P., 134, 141 Schaff, J. E., 171, 189 Schena, M., 84, 97, 115 Schlu¨ter, U., 222, 223, 242 Schmull, M., 220, 221, 242 Scholl, E. H., 156, 189 Schubert, D., 8, 21 Schwab, R., 15, 21 Seah, S., 170, 190 Segal, E., 2, 21 Sekmen, A. H., 131, 142 Semblat, J. P., 172, 190 Seo, H. S., 72, 115 Sera, T., 28, 39, 62 Sessa, G., 84, 87, 89, 101, 102, 103, 107, 115 Shabala, L., 122, 142 Shabala, S., 219, 221, 222, 243 Shaked, A., 207, 242 Shakirova, F. M., 134, 143 Shani, U., 215, 217, 242 Shannon, M. C., 121, 144 Sharrock, R. A., 106, 115 Shen, H., 82, 115 Shendure, J., 45, 62 Shi, H., 122, 123, 142 Shilatifard, A., 8, 19 Shin, J., 80, 115 Shinozaki, K., 124, 133, 143 Shultz, J. L., 38, 62 Silber, A., 199, 200, 243
255
Silberbush, M., 202, 243 Silva, J.Md., 129, 143 Silveira, S. R., 28, 62 Silvestre, J., 218, 219, 220, 221, 223, 240 Silvestrini, M., 29, 62 Simkin, A. J., 43, 62 Sindhu, A. S., 179, 190 Singh, A. K., 129, 138 Singh, D. P., 83, 115 Singh, R. P., 226, 243 Slama, I., 125, 127, 141, 143 Smant, G., 155, 190 Smethurst, C. F., 219, 220, 221, 222, 223, 243 Smirnoff, N., 125, 143 Smith, F. W., 203, 243 Smith, H., 67, 68, 69, 70, 76, 78, 79, 105, 109, 111, 115 Sobczak, M., 169, 179, 190 Sodek, L., 219, 243 Sojka, R. E., 221, 243 Solano, R., 105, 112 Solfjeld, I., 227, 243 Somers, D. E., 75, 115 Sonneveld, C., 199, 214, 243 Sorin, C., 85, 86, 89, 97, 99, 103, 108, 115 Sousa, C. A. F., 219, 243 Soussi, M., 215, 216, 243 Spence, K. O., 165, 190 Spollen, W. G., 132, 143 Springer, N. M., 4, 21 Staal, M., 121, 143 Staiger, D., 92, 111 Stavang, J. A., 94, 115 Stec, I., 9, 21 Steele, A. E., 168, 192 Steeves, R. M., 179, 190 Steiger, D. L., 28, 62 Steindler, C., 84, 96, 97, 115 Stewart, G. R., 153, 155, 190 Stockle, C. O., 208, 243 Stoimenova, M., 223, 243 Stout, R. G., 225, 243 Strong, W. M., 201, 243 Suchitra, S., 156, 190 Sultana, N., 216, 243 Sun, Y., 205, 243 Swain, S. M., 83, 115 Sybenga, J., 34, 62 T Taiz, L., 209, 210, 212, 214, 215, 216, 217, 219, 220, 221, 243
256
AUTHOR INDEX
Takabe, T., 125, 139 Takihara, Y., 157, 190 Tanaka, O., 207, 244 Tanaka, S., 106, 115 Tanaka, Y., 216, 243 Tang, J., 36, 63 Tanksley, S. D., 26, 28, 54 Tao, Y., 78, 81, 94, 96, 97, 101, 103, 104, 106, 107, 115 Tariq, M., 3, 11, 21 Tavakkoli, E., 215, 217, 244 Terry, N., 215, 216, 244 Tester, M., 119, 143, 209, 240 Tewari, R. K., 204, 207, 244 Tezara, W., 210, 212, 244 Thomas, F. M., 220, 221, 242 Thomas, J. C., 133, 143 Thomas, W., 202, 244 Thoquet, P., 170, 190 Thorstensen, T., 9, 13, 14, 21 Touraine, B., 203, 214, 236, 244 Triantaphyllou, A., 172, 181 Triantaphyllou, A. C., 151, 172, 174, 188, 190 Trievel, R. C., 7, 15, 18, 21 Trojer, P. L. G., 4, 21 Trudgill, D. L., 165, 172, 190 Tshilenge, P., 28, 63 Tu¨rkan, I., 119, 123, 124, 125, 126, 131, 132, 137, 142, 143 Turner, J. G., 104, 116 Tuteja, N., 122, 140 Twumasi, P., 209, 244 Tyagi, A., 119, 122, 125, 126, 133, 134, 142 U Udomprasert, N., 227, 244 Uefuji, H., 42, 43, 63 Unoki, M., 11, 21 Unsworth, M., 209, 239 Uozumi, N., 122, 143 Urrestarazu, M., 214, 218, 244 Urwin, P. E., 179, 190 V Valkonen, J. P. T., 170, 183 Valladares, F., 67, 115, 116 Vandenbussche, F., 69, 96, 116 Van De Peer, Y., 36, 63 Vandepoele, K., 36, 63 Van Der Hoeven, R., 36, 63 van der Vossen, E. A., 170, 191
van der Vossen, E. A. G., 170, 191 Vanholme, B., 153, 179, 183, 191 Van Iersel, M. W., 213, 240 Van Ooijen, G., 170, 191 Van Os, E., 194, 204, 244 Vartapetian, B. B., 220, 221, 244 Veech, J. A., 164, 183 Veen, B. W., 218, 244 Verbruggen, N., 127, 143 Vercauteren, I., 158, 161, 191 Verdonck, O., 208, 234 Vernon, D. M., 130, 144 Verslues, P. E., 209, 244 Vert, G., 101, 116 Vieira, L. G. E., 37, 47, 63 Vinocur, B., 118, 124, 129, 144 Voilley, A., 42, 63 Voogt, W., 199, 214, 243 Vos, P., 28, 63, 167, 191 Vuong, H., 37, 60 W Waditee, R., 125, 126, 144 Wagner, D., 4, 5, 20 Wagner, P. A., 219, 221, 222, 244 Walker, C. D., 207, 244 Wang, C., 166, 191 Wang, C. L., 167, 191 Wang, G. L., 38, 63 Wang, H. Y., 118, 119, 120, 124, 127, 144 Wang, J., 132, 144 Wang, X., 3, 21, 161, 191 Wang, Y., 9, 21 Wang, Z., 210, 245 Warner, A. J., 226, 233 Wei, C., 175, 191 Weigel, D., 69, 112 Weinig, C., 93, 116 Weller, J. L., 100, 116 Weng, J. H., 220, 221, 245 Whitelam, G., 76, 78, 79, 115 Whitelam, G. C., 68, 79, 92, 94, 95, 101, 103, 107, 108, 111 Wieczorek, K., 161, 162, 191 Wienkoop, G., 157, 191 Wiggers, R. J., 160, 191 Williamson, V. M., 147–180, 185, 187, 191 Wilson, C., 121, 144 Winicov, I., 127, 142 Winston, E. C., 43, 63 Wood, C., 8, 22 Woo, H. R., 11, 22
AUTHOR INDEX Wubben, M. J., 159, 192 Wubben, M. J., II, 159, 161, 192 Wu, F., 29, 63 Wu, S. J., 122, 144 Wu, X. L., 167, 192 Wylie, T., 148, 192 Wyss, U., 149, 168, 192 X Xiao, B., 7, 22 Xiong, L., 120, 123, 124, 132, 133, 135, 144 Xu, L., 9, 22 Xu, Q., 224, 225, 226, 227, 228, 245 Y Yadav, B. C., 179, 192 Yamaguchi-Shinozaki, K., 124, 133, 143 Yancey, P. H., 129, 144 Yang, J., 88, 116 Yan, Y., 104, 105, 116 Ying, Z., 15, 22 Yordanova, R. Y., 221, 222, 245 Yoshida, S., 221, 226, 245 Yu, M. C., 3, 22 Yu, M. H., 168, 192 Yu, Q., 204, 207, 245
257
Z Zamir, D., 170, 192 Zarkower, D., 177, 192 Zawadzki, J. L., 178, 192 Zeiger, E., 209, 210, 212, 214, 215, 216, 217, 219, 220, 221, 243 Zeltz, P., 29, 63 Zentella, R., 89, 116 Zerche, S., 203, 245 Zhang, J., 205, 206, 210, 212, 221, 237, 238, 245 Zhang, K., 4, 15, 22 Zhang, L., 4, 22 Zhang, T., 120, 144 Zhang, X., 3, 8, 10, 22 Zhang, Y., 104, 116, 224, 228, 245 Zhang, Y. P., 227, 245 Zhao, D., 204, 205, 206, 245 Zhao, G. Q., 215, 245 Zhao, Z., 9, 22 Zheng, Y., 198, 245 Zhu, A., 43, 63 Zhu, J. K., 119, 120, 121, 122, 123, 124, 133, 140, 143, 144 Zribi, L., 215, 216, 245 Zude, M., 218, 219, 220, 222, 237
SUBJECT INDEX
Note: The letters ‘f’ and ‘t’ following locators refer to figures and tables respectively. A ABA. See Abscisic acid (ABA) Abscisic acid (ABA), 120, 133–134, 221, 226–227 Actin, 160 Adenophorean parasites, 149 ADH. See Alcohol dehydrogenase (ADH) AFLPs. See Amplified fragment length polymorphisms (AFLPs) AGAMOUS, 8 Air temperature, 224–225 Alcohol dehydrogenase (ADH), 220 Amino acids and amides Pro biosynthesis, precursors in, 126–127 Glu and Orn pathways, stress sensitive/ tolerant plants, 127 Pro concentration, osmotic stress effects, 127 Pro, exogenous application of Allenrolfea occidentalis, 129 Pro over-production, transgenic approaches, 127–128 SRO5-P5CDH nat-siRNAs, role in saltstress regulatory loop, 128f Pro, role in cytoplasmic acidosis, 127 Amphidial secretions, 154 Amphids, 153, 153f Amphiploidy, 49 Amplified fragment length polymorphisms (AFLPs), 27 conversion into SCAR, 28 as molecular marker, functions, 28 Anoxia stress, 218, 223 Anthurium andreanun, 198 APX. See Ascorbate peroxidase (APX) Arabidopsis class V HKMTS SUVH proteins. See SUVH proteins SUVR proteins. See SUVR proteins HKMT classification in gene organization/evolution, 4–5, 5f SET domain, 5–7 HKMTs, role in plant development
class I HKMTS, 7–8 class II HKMT enzymes, 8–9 class III HKMT enzymes, 9–10 class IV HKMT enzymes, 10–11 lysine methylation sites, 4 Arabidopsis HD-Zip class II subfamily, phylogenetic tree of, 84f Arabidopsis SUVR5 (AtCZS), 14 Arabidopsis Trithorax-like proteins 1-5 (ATX1-5), 9 See also Class III HKMTS Arabinases, 176 ASC. See Ascorbic acid (ASC) Ascorbate peroxidase (APX), 131, 132, 207, 212, 216, 222, 228 Ascorbic acid (ASC), 131, 207, 212, 224 AtCZS. See Arabidopsis SUVR5 (AtCZS) ATX1-5. See Arabidopsis Trithorax-like proteins 1-5 (ATX1-5) ATXR7 (SDG25), 10 Auxins, 96–97, 101 B BAC. See Bacterial artificial chromosome (BAC) Bacillus thuringiensis, 179 BAC libraries in Coffea, 40t C. canephora BAC libraries clone IF126, use of, 41 clone IF200, use of, 41 C. eugenioides construction, aim, 41 genomic applications, 38–39 Bacterial artificial chromosome (BAC), 29 Basic–helix–loop–helix (bHLH) protein, 80, 87–89, 102 Betaines, 125–126 GB pre-treatment of rice cultivars, effects, 126 GB, salinity tolerance improvement, 125 GB synthesis by oxidation reactions, 125 BF/CF. See Blue fluorescence to chlorophyll fluorescence (BF/CF) 259
260
SUBJECT INDEX
bHLH protein. See Basic–helix–loop–helix (bHLH) protein Blue fluorescence to chlorophyll fluorescence (BF/CF), 205 B. malayi, 177, 178 Brassinosteroids, 96, 98–99 C Caþ-dependent protein kinase (CDPKs), 123, 135f role in abiotic stress signalling, 123 Caffeine synthesis, coffea, 42 Calreticulin, 156, 157 Calvin Cycle, 130, 197f, 205, 206, 211 Canopy shade treatment, 76 Carbohydrate-active enzymes (CAZymes), 175 CAROTENOID ISOMERASE, 9 CAZymes. See Carbohydrate-active enzymes (CAZymes) CCS52, 160 CDPK-interacting protein (CSP1), 123 CDPKs. See Caþ-dependent protein kinase (CDPKs) C. elegans, 174–179 C. elegans vs. M. incognita, 177 Cell expansion, 209 Center for Biology of Nematode Parasitism in Raleigh NSCU (USA), 173 CF technique. See Chlorophyll fluorescence (CF) technique CGA biosynthesis, 43 Chlorophyll fluorescence (CF) technique, 205 Chlorophylls/carotenoids, photosynthetic pigments, 69–70 Choline-fed transgenic plants, 126 Chromatin condensed/decondensed, distinction, 2–3 in gene expression, role, 2 Circadian clock and SAS signalling clock components and photoreceptors, relation, 92 expressed in sinusoidal wave form, 92 gating, 92 photoentrainment, 92 PIL1/PIL2, gated response of hypocotyl elongation, 93 shade-responsive genes, gating of, 92–93 transient simulated shade tratments, 92–93 Class I HKMTS Arabidopsis, genome encoding of CLF, 7–8
MEA, 7–8 SWN, 7–8 E(Z), homologues of, 7 Class II HKMTS SDG4 deficiency, effects, 9 SDG4/SDG8 mutations, 9 Class III HKMTS, 9 Class IV HKMT enzymes ATXR5/ATXR6, H3K27 monomethylation, 10–11 PCNA, interaction with, 10 CLA-VATA/ESR (CLE), 157 CLE. See CLA-VATA/ESR (CLE) CLE peptides, role in plant growth/ development, 157 CLF. See CURLY LEAF (CLF) Climatic sensors, 208 radiometer, 208 tensiometer, 208 CND. See Compositional Nutrient Diagnosis (CND) CNL. See Critical Nutrient Level (CNL) Coffea aroma precursors, 42 genetic maps of C.canephora by Nestle´, 31f interspecific genetic linkage maps, aim of, 30 genomic resources available on web, 47t–48t BAC libraries in, 38–41 ESTs in, 35–38 genes and metabolism, 42–45 genome size and cytogenetics, 34–35 whole genome sequencing, 49–53 genomics early 21st century, WGS strategy, 26 in 1980s, linkage mapping strategies, 25–26 in 1990s, PCR technology, 27 genus classification by botanical sections, 25 main species C. arabica, 25, 25f C. canephora, 25, 25f molecular markers AFLPs, 28 COS markers, 29 ISSR/ISTR, 28 PCR-based markers, 28 RFLPs, 28 SSR, microsatellite markers, 29 QTL identification, 33–34
SUBJECT INDEX Rubiaceae family, member of, 25 trading/exports, 24 CoffeaCyc, 49 Coffee DNA database, 46 Colchicine, 160 Compositional Nutrient Diagnosis (CND), 202 Composition of nutrient solution diagnosis of plant stress CF technique, 205 EC/nutrient deficiency, factors, 204 mineral deficiency, consequences, 205 nutrient deficiency, symptoms, 204 See also Mineral nutrients nutrient uptake regulation, factors, 201– 204 theories. See Theories based on nutrient absorption optimum composition, considerations high nutrient concentrations, positive/ negative impact, 198 ion antagonism, 199 lower concentrations, advantages, 198 nutrient uptake at root surface, importance. See Root surface, nutrient uptake at plant absorption of NH4þ/NO3 (N sources), importance, 199–200 plant nutrient uptake, measurement of measurement of nutrient content in plant tissues, 200–201 measurement of nutrient depletion in root environment, 200 Conserved ortholog set (COS), 27, 29 Constitutive shade-avoidance 1 (CSA1), 91 Controlled structured vocabularies, 52 COS. See Conserved ortholog set (COS) Critical Nutrient Level (CNL), 202 Cry proteins, 179 CSA1. See Constitutive shade-avoidance 1 (CSA1) CURLY LEAF (CLF), 7–8 CuZn-SOD, 131 CXC. See Cysteine-rich (CXC) region Cysteine proteinase inhibitors (cystatins), 179 Cysteine-rich (CXC) region, 7 Cyst nematodes life cycle of, 152f vs. RKNs feeding sites, 151–152 migration sites, 150–151 reproduction, 151
261
D 16D10, 157 Dark reversion, 72 dAS plants. See Double-antisense (dAS) plants Daylight, 69 Deficit irrigation method, 209 DELLA genes in SAS regulation, 81–83 DELLA proteins, 89 Deviation from Optimum Percentage (DOP), 202 Diagnosis and Recommendation Integrated System (DRIS), 202 Diagnosis techniques, oxygen deficiency, 223 Differential library screening, 158 DNA array technology, 159 DNA methylation, 2, 3, 11, 12, 13, 15–16 DOP. See Deviation from Optimum Percentage (DOP) Dosage compensation pathway, 177 Double-antisense (dAS) plants, 132 Double-stranded RNA (dsRNA), 178 DRIS. See Diagnosis and Recommendation Integrated System (DRIS) Drought-sensitive species, 210 Drought-tolerant species, 210 dsRNA. See Double-stranded RNA (dsRNA) E EC. See Electrical conductivity (EC) Effectors in induction of nematode feeding site, 157–158 in plant cell penetration/nematode migration, 155–156 in plant defence suppression, 156 Electrical conductivity (EC), 195, 195t, 196t, 197f, 213–217 End-of-day FR (EOD-FR) treatment, 76 Enhancer of Zeste E(Z), 4, 7 Enzymatic/non-enzymatic anti-oxidants, 131 EOD-FR treatment. See End-of-day FR (EOD-FR) treatment ERF1. See Ethylene response factor 1 (ERF1) EST. See Expressed sequence tag (EST) ESTs in Coffea determination of conserved genes between genomes, 36 ESTs of selective plant organisms, GenBank, 36t, 37–38 large-scale ESTs, uses, 36 Ethylene, 99–100
262
SUBJECT INDEX
Ethylene receptors, 44 Ethylene response factor 1 (ERF1), 104 Eucoffea, 25 Expansins, 176 Expressed sequence tag (EST), 29, 36–37 E(Z). See Enhancer of Zeste E(Z) E(Z)-like proteins, 7–8 F Far-red (FR) region, 69 Feeding cell formation signal transduction pathways AtSUC2 expression, 163 Hahsp17.7G4 expression in tobacco galls, 162 laser capture microscopy technology, soybean genes, 164 OPPP, 164 TobRB7 expression, 163 WRKY23 expression, 162–163 Feeding cells, 151–154, 158–164, 168, 179 Fermentation ADH, indicator of hypoxia, 220 early ethanolic fermentation pathway, anoxia tolerance, 220 pathways, 219 positive/negative effects, 219–220 prolonged oxygen deficiency, effects, 220 root hypoxia, effect on photoassimilates, 220 Fe-SOD, 131 FLC. See FLOWERING LOCUS C (FLC) FLOWERING LOCUS C (FLC), 8 FR region. See Far-red (FR) region FR sensing, use, 70 FYRC. See F/Y-rich C-terminus (FYRC) F/Y-rich C-terminus (FYRC), 6f, 9 F/Y-rich N-terminus (FYRN) domain, 6f, 9 FYRN. See F/Y-rich N-terminus (FYRN) domain G Gating process, 92 Genes/metabolism, coffea alkaloid biosynthetic pathways caffeine biosynthesis, 42–43 aroma formation process, 42 CGA biosynthesis, 43 coffea breeding, aim, 42 coffee fruit ripening, influencing factors, 43–44 next-generation sequencing technologies, 45
Northern Blot/qPCR, expression studies, 44 ‘PUCE CAFE,’ 45 sugars/amino acids, biosynthesis, 43 Genome size/cytogenetics, coffea FISH/heterochromatin staining techniques, 34–35 flow cytometry, genome size estimation, 34 gene-rich regions, organization, 35 GISH technique, 35 Giant cells AtFH6, actin nucleation in, 160 MAP65-3, formation by, 160 treatment with colchicine/oryzalin, effects, 160 Gibberellins, 100–101, 226 Glucose-6-phosphate dehydrogenase (G6PDH), 164 Glutathione (GSH), 131, 207, 223, 228 Glutathione reductase (GR), 131, 207 Glycophytic crops, 119 G6PDH. See Glucose-6-phosphate dehydrogenase (G6PDH) G-protein coupled receptors, 176 GR. See Glutathione reductase (GR) GSH. See Glutathione (GSH) H Haemoglobin (Hb)/nitric oxide (NO) cycle, 223 Hahsp17.7G4 expression in tobacco galls, 162 Halophytes abiotic stress, major threat to agriculture, 118 anti-oxidative responses dAS plants, study, 132 Jerba and Tabarka, salinity responses, 131–132 over-expression of ROS-scavenging enzymes, results, 132 compatible metabolites, prevention of detrimental changes amino acids and amides, 126–129 betaines, 125–126 sugars and sugar alcohols, 129–130 future perspectives signalling types for plants during drought/salt stress, 135f intermediary signalling components/ion homeostasis regulation ATPase activities, 121 CDPKs, role in abiotic stress signalling, 123
SUBJECT INDEX CSP1, transcriptional activator, 123 MAPK pathways, 123–124 Naþ/Hþ antiporters, function of, 121 SOS1 activation/apoplastic alkalinization, effects, 123 SOS pathway, components, 122 Naþ/ Cl, toxicity effects, 119 osmotic stress, consequences/results, 119 plant hormones, transduction of stress signal ABA, 133–134 jasmonic acid, 134 salicylic acid, 134 saline habitats, characteristics, 119 salt stress adaptibility, molecular mechanisms, 120 salt stress tolerance plants, categories, 120 Hero A, 179 Heterochromatin, 3, 12, 13, 14, 34, 35 Heterochromatin staining technique, 34–35 Heterodera schachtii, 150f Heterozygosity, 174 ‘Histone code,’ 3 Histone lysine methyltransferases (HKMTs), 1–16 HKMT classification in Arabidopsis gene organization/evolution, 4–5, 5f SET domain, 4, 5–7 pre-SET/post-SET domains, 7 SUV39H1, structural/functional analyses, 6 Histone methylation, 3 ‘Hit-and-run’ strategy, 149 H3K27 monomethylation, 11 HKMT classification in Arabidopsis gene organization/evolution, 4–5, 5f A. thaliana and Zea mays, genome encoding, 4 domain architecture of histones, 6f H3 and H4 lysines, target, 7f HKMTs. See Histone lysine methyltransferases (HKMTs) Homozygosity, 174 Horizontal gene transfer, 176 Hypocotyl elongation, 72, 75, 77, 79, 86, 87, 90f, 91, 92–94, 97, 99, 100, 101, 105, 108 I IMMUTANS, 132 Infra-optimal temperature cytokinin level, effects on, 226–227
263
nutrient uptake, effects on, 226 root temperature stress, effects on, 225 Initial syncytium cell, 151 In situ hybridization, 158 Intersimple sequence repeat (ISSR), 28 Interspecific genetic linkage maps, 30 Inverse sequence tagged repeat (ISTR), 28 Invertases, 176 Ion antagonism, 199 Ionic stress, 119, 135f ISSR. See Intersimple sequence repeat (ISSR) ISTR. See Inverse sequence tagged repeat (ISTR) J Jasmonates, 104 ERF1/JAZ, repressors of, 104–105 management of ‘dilemma’of plants, dual role, 104 Jasmonate zim-domain (JAZ), 105 Jasmonic acid, 104, 120, 134 JAZ. See Jasmonate zim-domain (JAZ) L Leaf hyponasty, 78 Lipid peroxidation, 197f, 206, 208, 212, 213, 223, 229 Long day plants Arabidopsis thaliana, 69 tobacco (Nicotiana tabacum), 69 M Maillard and Strecker’s reactions, 42 Maleness-promoting genes, 177 MAP65-3. See Microtubule associated protein 65-3 (MAP65-3) MAPK. See Mitogen-activated protein kinase (MAPK) MAPK pathways, 123–124 Mascarocoffea, 25 MDHAR. See Monodehydroascorbate reductase (MDHAR) MEA. See MEDEA (MEA) MEDEA (MEA), 7–8 Mehler reaction, 130 Meloidogyne incognita, 150f “Merlot” grapes and wine, 209 M. hapla genome project, 173 homozygosity, reproduction approach, 174 M. hapla VW9 genome, 173–174
264
SUBJECT INDEX
Mi-1, 178 Microtubule associated protein 65-3 (MAP653), 160 Microtubule depolymerization, 160 M. incognita genome project, 173 genome sequencing, 173–174 heterozygosity, reproduction approach, 174 vs. M. hapla GþC content, 174 gene content, 174 gene density, 175 reproduction, 174 Mineral nutrients, 205 deficiency, impact on Calvin cycle products, 206 K/Mg deficiency, effects, 206 measurement of enzymes activity, indicators of deficiency, 207 N, Mg, K and Zn deficiency photooxidative damage, cause, 206–207 plant defence mechanisms, protection against ROS, 207 N/P deficiency BF/CF and dual fluorescence, effects, 205–206 CF technique, assessment, 205 N stress, indicators, 205 Mitogen-activated protein kinase (MAPK), 123 Mn-SOD, 131 MoccaDB, 46 Molecular mechanisms in SAS signalling molecular components regulating SAS responses PAR, plant proximity perception, 81 PIF3, regulators of seedling deetiolation, 80 role of ATHB and HAT genes, 83–87 role of bHLH-encoding PAR genes, 87– 89 role of PIF and DELLA genes, 81–83 SAS regulatory components into transcriptional networks, intregration COP1 and DET1, role, 90–91 CSA1, role, 91 genetic components in SAS regulation, scheme, 90f Monodehydroascorbate reductase (MDHAR), 131, 207 Mozambicoffea, 25
N Naþ/Hþ antiporters (NHX), 121, 122 Natural plant resistance and nematode virulence genetic diversity/virulence development, 171–173 inheritance of nematode resistance, 166– 167 nematode resistance genes, cloning/ characterization, 169–171 non-host, resistant host, tolerant host, 164– 166 resistance phenotypes, 167–169 N deficient plants, 205 Nematicides, 149 Nematode (endoparasitic), anterior region of, 153f Nematode feeding cells, 158 Nematode resistance genes, cloning/ characterization, 169–171 Nematode resistance, inheritance of, 166–167 Nematodes, definition, 148 Nematodes, plant parasitism in adaptation to plant parasitism, 153f amphids and stylet, secreting molecules, 153 feeding tubes, structure/composition, 154 salivary/amphidial secretions, model proposed, 154 Adenophorea and Secernentea, classes of parasites, 149 ‘hidden’ feeding activity, 149 nematicides, use in control of nematodes, 149 the parasitic life cycle cyst nematodes and RKNs, four juvenile stages, 150–152 cyst nematodes, reproduction by mitotic parthenogenesis, 151 sedentary period, characterization, 151 sexual dimorphism, sedentary lifestyle, 151 sycytium and gall sections of infected Arabidopsis roots, 150f PPNs, 148 NHX. See Naþ/Hþ antiporters (NHX) “Nitrate respiration,” 223 Non-photochemical quenching, 210–212, 211f Nucleosome, 2 composition, 2 positioning, 2 Nutrient deficiency, 204, 205, 207, 222
SUBJECT INDEX Nutrient solution temperature diagnosis of plant stress decrease in water uptake, effects, 226 nutrient deficiencies, long cold periods, 226 oxidative stress, cause/effects. See Oxidative stress reduced nutrient uptake, effects, 228 reduced water uptake, effects, 227 root hydraulic resistance, 226 root temperature and root respiration, 225 root temperature/growth and morphology, 225 root temperature, importance over air temperature, 224–225 root temperature stress, symptoms, 225 synthesis/translocation of hormones, impact on, 226–227 O O-methyltransferase, caffeic acid, 41 OPPP. See Oxidative pentose phosphate pathway (OPPP) Oryzalin, 160 Osmolytes, 120, 123, 125, 130, 135f Osmotic adjustment, 127, 129, 130, 197f, 210, 213, 216, 230 Osmotic stress, 94, 119, 123, 124, 125, 127, 130, 133, 135f, 196t, 217 Osmotic/water-deficit effect of salinity, 215 Oxidative pentose phosphate pathway (OPPP), 164 Oxidative stress membrane injury, cause, 228 prevention plant tolerance to non-optimal temperatures, techniques, 228–229 ROS accumulation, 228 Oxygen concentration in nutrient solution diagnosis of plant stress low oxygen. See Fermentation; Oxygen deficiency optimum concentration, considerations, 218–219 Oxygen deficiency decrease in photochemical quenching, 222 diagnosis techniques, 223 “nitrate respiration” increase of NR activity, 223 nitrate reduction, effects, 223
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oxidative stress in plants, cause anoxia stress, formation of ROS, 223 higher antioxidant activity, 222–223 reduced absorption of nutrients, 220–221 root aerobic respiration, restriction, 219–220 stomatal closure, effect on photosynthesis/ chlorophyll, 221–222 P PAR. See Photosynthetically active radiation (PAR) PAR genes. See Phytochrome rapidly regulated (PAR) genes Partial root-zone drying, 209 PCNA. See Proliferating cell nuclear antigen (PCNA) PCR-based technologies. See Polymerase chain reaction (PCR)-based technologies PCWM arabinases, 176 PCWM enzymes. See Plant cell wallmodifying (PCWM) enzymes P deficient plants, 205 Penman-Monteith equation, 208–209 Petiole elongation, 78, 79, 99, 100, 108 PGM/bPG enzyme. See Phosphoglycerate mutase/bisphosphoglycerate (PGM/bPG) enzyme pH and EC of nutrient solution diagnosis of plant stress crops under salinity stress, effects, 215–216 osmotic/water-deficit effect of salinity, 215 salt-specific/ion-excess effect of salinity, 215, 216–217 two-phase model of salt injury (Munns), 217 optimum value, considerations 1.5 and 2.5 dS/m, ideal EC range, 213 fertigation management, 213 high EC, higher product quality, 214 high irrigation frequency/long irrigation events, results, 214 salinity problems, causes, 213–214 PHD. See Plant homeodomain (PHD) pH, definition, 214 Phenylpropanoid pathway, 42, 43, 44 Phosphoglycerate mutase/ bisphosphoglycerate (PGM/bPG) enzyme, 164
266
SUBJECT INDEX
Photoentrainment process, 92 Photon fluence rate, 70 Photosynthetically active radiation (PAR), 69 Photosystem II (PS-II) complex, 125, 205 ‘Physiological drought,’ 119 Phytochrome interacting factor 3 (PIF3). See Basic–helix–loop–helix (bHLH) protein Phytochrome photoreceptors, 69, 72, 82 Phytochrome rapidly regulated (PAR) genes, 81 PIF genes in SAS regulation, 81–83 Plantago maritima, salt-tolerant species, 121 Plant cell wall, 161 Plant cell wall enzymes, 161 Plant cell wall-modifying (PCWM) enzymes, 175 Plant defence mechanisms, 207 Plant functions in nematode infection feeding cell formation signal transduction pathways. See Signal transduction pathways in feeding cell formation syncytia vs. giant cells, 158 feeding site formation, A. thaliana (model) DNA array technology, 158–159 suppression of plant defences, 159 nematode development treatment of giant cells with oryzalin/ colchicine, effects. See Giant cells plant–nematode interactions molecular approaches, 158 plant hormones, role, 160–161 syncytia vs. giant cells, cytological changes, 159–160 Plant homeodomain (PHD), 6f, 9 Plant hormones plant-nematode interactions, role in, 160–161 cell wall-modifying enzymes, feeding cell formation, 161 transduction of stress signal, role ABA, 133–134 jasmonic acid, 134 salicylic acid, 134 Plant hydrolases, 161 Plant nematode interaction genomic analysis of RKN developmental pathways conserved in nematodes, 177–178 genome structure, 173–175
RKN genomes shed light on nematode diversity, 176–177 the secretome, 175–176 natural plant resistance and nematode virulence genetic diversity/virulence development, 171–173 inheritance of nematode resistance, 166–167 nematode resistance genes, cloning/ characterization, 169–171 non-host, resistant host, tolerant host, 164–166 resistance phenotypes, 167–169 novel strategies, control of plant-parasitic nematodes, 178–180 See also Strategies, control of plantparasitic nematodes parasitism genes in sedentary nematodes induction of the nematode feeding site, effectors in, 157–158 plant cell penetration/nematode migration, effectors in, 155–156 plant defence suppression, effectors in, 156 plant functions (manipulated)in nematode infection. See Plant functions in nematode infection plant parasitism in nematodes adaptation to plant parasitism, 152–154 the parasitic life cycle, 150–152 See also Nematodes, plant parasitism in Plant-parasitic nematodes (PPNs), 148, 178–180 Plant stress diagnosis, methods, 197f Plant tolerance to non-optimal temperatures, techniques, 228–229 Plant water content decrease in, effects cell expansion, 209 water potential, measurement of, 209–210 effects on drought sensitive/tolerant species, 210 water loss prevention Fs, water stress detection, 210 water loss, stomatal closure by effect on chloroplasts, 211–212 light energy dissipation by nonphotochemical quenching, 210, 211f transpiration rate and leaf temperature, proportionality, 212
SUBJECT INDEX water stress conditions, 210 NR activity/N content, 213 ROS or lipid peroxidation, measurement, 212 PLP. See Pyridoxal-5-phosphate (PLP) P. media, salt-sensitive species, 121 Polycomb Repressive Complex 2 (PRC2), 7 Polymerase chain reaction (PCR)-based technologies, 27 Polyols, 129–130 osmotic adjustment and osmoprotection, 130 PPNs. See Plant-parasitic nematodes (PPNs) PRC2. See Polycomb Repressive Complex 2 (PRC2) Pro (imino acid) biosynthesis, precursors in, 126–127 Glu and Orn, 126–127 exogenous application of Allenrolfea occidentalis, 129 osmotic stress, effect on Pro concentration, 127 over-production, transgenic approaches, 127–128 SRO5-P5CDH nat-siRNAs, role in saltstress regulatory loop, 128f role in cytoplasmic acidosis, 127 Proliferating cell nuclear antigen (PCNA), 10 Proline-tryptophane-tryptophane-proline (PWWP) domain, 9 Promoter–reporter gene fusions, 158 Promoter–trap strategies, 158, 164 Protein immunolocalization, 158 PS-II complex. See Photosystem II (PS-II) complex ‘PUCE CAFE,’ 45 PWWP. See Proline-tryptophanetryptophane-proline (PWWP) domain Pyridoxal-5-phosphate (PLP), 122–123 R Random amplified polymorphic DNAs (RAPDs), 27 RAPDs. See Random amplified polymorphic DNAs (RAPDs) Resistance genes, 178 Restriction fragment length polymorphisms (RFLPs), 27, 28 RFLPs. See Restriction fragment length polymorphisms (RFLPs) RKN, genomic analysis of
267
developmental pathways in nematodes dosage compensation pathway, 177 male differentiation and behaviour, control, 177 maleness-promoting genes, 177 sex determination pathway, 177–178 genome structure, 173–175 See also M. hapla; M. incognita RKN genomes, nematode diversity C. elegans vs. M. incognita, 177 nematode NRs, role, 177 the secretome horizontal gene transfer, 176 PCWM arabinases/invertases/expansins, gene encoding, 176 PCWM/CAZymes, PPN study, 175, 175t RKNs. See Root-knot nematodes (RKNs) RNA blotting, 158 Roche 454 Titanium sequencing technology, 53 Root aerobic respiration, restriction, 219–220 Root hydraulic resistance, 226, 228 Root hypoxia, effect on photoassimilates, 220 Root-knot nematodes (RKNs), 39, 149, 152f life cycle of, 152f vs. cyst nematodes, 150–152 See also RKN, genomic analysis of Root respiration, 197f, 223, 225, 228, 230 Root surface, nutrient uptake at fertigation frequency, impact, 199 frequency of irrigation, effects, 199 transpiration rate, 198–199 Root temperature, 224–225 Root temperature stress, symptoms, 225 RUBISCO, 15, 125, 206, 208, 211, 213, 216, 222, 223 S Saccharomyces cerevisiae (budding yeast), 4 SAGE. See Serial analysis of gene expression (SAGE) Salicylic acid, 132, 134, 156, 171 Salivary secretions, 154 Salt overly sensitive (SOS), 121 Salt-specific/ion-excess effect of salinity mineral deficiency, effects, 216–217 toxicity effects, 217 Salt stress tolerance plants, categories, 120 SAS. See Shade avoidance syndrome (SAS) SAS, regulatory components of low red to far red ratio light
268
SUBJECT INDEX
SAS, regulatory components of (Continued) Aspidistra leaf, spectral distribution of, 70f FR radiation, 70 PAR/FR absorption by Aspidistra leaf, 68f, 71 R:FR, definition, 70 R:FR, light quality parameter, 70 visible region, PAR, 69–70 SAS regulation, photoreceptors in Arabidopsis, simulated/canopy shade treatments, 73t–74t PHYA to PHYE gene family, phytochrome encoding in, 72–75 phytochrome photoreceptors, forms, 72 SAS responses in different organs/ developmental stages canopy shade treatments, 76 EOD-FR treatment, 76 germination, maternal effects on, 76 seed maturation/germination, stages, 76 simulated shade treatments, 76 SAS signalling and other regulatory pathways circadian clock, 92–93 hormones, 96–101 hormone/shade-regulated transcriptional networks, SAS control, 101–103 low and high temperature, 93–96 plant defence, 103–105 SAS signalling, molecular mechanisms in. See Molecular mechanisms in SAS signalling SAS signalling, spatial and temporal aspects intracellular/intercellular signalling, 105 long-distance communication, importance, 105 transgenic lines PATHB2:GFP-GUS and PATHB4: GFP-GUS, histochemical analysis, 107f SAS traits and gene expression, regulatory modules, 108 set of responses to plant canopy shade environmental conditions, role in photoperiodic signal variations, 68–69 light quality/quantity, importance, 68 shade tolerance vs. shade avoidance, 67–68 shade tolerance definition, 67
plants that reproduce with low light, examples, 67 shade-tolerant species, qualities, 67 SAS response regulation, molecular components PAR, plant proximity perception, 80–81 PIF3, regulators of seedling de-etiolation, 80 role of ATHB and HAT genes Arabidopsis HD-Zip class II subfamily proteins, phylogenetic tree of, 84f role of bHLH-encoding PAR genes, 87–89 role of PIF and DELLA genes, 81–83 SAS responses, different organs/ developmental stages canopy shade treatments, 76 EOD-FR treatment, 76 germination, maternal effects on, 76 responses to low R:FR light, 78–79 seed maturation/germination, stages, 76 simulated shade treatments flowering time, 78–79 hypocotyl elongation, strongest response, 77 petiole elongation, effects, 78 stem elongation combined with leaf hyponasty, effects, 78 wild-type seedling phenotypes, W or WþFR conditions, 77f SAS signalling and other regulatory pathways circadian clock clock components and photoreceptors, relation, 92 expressed in sinusoidal wave form, 92 gating, 92 photoentrainment, 92 PIL1/PIL2, gated response of hypocotyl elongation, 93 shade-responsive genes, gating of, 92–93 transient simulated shade tratments, 92–93 hormone and shade-regulated transcriptional networks, SAS control auxin-, BR- and GA-related gene expression, 101, 103 auxin/BR signalling pathways, 101 bHLH transcription factors, expression of, 102 interaction mechanisms, 102 hormones auxins, 96–97
SUBJECT INDEX brassinosteroids, 98–99 ethylene, 99–100 gibberellins, 100–101 low and high temperature effects on hypocotyl elongation, examples, 93–94 simulated shade and low-temperature treatments, experimments, 94–95, 95f plant defence ‘dilemma’of plants, 103 ERF1/JAZ, repressors of jasmonate responses, 104–105 jasmonates, dual role in, 104 Nicotiana longiflora and Arabidopsis, study, 104 SAV3. See Shade avoidance 3 (SAV3) SCAR. See Sequence-characterized amplified region (SCAR) SDG4 deficiency, 9 SDG4/SDG8 mutations, 9 Secernentean parasites, 149 Sedentary endoparasitic nematodes. See Plant-parasitic nematodes (PPNs) Sedentary nematodes, parasitism genes in induction of nematode feeding site, effectors in, 157–158 plant cell penetration/nematode migration, effectors in, 155–156 plant defence suppression, effectors in, 156 Sequence-characterized amplified region (SCAR), 28 Serial analysis of gene expression (SAGE), 45 Sesuvium portulacastrum, 121, 127 SET. See Suppressor of variegation, Enhancer of zeste and Trithorax (SET) Sex determination pathway, 177–178 Sexual dimorphism, 151 Shade avoidance 3 (SAV3), 94 Shade avoidance syndrome (SAS), 65–108 Shade tolerance definition, 67 vs. shade avoidance, 67–68 SHOOTMERISTEMLESS (STM), 8 Short day plants Japanese morning glory (Pharbitis nil), 69 rice (Oryza sativa), 69 Signal transduction pathways in feeding cell formation AtSUC2 expression, 163 Hahsp17.7G4 expression in tobacco galls, 162
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laser capture microscopy technology, soybean genes, 164 OPPP, 164 TobRB7 expression, 163 WRKY23 expression, 162–163 Silencing nematode genes, strategy, 179 Simulated shade treatment, 76 Single nucleotide polymorphism (SNP), 27 SMRT sequencing technology, 53 SNP. See Single nucleotide polymorphism (SNP) SOD. See Superoxide dismutase (SOD) Soilless systems, optimization of nutrition in abiotic factors affecting roots/shoots of, 195t stresses caused by incorrect management of, 196t advantages/disadvantages, 195 dissolved oxygen concentration in nutrient solution diagnosis of plant stress, 219–223 optimum concentration, considerations, 218–219 See also Oxygen concentration in nutrient solution electrical conductivity/pH in the nutrient solution diagnosis of plant stress, 215–217 optimum value, considerations, 213–215 See also PH and EC of nutrient solution methods used to diagnose plant stress, 197f nutrient solution composition diagnosis of plant stress, 204–208 nutrient uptake regulation, factors, 201– 202 optimum composition, considerations, 198–200 plant nutrient uptake, measurement of, 198–199 See also Composition of nutrient solution nutrient solution temperature diagnosis of plant stress, 224–229 optimum temperature, considerations, 224 soilless culture definition, 194 greenhouse building/automation/ computerization, link to, 194–195 use in Mediterranean countries, limitations, 195
270
SUBJECT INDEX
Soilless systems, optimization of nutrition in (Continued) water supply. See Water supply SOS. See Salt overly sensitive (SOS) SOS pathway, components SOS1, 122 SOS2, 122 SOS3, 122 SRA. See Sufficiency Range Approach (SRA) SRO5-P5CDH nat-siRNAs, role in salt-stress regulatory loop, 128f SSR markers, 29 Strategies,control of plant-parasitic nematodes Cry proteins, control of insects, 179 cystatin, transgenic expression of, 179 silencing nematode genes, strategy RNAi experiment repository in WormBase, 179 Stylet, 153, 153f Sucrose metabolism pathway, 43 Sufficiency Range Approach (SRA), 202 Sugars and sugar alcohols acyclic/cyclic polyols, 129–130 starch/sugar accumulation, short/long term reactions, 129 trehalose, role in protection of salt-stress, 129 Superoxide dismutase (SOD), 131, 207 Suppressor of variegation, Enhancer of zeste and Trithorax (SET), 4 Supra-optimal temperature nutrient uptake, effects on, 226 root temperature stress, effects on, 225 SUV39H1, 6 SUVH proteins activity, 12 characteristics, 11–12 discovery, 11 functions, 12–13 SUVR proteins characteristics, 13–14 functions, 14 SWI3, ADA2, N-CoR and TFIIIB DNAbinding (SANT), 7 SWINGER (SWN), 7–8 Syncytia vs. giant cells cytological changes, 159–160 feeding cell formation, 158 T T. halophila, 120, 134–135 Theories based on nutrient absorption
combination of theories, benefits, 204 nutrient supply, only factor driving nutrient uptake carrier-mediated ion transport (Epstein and Hagen), 201–202 designing optimum solution, criteria, 202 DRIS method, advantages, 202 errors in the theory, 202 nutrient uptake according to the demand limitations, 203–204 plant nutrition optimization, measurable parameters, 203 root surface area, importance, 203 Trehalose, 129 Trithorax-like (ATX) proteins, 4 Tubulin, 160 Twilight, 71 Two-phase model of salt injury (Munns), 217 W Water potential, 120, 124, 134, 208, 209–210, 213, 216, 221, 227, 230 Water stress, higher product quality examples “Merlot” grapes and wine, 209 Zinnia elegans, 209 Water supply diagnosis of plant stress decrease in plant water content, effects. See Plant water content optimum supply, considerations deficit irrigation and partial root-zone drying, optimal use of water, 209 effective management of irrigation, approach, 208 irrigation computer control system, 208 water stress, higher product quality, 209 Water uptake reduction, effects reduced leaf water potential and leaf turgor, 227 stomatal closure decline in photosynthetic rate/ transpiration, 227 WGS. See Whole genome sequencing (WGS) WGS, coffea BAC-by-BAC genome sequencing approach, 53 BAC end sequencing, functions, 51 BAC fingerprinting techniques, 51, 52 molecular markers, applications in, 49 resources/tools to be developed, 51
SUBJECT INDEX Roche 454 Titanium sequencing technology, 53 sequence-tagged genetic map, need for, 50 SGN database, use in Solanaceae family, 52 SMRT sequencing technology, 53 transferability of coffea markers, significance CcEIN4 gene region, microcolinearity, 50 whole genome shotgun approach software packages, 53
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Whole genome sequencing (WGS), 26, 49–53 See also WGS, coffea WRKY23 expression, 162–163 Z Zea mays (maize), 4, 36t vs. A. thaliana, genome encoding, 4 Zeaxanthin, 133, 205, 208 Zinnia elegans, 209
