Advances in Botanical Research, Volume 41

Advances in

BOTANICAL RESEARCH Incorporating Advances in Plant Pathology

Editor-in-Chief J. A. CALLOW

School of Biosciences, The University of Birmingham, UK

Editorial Board A. R. HARDHAM J. S HESLOP-HARRISON M. KREIS R. A. LEIGH E. LORD D. G. MANN P. R. SHEWRY D. SOLTIS

Australian National University, Canberra, Australia University of Leicester, UK Universite de Paris-Sud, Orsay, France University of Cambridge, Cambridge, UK University of California, Riverside, USA Royal Botanic Garden, Edinburgh, UK IACR-Long Ashton Research Station, UK University of Florida at Gainesville, USA

CONTRIBUTORS TO VOLUME 41

ARNAUD COMPLAINVILLE Institut des Sciences du Ve´ge´tal, Centre National de la Recherche Scientifique, 91198 Gif sur Yvette, France JAMES E. COOPER Department of Applied Plant Science, Queen’s University Belfast, Belfast BT9 5PX, United Kingdom MARTIN CRESPI Institut des Sciences du Ve´ge´tal, Centre National de la Recherche Scientifique, 91198 Gif sur Yvette, France CLAIRE HALPIN University of Dundee, Plant Science Research Group, Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, United Kingdom CELIA HANSEN Department of Biology, University of Leicester, Leicester LE1 7RH, United Kingdom J. S. HESLOP-HARRISON Department of Biology, University of Leicester, Leicester LE1 7RH, United Kingdom HAMLYN G. JONES University of Dundee, Plant Science Research Group, Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, United Kingdom

CONTENTS OF VOLUMES 30–40

Contents of Volume 30 Nitrate and Ammonium Nutrition of Plants: Physiological and Molecular Perspectives G. FORDE and D. T. CLARKSON Secondary Metabolites in Plant–Insect Interactions: Dynamic Systems of Induced and Adaptive Responses J. A. PICKETT, D. W. M. SMILEY and C. M. WOODCOCK Biosynthesis and Metabolism of Caffeine and Related Purine Alkaloids in Plants H. ASHIHARA and A. CROZIER Arabinogalactan-Proteins in the Multiple Domains of the Plant Cell Surface M. D. SERPE and E. A. NOTHNAGEL Plant Disease Resistance: Progress in Basic Understanding and Practical Application N. T. KEEN

Contents of Volume 31 PLANT TRICHOMES Edited by D. L. Hallahan and J. C. Gray Trichome Diversity and Development E. WERKER

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Structure and Function of Secretory Cells A. FAHN Monoterpenoid Biosynthesis in Glandular Trichomes of Labiate Plants D. L. HALLAHAN Current and Potential Exploitation of Plant Glandular Trichome Productivity S. O. DUKE, C. CANEL, A. M. RIMANDO, M. R. TELLEZ, M. V. DUKE and R. N. PAUL Chemotaxonomy Based on Metabolites from Glandular Trichomes O. SPRING Anacardic Acids in Trichomes of Pelagonium: Biosynthesis, Molecular Biology and Ecological Effects D. J. SCHULTZ, J. I. MEDFORD, D. COX-FOSTER, R. A. GRAZZINI, R. CRAIG and R. O. MUMMA Specification of Epidermal Cell Morphology B. J. GLOVER and C. MARTIN Trichome Initiation in Arabidopsis A. R. WALKER and M. D. MARKS Trichome Differentiation and Morphogenesis in Arabidopsis ¨ LSKAMP and V. KIRIK M. HU Trichome Plasmodesmata: A Model System for Cell-to-Cell Movement F. WAIGMANN and P. ZAMBRYSKI

CONTENTS OF VOLUMES 30–40

Contents of Volume 32 PLANT PROTEIN KINASES Edited by M. Kreis and J. C. Walker Plant Protein-Serine/Threonine Kinases: Classification into Subfamilies and Overview of Function D. G. HARDIE Bioinformatics: Using Phylogenetics and Databases to Investigate Plant Protein Phosphorylation E. R. INGHAM, T. P. HOLTSFORD and J. C. WALKER Protein Phosphatases: Structure, Regulation and Function S. LUAN Histidine Kinases and the Role of Two-component Systems in Plants G. E. SCHALLER Light and Protein Kinases J. C. WATSON Calcium-dependent Protein Kinases and their Relatives E. M. HRABAK Receptor-like Kinases in Plant Development K. U. TORII and S. E. CLARK A Receptor Kinase and the Self-incompatibility Response in Brassica J. M. COCK Plant Mitogen-activated Protein Kinase Signalling Pathways in the Limelight S. JOUANNIC, A.-S. LEPRINCE, A. HAMAL, A. PICAUD, M. KREIS and Y. HENRY

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Plant Phosphorylation and Dephosphorylation in Environmental Stress Responses in Plants K. ICHIMURA, T. MIZOGUCHI, R. YOSHIDA, T. YUASA and K. SHINOZAKI Protein Kinases in the Plant Defence Response G. SESSA and G. B. MARTIN SNF1-Related Protein Kinases (SnRKs) – Regulators at the Heart of the Control of Carbon Metabolism and Partitioning N. G. HALFORD, J.-P. BOULY and M. THOMAS Carbon and Nitrogen Metabolism and Reversible Protein Phosphorylation D. TOROSER and S. C. HUBER Protein Phosphorylation and Ion Transport: A Case Study in Guard Cells J. LI and S. M. ASSMANN

Contents of Volume 33 Foliar Endophytes and Their Interactions with Host Plants, with Specific Reference to the Gymnospermae W.-M. KRIEL, W. J. SWART and P. W. CROUS Plants in Search of Sunlight D. KOLLER The Mechanics of Root Anchorage A. R. ENNOS

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Molecular Genetics of Sulphate Assimilation M. J. HAWKESFORD and J. L. WRAY Pathogenicity, Host-specificity, and Population Biology of Tapesia spp., Causal Agents of Eyespot Disease of Cereals J. A. LUCAS, P. S. DYER and T. D. MURRAY

Contents of Volume 34 BIOTECHNOLOGY OF CEREALS Edited by Peter Shewry Cereal Genomics K. J. EDWARDS and D. STEVENSON Exploiting Cereal Genetic Resources R. J. HENRY Transformation and Gene Expression P. BARCELO, S. RASCO-GAUNT, C. THORPE and P. A. LAZZERI Opportunities for the Manipulation of Development of Temperate Cereals J. R. LENTON Manipulating Cereal Endosperm Structure, Development and Composition to Improve End Use Properties P. R. SHEWRY and M. MORELL Resistance to Abiotic Freezing Stress in Cereals M. A. DUNN, G. O’BRIEN, A. P. C. BROWN, S. VURAL and M. A. HUGHES

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Genetics and Genomics of the Rice Blast Fungus Magnaporthe grisea: Developing an Experimental Model for Understanding Fungal Diseases of Cereals N. J. TALBOT and A. J. FOSTER Impact of Biotechnology on the Production of Improved Cereal Varieties R. G. SOLOMON and R. APPELS Overview and Prospects P. R. SHEWRY, P. A. LAZZERI and K. J. EDWARDS

Contents of Volume 35 Recent Advances in the Cell Biology of Chlorophyll Catabolism ¨ RTENSTEINER H. THOMAS, H. OUGHAM and S. HO 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 VOLUMES 30–40

Contents of Volume 36 PLANT VIRUS VECTOR INTERACTIONS Edited by R. Plumb 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

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Contents of Volume 37 ANTHOCYANINS IN LEAVES Edited by K. S. Gould and D. W. Lee 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

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A Unified Explanation for Anthocyanins in Leaves? K. S. GOULD, S. O. NEILL and T. C. VOGELMANN

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

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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 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

Multiple Responses of Rhizobia to Flavonoids During Legume Root Infection

JAMES E. COOPER

Department of Applied Plant Science, Queen’s University Belfast, Belfast BT9 5PX, United Kingdom

I. II. III. IV. V.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of Early Legume-Rhizobia Interactions . . . . . . . . . . . . . . . . . . . . . Synthesis and Release of Flavonoids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Identification of Flavonoids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diversity and Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Chemoattractants and Growth Stimulators . . . . . . . . . . . . . . . . . . . . . . . . B. Inducers of Nodulation Genes Required for Nod Factor Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Induction of a Type III Secretion System and a Type I Secreted Protein. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Further Flavonoid-Dependent Gene Expression in Rhizobia . . . . . . . VI. Reception of Flavonoids by Rhizobia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Interactions with Regulatory NodD Proteins . . . . . . . . . . . . . . . . . . . . . . B. Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 3 7 12 15 16 17 19 26 31 31 35 41 42 42

ABSTRACT In the formation of legume-rhizobia symbioses flavonoids released from roots and seeds are best known as inducers of the bacterial nodulation genes that control the synthesis of reciprocal chitolipooligosaccharide signals (Nod factors) to the prospective host plant. However, successful symbiotic development requires the transmission Advances in Botanical Research, Vol. 41 Incorporating Advances in Plant Pathology 0065-2296/04 $35.00

Copyright 2004, Elsevier Ltd. All rights reserved.

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of a variety of other signals from rhizobia to plant roots and flavonoids initiate or mediate the production of most of them. This review considers the wide range of rhizobial responses to these secondary plant metabolites in the early phases of symbiotic interaction, including chemotaxis, growth stimulation, degradation, Nod factor synthesis, protein secretion by type I and III systems, surface polysaccharide production and expression of many new genes and proteins whose functions are only beginning to be analysed. Attention is also drawn to aspects of flavonoid-rhizobia interaction, such as release of compounds from inoculated roots and the reception of nodulation gene inducers by regulatory NodD proteins, that will need to be revisited by researchers if a complete understanding of the molecular dialogue between the partners is to be achieved.

I. INTRODUCTION The ability of legumes to capture (fix) atmospheric nitrogen endows them with special significance among agricultural plants: their productivity is theoretically independent of soil nitrogen status and extraneous fertilizer applications and they provide important grain and forage crops, both in temperate and in tropical zones. Nitrogen fixation can only occur when these plants are in the symbiotic state and the agents of fixation are soil bacteria from the genera Rhizobium, Bradyrhizobium, Sinorhizobium, Mesorhizobium and Azorhizobium—collectively known as rhizobia—that invade the root or stem cortex. Successful infections result in the formation of nodules into which atmospheric nitrogen diVuses to be reduced to ammonia by the nitrogenase enzyme of rhizobial bacteroids. Rhizobia exhibit varying degrees of specificity toward their hosts; with one known exception, Parasponia, infections are confined to the Leguminosae, and among rhizobial species and biovars particular specificity is often further displayed toward individual or small groups of legume genera, especially those originating in temperate or cool regions (Table I). Symbiotic promiscuity is a feature of some rhizobia and may be more widespread, especially among tropical strains, than hitherto appreciated (Perret et al., 2000). Strains with very broad legume host ranges do exist, an example being Rhizobium sp. NGR234, which can nodulate at least 112 legume genera (Pueppke and Broughton, 1999), and certain legumes (e.g., Phaseolus and Vigna) are considered to be nonselective hosts for rhizobia (Lewin et al., 1987; Michiels et al., 1998). The progression to the symbiotic state by two initially independent, freeliving partners is governed by reciprocal signal generation and perception, which has been described as a ‘‘molecular dialogue’’ (De´ narie´ et al., 1993). A class of plant secondary metabolites, the flavonoids, is responsible not only for initiating the formation of a symbiosis but also for influencing many of the subsequent events needed for successful root infections. This chapter reviews the full range of rhizobial responses to legume flavonoids,

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TABLE I Some Species of Rhizobia and Their Legume Hosts Speciesa,b

Hosts nodulated

Rhizobium leguminosarum bv. phaseoli bv. trifolii bv. viciae Rhizobium etli Rhizobium galegae Rhizobium lupini Rhizobium tropici Sinorhizobium fredii c Sinorhizobium meliloti Mesorhizobium loti Bradyrhizobium japonicum Azorhizobium caulinodansd Rhizobium spp.e Bradyrhizobium spp.

Phaseolus Trifolium Pisum, Lens, Vicia Phaseolus Galega Lupinus Phaseolus, Leucaena Glycine, etc. Medicago, Melilotus, Trigonella Lotus, Astragalus Glycine, Macroptilium, Vigna Sesbania Vigna, Arachis, Desmodium, Lotus, etc. Sarothamnus, Ulex, etc.

a The taxonomy and nomenclature of the rhizobia are the subjects of much debate and controversy. See Broughton (2003); Farrand et al. (2003); Sawada et al. (2003); Young et al. (2001, 2003). b Rhizobium and Sinorhizobium species are relatively fast growing in laboratory culture media. Bradyrhizobium species grow more slowly and Mesorhizobium species display an intermediate growth rate. c Includes strain USDA257, which has a very broad host range. d Stem-nodulating and exceptional among rhizobia in fixing nitrogen in the free-living state. e Includes strain NGR234, which can nodulate at least 112 legume genera. Sometimes referred to as Sinorhizobium sp. strain NGR234.

including chemotaxis, growth stimulation, degradation, protein secretion, and expression of nodulation (nod) and other symbiotically active genes. Particular attention is paid to the recently characterized type III protein secretion systems in rhizobia, the release of flavonoids from inoculated roots and their reception by regulatory NodD proteins. The last two aspects of symbiotic interaction, despite having received much research attention for more than one-and-a-half decades, will need to be revisited if their precise biochemical and molecular mechanisms are to be clarified.

II. OVERVIEW OF EARLY LEGUME-RHIZOBIA INTERACTIONS The currently known components of the early infection phase are identified in Fig. 1. In virtually all examples studied to date symbiotic interaction is initiated by micromolar or nanomolar concentrations of flavonoids or isoflavonoids

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Fig. 1. Early signalling events in legume-rhizobia symbioses. Pathways dependent on or mediated by flavonoids are shown in gray. Some pathways are not operative in all symbioses. AHL, N-acyl homoserine lactone; EPS, extracellular polysaccharides; KPS, capsular polysaccharides; LPS, lipopolysaccharides.

in legume root or seed exudates. These compounds may initially assist rhizosphere colonization by acting as chemoattractants or, less likely, as growth enhancers for rhizobia. Other factors may play a more prominent role than flavonoids in stimulating rhizobial growth in the legume rhizosphere. For example, Sinorhizobium meliloti responds to very low concentrations of

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external biotin through a regulatory locus, bioS (Streit and Phillips, 1997), resulting in increased growth rate and root colonization of Medicago sativa (Streit et al., 1996). A crucial contribution to the infection process is made when flavonoids interact with the constitutively expressed internal proteins of rhizobial regulatory nodD genes to form a transcriptional activator of other nod genes whose protein products are responsible for the synthesis of reciprocal signal molecules to the host plant root—the chitolipooligosaccharide Nod factors. This interaction constitutes the first of many elements that influence host specificity in legume-rhizobia symbioses. Flavonoids and isoflavonoids are not, however, inert compounds in this context, because rhizobia are capable of metabolizing them to yield a plethora of polycyclic and monocyclic phenolic products, some of which themselves possess nod gene-inducing or gene-inhibiting properties. Nonflavonoid nod gene inducers are also secreted by some legumes, in the form of betaines or aldonic acids, but compared to flavonoids these compounds are active only at higher concentrations. The term nod is used at this point and hereafter in the manner adopted by Downie (1998): as a generic designation for nodulation genes (e.g., nod, nol, and noe), except when referring to specific examples (e.g., nodA). Chitolipooligosaccharide Nod factors are essential signals for rhizobial entry into legume roots (Relic´ et al., 1994), and the success or otherwise of the infection process is in large part determined by their structural features. Application of nanomolar or femtomolar concentrations of purified rhizobial Nod factor to the roots of an appropriate legume host elicits the following responses, which can be detected by biochemical, molecular, and microscopical analysis: (1) deformation of root hairs (Lerouge et al., 1990) accompanied by root hair plasma membrane depolarization (Ehrhardt et al., 1992; Felle et al., 1995); (2) rapid increases then oscillations in intracellularfree calcium in root hairs, often referred to as calcium spiking (Ehrhardt et al., 1996; Gehring et al., 1997; Wais et al., 2000, 2002; Walker et al., 2000); (3) changes in the root hair cytoskeleton (Ca´ rdenas et al., 1998; Timmers et al., 1998); (4) preinfection thread formation in deformed root hairs (van Brussel et al., 1992); and (5) localized cortical cell division at the sites of root nodule primordia (Lo´ pez-Lara et al., 1995; Spaink, 1992; Spaink et al., 1993). Inhibition by Nod factor of the reactive oxygen-generating system in Medicago truncatula roots, indicating a plant defense suppression function, has recently been reported (Shaw and Long, 2003). Nod factors alone can induce some of the plant genes (nodulins) that are expressed in the preinfection, infection, nodule development, and nodule function phases of symbiotic interaction, some examples of the more rapidly expressed genes being enod12 (Scheres et al., 1990), enod40 (Kouchi and Hata, 1993), rip1

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(Cook et al., 1995), and dd23b (Crockard et al., 2002). Auxin flow in roots at the earliest stages of nodule formation is perturbed by Nod factors in conjunction with endogenous root flavonoids acting as auxin transport inhibitors (Boot et al., 1999; Mathesius et al., 1998). The contribution of flavonoids in this case appears to involve regulation of auxin breakdown by peroxidase (Mathesius, 2001). These findings confirm the earlier conclusion of Jacobs and Rubery (1988) that flavonoids regulate auxin transport in plant roots. Nod factors also control the number of nodules formed on a root system by inducing an autoregulation response in the host plant (van Brussel et al., 2002). The nature of signal-transduction pathways leading from the perception of Nod factors to symbiosis-related gene activation is currently the subject of intensive research (Cullimore et al., 2001; Goedhart et al., 2003; Limpens and Bisseling, 2003; Oldroyd, 2001). A breakthrough was achieved with the discovery of a symbosis receptor-like kinase (SYMRK) gene in Lotus (Stracke et al., 2002) and a nodulation receptor Kinase (NORK) in Medicago (Endre et al., 2002) that is required for early signal transduction in both rhizobial and mycorrhizal symbioses. More recently two genes that encode LysM receptor-like kinases that function upstream of SYMRK and could be direct receptors for rhizobial Nod factors were discovered in Lotus japonicus (Madsen et al., 2003; Radutoiu et al., 2003). Likewise, in Medicago truncatula two receptor-like kinase genes have been recognized as encoders of potential Nod factor receptors (Limpens et al., 2003). Other genes that are involved in the transduction of rhizobial Nod factor signals but are not required for mycorrhizal infection have also been identified in this legume (Ben Amor et al., 2003; Oldroyd and Long, 2003). In addition to Nod factors, flavonoids induce the synthesis and release by rhizobia of proteins that fulfill a variety of functions during plant infection. They include several that are associated with a type III secretion system and another, NodO, which is secreted by a type I system. Transcriptional and proteomics analyses have identified many other rhizobial genes and proteins whose expression is flavonoid-dependent but whose functions have yet to be defined. Flavonoids, either directly or via the Nod factors and secreted proteins whose synthesis depends on them, are therefore of prime significance as signal molecules and mediators of host specificity in legume-rhizobia symbioses. Other compounds are also required for successful symbiotic development, and their biosynthesis and structural features are influenced by flavonoids in many cases. Included in this category are the various surface polysaccharides of rhizobia that fulfill host recognition or defense avoidance/suppression functions: extracellular polysaccharides (EPS), lipopolysaccharides (LPS), K-antigen or capsular polysaccharides (KPS), and cyclic glucans (for reviews,

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see Becker and Pu¨ hler, 1998; Becker et al., 2000; Carlson et al., 1999; Fraysse et al., 2003; Kannenberg and Brewin, 1994; Mitho¨ fer, 2002; Noel and Duelli, 2000; Price, 1999; Spaink, 2000). Quorum-sensing N-acyl homoserine lactone (AHL) signals, used by rhizobia to coordinate the behaviour of individual cells in a population, act as autoinducers of rhizosphere-expressed (rhi) genes in rhizobia that influence host nodulation (Wisniewski-Dye and Downie, 2002) and are essential for the expression of certain exopolysaccharide synthesis genes in S. meliloti (Marketon et al., 2003). AHL signals also elicit responses from prospective host plants in the form of changes in the accumulation of proteins (Mathesius et al., 2003). In the legume partner carbohydrate-binding proteins on root surfaces, the lectins, have been regarded as important determinants of host recognition following the pioneering studies of Bohlool and Schmidt (1974), Dazzo and Hubbell (1975), and Hamblin and Kent (1973). Despite the eVorts of various research groups in the intervening period, many details of lectin function remain unresolved, although it appears likely that they mediate host specificity through selective interactions with Nod factors and/or rhizobial surface polysaccharides (Bhattacharya et al., 2002; Etzler et al., 1999; Hirsch, 1999; Kalsi and Etzler, 2000; van Rhijn et al., 1998, 2001).

III. SYNTHESIS AND RELEASE OF FLAVONOIDS Flavonoids are secondary metabolic products of the central phenylpropanoid pathway and the acetate-malonate pathway of plants. Thus all flavonoids are derivatives of phenylalanine from the shikimic acid pathway and malonyl CoA from the acetyl CoA carboxylase reaction. Condensation of 4-coumaroyl CoA from the phenylpropanoid pathway with three molecules of malonyl CoA by chalcone synthase (CHS) creates the central chalcone precursor from which all other flavonoid structures are ultimately derived (Fig. 2). The main flavonoid subclasses (e.g., chalcones, flavones, flavanones, flavonols, flavan 3-ols, proanthocyanidins, isoflavones, isoflavans, pterocarpans) contain numerous compounds involved in many plant functions (Shirley, 1996; Woo et al., 2002), including pigmentation, protection against ultraviolet (UV) light, pollen fertility, regulation of auxin transport, and hydrogen peroxide scavenging, as well as interactions with symbiotic microorganisms or defense against pathogens. Detailed accounts of their structures and biosynthesis are available (Aoki et al., 2000; Dixon, 1999; Forkmann and Heller, 1999), and their significance for metabolic engineering in plants has been emphasized by Dixon and Steele (1999).

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Fig. 2. Flavonoid biosynthesis in legumes. CA4H, cinnamic acid 4-hydroxylase; CHI, chalcone isomerase; CHR, chalcone reductase; CHS, chalcone synthase; CL, coumaroyl-CoA ligase; FLS, flavonol synthase; FNS, flavone synthase; F3,4R (FDR), flavan 3,4-diol reductase; F3H, flavanone 3-hydrolase; HFR (DFR), 3-OHflavanone 4-reductase (dihydroflavonol 4-reductase); IFS, isoflavone synthase; IFR, isoflavone reductase; I20 H, isoflavone 20 -hydroxylase; PAL, phenylalanine ammonia lyase; PAS, proanthocyanidin synthase; PTS, pterocarpan synthase; VER, vestitone reductase. (From Jain and Nainawatee, 2002; StaVord, 1997.) Reprinted by permission from The Botanical Review, 63(1) ß 1997. The New York Botanical Garden Press.

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Flavonoids acting as primary signals to rhizobia have been found in legume seed coat and root exudates. When deposited on seed coats, flavonoids are simply released into the surrounding aqueous environment during imbibition without involvement of any metabolic regulation (Hartwig and Phillips, 1991). Storage of flavonoids in roots and their release from epidermal tissues are, however, subject to internal metabolic controls, and strong evidence exists for a process of concurrent synthesis and release. For example, U-14C incorporated into phenylalanine was found in root exudate flavonoids (Maxwell and Phillips, 1990). Other data linking synthesis to release have been reviewed by Jain and Nainawatee (2002), including inhibition of phenylalanine ammonia lyase (PAL) by 2-aminooxy-3-phenylpropionic acid (AOPP), which decreased the synthesis of 7,40 -dihydroxyflavanone by 90–95% and its exudation by 50% (Amrhein and Godeke, 1977). Exudation of both 7,40 -dihydroxyflavone and 4,40 -dihydroxy-20 -methoxychalcone was also tightly linked to their concurrent synthesis. A relatively high proportion of unlabelled to labelled 7,40 -dihydroxyflavanone in the root exudate of AOPP-treated plants indicated that this compound could also be released from a presynthesized pool within the root. Indirect evidence for a linkage between synthesis and release of flavonoid signals to rhizobia comes from experiments in which inhibition of PAL by 2-aminoindan-2-phosphonic acid (AIP) was accompanied by decreased nodulation of Medicago roots (Zon and Amrhein, 1992). Flavonoids may be released as aglycones or glycosidic conjugates (Maxwell and Phillips, 1990). The latter are inherently more soluble and may therefore have a greater potential for diVusion from the root surface before being hydrolyzed to the aglycone form by rhizobia, other soil microorganisms, or plant exoenzymes (Hartwig and Phillips, 1991). Rhizobia themselves may be able to alter the hydrophobicity of flavonoid aglycones: complexation of luteolin with cyclosophoraoses produced by S. meliloti markedly enhances the solubility of this nod gene inducer (Lee et al., 2003). The presence of rhizobia in the legume rhizosphere also influences the quantity and perhaps the types of flavonoids released from roots. Increases in overall flavonoid-dependent nod gene-inducing activity of root extracts or exudates following inoculation with homologous rhizobia (the so-called Ini response—increase in nod gene-inducing flavonoids) have been reported for white clover (Rolfe et al., 1988), vetch (van Brussel et al., 1990), soybean (Cho and Harper, 1991), and alfalfa (Dakora et al., 1993a). Inoculation with heterologous rhizobia can also generate an Ini eVect in alfalfa that is lower than that produced by homologous S. meliloti (Dakora et al., 1993a) and in white clover, in which inoculation with Rhizobium leguminosarum bv. viciae gave a fourfold increase in nod gene-inducing activity of root extracts

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compared to inoculation with homologous R. leguminosarum bv. trifolii (Rolfe et al., 1988). In terms of specific compounds, root exudates of Phaseolus vulgaris inoculated with R. leguminosarum bv. phaseoli contained greater quantities of the phytoalexin coumestrol (3,9-dihydroxycoumestan) and its isoflavonoid precursor daidzein (40 ,7-dihydroxyisoflavone) than did exudates of sterile plants (Dakora et al., 1993b). Bolanos-Vasquez and Werner (1997) also found increased quantities of daidzein, naringenin (40 ,5,7-trihydroxyflavanone), liquiritigenin (40 ,7-dihydroxyflavanone), and isoliquiritigenin (20 ,40 ,4-trihydroxychalcone) in root exudates of this legume following inoculation with homologous rhizobia. In soybean root exudate elevated concentrations of daidzein, genistein (40 ,5,7-trihydroxyisoflavone), and coumestrol were detected after inoculation with wild-type Bradyrhizobium japonicum. Purified Nod factors from B. japonicum and Rhizobium sp. NGR234 produced the same eVect, whereas a B. japonicum mutant lacking the ability to synthesize Nod factors had no influence on the release of these compounds (Schmidt et al., 1994). Root exudates of Medicago sativa inoculated with S. meliloti were qualitatively diVerent with respect to flavonoid content compared with exudates from sterile plants (Dakora et al., 1993a); the former contained three extra compounds in the form of an aglycone and a glycoside of medicarpin and a nod gene inducer identified as formononetin-7-O-600 -O-malonylglucoside, a conjugate of the medicarpin precursor formononetin (7-hydroxy-40 methoxyisoflavone). This conjugate may have been identified incorrectly on account of the unavailability of an authentic standard for comparison of nuclear magnetic resonance (1H-NMR) and fast atom bombardment mass spectrometry (FAB-MS) (Schlaman et al., 1998); when formononetin-7-O600 -O-malonylglucoside was later purified from alfalfa roots, it failed to induce nod genes in S. meliloti (Coronado et al., 1995). Recourt et al. (1991) reported six new flavanones and two new chalcones in root exudates of Vicia sativa inoculated with R. leguminosarum bv. viciae, although it is not clear whether some of the flavanones were among those previously isolated, but not identified to the level of individual compounds, from sterile root exudate of the same legume by Zaat et al. (1989). In Trifolium subterraneum an additional nod gene-inducing compound, thought to be 40 ,7-dihydroxyflavone, was found in root exudates 3 days after inoculation with R. leguminosarum bv. trifolii and a diVerent, uncharacterized inducer was detected 2 days later (Lawson et al., 1996). Whether or not new flavonoids found in root exudates of plants after inoculation with rhizobia are the products of altered internal plant biosynthetic pathways, changes in the patterns of release of preformed compounds from internal pools, or biotransformations by rhizobia in the

RESPONSES OF RHIZOBIA TO FLAVONOIDS

11

rhizosphere, is often diYcult to ascertain. Rhizobia have been shown to degrade legume flavonoids to yield a great variety of flavonoid and other phenolic metabolites. Degradative activity was displayed toward flavonoids presented as authentic compounds at 100 mM (Rao et al., 1991), 10 mM (Rao and Cooper, 1994, 1995), and an even lower, nod gene-inducing concentration of 2 nM (Rao et al., 1996), as well as toward naturally occurring flavonoids in unconcentrated root exudates (Steele et al., 1999). Incubations of exudates from sterile plants with wild-type rhizobia have yielded conflicting results: the medicarpin found in inoculated root exudates of Medicago sativa by Dakora et al. (1993a) could not be identified in exudate from uninoculated plants after its incubation with S. meliloti, but Steele et al. (1999) detected new flavonoids in root exudate from uninoculated Lotus pedunculatus after incubation with Mesorhizobium loti. No studies of this type have included mutant rhizobia lacking degradative activity toward flavonoids. Challenging legume roots with Nod factor alone would eliminate the possibility of rhizobial degradation and permit the unequivocal identification of any new flavonoids released into root exudate. Nod factor inoculation does produce a general Ini response in Vicia sativa (van Brussel et al., 1990). However, in the only study to combine a Nod factor treatment with detection of individual flavonoids, analysis was restricted to three compounds already present in sterile soybean root exudate, all of which were released in greater quantities from Nod factor treated roots (Schmidt et al., 1994). If new flavonoids were released from roots after Nod factor treatment, in this study they would have remained unidentified. There is much evidence to show that the presence of rhizobia or their Nod factors elicits changes in enzyme activity and gene expression in the plant phenylpropanoid biosynthetic pathway. Increases in PAL and, especially, CHS expression have been reported in several legumes, but the timing of the response varies from several hours to several days postinoculation. PAL is encoded by a multigene family, and at least seven diVerent CHS isoforms have been characterized in Medicago sativa (McKhann and Hirsch, 1994) and six in Phaseolus vulgaris (Ryder et al., 1987). Inoculation of Glycine max with B. japonicum induces expression of subsets of the PAL and CHS gene families, but this was considered to be a postinfection event (Estabrook and SenguptaGopalan, 1991). CHS transcript accumulation in Vicia sativa reached its maximum approximately 2 days after inoculation with R. leguminosarum bv. viciae (Recourt et al., 1992), the CHS5 gene in Trifolium subterraneum roots was up-regulated within 6 hours of inoculation (Lawson et al., 1994), and in Medicago sativa root hairs CHS6-4 and CHS4-1 were up-regulated several days after inoculation with S. meliloti (McKhann et al., 1997). Nod factor stimulated CHS1 expression in root hairs of Vigna unguiculata 1 day

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after treatment (Krause et al., 1997) and in Medicago microcallus suspensions isoflavone reductase (IFR) gene expression was induced by relatively high concentrations of Nod factor from the cognate S. meliloti (Savoure´ et al., 1994). Enhanced expression of flavonoid biosynthesis genes has sometimes been accompanied by detection of new flavonoids in roots or root exudates (Lawson et al., 1996; McKhann et al., 1997; Recourt et al., 1991). However, it is often diYcult to distinguish changes connected with internal processes, such as auxin transport inhibition (Mathesius et al., 2000), from those that may aVect nod gene induction. Linking increases in gene transcription to the appearance of specific flavonoids is another problem; it is worth noting that the only certain consequence of increased CHS expression is the production of more chalcones. Radio-carbon tracing studies together with precise histochemical localizations of individual flavonoids in roots, as advocated by StaVord (1997), would help to clarify this aspect of plant response to rhizobial inoculation.

IV. IDENTIFICATION OF FLAVONOIDS For many years the preferred technique for separating and identifying flavonoids in plant tissues and their exudates has been high-performance liquid chromatography (HPLC), usually employing a variable wavelength UV detector. HPLC protocols have been developed specifically for the analysis of flavonoids in legumes (Graham, 1991a,b). UV spectroscopy is often supplemented by mass spectrometry (MS) or nuclear magnetic resonance (NMR) for confirmation of compound structures. The coupling of instruments for separation with those providing structural data has had a profound eVect in the field of phytochemical analysis, opening the way for more sophisticated approaches that are particularly suitable for screening tissue extracts or exudates for their full complement of flavonoid compounds (Kite et al., 2003). Hostettmann et al. (1996) recommended the application of hyphenated techniques such as liquid-chromatography-mass spectrometry (LC-MS) and HPLC-UV (LC-UV) as screening systems for flavonoids and related compounds in plants on the grounds that they oVer a complete and rapid analysis of small amounts of material. Because flavonoids exhibit characteristic UV spectra, photodiode array detection and postcolumn derivatization can provide much information on these compounds and their substitution patterns. A schematic diagram of the instrumentation used by Hostettmann et al. to analyze plant extracts for phenolic compounds of potential medicinal value is shown in Fig. 3. A single injection of a plant extract or exudate suYces to provide all MS and UV data for its polyphenolic constituents (excluding UV spectral shift measurements).

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Fig. 3. Scheme for LC-UV-MS analysis of plant tissue extracts or exudates for flavonoids, incorporating shift reagents to provide extra UV spectral information. (Modified from Hostettmann et al., 1996.)

This system was used successfully to identify flavonol glycosides in nonderivatized methanolic extracts of Epilobium leaves (Ducrey et al., 1995), and further information on hydroxylation patterns and sugar positions was obtained by means of postcolumn derivatization with up to five diVerent UV shift reagents prior to diode array detection. A variant of this arrangement, with separate LC-UV and gas chromatography-mass spectrometry (GC-MS) analyses, was used by Bolanos-Vasquez and Werner (1997) to identify isoflavonoids and flavonoids in methanolic extracts of Phaseolus vulgaris root exudates. Another analytical system, again based on multiple separation-identification techniques, enabled Steele et al. (1999) to identify flavonoids in seed and root exudates of Lotus pedunculatus. In this case a preseparation step, which involved high-performance thin-layer chromatography with densitometry (HPTLC-UV) to provide UV absorption spectra of individual spots in the range 200–500 nm (Scheidemann and Wetzel, 1997), was inserted before two further hyphenated techniques operating in parallel: capillary zone electrophoresis coupled to a diode array detector (CZE-UV) and GC-MS (Fig. 4). Postrun analysis software permitted comparisons between unknown sample compound UV spectra and a reference library of spectra from authentic flavonoids and related compounds. Examples of results for two flavonoids—quercetin (3,30 ,40 ,5,7-pentahydroxyflavonol) and

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Fig. 4. Scheme for separation and identification of flavonoids in legume seed and root exudates using a combination of high-performance thin-layer chromatographydensitometry (HPTLC-UV), capillary zone electrophoresis coupled to a diode array detector (CZE-UV), and gas chromatography-mass spectrometry (GC-MS). (From Steele et al., 1999.)

catechin (3,30 ,40 ,5,7-pentahydroxyflavan)—detected in seed exudates of Lotus pedunculatus by CZE-UV and GC-MS are shown in Fig. 5. Capillary electrophoresis has been applied to the profiling of isoflavonoids and their glycosidic conjugates in legume root extracts (Baggett et al., 2002), with results from micellar electrokinetic capillary chromatography (MEKC) showing good correlation with results from HPLC. Capillary electrophoresis coupled to mass spectrometry (CE-MS) can also be an eVective alternative to LC-MS for flavonoid identification in plant extracts (Huck et al., 2002).

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Fig. 5. Identification of quercetin (a) and catechin (b) in seed exudates of Lotus pedunculatus from UV and mass spectral comparisons with authentic standard compounds. (From Steele et al., 1999.)

V. DIVERSITY AND FUNCTIONS In the earliest phases of their interaction with legumes, rhizobia display a variety of responses to the presence of flavonoids in the rhizosphere. Some compounds are chemoattractants for rhizobia and may stimulate their growth. The same, or other, flavonoids act either as inducers or anti-inducers for the transcription of rhizobial nod genes. Compounds that induce nod

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genes can also induce genes that encode the synthesis and release of proteins from some rhizobia via type I or type III secretion systems. Surface polysaccharide structures may be modified in the presence of flavonoids, and the expression of other genes, whose functions in many cases have not yet been determined, is also known to be flavonoid-dependent. A. CHEMOATTRACTANTS AND GROWTH STIMULATORS

Rhizobia exhibit positive chemotaxis toward unfractionated legume epidermal exudates (Gaworzewska and Carlile, 1982) and to individual compounds found therein, including a number of flavonoids. Luteolin, 4,40 -dihydroxy-20 -methoxychalcone, 7,40 -dihydroxyflavone, and 7,40 -dihydroxyflavanone from alfalfa all induce positive chemotaxis in S. meliloti (Caetano-Anolle´ s et al., 1988; Dharmatilake and Bauer, 1992), and in the case of luteolin chemotaxis is a nodDdependent process (Caetano-Anolle´ s et al., 1988). For R. leguminosarum bv. phaseoli, apigenin (40 ,5,7-trihydroxyflavone), luteolin, umbelliferone, and acetosyringone all act as chemoattractants (Aguilar et al., 1988), whereas naringenin, kaempferol (3,40 ,5,7-tetrahydroxyflavonol)and apigenin are chemoattractants for R. leguminosarum bv. viciae (Armitage et al., 1988). In the case of B. japonicum, Kape et al. (1991) found no chemotaxis to isoflavonoids from its soybean host; however, hydroxycinnamic acids were strong chemoattractants. Similarly, Barbour et al. (1991) concluded that isoflavones were not the principal attractants of B. japonicum in soybean seed and root exudate. In at least one legume, Medicago sativa, rhizobia are attracted to that region of the root from which nod gene-regulating flavonoids are exuded (Gulash et al., 1984; Peters and Long, 1988). Rhizobia are positively chemotactic to many other compounds and sometimes more strongly so than toward flavonoids. Examples include sugars (Bowra and Dilworth, 1981), common amino acids (Barbour et al., 1991; Go¨tz et al., 1982), dicarboxylic acids (Barbour et al., 1991), a glycoprotein (Currier and Strobel, 1977), as well as aromatic acids, hydroxyaromatic acids, and simple phenolic compounds (Aguilar et al., 1988; Parke et al., 1985). Chemotaxis to flavonoids or any other compounds does not, however, appear to be an essential component of the infection process: rhizobial mutants lacking flagellae, motility, or chemotactic behaviour produce as many nodules as wild-type strains (Ames et al., 1980). Depending on concentration, flavonoids are potentially toxic to bacteria, and inhibitory eVects on rhizobial growth have been reported. Medicarpin and kievitone from soybean roots were strong inhibitors of growth for B. japonicum, R. lupini, and two fast-growing Lotus rhizobia, whereas phaseolin and maakiain were slightly less inhibitory. These compounds had no eVect on the growth of clover, pea, and alfalfa rhizobia (Pankhurst and

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Biggs, 1980). Soybean rhizobia are normally sensitive to the phytoalexin glyceollin (Parniske et al., 1991). In contrast, Lameta and Jay (1987) reported a stimulatory eVect of low concentrations of daidzein on the growth of B. japonicum. At concentrations of 1–10 mM, quercetin, quercetin-3-Ogalactoside, luteolin, and luteolin-7-O-glucoside increased the growth rate of S. meliloti in a defined minimal medium (Hartwig et al., 1991). The mechanism underlying growth stimulation in S. meliloti by luteolin appears to be independent of nod gene induction, because mutants lacking regulatory nodD genes also exhibited enhanced growth rates (Hartwig et al., 1991). A positive eVect of quercetin on the growth of S. meliloti was also recorded by Jain and Nainawatee (1999). In the same study elevated levels of exopolysaccharide production and increased activity of citric acid cycle and 6-phosphoglucanate pathway enzymes was observed after culture medium supplementation with naringenin; however, there was no eVect on growth and protein content. Genistein, naringenin, chrysin, and apigenin all promoted the growth of S. fredii USDA257 in late log phase (Lin et al., 1999). The degradative activity of rhizobia toward flavonoids is considered in detail in the section on metabolism, appearing later in this chapter. B. INDUCERS OF NODULATION GENES REQUIRED FOR NOD FACTOR SYNTHESIS

The availability of lacZ reporter fusions to some of the genes required for Nod factor synthesis (e.g., nodABC) permitted the discovery that transcription of these genes required a factor or factors from the host plant (Innes et al., 1985; Mulligan and Long, 1985; Rossen et al., 1985; Zaat et al., 1987). The first nod gene-inducing flavonoids to be discovered in this way were the flavones luteolin (5,7,30 ,40 -tetrahydroxyflavone) (Peters et al., 1986) and 7,40 -dihydroxyflavone (Redmond et al., 1986), the former having been isolated from the seed coat of Medicago sativa and the latter from roots of Trifolium repens. They are nod gene inducers for S. meliloti and R. leguminosarum bv. trifolii, respectively. Shortly after these important initial discoveries, apigenin-7-O-glucoside (5,7,40 -trihydroxyflavone-7-O-glucoside) and eriodictyol (5,7,30 ,40 -tetrahydroxyflavanone) in Pisum sativum (pea) were identified as inducers for R. leguminosarum bv. viciae (Firmin et al., 1986). Inducers from these and other flavonoid subclasses such as chalcones, flavonols, anthocyanidins, and isoflavonoids have, subsequently, been isolated from a variety of legumes, including Glycine max (soybean) (Banfalvi et al., 1988; D’Arcy-Lameta, 1986; Kape et al., 1992; Kosslak et al., 1987; Smit et al., 1992); Phaseolus vulgaris (common bean) (Bolanos-Vasquez and Werner, 1997; Hungria et al., 1991a,b, 1992); Medicago sativa (alfalfa) (Hartwig et al., 1990a; Maxwell et al.,

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1989; Phillips et al., 1994); Vicia sativa (vetch) (Zaat et al., 1989); Sesbania (Messens et al., 1991); Vigna subterranea (African Bambara groundnut); Vigna unguiculata (cowpea) (Dakora, 2000), and Galega orientalis (Suominen et al., 2003). Combinations of inducers can be more eVective than single flavonoid compounds. Synergistic inducing eVects of eriodictyol and naringenin with genistein, and liquiritigenin or isoliquiritigenin with daidzein, were observed for R. leguminosarum bv. phaseoli (Bolanos-Vasquez and Werner, 1997; Hungria et al., 1992). Hesperetin (30 ,5,7-trihydroxy-40 -methoxyflavanone) and naringenin were found by Begum et al. (2001) to generate higher levels of nod gene expression in pea rhizobia than either compound alone. Some flavonoids act as antagonists (anti-inducers) of nod gene induction by other flavonoids (Djordjevic et al., 1987; Firmin et al., 1986; Kosslak et al., 1990; Peters and Long, 1988; Zuanazzi et al., 1998), an eVect that may be based on competitive inhibition because it can be overcome by increasing inducer concentration (Peters and Long, 1988). The fact that inducers and anti-inducers are often present in the exudates of a single legume species has prompted the suggestion that in vivo levels of nod gene induction are the net outcome of positive and negative flavonoid eVects on the process (Jain and Nainawatee, 2002; Rolfe et al., 1988; Zuanazzi et al., 1998). Compounds that are inducers for certain rhizobia can be anti-inducers for others: the isoflavones genistein and daidzein are inducers of nod gene expression in B. japonicum and Rhizobium sp. NGR234, but they are anti-inducers for R. leguminosarum bv. trifolii and viciae. The ecological significance of antiinducers is unclear; some workers have used anti-inducing compounds that are not known to be released by the host of the microsymbiont under study, and Schlaman et al. (1998) pointed out that levels of nod gene-inducing activity recoverable from alfalfa rhizosphere soil (Leo´ n-Barrios et al., 1993) were so low that no plant benefit could be envisaged from decreasing them further. On the other hand, flavonoid-independent repression of nod gene expression by proteins such as NolR and NolA does occur (see the section, ‘‘Interactions with Regulatory NodD Proteins,’’ later in the chapter) and, in S. meliloti, mutants displaying significantly reduced expression of nodF (involved in providing key structural components to the nonreducing terminus of a Nod factor; see Table III and Fig. 7) are fully capable of nodulating alfalfa (Wells and Long, 2003). The fact that strains within B. japonicum respond diVerentially to flavonoid inducers and anti-inducers (Cunningham et al., 1991; Kosslak et al., 1990) casts doubt on the idea that plants release certain flavonoids simply to inhibit nod gene induction by other flavonoids. Some root exudate flavonoids have a dual function in symbiotic communication: daidzein, genistein, and isoliquiritigenin are

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inducers both of nod genes and of resistance to the soybean phytoalexin glyceollin in B. japonicum (Kape et al., 1992; Parniske et al., 1991). Certain nonflavonoid compounds also act as nod gene inducers but only at much higher concentrations (in the micromolar or millimolar range compared with nanomolar concentrations for most flavonoids). Examples are the betaines stachydrine and trigonelline from Medicago species (Phillips et al., 1992, 1995) and the aldonic acids erythronic and tetronic acid from Lupinus albus (Gagnon and Ibrahim, 1998). As noted by Aoki et al. (2000), these aldonic acids are inducers for Lotus-nodulating rhizobia (as measured by Nod factor production in M. loti rather than the usual nod gene expression assay using reporter gene fusions) as well as lupin rhizobia (R. lupini), but their presence in Lotus tissues or epidermal exudates has not been established. The natural nod gene inducers for M. loti remain unidentified despite the detection of many flavonoids in Lotus roots, root nodules, and root exudates (Cooper and Rao, 1992; Morris and Robbins, 1992; Pankhurst and Jones, 1979; Robbins et al., 1995; Steele et al., 1999; Wagner et al., 1996) and attempts to determine the inducing properties of most of them (Lo´pez-Lara et al., 1995). An example of nonflavonoid induction of Rhizobium sp. NGR234 nod genes by the simple phenolic compounds vanillin and isovanillin in seedling extracts of a nonlegume (i.e., wheat) has also been reported (Le Strange et al., 1990). A list of nod gene inducers of legume origin is presented in Table II, and the structures of inducing compounds from four flavonoid subclasses are shown in Fig. 6. C. INDUCTION OF A TYPE III SECRETION SYSTEM AND A TYPE I SECRETED PROTEIN

It has recently been established that flavonoid nod gene inducers are also required for the transcription of type III secretion system (TTSS) genes that are found in some rhizobia. TTSS genes occur in several gram-negative plant and animal pathogens and are characterized by secretion of proteins into the extracellular environment or directly into eukaryotic cytoplasm when contact is made with host cells (Cornelis, 2000; Cornelis and Van Gijsegem, 2000; He, 1998; Hueck, 1998). The functions of such proteins are subversion of the mammalian immune system in the case of animal pathogens such as Shigella and Yersinia (Cornelis and Wolf-Watz, 1997) and elicitation of a hypersensitive response in resistant plants or disease in susceptible ones by plant pathogenic bacteria (He, 1998). Probably the first indication that TTSS components occurred in rhizobia was obtained by Sadowsky et al. (1988), who discovered two daidzein- and genistein-inducible genes in S. fredii. These genes showed no significant

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TABLE II Rhizobial nod Gene Inducers Isolated from Legumes Under Sterile Conditions Host legume Medicago sativa

Vicia sativa Trifolium repens Glycine max

Luteolin (5,7,30 40 -tetrahydroxyflavone) Chrysoeriol (30 -methoxy-5,7,40 -trihydroxyflavone) Liquiritigenin (7,40 -dihydroxyflavanone) 7,40 -Dihydroxyflavone Methoxychalcone (4,40 -dihydroxy-20 -methoxychalcone) Stachydrine (betaine) Trigonelline (betaine) Apigenin-7-O-glucoside (5,7,40 -trihydroxyflavone-7-O-glucoside) Eriodictyol (5,7,30 ,40 -tetrahydroxyflavanone) 3,5,7,30 -Tetrahydroxy-40 -methoxyflavanone 7,30 -Dihydroxy-40 -methoxyflavanone Four more partially characterized flavanones 7,40 -Dihydroxyflavone Geraldone (7,40 -dihydroxy-30 -methoxyflavone) 40 -Hydroxy-7-methoxyflavone Daidzein (7,40 -dihydroxyisoflavone) Genistein (5,7,40 -trihydroxyisoflavone) Coumestrol (3,9-dihydroxycoumestan) Isoliquiritigenin (4,20 ,40 -trihydroxychalcone) Genistein-7-O-glucoside Genistein-7-O-(600 -O-malonylglucoside) Daidzein-7-O-(600 -O-malonylglucoside)

Reference Peters et al. (1986) Hartwig et al. (1990a) Maxwell et al. (1989) Maxwell et al. (1989) Maxwell et al. (1989) Phillips et al. (1992) Phillips et al. (1992) Firmin et al. (1986) Firmin et al. (1986) Zaat et al. (1989) Zaat et al. (1989) Zaat et al. (1989) Redmond et al. (1986) Redmond et al. (1986) Redmond et al. (1986) Kosslak et al. (1987) Kosslak et al. (1987) Kosslak et al. (1987) Kape et al. (1992) Smit et al. (1992) Smit et al. (1992) Smit et al. (1992)

J. E. COOPER

Pisum sativum

Compound

Phaseolus vulgaris

Sesbania rostrata Lupinus albus Galega orientalis

Hungria et al. (1991a) Hungria et al. (1991a) Hungria et al. (1991a) Hungria et al. (1991a) Hungria et al. (1991a) Hungria et al. (1991a) Hungria et al. (1991b) Hungria et al. (1991b) Hungria et al. (1991b) Bolanos-Vasquez and Werner (1997) Bolanos-Vasquez and Werner (1997) Bolanos-Vasquez and Werner (1997) Bolanos-Vasquez and Werner (1997) Dakora (2000) Dakora (2000) Dakora (2000) Messens et al. (1991) Gagnon and Ibrahim (1998) Gagnon and Ibrahim (1998) Suominen et al. (2003)

RESPONSES OF RHIZOBIA TO FLAVONOIDS

Cowpea

Delphinidin (3,5,7,30 ,40 ,50 -hexahydroxyflavylium) Kaempferol (3,5,7,,40 -tetrahydroxyflavonol) Malvidin (3,5,7,40 -pentahydroxy-30 ,50 -dimethoxyflavylium) Myricetin (3,5,7,30 ,40 ,50 -hexahydroxyflavone) Petunidin (3,5,7,40 ,50 -pentahydroxy-30 -methoxyflavylium) Quercetin (3,5,7,30 ,40 -pentahydroxyflavonol) Eriodictyol Genistein Naringenin (5,7,40 -trihydroxyflavanone) Daidzein Liquiritigenin Isoliquiritigenin Coumestrol Daidzein Genistein Coumestrol Liquiritigenin Erythronic acid (aldonic acid) Tetronic acid (aldonic acid) Uncharacterized chalcone

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Fig. 6. Rhizobial nod gene inducers from four flavonoid subclasses. The carbon numbering convention for chalcones diVers from that in flavones, flavanones, and isoflavones. B-ring numbering in both the flavone and isoflavone is shown for comparison.

homologies to any proteins in the databases available at that time and lacked the highly conserved nod box sequence in the 50 region that is required for the expression of many nodulation genes. One of these genes (ORF1) is now known to share a high degree of homology with an ORF (y4yP) that has been only recently identified in the TTSS of S. fredii USDA257 and Rhizobium sp. NGR234 (Krishnan et al., 2003). Subsequently, the other gene (ORF2) was shown to need both a flavonoid and a functional nodD1 gene for induction and has been named nolJ (Boundy-Mills et al., 1994). It has not been identified as a rhizobial TTSS component. Another clue to the presence of such a system in rhizobia came from work by Krishnan and Pueppke (1993), which demonstrated that S. fredii USDA257 exported new proteins, designated signal responsive (SR), subsequent to nod gene induction by flavonoids. Protein production and secretion were dependent on the presence of isoflavonoid or flavonoid nod gene inducers (e.g., genistein, luteolin, naringenin) and export occurred without N-terminal processing— a characteristic of protein secretion by TTSSs. A plasmid-borne locus in USDA257 involved in the regulation of soybean cultivar specificity,

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23

nolXWBTUV, contains flavonoid-inducible genes whose protein products share sequence homologies with components of TTSSs (Meinhardt et al., 1993). The finding that disruption of the locus was accompanied by absence of SR protein secretion strengthened the possibility that some products of the nolXWBTUV locus are responsible for synthesis of TTSS components (Krishnan et al., 1995). This study also proved that, as with nod gene induction in rhizobia, flavonoid-induced protein secretion requires the presence of regulatory NodD proteins. In addition, other intermediary regulators such as y4xI (a homologue of the Xanthomonas campestris hrp regulator HrpG), which is under NodD1 control and has recently been renamed TtsI (Marie et al., 2003), appear to be required for transcriptional activation of TTSS genes (Viprey et al., 1998). The presence of TTSSs in several rhizobia has now been established by nucleotide sequencing, including Rhizobium sp. NGR234 (Freiberg et al., 1997); M. loti MAFF303099 (Kaneko et al., 2000); B. japonicum USDA110 (Go¨ ttfert et al., 2001); R. etli CFN42 (National Center for Biotechnology Information [NCBI] database, accession number U80928); and S. fredii USDA257 (Krishnan et al., 2003). In the case of B. japonicum, Krause et al. (2002) have proposed a model for a regulatory cascade (initiated by genistein reception by NodD1 and NodV and involving the transcriptional activator protein NodW) controlling expression of genes in the TTSS cluster. Protein secretion by TTSS has been confirmed in Rhizobium sp. NGR234 (Marie et al., 2003; Viprey et al., 1998) and S. fredii (Krishnan et al., 1995). A model for the process involving two cytoplasmic proteins, five inner membrane proteins, two outer membrane proteins, and one lipoproteinassociated outer membrane protein was first proposed by Viprey et al. (1998). This has since been modified by Marie et al. (2003) to allow allocation of putative transfer or eVector functions to secreted proteins, with the latter type determining the plant response to the rhizobial TTSS. The genetic organization of TTSSs in NGR234, which cannot form N-fixing nodules on soybean rhizobia, and in USDA257, which can, is 98% identical (Krishnan et al., 2003). In contrast, the TTSSs of two soybean-nodulating rhizobia— S. fredii USDA257 and B. japonicum USDA110—share only limited sequence homology and markedly diVerent genetic organization. Rhizobial genes encoding TTSSs appear to be clustered within regions of 35–47 kb (Marie et al., 2001), and the clusters contain genes that encode the secretion apparatus as well as the secreted proteins themselves. Fully sequenced TTSS clusters in rhizobia contain ORFs that are homologous to TTSS genes in the animal pathogen Yersinia (ysc genes) and plant bacterial pathogens (hrc genes) and are designated rhc (Rhizobium conserved) (Viprey et al., 1998). In animal and plant pathogens, as well as the rhizobial symbiont NGR234, nine

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such conserved genes in the TTSS clusters are thought to encode the core protein secretion apparatus (Bogdanove et al., 1996; Freiberg et al., 1997). Other genes that play a role in protein secretion are termed tts (Marie et al., 2003). Homologues of rhc genes have been found via genomic hybridizations in Bradyrhizobium elkanii USDA76, B. japonicum CB756, and by mutagenesis in S. fredii HH103 but not in S. meliloti 2011 (Viprey et al., 1998). Another strain of S. meliloti, 1021, also lacks homologues of genes encoding a TTSS (Galibert et al., 2001), as does R. leguminosarum bv. viciae 3841 (J.A. Downie, personal communication). Marie et al. (2001) proposed that proteins secreted by rhizobial TTSSs be termed Nops (Nodulation outer proteins, encoded by nop genes) to reflect the accepted nomenclature for Yersinia outer proteins (Yops). Five flavonoid-, NodD1-, TtsI-, and Rhcdependent secreted Nops have been identified in NGR234, and wild-type USDA257 appeared also to secrete the same five proteins plus one other of approximate size 36 kilodaltons (kDa), after induction with apigenin (Marie et al., 2003). One 7-kDa secreted protein from NGR234, NopA, shared 99% identity with a homologue from USDA257, and database searches established that NopA homologues were present in all other TTSS-containing rhizobia but not in those lacking a TTSS. NopA is the NGR234 homologue of Nop7 from USDA257 (H. B. Krishnan, personal communication), which was recently shown to be associated with pili structures (see next paragraph and Krishnan et al., 2003) and may fulfill a transfer function. Two other secreted proteins from NGR234, NopX and NopL, were found to be absent from the TTSSs of some other rhizobia and were considered to possess transfer and eVector functions, respectively (Marie et al., 2003). Homologues of NGR234 NopX and NopL exist in USDA257; the first of these, and probably also the second (designated Nop38), has also been found in association with USDA257 pili (next paragraph and Krishnan et al., 2003). Plant and animal pathogens with TTSSs are thought to deliver secreted proteins into the plasma membrane and cytosol of eukaryotic host cells (Brown et al., 2001; Casper-Lindley et al., 2002; Gala´ n and Collmer, 1999; Lee, 1997) via structures, termed needle complexes (Kubori et al., 1998), or other surface appendages including pili (Roine et al., 1997). Recently, pili, whose production depends on a functional nodD1 gene and the presence of a flavonoid or isoflavonoid nod gene inducer, have been discovered in S. fredii USDA257 (Krishnan et al., 2003). The S. fredii nod gene inducers genistein, daidzein, apigenin, and luteolin all stimulated pili production, as did soybean seed exudate, but no pili were produced in the absence of genistein or the presence of the noninducers biochanin A and umbelliferone. Mutations in some USDA257 rhc genes also negated pili formation in genistein-induced cells containing a functional nodD1 gene. Furthermore, biochemical analysis

RESPONSES OF RHIZOBIA TO FLAVONOIDS

25

showed that at least three Nops—Nop7 (now designated NopA), Nop38 (thought to be NopL, but confirmation by Western blotting with NopL antibodies is required; H.B. Krishnan, personal communication), and NopX—were associated with purified pili. It is not known whether eVector Nops of symbiotic bacteria are delivered directly into legume host cell cytoplasm, but NopL from Rhizobium sp. NGR234 has recently been shown to serve as a substrate for plant protein kinases, and its phosphorylation was inhibited by a mitogen-activated protein (MAP) kinase inhibitor, pointing to a role in the modulation of MAP kinase pathways (Bartsev et al., 2003). More work is needed to determine the mechanisms governing the delivery and reception of Nops, but in terms of function they do appear to make an important contribution to the formation of successful symbioses. TTSS mutants exhibit diVerent and inconsistent symbiotic phenotypes compared to wild-type strains, ranging from no eVect, to increases or decreases in nodule number and changes in nodule N fixation capacity, to altered host specificity. To cite a few examples: wild-type Rhizobium sp. NGR234 and S. fredii HH103 form ineVective (nonfixing) nodule-like structures on the roots of Crotalaria juncea and Erythrinia variegata, respectively, but TTSS defective mutants form eVective nodules (Marie et al., 2001). Further studies with C. juncea showed that Nops from wild-type NGR234 were impairing nodule development in this host (Marie et al., 2003). An even more drastic alteration of phenotype is displayed by USDA257 after TTSS disruption. In this case rhc mutants acquire the ability to form eVective root nodules on a soybean cultivar, McCall, which cannot be nodulated at all by the wild-type parent strain (Krishnan et al., 2003). Interestingly, the same rhc mutants form significantly fewer nodules than wild-type USDA257 on another soybean cultivar, Peking (Krishnan et al., 2003). Clearly, TTSSs in rhizobia can influence nodulation in positive or negative ways, depending on the type of host plant involved, and various explanations have been invoked to account for their eVects. These include diVerences in flavonoid inducer exudation among host plants, varying levels of recognition of Nops among prospective hosts, the absence or modification of a Nops receptor, and the elicitation or avoidance of a defense response (Marie et al., 2001). Another flavonoid-inducible, NodD-dependent rhizobial gene encoding a secreted protein, NodO, has been found, but only in very few rhizobia: R. leguminosarum bv. viciae (de Maagd et al., 1989a, b) and a broad host range strain, Rhizobium sp. BR816, isolated from Leucaena leucocephala (van Rhijn et al., 1996). This protein is released by a diVerent, type I, secretion system that, like type III, is also found in gram-negative bacteria, where it controls the release of compounds such as the a-haemolysin toxin of Escherichia coli into the extracellular space. Unlike TTSSs, the genes required for the type

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I secretion apparatus (the protein secretion genes prsDE) are not linked to the gene encoding the secreted protein, and their expression is not flavonoiddependent (Scheu et al., 1992). NodO is a Ca2þ-binding protein with partial homology to E. coli haemolysin (Economou et al., 1990; Sutton et al., 1994). In R. leguminosarum bv. viciae, nodO can partially complement the nodulation defect of a nodEFL deletion mutant (Downie and Surin, 1990), and in R. leguminosarum bv. trifolii transfer of nodO to a nodE-deficient mutant extends the host range to include Vicia hirsuta (Economou et al., 1994). Host ranges of other rhizobia are also extended on receipt of nodO even when they possess a functional nodE gene (van Rhijn et al., 1996; Vlassak et al., 1998). The nodE gene encodes a -ketoacyl synthase responsible for synthesis of an unsaturated fatty acid, located at the nonreducing terminus of Nod factors (Table III and Fig. 7), which influences host specificity (Demont et al., 1993; Pacios Bras et al., 2000; Spaink et al., 1991). In R. leguminosarum bv. viciae and trifolii both NodE and NodF are involved in fatty acid synthesis, for which a model combining elongation of the unsaturated acyl chain by NodE and the provision of acyl groups by NodF has been proposed (Geiger et al., 1998). Although NodO is not involved in synthesizing the fatty acid component of Nod factors (or in any other facet of Nod factor synthesis), it can suppress nodulation defects brought about by the absence of this or another Nod factor substituent, a carbamoyl group, in several rhizobial species (Vlassak et al., 1998). Original proposals for the mode of action of NodO invoked a capacity to form ion channels that permit cation movement across and concomitant depolarization of the plasma membrane of plant cells (Economou et al., 1994; Sutton et al., 1994). Such changes are among the first to be observed when roots are challenged with Nod factors (Ehrhardt et al., 1992, 1996; Felle et al., 1996; Gehring et al., 1997). More recently nodO has been identified as a gene that promotes infection thread development in root hairs (Walker and Downie, 2000). D. FURTHER FLAVONOID-DEPENDENT GENE EXPRESSION IN RHIZOBIA

In addition to the examples given in the preceding sections, it is apparent from both transcriptional (Ampe et al., 2003; Perret et al., 1994, 1999) and proteomics (Chen et al., 2000; Guerreiro et al., 1997, 1999) studies that the expression of other rhizobial genes, located on symbiotic plasmids or in the chromosome, is flavonoid dependent. Perret et al. (1994), using a combination of competitive RNA hybridization, subtractive DNA hybridization, and shotgun sequencing, found several flavonoid-inducible transcripts on the large symbiotic plasmid of Rhizobium sp. NGR234 (pNGR234a) that shared no homologies with known nodulation genes but strong homologies

RESPONSES OF RHIZOBIA TO FLAVONOIDS

27

TABLE III Nodulation Gene Products Required for Synthesis and Release of Nod Factors Protein

Function

Biosynthesis of glucosamine (chitin) oligosaccharide backbone NodM Glucosamine synthase NodCa N-acetyl-glucosamine transferase NodBa Deacetylase, acting at the nonreducing end of glucosamine oligosaccharide Biosynthesis and transfer of fatty acid moiety at nonreducing terminus NodF Acyl carrier protein NodE -Ketoacyl synthase NodAa Acyl transferase involved in N-acylation of deacetylated nonreducing terminus of glucosamine oligosaccharide Modification of nonreducing terminus NodS Methyl transferase NodU Carbamoyl transferase NolO Carbamoyl transferase NodL O-acetyl transferase, O-acetylates at 6-C position Modification of reducing terminus NodP,Q ATP sulphurylase and APS kinase, provide activated sulphur for sulphated Nod factors NodH Sulphotransferase NoeE Sulphotransferase involved in sulphation of fucose NolK GDP fucose synthesis NodZ Fucosyl transferase NolL O-acetyltransferase; involved in acetyl-fucose formation NodX O-acetyltransferase, specifically O-acetylates the 6-C of the terminal nonreducing sugar of the penta-N-acetylglucosamine of R. leguminosarum TOM from Afghanistan pea NoeI 2-O-methyltransferase involved in 2-O-methylation of fucose Secretion of Nod factors NodIa ABC transporter component carrying an ATPase domain NodJa ABC transporter sub-unit APS, adenosine-50 -phosphosulphate kinase; ATP, adenosine triphosphate; GDP, guanosine diphosphate. Sources: Downie (1998); Pacios Bras et al. (2000); Vance (2002). a

Present in all rhizobia.

to a number of other prokaryotic genes and proteins. One symbiotically active ORF was highly homologous to the leucine responsive regulatory protein (Lrp) of E. coli; it was present in Rhizobium sp. NGR234 but not in the closely related S. fredii USDA257. Further, detailed studies showed that daidzein enhanced the transcription of 147 previously silent ORFs on pNGR234a and that genes involved in Nod factor biosynthesis were more rapidly induced than some others whose products are required at a later stage of interaction with a host plant (Kobayashi et al., 2004; Perret et al., 1999).

Fig. 7. Composite Nod factor structure showing the range of possible substitutions on the oligochitin backbone. Nod proteins responsible for structural modifications are indicated where known. Ac, acetyl; Ara, arabinosyl; Cb, carbamoyl; Fuc, fucosyl; Gro, glycerol; Man, mannosyl; Me, methyl; S, sulphate. (From Bladergroen and Spaink, 1998; Pacios Bras et al., 2000.)

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Proteomics analyses have identified new proteins whose expression levels are influenced by the presence of nod gene-inducing flavonoids in the bacterial growth medium. For example, two proteins that did not show sequence matches with any known nod gene products were induced in R. leguminosarum bv. trifolii by 7,40 dihydroxyflavone (Guerreiro et al., 1997). The expression of several proteins that appeared to be encoded by pSyma of S. meliloti was positively regulated by luteolin and none of them matched the products of any previously identified luteolin-regulated gene (Chen et al., 2000). Other proteins were down-regulated in the presence of luteolin, or expressed only in the absence of pSyma, or accumulated in maximum amounts when pSyma was either present or absent. At the level of protein expression it is clear therefore that luteolin exerts both positive and negative regulatory eVects on plasmid and chromosomal genes in S. meliloti. Two proteins with homologies to a molecular chaperone, GroEL, that is thought to assist partially folded proteins in attaining a correctly folded configuration (Ogawa and Long, 1995) were up-regulated by luteolin. It was suggested that this fulfilled the need for specific folding requirements of other luteolininduced proteins and that another up-regulated, 30S ribosomal protein was indicative of a luteolin influence on the cell’s translational machinery (Chen et al., 2000). Proteomics analyses are likely to underestimate the extent of flavonoid-dependent gene expression in rhizobia for several reasons, such as culture conditions that aVect transcriptional activity (Girard et al., 1996) and, consequently, the numbers and amounts of proteins expressed, and the limited resolving capability of two-dimensional gel electrophoresis which may limit their discrimination. Also, some nod gene products known to be induced by flavonoids (e.g., NodABC) are not always detectable on gels (Chen et al., 2000; Guerreiro et al., 1999). The three main types of surface polysaccharide in rhizobia (EPS, KPS, and LPS) all contribute to symbiotic development, and flavonoids have been shown to influence their structures either during or after biosynthesis. EPS produced by S. fredii USDA193 were of lower average molecular mass and had a reduced uronic acid content when genistein was present as a nod gene inducer in the growth medium. Genistein-mediated changes in EPS structure were also Sym plasmid-dependent (Dunn et al., 1992). In Rhizobium sp. S-2, isolated from the pigeon pea plant, EPS production was both naringeninand Sym plasmid-dependent and EPS determinants were found on the large symbiotic plasmid of this strain (Pandya and Desai, 1998). Fraysse et al. (2003) proposed that EPS, even when encoded by chromosomal genes, could be subject to modification by symbiotic plasmid genes whose expression was directly or indirectly induced by plant flavonoids. In the case of KPS, production of the secondary 2-O-MeMan-containing K-antigen of S. fredii

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USDA205 was up-regulated by the nod gene inducer apigenin (Forsberg and Reuhs, 1997; Reuhs et al., 1993, 1994,1995). A reciprocal influence of K-antigens from S. meliloti on (iso)flavonoid biosynthesis in alfalfa was noted by Becquart de Kozak et al. (1997). The O-antigen polysaccharide (OPS) component of LPS in S. fredii USDA205 is altered in both carbohydrate composition and mass range when apigenin is introduced into the growth medium (Reuhs et al., 1994). When cultured in the presence of nod gene-inducing flavonoids, Rhizobium sp. NGR234 produces large quantities of a new high molecular weight, rhamnose-rich LPS, but in the absence of inducers only fast-migrating lower molecular weight LPSs are synthesized (Fraysse et al., 2002). Furthermore, LPS biosynthesis genes are located in nod regions of the NGR234 large symbiotic plasmid (Freiberg et al., 1997), an arrangement that could permit structural adaptation of LPSs before bacterial contact with a prospective host (Fraysse et al., 2003). Interestingly, LPS structural changes in some rhizobia appear to be brought about directly by flavonoids without a requirement for nodD or any other gene on the symbiotic plasmid (Noel et al., 1996; Tao et al., 1992). Such is the case with R. etli, in which LPS alterations induced by low pH and anthocyanins (Duelli and Noel, 1997) appear to involve methylation of particular OPS residues (Noel and Duelli, 2000). Some of the flavonoid-dependent eVects on LPS that were described previously can be understood in the context of a highly integrated glycolipid chemistry in rhizobia (Cedergren et al., 1995; Price, 1999) that can provide a common origin for structural elements, such as sulfation, methylation, and fatty acyl composition, that are shared between chitolipooligosaccharides (Nod factors) and lipopolysaccharides. This applies also to membrane phospholipids, which can contain flavonoid-/NodD-dependent, nodFE-derived fatty acids found in Nod factors produced by the same organism (Cedergren et al., 1995; Geiger et al., 1994), as well as other fatty acids that are syntheszsed by the nodFE gene products but not incorporated into Nod factors (Geiger et al., 1998). One membrane phospholipid in S. meliloti, phosphatidylcholine, has been shown to be essential for nodulation of Medicago (Lo´ pez-Lara et al., 2003; Sohlenkamp et al., 2003). It can be synthesized using choline exuded from the host plant (de Rudder et al., 1999), but it is not known whether flavonoid-/NodD-dependent, nodFEderived fatty acyl residues are components of its structure (O. Geiger, personal communication). Turning from synthesis to catabolism, a recent study (Baumberger et al., 2003) has identified two genistein-inducible polysaccharide degradation genes in B. japonicum that would be of great significance for infection should they be needed for localized degradation of root hair cell walls. These genes

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are gunA2 and pgl, encoding, respectively, an endoglucanase (cellulase) and a polygalacturonase. Finally in this section, flavonoid repression of a group of three rhi genes, rhiABC, on the symbiotic plasmid of R. leguminosarum bv. viciae, has been reported. The rhiABC operon requires AHLs for induction (Gray et al., 1996; Rodelas et al., 1999) and is regulated by a fourth rhi gene product, RhiR, whose expression is repressed by flavonoids (e.g., hesperetin) that are nod gene inducers for this organism. Thus transcription of rhiA is decreased in the presence of flavonoids and the eVect is NodD-dependent (Cubo et al., 1992; Economou et al., 1989). Mutations in rhi genes do not appear to aVect nodulation of Vicia hirsuta adversely unless the closely linked nodFEL genes are missing (Cubo et al., 1992).

VI. RECEPTION OF FLAVONOIDS BY RHIZOBIA A. INTERACTIONS WITH REGULATORY NodD PROTEINS

Activation of nodulation genes whose protein products are required for Nod factor synthesis is mediated by regulatory NodD proteins, the constitutively expressed products of the nodD genes. NodD belongs to the LysR family of transcriptional regulators (Schell, 1993), and it binds in the absence of inducers to conserved nucleic acid sequences, termed nod boxes (Fisher and Long, 1993; Rostas et al., 1986), in the promoter regions of inducible nodulation genes (Goethals et al., 1992). In the presence of appropriate plant flavonoids and NodD these genes are transcribed; their various protein products (see Table III) act collectively to synthesize the reciprocal Nod factor signal (see Fig. 7) to the plant root that is a determinant of host specificity. In B. japonicum and R. etli the nodD1 gene itself is preceded by a nod box sequence and transcription is enhanced in the presence of its own product (NodD1) and certain isoflavonoid glycosides (Smit et al., 1992). The regulation of nodulation genes, their functions, and the structures and properties of the Nod factors they ultimately encode have been extensively and exhaustively reviewed (Broughton et al., 2000; D’Haeze and Holsters, 2002; Downie, 1998; Pacios Bras et al., 2000; Perret et al., 2000; Schlaman et al., 1998; Schultze and Kondorosi, 1998; Spaink, 2000). Consequently, this section is principally concerned with the reception of flavonoids by rhizobia and the uncertainties surrounding the nature of their interactions with NodD proteins. All rhizobia possess nodD genes, but the number of homologues varies from one in R. leguminosarum bv. trifolii and R. leguminosarum bv. viciae to

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between two and five in B. japonicum, Rhizobium sp. NGR234, S. meliloti, R. etli, and R. tropici. Mutation of the single nodD gene in R. leguminosarum bv. trifolii results in loss of nodulating ability on clovers, and inactivation of nodD1 in NGR234 similarly produces a Nod phenotype on its many host genera (Broughton et al., 2000). In some other rhizobia such as S. meliloti and B. japonicum, nod gene induction appears to involve more complicated regulatory mechanisms. The three NodD proteins in S. meliloti do not all act as flavonoid receptors; NodD1 interacts with flavonoids and NodD2 with betaines and 4,40 -dihydroxy-20 -methoxychalcone (Hartwig et al., 1990b; Phillips et al., 1992), but, exceptionally, NodD3 regulates expression of nod genes in the absence of any compound in plant exudates. Mutations in all three nodD genes are required to eliminate host nodulation by S. meliloti. In this organism another symbiotic regulatory gene, syrM, (which is also flavonoid independent) acts in conjunction with nodD3 to provide selfamplifying positive regulation of nod genes in developing root nodules (Swanson et al., 1993). Recent work with mutants of S. meliloti that lack a-isopropyl-malate synthase, the first enzyme in the leucine biosynthetic pathway, has shown that a leucine-related metabolic intermediate may also be required as an inducer, in addition to luteolin, for activation of nodulation genes by NodD1 (Sanjua´ n-Pinilla et al., 2002). A further example of diVerential responses to flavonoids can be found in B. japonicum: Glycine max releases genistein and daidzein that induce the B. japonicum nodYABCSUIJ operon and certain isoflavone glycosides that do not, except in the presence of a suboptimal genistein concentration (Smit et al., 1992). B. japonicum possesses two genes, nodVW, which are distinct from and supplementary to nodD and are involved in the regulation of Nod factor synthesis via isoflavonoid inducers. This two-component system relies on NodV, a sensory kinase, to recognize flavonoids that do not normally interact with NodD, whereas NodW activates gene transcription (Go¨ ttfert et al., 1990, 1992; Loh et al., 1997; Sanjua´ n et al., 1994). The system is responsible for extending the host range of this organism to legumes such as Macroptilium atropurpureum, Vigna radiata and Vigna unguiculata. The previously mentioned isoflavonoid-inducible cellulase (gunA2) and polygalacturonase (pgl) genes in B. japonicum (Baumberger et al., 2003) require both NodD and NodW for their expression, even though neither of these two putative targets contains a promoter with a nod box consensus. It was suggested that in this case a hierarchical regulatory cascade may operate, in which NodD or NodW (or both) control an unidentified (so far) regulatory gene whose product in turn controls gunA and pgl expression. Yet another regulatory system is present in S. fredii and involves the nolJ, nolBTUV, and nolX transcriptional units (Bellato et al., 1996; Meinhardt et al., 1993). These

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33

genes also lack nod box sequences in their promoters, but they too are isoflavonoid-inducible and NodD dependent. In contrast to these examples of flavonoid selectivity by diVerent regulatory Nod proteins in a single bacterium, all three separate NodDs of R. etli appeared to react in the same way to a range of flavonoid inducers from common bean (Hungria et al., 1992). Regulation of nod gene expression is also subject to negative control by repressor proteins such as NolR and NolA whose production is flavonoid independent (Schlaman et al., 1998). An excess of Nod factors in the rhizosphere is apparently detrimental to eYcient nodulation and can aVect the spectrum of hosts that are nodulated (Fellay et al., 1998; Gillette and Elkan, 1996; Knight et al., 1986; Sadowsky et al., 1991). It may also trigger unwanted host defense reactions (Savoure´ et al., 1997). In R. leguminosarum bv. viciae the single nodD gene is negatively autoregulated by its own product, NodD (Rossen et al., 1985). Relationships between flavonoid structure and nodulation gene-inducing or inhibiting properties are apparent. A hydroxyl group at the 7-carbon position in the flavonoid skeleton, regardless of other OH substitutions, is a feature of inducing compounds for Rhizobium sp. NGR234. Hydroxylation at the 7- and 40 -carbon positions is a common feature of inducers from many legumes (see Table II), and testing of more than 1000 flavonoids led to the conclusion that hydroxylation at the position equivalent to the 7-carbon was a feature of compounds that had inducing or anti-inducing activity in many diVerent rhizobia (Cunningham et al., 1991). Rhizobia with narrower host ranges appear to require a more specific pattern of substitutions in the basic flavonoid structure to ensure interaction with NodD. Thus the nod genes of R. leguminosarum bv. viciae are induced by flavonoids with hydroxyl groups at the 5-, 7-, and 40 -carbon positions. Is narrow host range in rhizobia attributable to a high degree of specificity between flavonoids and NodD? Certainly an organism such as Rhizobium sp. NGR234 can interact with many flavonoid inducers and nodulates legumes in many genera. However, other examples do not provide such a correlation; R. leguminosarum bv. viciae responds to a wide variety of flavonoids but has a narrow host range. In general it can be concluded that there is no universal flavonoid inducer for rhizobial nodulation genes; rhizobia associated with a particular host tend to be more responsive to inducers released from that plant and variation among NodDs of diVerent rhizobia with regard to flavonoid reception is not of itself suYcient to define host specificity. How do NodD proteins interact with coinducing flavonoids to activate nodulation gene transcription? Despite the presumption, often reiterated in the literature over many years (a notable exception being the review of

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rhizobial nod gene regulation by Schlaman et al., 1998), that flavonoids form a complex with NodD and in so doing eVect conformational changes at the nod box binding sites that activate transcription, there is no direct evidence for physical interaction between the two molecules. Specific binding of NodD itself to DNA, in the presence or absence of flavonoids, has been confirmed by gel electrophoresis retardation assays (Fisher and Long, 1989, 1993; Goethals et al., 1992; Hong et al., 1987; Kondorosi et al., 1989; Schlaman et al., 1992). NodD binding to nodulation gene promoters protects a 75- to 20-base pair (bp) region from the transcriptional initiation sites, including nearly the entire nod box sequence (Fisher and Long, 1989; Kondorosi et al., 1989; Machado et al., 1998). Working with R. leguminosarum bv. viciae, Okker et al. (2001) confirmed that NodD binds to DNA in the absence of inducer compounds, and by means of single base pair substitutions along the nodF nod box, they were able to show that mutations in the nod box LysR motif abolished flavonoid-dependent promoter activity even though binding of NodD was unaVected. This finding implies that flavonoids are required as coinducers when NodD interacts with the entire nod box or that the LysR motif is not the only sequence involved in NodD binding (Schlaman et al., 1998). NodD interacts with two binding sites in the nod box (Fisher and Long, 1993), and a recent study (Feng et al., 2003) provided evidence that it binds to target DNA through anchoring the two half-sites of the nod box as a tetramer. An imperfect inverted repeat, (AT-N10-GAT) in each half-site is critical for NodD binding, and mutation of the inverted repeat at the distal half-site allowed NodD to activate nodA transcription in vivo in the absence of a flavonoid coinducer. Yeh et al. (2002) showed that the chaperonin GroESL is required for recombinant S. meliloti NodD1 to respond to its inducer, luteolin, in vitro. This interaction results in increased binding of NodD1 to target DNA at luteolin concentrations up to 100 mM and decreased binding at even higher concentrations. A decrease in NodD binding to nod box DNA at naringenin concentrations higher than 10 mM was noted in R. leguminosarum by Hu et al. (2000), who argued that inducer concentrations of this order could accumulate in the cytoplasmic membranes of rhizobia in vivo. Speculation on the mode of transcriptional repression of nod genes by NodD in the absence of flavonoids takes account of the fact that this protein can induce a bend in DNA at the site of nod gene promoters (Feng et al., 2003; Fisher and Long, 1993; Schlaman et al., 1998). The flavonoid coinducer may interact with NodD in an as-yet undefined manner and location to produce a change in bend that enables RNA polymerase to initiate transcription of the nod gene promoters (Hu et al., 2000). As noted by Yeh et al. (2002), even if a general mechanism for flavonoid-NodD interaction can be established, the relationships among

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coinducer structural specificity, NodD activity, and legume host range will still require explanation. B. METABOLISM

Many microorganisms can degrade phenolic compounds of plant origin, including condensed tannins and flavonoids (Arunachalam et al., 2003; Bhat et al., 1998; Ibrahim and Abul-Hajj, 1990). Degradation may occur under aerobic or anaerobic conditions, and metabolites (e.g., acetate, butyrate, -ketoadipate) from various pathways are channelled into the citric acid cycle (Bhat et al., 1998). Barz (1970) and Barz et al. (1970) demonstrated that bacteria in legume rhizospheres could degrade flavonoids such as formononetin and daidzein, and rhizobia themselves are known to be capable of catabolizing many aromatic substances (Latha and Mahadevan, 1997). An indication that bacteria could participate in the aerobic degradation of flavonoids came from a study of the utilization of the flavan-3-ol catechin by B. japonicum, in which protocatechuic acid was detected among the metabolites (Muthukumar et al., 1982). More detailed analyses of degradation products resulted in the detection of phloroglucinol carboxylic acid, phloroglucinol, resorcinol, hydroxyquinol, maleyl acetate, and -carboxy-cis,cismuconate, in addition to protocatechuic acid (Hopper and Mahadevan, 1997). Another rhizobial isolate from root nodules of Leucaena leucocephala was shown to use catechin as a sole carbon source and to yield protocatechuic acid, phloroglucinol carboxylic acid, phloroglucinol, resorcinol, catechol, and hydroxyquinol as by-products (Gajendiran and Mahadevan, 1988). These and many other simple phenolics can be used by rhizobia as sole carbon and energy sources (Gajendiran and Mahadevan, 1990; Parke and Ornston, 1984; Vela et al., 2002). Indirect evidence for degradation of the flavone chrysin and the flavanone naringenin by B. japonicum was obtained from HPLC analyses of supernatants from spent flavonoid-supplemented cultures in which no trace of either compound could be detected (Kosslak et al., 1990). Rhizobial cleavage of the flavone nucleus of a flavonoid was first demonstrated by Rao et al. (1991); incubation of the pentahydroxyflavonol quercetin, supplied at a concentration of 100 mM in an arabinose-based, defined growth medium, with M. loti or a Lotus-nodulating Bradyrhizobium strain, yielded protocatechuic acid and phloroglucinol among the metabolites after 3 days (Fig. 8). A pattern of multiple C-ring fissions with the potential for transient chalcone formation was proposed to account for the production of these conserved A- and B-ring products. Among bacteria, reports of flavone type ring cleavage have been largely confined to anaerobes such as Clostridium and Eubacterium in the mammalian intestinal tract (Hur and

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Fig. 8. Proposed mechanism for fission of the flavone nucleus (C-ring) in the pentahydroxyflavonol quercetin and principal degradation products, phloroglucinol, and protocatechuic acid, detected in extracted culture supernatants by GC-MS. (From Rao et al., 1991.)

Rafii, 2000; Hur et al., 2000, 2002; Krumholz and Bryant, 1986; Schoefer et al., 2002; Winter et al., 1989). However, Pillai and Swarup (2002) found that a plant-growth-promoting rhizobacterial strain of Pseudomonas putida could utilize a variety of flavonoids as sole carbon sources and was capable of aerobic catabolism of quercetin by a similar, if not identical, mechanism to that proposed by Rao et al. (1991) for Lotus-nodulating rhizobia. In the case of P. putida the first step is dehydroxylation to naringenin, followed by hydrolysis and C-ring cleavage to yield phloroglucinol and protocatechuate among the products, a process that can be viewed as a reversal of quercetin biosynthesis in plants, which involves the hydroxylation of naringenin by flavanone-3-hydroxylase. Before this finding, flavonoid degradation by Pseudomonas spp. was thought to occur primarily via A-ring cleavage mechanisms (JeVrey et al., 1972a, b; Rao and Cooper, 1994; Rao et al., 1991; Schultz et al., 1974). Quercetin is not a nod gene inducer for M. loti (Lo´ pez-Lara et al., 1995), but other rhizobia can degrade their own presumptive nod gene inducers by mechanisms that originate with C-ring fission (Rao and Cooper, 1994, 1995). Other general features of the process are apparent, such as the formation of C-ring modification compounds in addition to conserved A- and B-ring monocyclic hydroaromatics. Also, flavonoids with OH substitutions at the 5- and 7-carbon positions yield phloroglucinol as the main conserved A-ring product, whereas those with a single OH substitution at the 7-carbon position yield resorcinol. Conserved B-ring products are more varied and include p-coumaric acid (4-hydroxycinnamic acid), p-hydroxybenzoic acid (4-hydroxybenzoic acid), protocatechuic, and phenylacetic and caVeic acids (Rao and Cooper, 1994). When incubated with its principal nod gene inducer, luteolin, S. meliloti yielded new closed C-ring metabolites (tetrahydroxyflavanone and apigenin) in addition to a suite of monocyclic phenolic degradation products (Cooper and Rao, 1995).

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The same general pattern of degradation applies to the isoflavonoid nod gene inducers daidzein and genistein during incubation with B. japonicum USDA110spc4, and a detailed analysis of products revealed that significant amounts of isoflavonoid were transformed (Rao and Cooper, 1995). Proposed degradation pathways for both compounds, based on products detected in culture supernatants by GC-MS and HPLC-UV, are shown in Fig. 9. As previously noted for the catabolism of quercetin by P. putida the rhizobial degradation of these isoflavonoids can be regarded as a reversal of their plant biosynthetic pathways. A notable feature of this scheme is the requirement for aryl (B-ring) migration from the 3-carbon to the 2-carbon position on the C-ring to account for the presence of chalcones among the metabolites. The pathways allow for the possibility of multiple sources for some products; for example, umbelliferone could be derived from any or all of the following compounds in the daidzein scheme: resorcinol, coumestrol, or p-coumaric acid. The formation of several new metabolites from single isoflavonoid precursors could have implications for the root infection process. For example, as already noted, isoliquiritigenin is a strong nod gene inducer for B. japonicum (Kape et al., 1992), whereas umbelliferone and coumestrol can act as moderately eVective inducers or inhibitors, depending on the strain of B. japonicum (Kosslak et al., 1990). Two degradation products from daidzein and genistein, namely umbelliferone and phenylacetic acid, reduced the nod gene-inducing activity of genistein in B. japonicum USDA110spc4 by significant amounts (Rao and Cooper, 1995). Current models of NodD-flavonoid interaction take no account of the degradation of nod gene inducers by the receiving organism and the formation of new compounds that may influence gene induction. Findings from studies of flavonoid degradation by rhizobia point to a more complicated version of flavonoid-induced nod gene expression than the one proposed by Hubac et al. (1993, 1994), which involved enhanced retention of a single, inert inducer compound (luteolin) in the outer membrane of S. meliloti. One study has revealed a positive correlation between inducer catabolism and nodulation: in S. meliloti, genes involved in demethylation of the betaine nod gene inducer stachydrine are required for eYcient host nodulation and are grouped on the symbiotic plasmid in a region that contains the nodulation genes (Goldmann et al., 1994). The reports of flavonoid and isoflavonoid nod gene-inducer degradation, as previously described, were based on experiments in which authentic compounds were added to defined growth media at a concentration of 10 mM. If one discounts studies of rhizobial influence on flavonoid release from roots, in which the occurrence of some compounds may be attributable to flavonoid modification or degradation in the rhizosphere (see the section on the synthesis

Fig. 9. Proposed pathways for degradation of the isoflavones genistein and daidzein by Bradyrhizobium japonicum, based on products detected by GC-MS and HPLC-UV analyses of extracted culture supernatants. Isomerization of chalcones and flavanones is indicated by reversible arrows. (From Rao and Cooper, 1995.)

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and release of flavonoids, earlier in this chapter), he or she should consider that very few experiments providing information on rhizobial biotransformations of flavonoids in legume epidermal exudates have been undertaken. Steele et al. (1999) detected two unidentified new flavonoids as well as elevated concentrations of protocatechuic acid and phloroglucinol in root exudate from Lotus pedunculatus following incubation with M. loti. Incubation of the same exudate with R. leguminosarum bv. trifolii also yielded two new phenolic compounds that were diVerent than those formed by M. loti. No nod gene induction assays were performed with the new compounds. The monocyclic phenolics 4-hydroxybenzoate and protocatechuate, which are often found among the products of rhizobial flavonoid metabolism (Rao and Cooper, 1994, 1995; Rao et al., 1991), are known to serve as growth substrates for rhizobia (Chen et al., 1984; Hussein et al., 1974; Lorite et al., 1998; Muthukumar et al., 1982; Parke and Ornston, 1984; Wong et al., 1991). Both compounds, together with another flavonoid degradation product, p-coumaric acid (4-coumaric acid), can be metabolized via the protocatechuate branch of the -ketoadipate pathway (Fig. 10) to enter the citric acid cycle via succinyl CoA and acetyl CoA (Parke, 1997; Parke et al., 1991). Some rhizobia can also metabolize 4-hydroxybenzoate via catechol or salicylic acid and gentisic acid prior to ring cleavage (Muthukumar et al., 1982). Microbial degradation of protochatechuate to -ketoadipate may also proceed via the catechol branch of the pathway, whereas phloroglucinol carboxylic acid and phloroglucinol may reach the same endpoint via intermediates such as resorcinol, hydroxyhydroquinone, and maleyl acetate (Bhat et al., 1998). Distribution of the -ketoadipate pathway is widespread among the rhizobia (Parke, 1997), and it appears that enzymes of the pathway are inducible in Rhizobium species but constitutive in Bradyrhizobium (Parke and Ornston, 1986). Catabolism of 4-hydroxybenzoate and protocatechuate via the protocatechuate branch of the pathway in Agrobacterium tumefaciens is mediated by a regulatory gene, pcaQ, which encodes an activator that responds to -carboxy-cis,cis-muconate and controls the expression of five genes in the pathway: pcaDCHGB (Parke, 1993, 1995). Like NodD, the activator protein PcaQ is a member of the LysR family of transcriptional regulators (Parke, 1996a), but in a phylogenetic tree of LysR-type proteins it falls outside the group that contains NodD (Schlaman et al., 1992). Homologues of pcaQ have been found in rhizobia (Parke, 1996b), and the pattern of induction of pca genes in A. tumefaciens is similar to that in R. leguminosarum bv. trifolii (Parke, 1995). The genetic organization and regulation of the -ketoadipate pathways in various bacteria, including rhizobia, have been reviewed by Harwood and Parales (1996). No definitive evidence has yet been obtained to show that flavonoid degradation

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Fig. 10. The protocatechuate branch of the -ketoadipate (3-oxoadipate) pathway in rhizobia with gene designations shown for each step.

by rhizobia is accompanied by channelling of some monocyclic phenolic metabolites into the protocatechuate branch of the -ketoadipate pathway. However, the detection of -carboxy-cis,cis-muconate during incubation of catechin with B. japonicum (Hopper and Mahadevan, 1997) and elevated

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levels of pcaH expression in S. meliloti growing in luteolin-supplemented culture medium (T. P. McCorry and J. E. Cooper, unpublished results) suggest that this is the case. Finally, an intriguing pointer to the fate of certain carbon atoms in a nod gene-inducing flavonoid during assimilation and metabolism by R. leguminosarum bv. viciae was obtained from a radio-carbon tracing study using A-ring labelled 14C-naringenin (Rao et al., 1996). Two hours after presenting this compound to the bacterium at a nod gene-inducing concentration of 2 nM (12.5 Kilobecquerels [kBq]), radio activity (2189 disintegrations per minute [dpm]) was detected in a supernatant fraction corresponding to one of the main biologically active Nod factors of this organism, NodRlv-IV (Ac, C18:4) (Spaink et al., 1991). Further analysis revealed that 95% of this signal was located in the fatty acid side chain at the nonreducing terminus that is a determinant of host specificity for this Nod factor. Incorporation of carbon atoms that were originally in naringenin, into the fatty acyl moiety occurred despite the fact that the flavonoid was supplied at a concentration that was 250 times lower than cold acetate in the incubation medium. This is the sole available example of the contribution of structural elements (carbon atoms) from a nod gene-inducing flavonoid to a Nod factor and it is not known whether such an intimate linkage between primary and reciprocal signal molecules is a general feature of rhizobial responses to inducing compounds. The contribution of various signalling events to the specificity of legumerhizobia symbioses has been referred to throughout this review, but, with the qualification that flavonoid reception determines the production or modifies the nature of many other rhizobial signal molecules, there appears to be no single step that ensures a successful outcome in the form of a nitrogen-fixing root nodule. Certainly, flavonoid interaction with NodD and/or Nod factor eVects on root hair curling and cortical cell division can be highly specific in some partnerships but, as argued by Fellay et al. (1995), symbiotic control can be spread over a number of steps, none of which need be specific. The infection process can therefore be viewed as a series of hurdles carrying varying probabilities of successful negotiation, which has the capacity to account for all known combinations of macrosymbiont and microsymbiont.

VII. CONCLUSION Since the identification of luteolin and 7, 40 -dihydroxyflavone as activators of rhizobial nod gene expression in 1986, it has been accepted that flavonoids are key participants in the molecular dialogue between plant and bacterium that eventually leads to a functioning root nodule. Appreciation of their

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contribution to this process was enhanced by the discovery that the reciprocal Nod factor signals, whose synthesis they induce, are the agents of numerous changes in the host root at the physical, biochemical, and molecular genetic levels, which are all associated with infection and nodule development. However, as this chapter has attempted to demonstrate, Nod factors represent only one of many types of signals transmitted from rhizobia to the host plant and a close analysis of the literature reveals that flavonoids initiate, regulate, or mediate the production of most of them. As examples, flavonoid inducers are required for the synthesis of type III proteins, and their secretion machinery, the type I secreted protein NodO, polysaccharide-degrading enzymes, and numerous other proteins whose functions have yet to be determined. The three main classes of rhizobial surface polysaccharides—EPS, KPS, and LPS—are all subject to flavonoid-mediated qualitative or quantititative changes either during or after biosynthesis. Although gene induction or up-regulation are the most common functions attributable to flavonoids, they can also act as repressors, as in the case of rhi genes in R. leguminosarum bv. viciae. Evidently, as initiators of the rhizobial response to the presence of a legume that involves multiple reciprocal signals whose synthesis is directly or indirectly dependent upon them, flavonoids cannot simply be regarded as one type of signal among several with approximately equal significance. On the contrary, they appear to be a sine qua non for infection of virtually all legumes studied to date. New flavonoid-inducible genes and their products continue to be discovered and their roles during host infection elucidated. Future transcriptional, proteomics, and mutational analyses, together with the mining of data from rhizobial genome sequencing projects, will undoubtedly uncover more examples to further expand the scope of flavonoid influence on symbiotic development.

ACKNOWLEDGMENTS I wish to thank Allan Downie, Otto Geiger, Hari Krishnan, and Wolfgang Streit for providing information on their recent research, J. R. Rao for many helpful discussions, and Oonagh McMeel for the preparation of figures.

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Investigating and Manipulating Lignin Biosynthesis in the Postgenomic Era

CLAIRE HALPIN

University of Dundee, Plant Science Research Group, Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, United Kingdom

I. II. III. IV.

V. VI.

VII. VIII. IX. X. XI.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lignification in Plant Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structure of the Lignin Polymer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Monolignol Biosynthetic Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. The Basic Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Recent Revisions to the Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. DiVerent Pathway Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monolignol Polymerization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cell Biology of Lignin Biosynthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Immunolocalization of Lignin Biosynthetic Enzymes . . . . . . . . . . . . . . B. Do Enzyme Complexes Promote Metabolite Channelling? . . . . . . . . . C. Export of the Monolignols to the Cell Wall . . . . . . . . . . . . . . . . . . . . . . . D. Alternative Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transcriptional Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Transcription Factors and Regulatory Elements . . . . . . . . . . . . . . . . . . . B. Transcript Profiling Using Microarrays . . . . . . . . . . . . . . . . . . . . . . . . . . . Metabolic Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analysis of Lignin Content, Structure, and Composition . . . . . . . . . . . . . . Genes Involved in Lignin Biosynthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Plants with Modified Expression of Lignin Biosynthetic Genes . . . . . . . . A. Phenylalanine Ammonia Lyase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Cinnamic Acid 4-Hydroxylase. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. 4-Coumarate:CoA Ligase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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D. Hydroxycinnamoyl-CoA:Shikimate/Quinate Hydroxycinnamoyl Transferase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. P-Coumarate 3-Hydroxylase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. CaVeoyl-CoA O-Methyltransferase. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. CaVeic Acid O-Methyltransferase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Ferulate 5-Hydroxylase. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. Cinnamoyl-CoA Reductase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Cinnamyl Alchohol Dehydrogenase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Manipulation of Multiple Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XII. Commercial Applications of Modified Lignin Plants. . . . . . . . . . . . . . . . . . . XIII. Conclusions and Perspectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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ABSTRACT Enormous progress has been made over the last decade in understanding and manipulating the pathway for lignin biosynthesis in plants. The roles of most of the genes on the pathway have now investigated by reverse genetic approaches in a variety of species, including trees, and field trails underpinning commercial exploitation have taken place. Despite this, many basic aspects of the lignin pathway are still very poorly understood. Little is known about the cell biology of the process, for example, and much has still to be learned about how the pathway is controlled and regulated at transcriptional and biochemical levels. The advent of post-genomic technologies such as transcript and metabolite profiling offer new opportunities for probing the lignin pathway and its inter-relationship with other pathways and developmental processes in plants. This review describes recent advances in understanding lignification while highlighting the areas where significant further work is needed.

I. INTRODUCTION Lignification is a fundamental developmental process unique to higher land plants. Over the past decade the biochemistry and molecular biology of lignification have been extensively studied, an eVort driven both by scientific and by commercial interests. Lignin is the second most abundant biopolymer on earth, constituting 20% of total organic carbon, and is thought to have played a critical role in the evolution of land plants by conferring structural rigidity to strengthening tissues (sclerenchyma fibers), and by waterproofing xylem vessels. Lignin has major influence on the ease of wood pulping during papermaking, on the digestibility of forage crops, and on the postharvest quality of certain vegetables. It is also critical for normal plant health, development, and disease resistance. Most of the genes involved in lignin biosynthesis have been cloned, and a significant amount of research eVort has been committed to determining the function of individual genes by reverse genetics. Genetic engineering of the

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pathway has been achieved in a variety of species including trees, and field trials to underpin commercial exploitation have already taken place. Despite, or perhaps because of, this headlong rush toward biotechnological applications, some basic aspects of lignification have largely been ignored. The spatial organization of the pathway, how it is regulated, and its role in the myriad complex interactions that result in normal plant development are all areas that have received relatively little research attention to date. Although the focus on the biotechnological applications of lignin manipulation in part explains the lack of progress in elucidating these more fundamental scientific questions, progress has also been hampered by the fact that such issues were diYcult to address with pregenomic technologies. The recent development of novel tools, particularly some of the technologies of functional genomics, is beginning to impact these areas of research. The application of techniques such as microarray analyses, metabolite profiling, and T-DNA mutagenesis oVer enormous potential for illuminating the full interactions of the lignin pathway with other plant metabolic processes and for elucidating the full consequences of manipulating lignin biosynthesis in transgenic crops. This chapter describes much of the recent work aimed at understanding lignification and highlights areas where a significant amount of further work is needed to give an overview of our current state of knowledge and ignorance of this important developmental process.

II. LIGNIFICATION IN PLANT DEVELOPMENT Lignin is deposited in the secondary wall of certain diVerentiating cell types. Although predominantly found in sclerenchyma fibers and in the tracheids and vessels of vascular tissue, lignin is also critical to the functioning of other, less obvious cell types. For example, loss of lignification in a subset of valve margin cells in fruits of the Arabidopsis shp1 shp2 mutant is implicated in the failure of the fruits to dehisce (Liljegren et al., 2000). Lignin can also be deposited in response to infection or certain abiotic stimuli, even in cell types that do not normally contain it. Nevertheless, lignin deposition during normal plant development is cell specific and tightly controlled both temporally and spatially. Several Arabidopsis mutants illustrate how alteration in this temporal and spatial control may result in premature lignification (Mele et al., 2003) and/or ‘‘inappropriate’’ lignification of pith parenchyma cells (Newman et al., 2004; Zhong et al., 2000a), endodermal cells (Cano-Delgado et al., 2000), or epidermal cells (Mele et al., 2003). The study of such ‘‘ectopic lignification’’ mutants is beginning to reveal the complex, interconnected networks that can influence and control lignin

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deposition. The eli1 (ectopic lignification 1) mutants, for example, have shown reduced cellulose synthesis as a result of mutations in the cellulose synthase gene CESA3 (Cano-Delgado et al., 2003). This cellulose deficiency is proposed to activate lignin synthesis and defense responses through jasmonate and ethylene and other signalling pathways. The induction of lignification in tissues with reduced cellulose synthesis suggests that plant cells may have mechanisms to monitor, and perhaps to attempt to maintain, cell wall integrity. Ethylene signalling was also altered in elp1 (ectopic deposition of lignin in pith 1), a mutant in a chitinase-like gene, AtCTL1. Ethylene production in the mutant was significantly greater than in the wild-type, and certain mutant phenotypes, including exaggerated hook curvature and increased root hair formation, could be rescued by ethylene inhibitors, whereas other phenotypes such as reduced root and hypocotyl length could be partially rescued (Zhong et al., 2002). The ectopic lignification was, however, unaVected by ethylene inhibitors, indicating that although these studies reveal aspects of the signalling cascades that may regulate lignification, no simple conclusions can yet be drawn. Further complexity is added by a very recent report indicating that misexpression of a specific transcription factor, AtMYB61, can cause ectopic lignification in Arabidopsis (Newman et al., 2004). In the det3 (de-etiolated 3) mutant the spatial control of AtMYB61 expression is lost and this apparently induces both the ectopic lignification and dark photo-morphogenic phenotypes of this mutant. It is still unclear how these two pleomorphic phenotypes are connected and how AtMYB61 expression is influenced by the det3 locus, which encodes the C subunit of a vacuolar ATPase (adenosine triphosphatase). Given that many cellular processes may be disrupted in this mutant, it is possible that ethylene and jasmonic acid signalling could be altered, as in the case of eli1. Further investigation is needed to determine whether common or diVerent mechanisms and pathways are responsible for the ectopic lignification phenotypes of these various, apparently unrelated, mutants. The importance of lignin to maintaining the integrity of certain cell walls is graphically illustrated by the collapsed xylem vessels seen in mutant or transgenic plants with reduced expression of certain specific lignin biosynthetic genes (Franke et al., 2002a; Jones et al., 2001; Piquemal et al., 1998). Many of these plants have radically reduced lignin content and show a range of developmental abnormalities including stunted growth and, often, altered leaf morphology, highlighting the critical role lignin plays in normal plant development. However, the link between reduced lignin content and adverse phenotypes is not absolute (Chabannes et al., 2001a; O’Connell et al., 2002) and nonpolymeric products of the lignin pathway may also have as yet illdefined roles in modifying development. When phenolic metabolism and

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lignin biosynthesis were repressed in tobacco by heterologous expression of a Myb gene from the Antirrhinum plant, growth was inhibited and leaves showed signs of premature cell death (Tamagnone et al., 1998a). A deficiency in dehydrodiconiferyl alcohol glucosides (DCGs; synthesized from the monolignol coniferyl alcohol) was implicated in causing the leaf phenotypes. DCGs are cytokinin-like growth promoters that have been implicated in the regulation of cell division and expansion (Binns et al., 1987; Tamagnone et al., 1998b; Teutonico et al., 1991). Levels of DCGs were greatly reduced in the Myboverexpressing plants, and suspension cultured cells from these plants regained a normal phenotype when fed the glucoside (Tamagnone et al., 1998b). Further work is needed therefore to resolve the relative roles of deficiencies in lignin and in soluble phenolics in causing the various phenotypes that indicate altered development in certain lignin-modified plants.

III. STRUCTURE OF THE LIGNIN POLYMER Lignin content and composition varies among plant taxa, species, cell types, and even from one part of the cell wall to another. Lignin is a polymer predominantly composed of para-coumaryl ( p-coumaryl) alcohol, coniferyl alcohol, and sinapyl alcohol, the three ‘‘monolignols’’ (Fig. 1). These units diVer only in their degree of methoxylation and give rise to three diVerent types of lignin; that is, p-hydroxyphenyl (H), guaiacyl (G), and syringyl (S) lignin, respectively. The monolignol composition of the polymer varies considerably among taxa, tissues, and cell types. In gymnosperms, lignin is composed almost entirely of guaiacyl units, with small amounts of p-hydroxyphenyl units. The proportion of H lignin is greatly increased in compression wood, which forms in regions under compressive stress, such as the underside of branches. Lignin in most dicot angiosperms is a combination of guaiacyl and syringyl monomers, present in varying proportions according to species. In the monocot

Fig. 1. alcohol.

The monolignols para-coumaryl alcohol, coniferyl alcohol, and sinapyl

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grasses, lignin additionally contains a significant proportion of H units. A variety of chemical linkages including noncondensed -O-4 ether bonds and condensed carbon-carbon bonds (e.g., - , -1, -5, and 5-5) can connect the monomers in lignin. Gymnosperm lignin has relatively more condensed carbon-carbon bonds than angiosperm lignin because of the predominance of G units where the aromatic C5 position is free to make ring-to-ring linkages. These carbon-carbon bonds are resistant to chemical degradation, which explains why gymnosperm woods are harder to pulp than angiosperm woods using chemical pulping processes.

IV. THE MONOLIGNOL BIOSYNTHETIC PATHWAY A. THE BASIC PATHWAY

Many aspects of the lignin biosynthetic pathway have been comprehensively discussed in recent reviews (see Anterola and Lewis, 2002; Baucher et al., 2003; Boerjan et al., 2003; Humphreys and Chapple, 2002), so only a brief description of the pathway and related unresolved issues will be given in this chapter. Lignin is a product of the phenylpropanoid pathway. Early reactions on the pathway are catalyzed by phenylalanine ammonia lyase (PAL), cinnamate 4-hydroxylase (C4H) and 4-coumarate:CoA ligase (4CL). Subsequent hydroxylation and methylation reactions add one or two methoxyl groups to the phenyl ring. Four enzymes—cinnamate 3-hydroxylase (C3H), caVeoyl-CoA O-methyltransferase (CCoAOMT), ferulate 5-hydroxylase (F5H), and caVeate O-methyltransferase (COMT)—are involved in these reactions. Two successive reduction reactions catalyzed by cinnamoyl-CoA reductase (CCR) and cinnamyl alcohol dehydrogenase (CAD) are involved in the final steps of monolignol synthesis. All nine of the enzymes referred to so far have been cloned, and their involvement in lignin biosynthesis has been clearly demonstrated in vivo because transgenic or mutant plants deficient in any of these activities have significant changes to either lignin content or lignin monomeric composition. B. RECENT REVISIONS TO THE PATHWAY

The monolignol biosynthetic pathway has undergone many recent revisions and it is likely that the substrate(s) and position(s) in the pathway of some of the enzymes have still not been definitively determined. A major revision concerns the position in the angiosperm pathway where guaiacyl precursors can be converted, by the addition of a methoxyl group, into syringyl lignin precursors. This occurs in two reactions catalyzed by the enzymes F5H and COMT. According to the traditional lignin pathway, these reactions were

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believed to occur at the level of the acids, converting ferulic acid into sinapic acid. However, recent work indicates these reactions really occur at the position of the aldehydes and possibly the alcohols (Chen et al., 1999; Humphreys et al., 1999; Osakabe et al., 1999). Although these pathway revisions were originally proposed on the basis of biochemical evidence, such as the substrate specificity of the enzymes assayed in vitro, they are entirely consistent with results previously obtained from plants manipulated or mutated to reduce COMT or F5H expression. Recent discoveries suggest the possible involvement of at least two additional enzymes in the production of the monolignols. The cloning of C3H and the identification of mutants defective in it (Franke et al., 2002a,b; Schoch et al., 2001) have indicated that p-coumaroyl shikimate and p-coumaroyl quinate are likely to be important intermediates in lignin biosynthesis. These two compounds and not p-coumarate as previously thought, are the preferred substrates for C3H (Schoch et al., 2001), which in turn suggests that a novel hydroxycinnamoyl-CoA:shikimate/quinate hydroxycinnamoyltransferase (HCT), capable of conjugating the shikimate and quinate groups onto p-coumarate, must also be involved in lignin biosynthesis. Recently the gene encoding HCT has been cloned (HoVmann et al., 2003), and an important role for it in plant development and lignin biosynthesis has been verified using reverse genetic approaches (HoVmann et al., 2004). Similarly, a second novel gene, encoding a sinapyl alcohol dehydrogenase (SAD), has recently been discovered in poplar (Li et al., 2001a). This enzyme apparently preferentially converts sinapaldehyde into sinapyl alcohol, indicating a potential role in the lignin pathway. SAD expression also correlates with S lignin biosynthesis both spatially and temporally. On the basis of these data a new pathway for lignin biosynthesis has been proposed where CAD exclusively catalyzes the conversion of coniferaldehyde to coniferyl alcohol while SAD converts sinapaldehyde to sinapyl alcohol (Li et al., 2001a). The data indicating a potential role for SAD in lignin biosynthesis are convincing but nonetheless circumstantial. Further work, particularly the production of SAD mutants or SADsuppressed transgenics, is needed to validate and further investigate the role of SAD in vivo. C. DIFFERENT PATHWAY MODELS

The current uncertainty about the exact sequence of reactions that contribute to lignin biosynthesis is reflected in the recent literature, in which numerous apparently diVerent models of the pathway can be found. Many still view the pathway as the traditional ‘‘metabolic grid’’ or network where

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certain routes may nevertheless be favored over others in diVerent tissues or cell types (see Anterola and Lewis, 2002; Boerjan et al., 2003). However, certain research groups favor models in which the main lignin pathway is a streamlined linear sequence of reactions leading to G lignin synthesis with a single branch point allowing for S lignin biosynthesis (Li et al., 2001a, 2003). Still others have suggested a metabolic channeling model that allows for essentially independent pathways for G and S lignin synthesis (Dixon et al., 2001). Although the distinction between these diVerent models is sometimes more apparent than real (most pathways allow for the potential existence of other minor routes), this situation must be very confusing for the nonexpert reader. Further research is needed to clarify this most basic aspect of lignin biosynthesis (i.e., to outline the full sequence of reactions on the main biosynthetic pathway in normal wild-type plants). The extent to which the pathway is the same in diVerent dicot angiosperms is also an area for further clarification because substrate specificities of some enzymes, notably 4CL, diVer among species.

V. MONOLIGNOL POLYMERIZATION Several diVerent possible mechanisms have been proposed for the polymerization of monolignols within the cell wall (reviewed by Boerjan et al., 2003; Lewis, 1999). Traditionally it has been believed that monolignols are converted into phenoxy radicals by cell wall oxygenases, although the identity of the oxygenases involved is still an issue of some debate. Peroxidases, laccases, and other phenoloxidases have all been proposed to be involved (Dean and Eriksson, 1994; McDougall et al., 1994; Richardson et al., 1997; Savidge and Udagama-Randeniya, 1992), but the multiplicity of such enzymes that exist in plants and the possibility of functional redundancy make the exact role of specific enzymes diYcult to investigate. Considerable controversy also exists as to whether the subsequent polymerization of the monolignols is a random, spontaneous phenomenon (Hatfield and Vermerris, 2001), or whether it is highly ordered (Gang et al., 1999) and perhaps mediated by dirigent proteins such as the one isolated from Forsythia, which is capable of catalyzing the stereoselective coupling of two coniferyl alcohol radicals into the lignan pinoresinol (Davin et al., 1997). This debate is not likely to be drawn to a rapid conclusion, particularly because good evidence from transgenic or mutant plants supporting the roles of particular cell wall enzymes is still lacking. New ideas also need to be incorporated into this discussion, such as the recent proposal that redox shuttle-mediated oxidation may be ¨ nnerud et al., 2002). involved (O

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VI. CELL BIOLOGY OF LIGNIN BIOSYNTHESIS A. IMMUNOLOCALIZATION OF LIGNIN BIOSYNTHETIC ENZYMES

Considering the enormous amount of research expended on lignin biosynthesis in recent years, it is surprising how little is definitely known about the cell biology of the process. It is generally accepted that the monolignols are synthesized inside the cell, then exported to the cell wall where they are polymerized into lignin. However, a degree of uncertainty still surrounds the subcellular localization of the lignin biosynthetic enzymes. It was originally believed that the enzymes might reside within the endomembrane system because this made it easy to envisage monolignol export to the cell wall in membrane-bound vesicles. However, this assumption was brought into question once lignin biosynthetic genes began to be cloned. Examination of the sequences of many lignin biosynthetic genes (including PAL, CCoAOMT, COMT, 4CL, CCR, and SAD) reveals that most of these genes do not contain signal sequences or other recognizable subcellular targeting domains. The absence of such targeting signals strongly suggests that many lignin enzymes may be cytosolic. However, the issue of the subcellular location of these enzymes is given further complexity by repeated claims of enzyme:enzyme complexes, perhaps membrane-bound, that might act to promote metabolic channelling of phenylpropanoid intermediates (Dixon et al., 2001; Guo et al., 2002; Rasmussen and Dixon, 1999; Winkel-Shirley et al., 1999). One might expect that many uncertainties about the cell biology of lignification might be simply resolved by immunolocalization of the individual lignin biosynthetic enzymes. Although attempts have been made to immunolocalize certain enzymes in xylem cells, results from diVerent groups have been somewhat contradictory. PAL, controlling the entry point to the phenylpropanoid pathway, is the most extensively studied enzyme involved in lignin biosynthesis. A small number of diverse studies have looked at its subcellular localization, and the results suggest that it may be present in multiple locations. In lignifying bean xylem, PAL was found by immunocytochemistry to be mostly cytosolic but sometimes membrane associated and sometimes in vacuoles (Smith et al., 1994). In diVerentiating Zinnia tracheary elements, PAL was found to be dispersed between cytosol, the endomembrane system, and cell walls (Nakashima et al., 1997). Similar confusion surrounds the localization of CAD, the final enzyme of monolignol synthesis. Two independent immunolocalization experiments contradict each other. Nakashima et al. (1997) report that in Zinnia, CAD is found in the cytosol, in Golgi-derived vesicles, and in secondary cell walls (an identical localization to that was reported for PAL in the same paper). Samaj et al. (1998)

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report that in poplar, CAD is found predominantly in the cytoplasm and is ‘‘randomly’’ associated with endoplasmic reticulum (ER) and Golgi vesicles but is not present in the cell wall. It is possible that some of these results are anomalous because of artifacts that arise because of the need to fix and section materials before visualization. For example, localization of CAD and PAL in cell walls could be a result of the spread of epitopes from the cytosol during the physical disruption of preparation and sectioning. By contrast, Takabe et al. (2001) report finding little evidence for PAL or CAD localization on membranes or in the cell wall in poplar, and concluding that these enzymes are cytosolic. A recent study immunolocalizing CAD, COMT, and CCR in maize and sugarcane also concludes that all three enzymes are mainly cytosolic (Ruelland et al., 2003). Similarly, other researchers have indicated a cytosolic location for CCoAOMT and/or COMT in alfalfa, poplar, and eucalyptus (Kersey et al., 1999; Takeuchi et al., 2001). Despite the uncertainly about the subcellular localization of many lignin biosynthetic enzymes, at least three are clearly localized on membranes. C4H, like other plant Cyt P450-dependent monoxygenases (P450s), is a membrane protein with its catalytic site in the cytoplasm. It has been purified from microsomes (Gabriac et al., 1991) and was shown by immunolocalization to be abundant in Golgi in French bean (Smith et al., 1994). However, more recent work expressing a C4H:GFP fusion in Arabidopsis suggests that C4H is exclusively localized to the ER (Ro et al., 2001). Although the other two P450 enzymes, C3H and F5H, that act downstream of C4H on the lignin pathway, are certain to be membrane proteins, their exact location within cells has not been formally determined. F5H activity has been demonstrated in microsome membranes (Grand et al., 1984). B. DO ENZYME COMPLEXES PROMOTE METABOLITE CHANNELLING?

Although many lignin biosynthetic enzymes are probably cytosolic, a peripheral association with membranes, mediated by interaction with membrane proteins, is possible. Assembly of complexes of lignin enzymes on membranes is an attractive idea, because this could facilitate the metabolic channelling that some research groups have claimed for the pathway. It is also easier to envisage how the monolignols might get sequestered into export vesicles if the enzymes producing those monolignols are membrane associated. A possible association of PAL and C4H on microsome membranes has been suggested to explain metabolic channelling from Phe to p-coumarate (Czichi and Kindl, 1977; Rasmussen and Dixon, 1999; Wagner and Hrazdina, 1984). However, no data directly supports interactions between lignin enzymes, and many of the enzymes, including CAD, can

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be purified to homogeneity from aqueous cell extracts without the use of detergents or reagents that disrupt protein:protein interactions. Channelling between F5H and COMT has also been repeatedly proposed. Although a small proportion of total COMT activity could be found together with F5H activity in crude microsomal preparations from alfalfa, no improved catalytic eYciency in the production of sinapaldehyde could be demonstrated (Guo et al., 2002). The authors concluded that their results provided only limited support for the concept of metabolic channelling in the biosynthesis of S monolignol precursors. C. EXPORT OF THE MONOLIGNOLS TO THE CELL WALL

It is assumed by many that the monolignols are exported as glucosides, although no direct evidence supports this. The cambial sap of conifers contains high concentrations of the monolignol glucoside coniferin (see Whetten and SederoV, 1995), which has been assumed to be a stored source of monomers for subsequent lignification of developing tracheids. Some angiosperms also store monolignol glucosides, but most do not. A large multigene family of uridine diphospho (UDP)-glucosyltransferases (UDPGs) has been identified in Arabidopsis (Li et al., 2001b), but so far only circumstantial evidence based on substrate specificities of the recombinant enzymes suggests which gene(s) could be involved in monolignol modification (Lim et al., 2001). Two of the recombinant UDPGs were able to 4-Oglucosylate sinapyl alcohol into syringin, and one of these could also glucosylate coniferyl alcohol to coniferin in vitro (Lim et al., 2001). However, a direct role for such reactions during lignin biosynthesis remains to be proven. Traditionally, it has been assumed that the monolignols are exported in membrane-bound vesicles, because vesicle-like structures filled with ultraviolet (UV)-fluorescent material are abundant in lignifying cells. However, cell wall phenolics such as ferulic acid are also highly UV fluorescent and no direct evidence confirms that these vesicles actually transport monolignols. More recently, high-resolution transmission electron microscopy of pine xylem has confirmed that developing tracheids actively making secondary walls have a highly developed trans-Golgi network with unusual structures and large associated vesicles (Samuels et al., 2002). These vesicles were apparently involved in exporting cell wall polysaccharides such as mannans to the cell wall, but they were also stained with osmium, indicating that they might also transport phenolics. A rarely considered alternative to the hypothesis of vesicular transport of monolignols is the possibility that the monomers might be able to cross the

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plasma membrane directly, either as glucosides or aglycones. Either way, it is not known whether the monolignols can cross the relevant membrane (either vesicle or plasma membrane) by simple diVusion or whether the action of a specific transporter is needed. If monolignols are indeed transported as glucosides, the activity of a cell wall glucosidase is needed to remove the sugar residue before polymerization. A coniferin -glucosidase capable of deglucosylating one of the monolignols has been cloned from pine (Dharmawardhana et al., 1999). Immunolocalization of this enzyme indicates that it is extracellular, residing in the secondary cell walls of developing xylem, a location compatible with its proposed role in deglycosylating lignin precursors before polymerization (Samuels et al., 2002). Reverse genetic approaches now need to be applied to more directly investigate whether lignin precursors are transported as glucosides and to what extent coniferin -glucosidase is needed for lignin biosynthesis. Given that such approaches would be technically diYcult to implement in pine, the identification of coniferin -glucosidase orthologues in more easily manipulated species such as Arabidopsis or tobacco should be a priority, but no information on this yet exists in the literature. D. ALTERNATIVE MODELS

A number of diVerent potential models can therefore be proposed to explain the cell biology of lignin biosynthesis. The simplest model is based purely on the fact that no direct evidence supports a more complicated scheme. According to this model, the lignin pathway operates by free diVusion of substrates between enzymes located in the cytoplasm and the P450 enzymes located on a membrane. Monolignol products either diVuse through the plasma membrane or are transported across via a specific transporter, either as free alcohols or as glucosides (Fig. 2A). A more complicated model takes into account the possibility of metabolite channelling on the pathway. According to this model, lignin biosynthetic enzymes located in the cytosol could be sequestered onto membranes by the membraneintegrated P450 enzymes (C4H, C3H, and F5H). The membrane involved might be the ER, Golgi, or a post-Golgi compartment. After synthesis, the monolignols would be glucosylated by a UDPG interacting with the membrane-associated complex of lignin enzymes (Plant UDPGs are assumed to be cytosolic enzymes.). The monolignol glucosides would then be transported in vesicles to the plasma membrane for export to the cell wall (Fig. 2B). The currently available evidence does not allow us to distinguish even between these two extreme models, and it is likely that in reality the

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Fig. 2. Two possible models of lignin biosynthesis. (A) Simple model—the lignin pathway operates by free diVusion of substrates between enzymes located in the cytoplasm, and the P450 enzymes (C4H, C3H, and F5H) located on a membrane and monolignol products diVuse through the plasma membrane. (B) More complicated model—lignin biosynthetic enzymes located in the cytosol are sequestered onto membranes by the membrane-integrated P450 enzymes. Monolignols are glucosylated by a UDP-glucosyltransferase, cross into vesicles (mediated by a specific transporter D), and are transported to the plasma membrane for export to the cell wall. Current knowledge does not allow us to distinguish between these two extreme models and it is likely that, in reality, elements of both models are involved.

route(s) of synthesis and transport of monolignols may involve elements of both models. To date, no conclusive model of the spatial organization of the lignin pathway exists and many aspects of the cell biology of the process have been largely neglected. The use of sophisticated modern techniques, including live-imaging microscopy of fluorescently labelled lignin biosynthetic enzymes, should enhance this area of research, but only one study so far has taken this approach (Ro et al., 2001). It is remarkable that more work has not been performed to reveal the cell biology underpinning this major export pathway of many plant cells. Knowledge of the spatial organization of the lignin pathway at the subcellular level will also ultimately underpin biotechnological advances and may lead to new ideas on how the processes of lignification and secondary cell wall deposition can be manipulated to useful ends.

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VII. TRANSCRIPTIONAL REGULATION A. TRANSCRIPTION FACTORS AND REGULATORY ELEMENTS

Few regulatory or transcription factor genes have been identified that can promote or inhibit expression of lignin biosynthetic enzymes. Results from the Arabidopsis genome initiative suggest that plants have a particularly large complement of transcription factor genes and only a small fraction have yet been characterized. Now that information on the full number of Arabidopsis transcription factors is available, rapid progress can be expected in this area. Quite a bit is already known about transcriptional regulation of the general phenylpropanoid pathway, which represents the early steps in lignin biosynthesis (Leyva et al., 1992; Sablowski et al., 1994), but little is understood about the regulation of the later steps more dedicated to monolignol production. A growing body of evidence suggests that Myb proteins are involved (Jin et al., 2000; Martin and Paz-Ares, 1997; Tamagnone et al., 1998a). Consistent with this premise, conserved motifs (AC elements or PAL-boxes), which are commonly recognition sites for Myb binding, are found in the promoters of some lignin biosynthesis genes, including PAL (Lois et al., 1989), C4H (Bell-Lelong et al., 1997), 4CL (HauVe et al., 1993), CCoAOMT (Chen et al., 2000), CCR (Lacombe et al., 2000), and CAD (Lauvergeat et al., 2002), although not all have been verified by experimental functional analyses. In some cases, AC elements have been shown to reside within promoter regions that are essential for expression in vascular tissues, particularly xylem, suggesting that these elements, in consort with other promoter sequences, may be important in directing tissue-specific expression (Hatton et al., 1995; Lacombe et al., 2000; Lauvergeat et al., 2002). This idea is supported by a recent genome-wide study of lignification genes in Arabidopsis, which described the presence of AC elements in the promoters of all but one (C4H) of the genes needed for the biosynthesis of G lignin during vascular development (Raes et al., 2003). Other conserved elements such as H-boxes or G-boxes are found in certain lignin biosynthetic genes and are thought to be associated with stress and defense responsiveness. Some lignin biosynthesis genes are induced by wounding, and the cis-elements responsible for wounding responsiveness have been localized within their promoters (see Lauvergeat et al., 2002). Certain lignin biosynthetic genes have also been shown to be circadianclock controlled in Arabidopsis (Harmer et al., 2000), and their expression can be induced by a whole range of other triggers, but the regulatory mechanisms underlying these responses remain largely undetermined. Some progress is beginning to be made to identify specific transcription factors or other regulatory proteins involved in modulating the

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expression of lignin biosynthesis genes. A Myb protein from Pinus taeda, PtMYB4, has recently been shown to bind to AC elements and to induce expression of certain lignin biosynthesis genes when overexpressed in tobacco (PatzlaV et al., 2003). Overexpression of another Myb protein, Arabidopsis PAP1, can activate all areas of phenylpropanoid metabolism, including lignin biosynthesis (Borevitz et al., 2000). A PAL-box binding protein, Ntlim1, with a zinc finger motif and similarity to members of the LIM protein family, has been isolated from tobacco (Kawaoka and Ebinuma, 2001). Transgenic tobacco containing an antisense Ntlim1 gene showed a 20% decrease in lignin content and reduced activity of PAL, 4CL, and CAD (Kawaoka and Ebinuma, 2001). Arabidopsis mutants in the BREVIPEDICELLUS (BP) gene, a KNOX gene primarily involved in internode patterning, showed increased and aberrant lignin deposition and the BP gene was shown to bind to promoters of some lignin pathway genes (Mele et al., 2003). Significant future work is necessary to delineate and characterize the diVerent, probably overlapping, regulatory elements and the corresponding proteins that bind to them, which are required for the control of lignin gene expression spatially, temporally, and in response to biotic and abiotic stresses.

B. TRANSCRIPT PROFILING USING MICROARRAYS

Transcript profiling of lignifying cells or tissues is beginning to add a new dimension to this area of research. Global gene expression analysis has the potential to reveal these networks of coordinated regulation and to suggest, perhaps, unexpected interactions among metabolic pathways and physiological processes. Similarly, comparison of coregulated lignin genes may reveal new common regulatory elements, an approach that has already proved useful in other systems. Transcript profiling in plants is just beginning, but even microarrays that cover only a small portion of a genome can yield useful data. A hybrid aspen xylem microarray containing 2995 unique expressed sequence tags (ESTs) has recently been produced and used to analyze tissue-specific transcript profiles from distinct development zones within the wood-forming tissues of wild-type (Hertzberg et al., 2001) and transgenic (Israelsson et al., 2003) plants. This analysis revealed that the genes for lignin biosynthetic enzymes and transcription factors and other potential regulators of xylogenesis are under strict transcriptional regulation, being expressed only in tissues at a particular developmental stage within the xylem (Hertzberg et al., 2001). Among other results this work indicated that a homologue for the dirigent protein gene was induced

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coincident with lignification, and that two peroxidases (not previously linked to lignification) and two laccases were expressed in lignifying cells, providing supportive evidence for a role for these genes in lignification. Transcript profiling of all known genes involved in monolignol production in conifers performed on Pinus taeda cell suspension cells indicated that PAL, C4H, C3H, 4CL, CCoAOMT, CCR, and CAD were coordinately up-regulated on transfer of cells to a medium containing 8% sucrose and 20 mM potassium iodide, whereas a putative acid/ester O-methyltransferase that had been suggested to be involved in lignin biosynthesis was not up-regulated, which suggests that it is not, in fact, involved in monolignol production (Anterola et al., 2002).

VIII. METABOLIC REGULATION In the aforementioned Pinus taeda study the eVects of increasing Phe supply on lignin gene transcript levels was also addressed. By increasing exogenously supplied Phe to saturating levels, transcript levels of PAL, 4CL, CCoAOMT, CCR, and CAD increased, whereas those for C4H and C3H were only slightly up-regulated. These data, along with metabolic profiling data, were interpreted in the light of preexisting literature on genetically manipulated plants to suggest that carbon allocation to the monolignol pathway and its distribution toward the synthesis of the diVerent monolignols in conifers is controlled by Phe supply and by diVerential modulation of C4H and C3H (Anterola et al., 2002). This work highlights the complex interrelationships that must exist both within and between plant metabolic pathways at the biochemical level. Metabolites are not just the products of these pathways but often also act as sensitive sensors and regulators so that changes in metabolite pattern can sometimes lead to complex changes in partitioning of flux within metabolic networks (Li et al., 2000). Although research into this area is just beginning, it is clear that metabolic regulation plays important roles on the phenylpropanoid pathway at various levels. Certain phenylpropanoid intermediates are known to have feedforward (Loake et al., 1992) or feedback (Blount et al., 2000) regulatory properties acting at the level of enzyme activity (Bolwell et al., 1986) or gene transcription (Loake et al., 1992; Mavandad et al., 1990). For example, PAL activity is reduced in transgenic tobacco deficient in C4H, presumably due to feedback modulation (Blount et al., 2000), because cinnamic acid has been shown to inhibit PAL expression at the transcriptional level (Blount et al., 2000; Mavandad et al., 1990). Phenylpropanoid intermediates can also modulate pathway reactions at the biochemical level. For

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example, 5-hydroxyconiferaldehyde, the preferred substrate for COMT, has been shown to inhibit the possible competing reactions of caVeate and 5-hydroxyferulate methylation (Li et al., 2000). Similarly, in mixed reactions in vitro containing both coniferaldehyde and ferulate, coniferaldehyde acted as a noncompetitive inhibitor of 5FH, suggesting that, in vivo, in the presence of coniferaldehyde, syringyl monolignols are only synthesized from coniferaldehyde (Osakabe et al., 1999). Although plant metabolite profiling is in its infancy, some researchers have already developed metabolite profiling protocols that can quantify lignin pathway intermediates and plant phenolic glucosides from xylem extracts of wild-type and transgenic plants (Meyermans et al., 2000). Analysis of xylem from CCoAOMT–down-regulated poplars indicated that while lignin content decreased, the amount of certain soluble phenolics, notably glucosides of phenolic acids, increased. In particular, O4- -D-glucopyranosyl-sinapic acid, a storage or detoxification product of sinapic acid, accumulated to 10% of soluble phenolics, allowing Meyermans et al. (2000) to infer that endogenously produced sinapic acid is not a major precursor in syringyl lignin biosynthesis in poplar. By contrast, Chen et al. (2003) were unable to detect diVerences in monolignol pathway intermediates in the soluble extracts of stems or leaves from COMT– or CCoAOMT–down-regulated alfalfa, although caVeoyl glucoside apparently accumulated in CCoAOMT– down-regulated stems (Chen et al., 2003; Guo et al., 2001a). Further development and application of metabolic profiling techniques will undoubtedly provide a productive area of research into the monlignol biosynthetic pathway in the future, potentially clarifying the accuracy or otherwise of the diVerent current models of the pathway, and illuminating the ways that the monolignol pathway interacts with other branches of phenylpropanoid metabolism.

IX. ANALYSIS OF LIGNIN CONTENT, STRUCTURE, AND COMPOSITION Determining the content, structure, and composition of lignin in plant materials is a challenging problem because of the complexity and heterogeneity of the polymer and its cross-links to other wall components. Indeed, it is not currently possible to fully elucidate lignin structure and composition in any species. Most techniques for lignin characterization rely on initial solubilization of a lignin fraction under relatively harsh chemical conditions. It is important to realize that the resulting soluble fraction does not represent native lignin either quantitatively or qualitatively. In particular, condensed

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lignin fractions resistant to degradation are absent from such preparations and solubilized fractions are often modified (by condensation, oxidation, addition, or substitution) during extraction. Lignin analysis techniques also vary considerably in sensitivity, and minor lignin components that can be detected by one technique may appear absent using a diVerent method. These problems mean that no single technique is completely reliable or unbiased and that it is usually necessary to use several methods to get representative data. This is particularly important when dealing with ‘‘novel’’ lignins from genetically manipulated plants in which apparent discrepancies in the literature can often be attributed to the distinct techniques used by diVerent laboratories. A detailed description of the various methods available for lignin determination is outside the scope of this review and therefore only a brief summary will be given. A more complete explanation of the methods and their limitations can be found elsewhere (see Anterola and Lewis, 2002; Baucher et al., 2003). Lignin is most frequently quantified by gravimetric techniques after extracting the other, more soluble, wall components (Klason lignin determination) or, alternatively, by extracting the lignin component itself (e.g., with thioglycolic acid or acetyl bromide). Lignin structure and composition can be determined by analysis of its degradation products derived by pyrolysis gas chromatography-mass spectrometry (pyrolysis GC-MS), alkaline nitrobenzene oxidation, thioacidolysis, or derivatization followed by reductive cleavage (DFRC). The targets of these techniques are predominantly the -O-4 linkages, the most abundant linkages in ‘‘normal’’ lignin. However, the proportion of -O–4 linked units may be dramatically reduced in certain genetically modified plants, where the degree of condensation of lignin tends to increase. Two-dimensional nuclear magnetic resonance (NMR) spectroscopy of isolated lignin fractions has allowed an improved picture of lignin structure to be obtained from particular genetically modified plants (Ralph et al., 1998, 2001). Methods that allow the whole of the polymer to be analyzed in situ, such as solid-state NMR or FT-IR (Fouriertransform infrared spectroscopy), although generally not as sensitive as ‘‘wet’’ chemical methods, can also be useful when dealing with novel lignins that are hard to extract. Recently a method has been described that is claimed to fully dissolve finely ground cell wall material in such a way that lignin structures remain intact, allowing the whole lignin fraction to be analyzed by high-resolution solution-state NMR methods for the first time (Lu and Ralph, 2003). This method oVers great potential for more complete determination of lignin structures and deserves further testing on a range of transgenic and mutant plants to determine the extent of its versatility and usefulness.

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X. GENES INVOLVED IN LIGNIN BIOSYNTHESIS Most of the genes involved in monolignol production have been cloned in a variety of species. In addition, the recent whole genome sequencing of the model plant Arabidopsis has enabled the assembly of complete inventories of the genes potentially involved in the monolignol biosynthetic pathway in this species (Costa et al., 2003; Goujon et al., 2003b; Raes et al., 2003). Comparison of 59,797 ESTs from wood-forming tissues of loblolly pine with the genome sequence of Arabidopsis suggests a substantial conservation of gene sequences between this woody gymnosperm and the herbaceous angiosperm (Kirst et al., 2003). For contigs of 1 kb or more of high-quality sequence, more than 90% had an apparent Arabidopsis homologue. These results suggest that a common set of genes for woodiness may exist in all seed plants, supporting the use of Arabidopsis for comparative genomics of other angiosperms and even gymnosperms. Analysis of sequences for putative monolignol biosynthetic genes in Arabidopsis show that most are encoded by multigene families, although evidence of a real involvement in lignification is available for only one or a few genes in each family. In contrast to this general redundancy of sequences, a couple of genes involved in lignin synthesis (e.g., C4H, HCT) are unique in Arabidopsis or have only one homologue (e.g., F5H) (Raes et al., 2003). There is also evidence that some lignin biosynthetic genes (e.g., PAL, 4CL, CAD) have been duplicated during Arabidopsis evolution (Goujon et al., 2003b).

XI. PLANTS WITH MODIFIED EXPRESSION OF LIGNIN BIOSYNTHETIC GENES A significant amount of research eVort has gone into determining the exact roles of diVerent genes in lignin biosynthesis by modifying their expression in transgenic plants. A variety of reverse genetic approaches have been used to either suppress gene activity with antisense RNA/cosuppressing transgenes, or to overexpress genes. This work has concentrated on herbaceous and woody species that are easy to transform, such as tobacco and poplar. Mutant plants defective in particular lignin biosynthetic enzymes have also been identified in maize, sorghum, loblolly pine, and Arabidopsis. With the advent of modern techniques allowing for saturation mutagenesis in Arabidopsis, it is to be expected that mutants for all lignin biosynthesis genes may soon be identified in this species. Such mutants often have the advantage of being ‘‘knockouts,’’ in which the function of a gene is completely disrupted. This is in contrast to the situation in transgenics made by gene suppression technologies, in which a certain amount of expression is still possible from

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the target gene. On the other hand, the significant possibility for functional redundancy within the multigene families that exist for many lignin genes in Arabidopsis means that knockout mutants in single genes may have no phenotype. Gene suppression technologies have the advantage here in potentially being able to target several genes within a gene family provided they have suYciently high homology to each other. Both knockout mutants and transgenics are therefore likely to be useful in elucidating the potential roles of the less well-studied genes likely to be involved in lignin synthesis (i.e., glucosyltransferases, -glucosidases, dirigent proteins) in the same way that they have been fundamental in promoting our current understanding of the roles of the main enzymes involved in monolignol production. The following section summarizes work to date for each enzyme. A. PHENYLALANINE AMMONIA LYASE

Phenylalanine ammonia lyase catalyzes the first step of the general phenylpropanoid pathway, a step that is common to the production of many metabolites including flavonoids, coumarins, and phytoalexins, not just lignin. PAL expression has been significantly suppressed in tobacco and results in a range of phenotypes including reduced growth, altered leaf shape, reduced pollen viability (Elkind et al., 1990), and increased susceptibility to the fungal pathogen Cercospora nicotianae (Maher et al., 1994). Plants with low PAL activity have thinner cell walls in the secondary xylem (Bate et al., 1994; Elkind et al., 1990) and reduced lignin content. In particular, the incorporation of G units into the noncondensed fraction of lignin is reduced and, consequently, S:G increases (Korth et al., 2001; Sewalt et al., 1997a). PAL overexpression results in a small increase in Klason lignin and a decrease in the amount of S units, yielding a twofold reduction in the S:G ratio when lignin was analyzed by thioacidolysis (Korth et al., 2001). The level of chlorogenic acid (3-caVeoylquinic acid) has been correlated with PAL activity in leaves and stems of both PAL-silenced and PAL-overexpressing tobacco (Bate et al., 1994; Blount et al., 2000; Elkind et al., 1990; Howles et al., 1996; Korth et al., 2001; Maher et al., 1994). PAL suppression decreases the chlorogenic acid content of tobacco leaves (Bate et al., 1994; Blount et al., 2000), whereas PAL overexpression increases it (Bate et al., 1994; Howles et al., 1996). No Arabidopsis PAL mutants have yet been described in the literature. Arabidopsis has four PAL genes and all are expressed in the inflorescence stem, a tissue with a high proportion of lignifying cells. However, the presence of an AC element in the promoters of the PAL1 and PAL2 genes suggests that these genes are the most likely candidates to be involved in monolignol synthesis in lignifying vascular cells (Raes et al., 2003). It has been suggested that PAL1 and PAL2 may be functionally redundant and

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that a mutation in just one of these genes may not be enough to induce a phenotype (Goujon et al., 2003b). B. CINNAMIC ACID 4-HYDROXYLASE

Like PAL, the enzyme before it on the general phenylpropanoid pathway, C4H, is necessary to the production of many plant metabolites including lignin, and its suppression in transgenic tobacco significantly reduces lignin content. However, in contrast to PAL, C4H suppression particularly aVects the incorporation of S units, promoting a corresponding decrease in the S:G ratio (Sewalt et al., 1997a). Thus C4H and PAL deficiencies cause opposing changes to lignin monomeric composition, a finding that is hard to reconcile with the sequential positioning of the two enzymes on current lignin biosynthesis pathways. A variety of explanations are possible but suggest either that C4H has a diVerent role and position on the pathway than that currently assumed, or that metabolic channelling, mediated by C4H and other enzymes in complex, can operate on the lignin pathway to direct precursors toward S lignin biosynthesis (Dixon et al., 2001). Arabidopsis contains a single C4H gene and ref3 plants (reduced epidermal fluorescence; Ruegger et al., 1999) have a mutation in this gene (Franke et al., 2002b). It is not clear whether these plants are null mutants, and it is possible that they possess some residual C4H activity. Reduction of C4H activity in this mutant has similar eVects to C4H suppression in tobacco (i.e., the plants have decreased lignin content with altered composition), being particularly deficient in S units. The mutant also has developmental abnormalities and is stunted with increased branching and is male sterile. C. 4-COUMARATE:COA LIGASE

Transgenic plants with reduced 4CL activity have been produced in tobacco (Kajita et al., 1996, 1997), Arabidopsis (Lee et al., 1997), and aspen (Hu et al., 1999; Li et al., 2003). In all cases, plants with significant deficiencies in 4CL had lignin content reduced by 25–50%. By combining data from all of these transgenics, Anterola and Lewis (2002) estimate that 4CL activity has to be suppressed by more than 60% before the deficiency has a significant impact on lignin content. Despite consistent results in terms of lignin content reduction, these diVerent transgenics appear to display inconsistent changes to lignin composition, with S units apparently predominantly reduced in tobacco, only G units reduced in Arabidopsis, and no change to the relative proportions of S and G units in aspen (Hu et al., 1999; Kajita et al., 1996, 1997; Lee et al., 1997). These diVerential eVects may result from suppression of distinct 4CL isoforms by the diVerent transgenes used in each experiment or from the diVerent substrate specificities of certain 4CL isoforms in diVerent species. Equally,

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these discrepancies could also simply reflect the diYculties in directly comparing results obtained from diVerent plants grown under varying conditions that were analyzed using diVerent lignin analytical techniques, each with their own general inadequacies and limitations (for discussion, see Anterola and Lewis, 2002). In tobacco and poplar, 4CL deficiency was associated with a higher amount of cell-wall–bound hydroxycinnamic acids ( p-coumaric, ferulic, and sinapic acids). Tobacco plants with the greatest lignin reductions were stunted and had collapsed xylem vessels (Kajita et al., 1997). However, in aspen, 4CL suppression has been reported to enhance growth (Hu et al., 1999), although the growth eVects were apparently not reproduced in a subsequent experiment in which the transgene was expressed from a diVerent promoter (Li et al., 2003). Increased cellulose contents were also reported for 4CL-suppressed aspen in both studies. These results may suggest that reduced carbon flow toward lignin synthesis increases the availability of carbon for cellulose synthesis, although it has been suggested that much of the apparent cellulose increase may be accounted for by the proportional increase expected in the cellulose component of whole tissues when the lignin component is reduced (see Anterola and Lewis, 2002 for discussion). No 4CL mutants have been identified in Arabidopsis, probably because of functional redundancy between the 4CL1 and 4CL2 genes (Goujon et al., 2003b). Of the four 4CL genes in Arabidopsis, these two (i.e., 4CL1 and 4CL2) are thought to be the best candidates for monolignol synthesis (Ehlting et al., 1999), and AC elements are present in their promoters (Raes et al., 2003). D. HYDROXYCINNAMOYL-COA:SHIKIMATE/QUINATE HYDROXYCINNAMOYL TRANSFERASE

HCT has only recently been cloned from tobacco (HoVmann et al., 2003) and a single report indicates the consequences of HCT deficiency in plants. HoVmann et al. (2004) describe HCT-silenced Nicotiana benthamiana which have decreased lignin syringyl units and increased p-hydrophenyl units, confirming the function of the acyltransferase in phenylpropanoid biosynthesis. E. P-COUMARATE 3-HYDROXYLASE

Because of its very recent isolation, no data exists on the eVects of C3H suppression in tobacco or woody plants. However, by screening Arabidopsis plants for reduced epidermal fluorescence, the ref 8 mutant was identified and subsequently shown to be defective in the CYP98A3 gene, the single Arabidopsis C3H gene (Franke et al., 2002a,b). Lignin content in this mutant is reduced by 60–80% and is almost entirely composed of p-coumaryl alcohol (H) units with large amounts of esterified p-coumaric acid. The mutant

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accumulates a range of soluble p-coumarate esters instead of sinapoyl malate. Ref8 plants are severely dwarfed and have collapsed xylem vessels, increased cell wall degradability, and a higher susceptibility to fungal colonization (Franke et al., 2002a). These data indicate that C3H is a major control point in the production of C3- and C5-substituted phenylpropanoid lignin precursors. F. CAFFEOYL-COA O-METHYLTRANSFERASE

Suppression of CCoAOMT reduces lignin content by 12–50% in transgenic tobacco (Pinc,on et al., 2001a; Zhong et al., 1998), alfalfa (Guo et al., 2001a; Marita et al., 2003a), and poplar (Meyermans et al., 2000; Zhong et al., 2000b). In all studies the amount of G units was reduced, consistent with the proposed role for CCoAOMT in G lignin synthesis. In some studies, S units were also reduced, although small increases in the S:G ratio reflected a predominant influence on G units (Meyermans et al., 2000; Zhong et al., 1998). In alfalfa, and in one tobacco study, the amount of S units was not reduced (Guo et al., 2001a; Pinc,on et al., 2001a). In poplar the lignin produced by CCoAOMT-suppressed plants has been shown to be less cross-linked than normal (Zhong et al., 2000b). Vessel cell walls also showed enhanced fluorescence, possibly a result of the increased levels of free and bound p-hydroxybenzoic acid that were detected. Similarly, increased amounts of methanol-extractable phenolics including the O-b-D-glucosides of caVeic acid, sinapic acid, and vanillic acid, were detected in the wood of the transgenic poplars (Meyermans et al., 2000), whereas soluble caVeoyl glucoside accumulated in stem extracts of transgenic alfalfa (Guo et al., 2001a). These glucosides may result from a detoxification of accumulating hydroxycinnamic acids, as indicated by feeding experiments with caVeic and sinapic acids (Meyermans et al., 2000). The accumulation of O-b-Dglucopyranosyl–sinapic acid in plants with reduced S lignin supports the hypothesis that sinapic acid is not the main precursor for S units in vivo. Of all the CCoAOMT plants produced, only tobacco plants from one experiment showed an obvious phenotype with altered growth and flower development (Pinc,on et al., 2001a). Arabidopsis mutants in CCoAOMT genes have not yet been identified. G. CAFFEIC ACID O-METHYLTRANSFERASE

COMT is one of the most well-studied lignin genes and data on the results of its suppression exist for tobacco, poplar, alfalfa, and maize transgenics (Atanassova et al., 1995; Dwivedi et al., 1994; Guo et al., 2001a; Jouanin et al., 2000; Ni et al., 1994; Tsai et al., 1998; Van Doorsselaere et al., 1995)

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and in maize, sorghum, and Arabidopsis mutants (Bout and Vermerris, 2003; Goujon et al., 2003c; Suzuki et al., 1997; Vignols et al., 1995). The predominant and consistent eVect of COMT suppression in all these species is a drastic reduction in the lignin S:G ratio resulting from a reduction in S unit amounts. An unusual monomer, the 5-hydroxyguaiacyl (5OHG) unit is also present in the polymer in both transgenic (Atanassova et al., 1995; Guo et al., 2001a; Jouanin et al., 2000; Lapierre et al., 1999; Marita et al., 2003a; Tsai et al., 1998; Van Doorsselaere et al., 1995) and mutant plants (Chabbert et al., 1994; Goujon et al., 2003c; Suzuki et al., 1997). In some plants the level of 5OHG units even exceeded that of S units (Jouanin et al., 2000), whereas in the Arabidopsis knockout mutant, no S units could be detected and the plants had increased levels of 5OHG and G units. These data are therefore consistent with a predominant and essential role for COMT in the production of S lignin units. Similarly, in COMT-suppressed alfalfa the reduction in both G and S units (Guo et al., 2001a; Marita et al., 2003a) is consistent with the work of Parvathi et al. (2001), who showed that in this species COMT is also involved in the methylation of caVeyl aldehyde. Severe suppression of COMT may sometimes moderately reduce total lignin content in most species. Reports on this are not consistent, however, and sometimes, similar plants are reported to have (Jouanin et al., 2000; Ni et al., 1994), or to have no (Atanassova et al., 1995; Dwivedi et al., 1994; Van Doorsselaere et al., 1995), reduced lignin, or the apparent reduction in lignin amount is dependent on the technique used to analyze the polymer (Guo et al., 2001a; Marita et al., 2003a). These data likely highlight the caution that has to be exercised when comparing lignin data from plants grown for diVerent lengths of time under varying environmental conditions and analyzed using diVerent techniques (Baucher et al., 2003). The changes in composition in lignin of COMT-suppressed plants have consequent eVects on lignin structure, which is also greatly altered. In poplar the proportion of -O-4 linkages appear to be reduced, while condensed C-C linkages are increased. Biphenyl (5–5) and phenylcoumaran ( -5) linkages are particularly more abundant (Guo et al., 2001a; Jouanin et al., 2000; Lapierre et al., 1999), making the lignin more similar to softwood lignin. These changes, and the reduction in free phenolic groups on -O-4-linked G units (Lapierre et al., 1999) probably contribute to the fact that this poplar wood is more diYcult to pulp (Pilate et al., 2002). In COMT-suppressed alfalfa, - , -1, and -5 linkages involving S units are absent (Guo et al., 2001a), whereas in COMT–down-regulated poplar, free phenolic groups in -O-4-linked G units were less abundant (Lapierre et al., 1999). In general, however, COMT-suppressed plants have no obvious external phenotype, although in poplar, when the bark is removed, the wood of the

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COMT-suppressed lines has a rose color (Van Doorsselaere et al., 1995) or a reddish-brown color (Jouanin et al., 2000; Tsai et al., 1998) compared to the whitish, wild-type wood; this attribute has been ascribed to an increased amount of coniferaldehyde (Tsai et al., 1998). H. FERULATE 5-HYDROXYLASE

Because of the recent appreciation that 5-hydroxylation of lignin precursors occurs preferentially at the cinnamaldehyde level, this enzyme is sometimes referred to as coniferaldehyde 5-hydroxylase or Cald5H, although many researchers still use the original name. F5H expression has recently been manipulated in tobacco, Arabidopsis, and poplar/aspen (Franke et al., 2000; Li et al., 2003; Meyer et al., 1998; Sibout et al., 2002), but its role in lignin biosynthesis has been appreciated for more than a decade because of the early identification of an Arabidopsis F5H mutant, fah1 (Chapple et al., 1992; Meyer et al., 1996). This mutant produces a lignin deficient in S units with enhanced proportions of phenylcoumaran ( -5) and dibenzodioxocin (biphenyl; 5-5) linkages (Marita et al., 1999). F5H overexpression in Arabidopsis, tobacco, or poplar results in lignin almost entirely composed of S units, containing no phenylcoumaran or dibenzodioxocin structures (Franke et al., 2000; Marita et al., 1999; Meyer et al., 1998). These studies confirm that F5H plays a crucial role in the production of S lignin units and in determining lignin monomer composition. I. CINNAMOYL-COA REDUCTASE

Reports describing the eVects of CCR deficiency in transgenic or mutant plants give a consistent indication of the important role the enzyme plays in controlling lignin content. CCR-suppressed transgenic tobacco (O’Connell et al., 2002; Piquemal et al., 1998; Ralph et al., 1998) and Arabidopsis (Goujon et al., 2003a) have approximately 50% less Klason lignin than wild-type plants, as does an Arabidopsis ccr mutant called irregular xylem (irx4) (Jones et al., 2001). Many of these CCR-deficient plants display similar aberrant phenotypes (Fig. 3) including stunted growth, altered leaf morphology, and collapsed or irregular xylem vessels. The mechanical weakness of the vessel walls appears to be the result of disorganization and loosening of the secondary walls of fibers and vessels, where there is a particular deficiency in noncondensed lignin units (Chabannes et al., 2001a,b; Goujon et al., 2003a; Pinc,on et al., 2001b). Thioacidolysis of lignin from both CCR-deficient Arabidopsis and tobacco has confirmed an overall reduction in noncondensed lignin, particularly in -O-4-linked guaiacyl

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Fig. 3. Phenotype of CCR-suppressed plants. Tobacco plants suppressed in CCR activity (left) frequently show altered leaf shape compared to wild-type plants (right).

units (Goujon et al., 2003a; O’Connell et al., 2002). The more pronounced decrease in noncondensed G units compared to S units likely accounts for the increased S:G ratio of lignin from CCR-deficient tobacco (O’Connell et al., 2002; Piquemal et al., 1998). A similar increase in thioacidolysis S:G units has also been seen in some CCR-deficient Arabidopsis lines, depending on the growth conditions (Goujon et al., 2003a). Increased amounts of ferulic acid and sinapic acid are incorporated into the cell walls of CCRdeficient plants (Goujon et al., 2003a; O’Connell et al., 2002; Piquemal et al., 1998) and, in tobacco, feruloyl-tyramine is also detected (Ralph et al., 1998), possibly representing an alternative sink for feruloyl-CoA. The presence of unusual phenolics such as ferulic acid and sinapic acid may account for the unusual orange-brown color of xylem cell walls in CCR-suppressed tobacco, because semi-in vivo incorporation of these two hydroxycinnamic acids into stem sections resulted in a comparable phenotype (Piquemal et al., 1998). The altered structure of lignin in CCR-suppressed tobacco is also indicated by the higher amount of alkali-labile material that can be released from the extractives-free polymer and by the fact that there is an increased proportion of free phenolic groups in the noncondensed fraction of lignin (O’Connell et al., 2002).

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J. CINNAMYL ALCOHOL DEHYDROGENASE

The roles of CAD in lignin biosynthesis have been extensively studied in a wide variety of species. Transgenic plants with reduced CAD activity have been produced in tobacco (Halpin et al., 1994; Hibino et al., 1995; Stewart et al., 1997; Yahiaoui et al., 1998), poplar (Baucher et al., 1996), and alfalfa (Baucher et al., 1999), whereas cad mutants exist in pine (MacKay et al., 1997), maize (Halpin et al., 1998), Arabidopsis (Sibout et al., 2003), and probably sorghum (Pillonel et al., 1991). CAD catalyzes the last step in the production of the monolignols, the reduction of cinnamaldehydes into cinnamyl alcohols. Despite its important role in monolignol production, lignin content is not reduced, or is only slightly reduced in most CAD-deficient plants. It appears that other phenolics such as the aldehyde substrates of CAD can be incorporated into lignin and to some extent compensate for the reduced availability of monolignols. The CAD-deficient bm1 mutant of maize has been reported to have significant reductions in lignin in certain genetic backgrounds (Colenbrander et al., 1973; Kuc and Nelson, 1964), but no change in lignin content was detected when the mutation was introduced into other maize varieties (Marita et al., 2003b), highlighting the way that genetic and environmental factors can potentially influence the results of diVerent experiments. The changes to lignin composition identified in CAD-deficient plants are for the most part consistent with the role traditionally envisaged for CAD in catalyzing the reduction of all three lignin cinnamaldehydes ( p-coumaryl-, coniferyl- and sinapyl-aldehyde). Increased levels of cinnamaldehydes have been detected in the lignin of CAD-antisense tobacco (Halpin et al., 1994; Ralph et al., 1998, 2001) and poplar (Kim et al., 2002), as well as in the pine (Ralph et al., 1997), Arabidopsis (Sibout et al., 2003), and maize (Marita et al., 2003b) cad mutants. In tobacco, poplar, and Arabidopsis, increases in both coniferaldehyde and sinapaldehyde were identified (Halpin et al., 1994; Kim et al., 2002; Ralph et al., 2001; Sibout et al., 2003), suggesting that CAD is indeed involved in the reduction of both of these cinnamaldehydes. Similarly, in CAD-antisense poplar and in the maize bm1 mutant (Halpin et al., 1998; Lapierre et al., 1999) the proportion of S and G units in lignin thioacidolysis products is not altered, whereas in CAD-antisense tobacco and alfalfa (Baucher et al., 1999; Halpin et al., 1994) the S:G ratio is reduced. This suggests that in CAD-deficient plants the deposition of S lignin units is equally or more aVected than that of G lignin units. All of this data is at variance with a recent claim that a specific sinapyl alcohol dehydrogenase, and not CAD, is responsible for sinapaldehyde reduction in angiosperms (Li et al., 2001a).

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Fig. 4. Wood color in CAD antisense tobacco. Tobacco plants with very low levels of CAD activity (right) have a bright red color in the woody xylem compared to the cream color of wild-type plants (left).

A striking characteristic of transgenic plants down-regulated for CAD is the red or brownish color of xylem tissues (Fig. 4), initially observed in the maize ‘‘brown-midrib’’ (bm) mutants. This color has been attributed to the incorporation of cinnamaldehydes in the polymer because synthetic dhydrogenation polymers (DHPs) of coniferyl alcohol and coniferaldehyde also form a red polymer (Higuchi et al., 1994). The cross-coupling of cinnamaldehydes into the lignin polymer may result in a more extended conjugated system, which causes the red color (Higuchi et al., 1994). The unusual monomer, dihydroconiferyl alcohol, is apparently also incorporated into the lignin of the pine cad mutant, where it accounts for 30% of the polymer compared to only 3% in wild-type plants (Ralph et al., 1997). Altered structure in the lignin polymer of CAD-suppressed plants is also indicated by its increased extractability in alkali (Baucher et al., 1996; Bernard-Vailhe et al., 1996; Halpin et al., 1994; MacKay et al., 1999; Yahiaoui et al., 1998). In transgenic poplar and tobacco, the lignin was enriched in free phenolic groups in both S and G units (Lapierre et al., 1999; O’Connell et al., 2002). Similarly, the proportion of G units with free phenolic groups was increased in the Arabidopsis Atcad-D mutant (Sibout et al., 2003). This increase in free phenolic groups may be important in altering the solubility of lignin, which in turn has implications for the ease with which wood from these plants can be pulped (Lapierre et al., 1999; O’Connell et al., 2002). K. MANIPULATION OF MULTIPLE GENES

Although the vast majority of work aimed at manipulating lignin biosynthesis in transgenic plants has focused on modifying the expression of single genes, some researchers are beginning to explore the possibilities of

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producing novel lignins by simultaneous manipulation of multiple genes. Stacking transgenes or traits in this way may potentially provide great opportunities for crop and tree improvement in the future (Halpin and Boerjan, 2003; Halpin et al., 2001). Both COMT and CCoAOMT have simultaneously been suppressed in tobacco (Pinc,on et al., 2001a; Zhong et al., 1998) and alfalfa (Guo et al., 2001a). In tobacco, a greater reduction in Klason lignin content was achieved in comparison with the respective single transformants (Pinc,on et al., 2001a), but this was not the case in alfalfa (Guo et al., 2001a). In both species, lignin S:G ratio was reduced. However, in alfalfa, only S units were decreased (Guo et al., 2001a), whereas in tobacco both G and S units were aVected (Zhong et al., 1998). It has been proposed that additional enzymes may be involved in the methylation of G unit precursors in alfalfa, to explain the relative preservation of G units in this species (Guo et al., 2001a). Simultaneous suppressions in COMT and CCR expression have been achieved in tobacco by crossing plants down-regulated in the single genes (Pinc,on et al., 2001b). Progeny of this cross had reduced activity of both enzymes and had intermediate phenotypes between those of the two parents. In terms of lignin modification, however, the eVects of CCR suppression appeared to predominate and plants had reduced lignin content and increased S:G ratio, whereas characteristics typical of COMT suppression were not detected (Pinc,on et al., 2001b). This may reflect insuYcient levels of COMT down-regulation to promote lignin modification. Homozygous transgenics suppressed in either CAD or CCR were similarly crossed to produce tobacco plants down-regulated in both genes (Chabannes et al., 2001a). Combinatorial suppression of both genes appeared to have a synergistic eVect in reducing lignin quantity, which was decreased by approximately 50% compared to 32% and 12% reductions in plants hemizygous for CCR- and CAD-suppressing transgenes, respectively. Nevertheless, NMR spectra of isolated lignin fractions showed that the structure of the polymer was closer to that of wild-type plants than to the CCR- or CAD-deficient parents. Similarly, the phenotype of the CAD/CCR suppressed plants was normal with only slight alterations in the vessel shape, indicating that under certain circumstances, plants can tolerate important reductions in lignin content without developing adverse phenotypes. Tobacco plants doubly suppressed in CCR/COMT, in CAD/CCR, or in CAD/COMT have been produced using chimeric constructs consisting of partial sense sequences for both target genes (Abbott et al., 2002). Plants suppressed in CAD and COMT were also produced by crossing transgenics suppressed in either enzyme so that the two diVerent strategies could be

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compared. Greater levels of enzyme suppression and greater changes to lignin were achieved using the single chimeric construct for CAD/COMT suppression, compared to the crossed transgenics, indicating the greater eYciency of the chimeric construct strategy. The same approach was used successfully to suppress all three genes (CAD, COMT, and CCR) together. The resulting plants were severely stunted and had characteristics of COMT, CCR, and CAD suppression in lignin (Abbott et al., 2002), further illustrating the usefulness and eVectiveness of this strategy. Suppression of 4CL has been combined with overexpression of F5H by cotransformation of two transgenes in aspen (Li et al., 2003). Additive eVects were observed compared to transformants with the individual transgenes. The trees had a higher S:G ratio along with a 52% decrease in lignin and proportional increases in cellulose. Thus multigene manipulation can be used to great eVect to concurrently improve several valuable wood quality traits.

XII. COMMERCIAL APPLICATIONS OF MODIFIED LIGNIN PLANTS Lignin has a major influence on a number of plant characteristics that aVect the agronomic quality or commercial value of crops or plantation trees. The digestibility of forage crops is improved in mutants where lignin content is reduced (see Cherney et al., 1991 for a review). Some of these increased digestibility ‘‘brown-midrib’’ mutants have been shown to be deficient in enzymes of monolignol biosynthesis, notably COMT and CAD (Halpin et al., 1998; Vignols et al., 1995), suggesting a route to digestibility improvement via genetic engineering of lignin biosynthetic genes. Consistent with this idea, transgenic tobacco and alfalfa plants suppressed in CAD have been shown to have slightly improved in situ cell wall degradability when fed to sheep (Baucher et al., 1999; Bernard-Vailhe et al., 1998), whereas alfalfa, tobacco, and Stylosanthes plants deficient in COMT also had improved digestibility (Barriere et al., 2003; Bernard-Vailhe et al., 1996; Guo et al., 2001b; Rae et al., 2001; Sewalt et al., 1997b). Cell walls of the Arabidopsis C3H mutant (ref8) have increased susceptibility to polysaccharide hydrolases (Franke et al., 2002a), whereas enzymatic digestibility of transgenic tobacco suppressed in PAL or transgenic Arabidopsis down-regulated for CCR indicated a similar digestibility improvement (Goujon et al., 2003a; Sewalt et al., 1997b). Obviously, to be useful, genetic manipulation of lignin content must be accomplished without adverse eVects on plant phenotype, disease resistance, or agronomic characters such as lodging resistance. In practice

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these restrictions are likely to restrict the choice of genes for manipulation to those later on the monolignol branch pathway, such as COMT and CAD. Lignin modification could also be used as a means to manipulate the rate with which plant residues decompose in soils. Lignin is inherently recalcitrant to decay and also protects the cell wall carbohydrates that are covalently linked to it from microbial attack. Reducing the amount of lignin in potentially disease-harboring plant residues could possibly improve decomposition rates, perhaps, reducing rotation times for certain crops. Although this area has received little attention to date, one study already illustrates the potential for altering decomposition rates by manipulating lignin (Hopkins et al., 2001). Lignin limits the ease with which wood can be pulped because removal of lignin from cellulose requires the use of harsh mechanical or toxic chemical processes. To make pulping easier and more environmentally benign, lignin contents of wood could be reduced or lignin extractability could be improved by modifying the polymer structure. Several pulping studies have been performed on transgenic lignin-modified plants and results illustrate the potential of genetic engineering for improving pulping properties of woods. Greenhouse-grown transgenic tobacco and poplar with down-regulated CAD activity have been subjected to chemical pulping analyses (Baucher et al., 1996; Jouanin et al., 2000; Lapierre et al., 1999; O’Connell et al., 2002). In both species the changes to lignin structure in CAD-suppressed plants resulted in a greater ease of pulping by the chemical Kraft process, and the kappa number, a measure of residual lignin in the pulp after cooking, was reduced compared to that of wild-type plants. Subsequent bleaching of the pulps was also easier and there were no detrimental changes to other pulp properties. Most importantly, these pulping improvements were maintained when the transgenic poplars were grown for 4 years in the field at two diVerent sites in France and the United Kingdom (Pilate et al., 2002). Wood from the pine cad mutant has also been subjected to Kraft processes, but in this case no enhanced delignification was evident (MacKay et al., 1999), probably reflecting the diVerences in lignin structure between angiosperms and gymnosperms. Soda pulping of the mutant, however, produced pulp with a lower kappa number than wild-type plants, providing a potential opportunity for lowering consumption of sulfide during pulping. Pulping of wood from field-grown COMT–down-regulated poplars indicates that the modified lignin is more diYcult to extract and kappa numbers are consequently higher, whereas pulp brightness after bleaching is lower than that of wild-type trees (Pilate et al., 2002). These data suggest that, contrary to original expectations, depleting COMT activity reduces wood quality for Kraft pulping.

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Transgenic tobacco with reduced levels of CCR has also been subjected to Kraft pulping. As in the case of CAD-deficient tobacco, the low-CCR plants produced pulp with a lower kappa number than that of wild-type plants (O’Connell et al., 2002). After bleaching, however, pulp from the CCRsuppressed plants had reduced brightness, which was apparently caused by a higher content of unextracted chlorophyll (O’Connell et al., 2002). Similarly, tobacco suppressed in 4CL has been shown to be improved for Kraft pulping and subsequent bleaching, exhibiting a higher eYciency of delignification and higher pulp yield than corresponding control material (Kajita et al., 2002). Analysis of greenhouse-grown poplar overexpressing F5H has demonstrated particularly significant improvements of Kraft pulping eYciency. Pulps had lower kappa numbers and increased brightness compared to wild-type (Huntley et al., 2003). The authors estimate that this genetic improvement could increase pulp throughput by 60% while concomitantly decreasing the consumption of pulping chemicals. Taken together the data currently available suggest that suppression of CAD, CCR, or 4CL and overexpression of F5H provide the best currently tested options for improving Kraft pulping eYciency. In particular, CAD suppression and F5H overexpression present the most promising opportunities, because manipulation of these genes in a variety of species has shown no adverse phenotypes to be associated with the genetic changes and clear pulping benefits have been demonstrated.

XIII. CONCLUSIONS AND PERSPECTIVES A decade of intensive research into the lignin biosynthesis has greatly improved our understanding of the genes and reactions involved and how to manipulate them toward useful ends, although some clarity is still needed on the accuracy of diVerent proposed pathway models. Progress in these areas is, however, balanced by a complete lack of consensus, based on conflicting pieces of incomplete information, on the cell biology of the process. Similarly, the transcriptional and metabolic regulation of the pathway and of its interactions with other cellular processes is only beginning to be investigated. Postgenomic technologies such as transcript and metabolite profiling are already being applied to address these questions and oVer the prospect of rapidly increasing our understanding and appreciation of the lignin pathway and its wider role in plant metabolism and development by comparing these processes in wild-type and transgenic plants modified in cell wall biosynthesis. Such analyses should provide a comprehensive and holistic view on cell wall assembly at a level that was not possible just a few years ago. In addition to the identification of novel

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genes involved in wood formation, whose function can be further studied by reverse genetics, applications of these techniques will shed light onto the interrelations between the biochemical pathways leading to the biosynthesis of the diVerent cell wall macromolecules and onto their relationship to plant growth and development.

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Application of Thermal Imaging and Infrared Sensing in Plant Physiology and Ecophysiology

HAMLYN G. JONES

University of Dundee, Plant Science Research Group, Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, United Kingdom

I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Leaf Temperature in Plant Physiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Improved Sensor Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Thermal Radiation and Remote Temperature Measurement Basics. . . . . A. Black Body Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Remote Measurement of Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Errors in Estimation of Temperature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Plant Energy Balance and Leaf Temperature. . . . . . . . . . . . . . . . . . . . . . . . . . A. Energy Balance Basics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Stomatal Conductance and Evaporation As Functions of Leaf Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Water loss, Transpiration, Stomatal Conductance, and Stress Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Biophysical and Aerodynamic Properties . . . . . . . . . . . . . . . . . . . . . . . . . . C. Metabolic Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Disease and Infection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Pollution and Agronomic EVects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Genetic Screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Frost Tolerance and Damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Concluding Comments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advances in Botanical Research, Vol. 41 Incorporating Advances in Plant Pathology 0065-2296/04 $35.00

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Copyright 2004, Elsevier Ltd. All rights reserved.

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ABSTRACT The applications of remote temperature sensing of plants by infrared thermography and infrared thermometry are reviewed and their advantages and disadvantages for various purposes discussed. The great majority of applications of thermography and of infrared thermometry depend on the sensitivity of leaf temperature to evaporation rate (and hence to stomatal aperture). In most applications, such as in the early or pre-symptomatic detection of disease or water deficits, what is actually being studied is the effect of the disease on stomatal behaviour or membrane permeability to water. Other applications of thermography in plant physiology include the study of thermogenesis as well as the characterisation of boundary layer transfer processes. Thermography is shown to be more than just a method for obtaining pretty pictures; it has particular advantages for the quantitative analysis of spatial and dynamic physiological information. Its capacity for large throughput has found application in screening approaches, such as in the selection of stomatal or hormonal mutants. The use of wet and dry reference surfaces for the enhancement of the power of thermal imaging approaches, especially in the field is reviewed, and the problems and potential solutions when applying thermography in the field and in the laboratory discussed.

I. INTRODUCTION A. LEAF TEMPERATURE IN PLANT PHYSIOLOGY

Leaf temperature is important to plants both because of the subtle eVects of small temperature changes on the rates of key physiological processes such as biochemical reactions and cell growth and division and because of the damaging eVects of extreme temperatures. Any study of physiological processes needs to take account of the temperature sensitivity of the process in relation to the likely natural variation (spatial and temporal) of temperature. Although many biochemical processes are fairly insensitive to temperature changes of several degrees around the temperature optimum, at the extreme, diVerences of a degree or less may become crucial. This should be put in the context of the potential spatial temperature variation over a single leaf, which may be as much as 3–5 8C (Raschke, 1956; Roth-Nebelsick, 2001) under appropriate conditions. Leaf temperature is also important as an indicator of aspects of physiological function, especially those related to evaporation rate; this will form the basis of much of the review in this chapter. It has been well known for many years (e.g., from the classical studies of Brown and Escombe, 1905; but also see reviews by Huber, 1935; Jackson, 1982; Raschke, 1960) that leaf temperature depends on stomatal opening, with temperatures decreasing as stomata open and as evaporation rates

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increase. The use of leaf temperature as an indicator of stomatal conductance or transpiration, however, is confounded by the fact that leaf temperature is also aVected by a wide range of other plant and environmental characters according to the leaf energy balance. Furthermore, as the environment is constantly changing, at least for plants in the field, it also becomes necessary to consider the dynamic behavior of leaf temperature in any precise study of leaf temperature. Until the development of remote infrared sensing of leaf or canopy temperature, applications involving the measurement of leaf temperature had been limited by the diYculty of using thermocouples in any large-scale field or even laboratory study. Following Raschke’s (1960) review of the principles of leaf energy exchanges in the early 1960s and the availability of new infrared remote sensors that could be used for the sensing of canopy temperature, there was a rapid development of their use for the study of plant water relations, especially to provide guidance for irrigation scheduling (see Jones, 1999b). Tanner (1963) and Fuchs and Tanner (1966) were among the earliest to publish papers describing the remote measurement of leaf temperature by infrared thermometry for the study of plant water relations in the field. A key milestone in the application of thermal sensing for irrigation management was the definition of what was termed a stress degree day (SDD) by Jackson et al. (1977) as the diVerence between the canopy temperature and air temperature. This provided a powerful way of normalizing for day-to-day and regional diVerences in environmental conditions. By measuring canopy-air temperature diVerences daily these researchers derived an integrated measure of ‘‘stress’’ experienced by the crop, the cumulative SDD. The next important step was the further normalization to take account of diVerences in atmospheric humidity and the comparison with a notional reference (well-watered) crop by means of the crop water stress index (Idso, 1982; Idso et al., 1981; Jackson et al., 1981). Further improvements involved the more general introduction of wet and dry reference surfaces to normalize for all aspects of current environmental conditions (Jones, 1999b; Jones et al., 1997) and allow absolute estimates of conductance or evaporation. An extensive literature developed over 30 years or so, making use of the temperature rise as stomata close under drought as an indicator of crop ‘‘stress.’’ This was greatly facilitated by the increasing availability of infrared thermometers. Most studies in this field have been based on infrared thermometry rather than on thermography, which is the process of obtaining thermal images. It is only relatively recently that thermography has become feasible for many laboratories, with the development of a new generation of

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uncooled imagers. In this chapter we concentrate on thermography but will refer to the literature on infrared thermometry wherever relevant advances have been made in that area.

B. IMPROVED SENSOR TECHNOLOGY

The availability of appropriate equipment limited the use of thermographic techniques until thermal scanners became available in the 1960s. Some early studies used aerial thermal scanners for the detection of water stress in plant canopies and for the estimation of crop evaporation rates (Bartholic et al., 1972; Heilman et al., 1976). Other studies used similar equipment for the study of temperature distributions and boundary layer transfer processes across plant leaves (Clark and Wigley, 1975). The earliest applications of thermal imagery in plant physiology were in the 1970s and early 1980s when Omasa and colleagues (Hashimoto et al., 1984; Omasa et al., 1981a, b) started using laboratory systems for the study of distributions of temperature and, by implication stomatal conductances, over plant leaves in the laboratory. In these early days instrumentation was heavy and the cameras required the sensor to be cooled using liquid nitrogen. The opportunities to develop methods based on infrared thermography have been greatly increased by the introduction of uncooled, handheld cameras with thermal resolution of better than 0.1 8C and by more aVordable pricing. For example, it is now possible to purchase such a camera for less than £7,000 sterling ($9,000). Advances in thermography have occurred in parallel with advances in a wide range of other imaging technologies that are or could be used to complement information from thermography. These additional techniques range from those that give information of internal structure such as x-ray or nuclear magnetic resonance (NMR) tomography, through conventional visible reflectance (red/green/blue) images familiar from standard digital cameras to fluorescence images. In this chapter we concentrate on a consideration of what information can and cannot be obtained from thermal imagery. Although our emphasis is on plant physiological and ecophysiological applications at the leaf to plant or crop scales, for completeness we also mention applications of thermal imagery at the larger scale (e.g., use of satellite imaging for studies of plant stress and evapotranspiration). Applications at the microscopic scale are limited by the lateral thermal conductivity of plant leaves (see the section on spatial resolution, later in this chapter).

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II. THERMAL RADIATION AND REMOTE TEMPERATURE MEASUREMENT BASICS A. BLACK BODY RADIATION

All bodies with a temperature above absolute zero (
273.15 8C) emit thermal radiation as a function of their temperature. For a perfect emitter, known as a ‘‘black body,’’ the wavelength dependence of the emitted energy is described by the Planck distribution function: Ll ¼ 2hc2 =ðl5 ðehc=lkT
1ÞÞ

ð1Þ

in which Ll is the spectral radiance (W m
2 sr
1 mm
1), defined as the radiant flux density emanating from a surface per unit solid angle per unit wavelength interval (centered on l), h is the Planck constant (6.6256  10
34 J s), k is the Boltzmann constant (1.38054  10
23 J K
1), c is the speed of light (2.998  108 m s
1) and l refers to the wavelength (mm). The wavelength (lm) with the peak emittance decreases with increasing temperature according to Wein’s displacement law: lm ¼ 2897=T

ð2Þ

The total radiant flux density emitted (radiant excitance) from unit area of surface (R, W m
2) over a hemisphere is p multiplied by the integral over all wavelengths of black body spectral radiance: Z 1 R ¼ ep Ll dl ¼ esT 4 ð3Þ 0

in which s is the Stefan-Boltzmann constant (5.6697  10
8 W m
2 K
4) and e is known as the emissivity. The concept of emissivity is introduced to take account of the fact that most real surfaces are not perfect emitters of thermal radiation, so that the actual radiant excitance may be less than the theoretical value defined for a black body. Emissivity relates the actual radiance of a body at a given temperature to that of a black body and falls between 0 and 1. Although the emissivity varies somewhat as a function of wavelength, for many purposes it is assumed that real surfaces approximate ‘‘gray bodies,’’ having a constant emissivity across the thermal infrared region. Some typical values for the emissivities of diVerent materials are summarized in Table I. B. REMOTE MEASUREMENT OF TEMPERATURE

The aforementioned relationships allow one to estimate the temperature of a surface from measurements of the amount of thermal radiation emitted.

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TABLE I Broad-band Thermal Emissivities a of DiVerent Surfaces b,c Plant leaves Plant canopies Dry leaves Dry grass Wood Bark Dry soil Wet soil Sand Distilled water Water

Emissivity

References

0.95 (0.92–0.99) 0.98–0.99 0.96 0.88 0.90 0.94–0.97 0.92 0.95 0.87–0.92 0.96 0.98–0.99

1, 2, 4, 5 4 1 1 1 1 1, 5 1, 5 2, 4, 5 5 1, 3, 5

a

8–14 mm unless otherwise stated. These values are guidelines only because values vary markedly with surface condition. c Spectral emissivities may be found in the MODIS emissivity library (www.icess.ucsb.edu/ modis/EMIS/html/em.html (Zhengmin Wan, University of California, Santa Barbara). 1 ¼ Rees, 2001; 2 ¼ Sutherland, 1986; 3 ¼ Campbell and Norman, 1998; 4 ¼ Idso 1969; 5 ¼ MODIS emissivity library. b

Because the amount of energy emitted in the thermal wave band is rather small, extremely sensitive detectors are necessary. With recent advances in sensor technology it is no longer necessary to cool the detector (e.g., with liquid nitrogen) to attain adequate accuracy and a range of room-temperature sensors are becoming available. The particular sensor technology depends on the wavelength and precision required. Indium-Gallium-Arsenide sensors and Indium-Antimonide sensors are often used for the shorter wavelengths of thermal infrared (up to approximately 5 mm; primarily used for sensing hightemperature objects in industry), whereas quantum well photodetectors and microbolometers are used for the longer wavelengths required for measurement outdoors. Pyroelectric sensors generate current, whereas bolometer sensors are based on changing electrical resistance of the sensing element. Because thermal sensors do not respond equally to all wavelengths of thermal radiation, conversion from sensor output to temperature is achieved in the built-in software by means of integration of Eq. 1 over the appropriate wavelengths. Most cameras use one of two atmospheric windows where the atmosphere does not absorb radiation markedly (3–5 mm or 8–14 mm). Only the latter of these is of much use to an ecophysiologist for daytime measurements outdoors because there is too much interference by the upper

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tail of the highly energetic solar radiation spectrum in the shorter of these two wavelength windows. An alternative remote measurement technique is to detect colour changes of calibrated liquid crystals painted on leaves (Smith et al., 2004). C. ERRORS IN ESTIMATION OF TEMPERATURE

1. Background temperature and emissivity errors The algorithms used to estimate surface temperatures must not only correct for the spectral sensitivity of the detector and the emissivity of the surface, but they must also determine the fraction of the total thermal radiation received at the detector, which originates from the object being measured. The radiation emitted is attenuated somewhat by the atmosphere through which it passes (as a function of atmospheric path length and humidity) and is supplemented by the thermal radiation originating in the surroundings that is reflected by the object, as well as any thermal radiation emitted toward the detector by the intervening air. The influence of environmental temperature on the accuracy of radiometric estimates of temperature has been discussed in some detail by Omasa et al. (1984) and Jones et al. (2003). These and other authors have pointed out that infrared thermography or thermometry detects the total thermal radiation flux density leaving a surface: that is the sum of the emitted thermal radiation, Re, and the reflected thermal radiation, Rr, together with any transmitted radiation (this latter is usually assumed to be negligible, at least in plant canopies). To estimate the surface temperature it is necessary to determine only the emitted radiation component; this requires an estimate of Rr. One approach to correcting for this ‘‘background’’ radiation is to replace the surface of interest by a highly reflective (low emissivity) diVuse reflector surface such as crumpled aluminium foil, which reflects the incoming thermal radiation, and then to record the apparent temperature of this surface when e is set equal to 1. The subsequent correction is often incorporated within the camera software. This background temperature can vary from close to ambient air temperature when making measurements within a canopy (because the background is largely composed of other leaves at close to air temperature), in which case emissivity errors have only a small impact, to 270 K or lower. These lower, apparent environmental temperatures arise when measuring from above the canopy in clear sky conditions, because the background is then dominated by the sky, which may have a radiative temperature of lower than 250 K (Jones et al., 2003).

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Substitution into Eq. 3 leads to the calculation that a 1% error in e would be expected to equate to approximately a 0.75 K error in the estimated temperature at 300 K. Errors in surface temperature estimates that arise from errors in emissivity (e) are often smaller than one might expect from this simple inversion because of the presence of reflected, incoming background thermal radiation, so that: R ¼ Rr þ Re ¼ ð1
eÞs T4background þ es T4s

ð4Þ

in which Tbackground is the eVective background temperature. Indeed for a leaf deep within a canopy, where the background temperature is close to that of the leaf itself, the apparent emissivity (i.e., the value of e required for substitution in Eq. 3 when the total outgoing radiation is substituted for Re) is close to unity. Therefore Eq. 4 reduces to the following equation: R ffi s T4s :

ð5Þ

In this case the brightness temperature (the apparent temperature assuming that the emitter has an emissivity equal to 1) is close to the actual temperature. If, however, one assumes a leaf temperature of 300 K with a typical leaf emissivity of 0.95 and calculates temperature by inversion of Eq. 3, one gets an apparent temperature of 303.9 K. As another example, when the background temperature is 260 K (clear sky) and the leaf temperature is 300 K, the brightness temperature (reflected and emitted radiation) is 298.4 K, but the apparent temperature calculated when using Eq. 3 is 302 K. In this latter case the apparent emissivity for substitution in Eq. 3 is 0.978 (when e ¼ 0:95). These examples illustrate the importance of obtaining both an accurate estimate of the emissivity and of the background temperature. Errors also arise as a result of thermal emission by the atmosphere between the object and the sensor and because the thermal transmissivity of the atmosphere is not perfect, declining with distance. These corrections are a strong function of humidity. The firmware or software provided with thermal imagers or thermometers makes some or all of these corrections automatically, so that temperature errors associated with errors in e are usually less than 0.3 K/ percentage error in e, whereas atmospheric eVects can usually be neglected for close-range viewing (within several tens of meters), although for satellite imagery the errors can be between 3 and 10 K (Campbell and Norman, 1998; Jones et al., 2004). Typical reported emissivities for diVerent surfaces are summarized in Table I. Most individual plant leaves have thermal emissivities between approximately 0.92 and 0.96. It is important to note, however, that the eVective emissivity (ecanopy ) of a closed plant canopy consisting of leaves

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with e ¼ 0:95 will be between 0.98 and 0.99 because of the internal reflections between diVerent leaves. Campbell and Norman (1998) showed that this eVect may be approximated by the following equation: ecanopy ffi 1
ðð1


pffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffi eleaf Þ=ð1 þ eleaf ÞÞ:

ð6Þ

An interesting phenomenon related to the background radiation was observed by Lamprecht et al. (2002) when studying thermogenesis in the water lily Victoria cruziana. They observed that reflections of flower parts in the water could appear up to 4.5 K warmer than direct thermal images of the flowers. They attributed this to the fact that the reflection includes a fraction of the directly emitted radiation from the water, together with the fraction of the emitted radiation from the flower that is reflected by the water. As can be readily shown by substitution into Eq. 4, however, this overestimate of object temperatures should not occur if the instrument is set up with correct estimates of e for both the water and the flower tissue and of the background correction. Indeed the occurrence of such phenomena can be used as a diagnostic for incorrect camera setup. 2. Gaussian noise and cross-talk A potential error with thermographic arrays is possible cross-talk between adjacent sensor pixels, as has been noted by Omasa (2002). For longdistance imaging there can also be significant scattering of radiation from adjacent pixels, which gives a slight blurring eVect; this can be minimized by the use of a Laplacian filter (Mather, 1999). The inherent Gaussian noise dependent on sensor quality and integration time is another problem that needs to be considered. This can be reduced by increasing integration time (or replication); although this is at the expense of dynamic resolution, most sensors currently in use are sensitive enough that this is not a practical limitation in most plant physiological applications. Alternatively, noise may be reduced at the expense of spatial resolution by the application of a low-pass smoothing filter such as a moving-average filter or a median filter across the image (see Mather, 1999; Tukey, 1977). The latter is generally preferred because it is less influenced by outliers. In the medical field, attempts have been made to develop algorithms based on techniques such as mean-field annealing to remove noise while preserving image edges (Snyder et al., 2000). There is much scope for enhanced use of such artificial data improvement methods, although the existing algorithms may be of limited value for typical plant subjects. Further techniques are discussed in the section on image enhancement, later in this chapter).

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3. Spatial errors in calibration across an image To achieve adequate accuracy for some plant water relations studies it has been found necessary, with at least one type of camera, to correct drifts in spatial calibration across the sensor array as the camera warms up. Jones et al. (2002) proposed a simple subtraction method to improve the comparability of data across the whole of an image. The procedure is to take a reference image of a homogeneous reference surface (in practice an outof-focus image with the lens cap on suYces with reasonable accuracy in the field), subtract this (pixel by pixel) from the sample image, and then add back to all pixels the average temperature of the reference image to obtain a corrected image. The fact that absolute temperature accuracy is not necessarily high is not usually a problem in plant physiological studies, in which most measurements are relative to reference areas within the image. 4. Radiometric versus aerodynamic temperature and other errors In the case of thermography of flat, single leaf surfaces it is only necessary to take account of the instrumental errors of the type outlined in the preceding paragraphs, because the suitably corrected radiative temperature equates well to the temperature required for substitution into heat and mass transfer equations. When studying plant canopies and other complex surfaces, however, the situation is more diYcult. The complexity of plant canopies results in significant temperature variation (e.g., between sunlit and shaded leaves), so that the apparent radiometric temperature (which depends on radiation from the visible upper layers of the canopy) may not truly reflect so-called aerodynamic temperature (the average canopy temperature of those surfaces exchanging heat and water vapor), a value that is required for calculation of heat and mass transfer (e.g., Campbell and Norman, 1998; Kimes, 1980). There has also been substantial discussion of the angular variation of emitted thermal radiation from a canopy and apparent emissivity. In addition to the instrumental errors outlined previously, there are many cases in which the apparent radiative temperature of a canopy varies with view zenith angle. In practice it is usually assumed that the individual canopy surfaces behave as Lambertian emitters (i.e., having the eVect that the apparent temperature is independent of view angle). The variation in apparent temperature of canopies with view angle (Franc,ois et al., 1997; Kimes, 1980; Otterman et al., 1999) is largely thought to result from varying fractions of soil and sunlit or shaded leaves as the zenith or azimuthal angles of view change. A less obvious error arises where the surface in the instantaneous field of view (a single pixel) is composed of an ensemble of surfaces at diVerent temperatures. In this case the wavelength distributions of the emissions from

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each surface will diVer slightly, even if the emissivities equal one, and the wavelength distribution of the radiation emitted by the ensemble will not correspond exactly to that of a black body. Norman and Becker (1995) have suggested that for extreme temperature diVerences of 10 8C this can lead to errors in the radiometric estimate of surface temperature of the order of 1 8C. 5. Spatial resolution The potential image resolution is clearly set by the number of pixels and the area viewed, which in turn depends on angle of view (a) of the camera lens and distance to the object (z). The width of the imaged area (w) is given by the following equation: w ¼ 2z tanða=2Þ ð7Þ in which a is the total field of view of the lens and z is the distance to the object. For example, for a typical thermal imager with a 17-degree field of view and 256 pixels across the image the width of the object viewed at one meter ¼ 2  1  tan(17/2) ¼ 0.299 m. Therefore one pixel corresponds to 256/299 mm ¼ 1.17 mm. The size of pixels viewed clearly varies with lens focal length and distance to the object. Whether temperature variation at this spatial scale is physiologically meaningful depends on a number of factors, probably most important of which is the lateral thermal conductivity of the leaf or object being imaged. Spatial resolution is potentially much greater for thin leaves than for thick leaves because of the smaller lateral thermal conductivity of the former. For leaves in which the energy balance is varying spatially over the surface of the leaf, as happens where the stomata are closed in patches, one needs to take account of the three-dimensional heat flows rather than the one-dimensional equation used thus far. For typical thin leaves such as Phaseolus vulgaris, Jones (1999a) calculated that the half distance (i.e., the distance over which half the total temperature change between two steady values either side of a step change in surface conductance from 0 to 160 mmol m
2 s
1) was approximately 3.1 mm. Therefore the maximum temperature gradient likely to be observed across the lamina of such a leaf would be of the order of only approximately 0.4 8C/mm, which agreed well with the observed values. For small lesions or thicker leaves the gradient would be smaller and the ability of thermal imaging to detect localized stomatal variation more limited. It follows that the potential of thermal microscopy is somewhat limited, although with a sensitive detector, even quite small perturbations (<10
8 m2) in the water flux field can be detected, even if absolute estimates of their magnitude are diYcult to determine. Typical spatial resolution for the study of leaf processes is illustrated in Plate 1A. These images show the smooth temperature

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Plate 1. Pseudocolor thermal images of the first trifoliate leaf of a Phaseolus vulgaris leaf. (A) The first frame shows the cooling eVect of high evaporation rates from droplets of water or abscisic acid (the two right-hand drops), whereas the second frame shows the area of raised temperature, indicating stomatal closure caused by the abscisic acid after the water has dried. This area was delimited by veins. The third frame shows the blurred outline at the edge of a rectangular strip of Sellotape aYxed to the lower surface of the leaf, thus inhibiting transpiration. (B) This is a series of images taken at 2-minute intervals. The leaf was severed from the plant 10 seconds after the first image and illustrates the rapid cooling as stomata open during the initial stages of developing water deficit (the Iwanov eVect). (Panel B from Jones, 1999a, ß 1999 Blackwell Publishing Ltd.)

gradients around defined droplets of water on an area of Sellotape stuck to the leaf surface, showing the blurring eVect that is caused by lateral thermal diVusion. 6. Temporal resolution Most handheld thermal cameras have charged-coupled device (CCD) array detectors with an eVective shutter speed of the order of tens of milliseconds (ms), although some have line scanners that may take up to a couple of

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seconds to collect the information for a whole image. It follows that the temporal resolution of the instrument is rarely limiting in studies of thermal dynamics of plant leaves, in which the half time for temperature changes is frequently of the order of 30 seconds or more (Gates, 1980). This temporal resolution is ample to allow the study of physiological processes such as stomatal dynamics, a good example of which is the Iwanov eVect (Iwanov, 1928), in which stomata open rapidly (over a time scale of minutes in response to a break in their water supply. Plate 1B shows a good example of the Iwanov eVect in practice. Much more rapid changes of temperature, however, occur during the propagation of freezing events in tissues (see the section on frost tolerance and damage, later in the chapter). D. IMAGE ANALYSIS

Without the ability to perform quantitative analysis on images, thermography is really no more than a method for producing ‘‘pretty’’ pictures. The real potential of the system lies in its ability to provide quantitative information on spatial variation of the quantity being studied (e.g., stomatal conductance or thermogenesis) with the added potential of studying the dynamic variation of such spatial expression. 1. Image enhancement The use of image enhancement techniques to minimize the eVects of random noise was discussed earlier in the section on Gaussian noise and cross-talk in Section II. Here we consider approaches to improve the visual contrast of an image. The images produced by a thermal imager comprise one channel of data, with each pixel having a numeric value corresponding to a particular temperature. The most straightforward way of representing the diVerent temperatures visually is to use a gray scale with black representing a low temperature and white a high temperature. Where the temperature range of the image covers only a small part of the full dynamic range of the sensor the corresponding image will be dull and lacking in contrast. To improve the visual discrimination it is common to enhance the contrast using a linear contrast stretch, or sometimes more sophisticated enhancement procedures such as histogram equalization (see Mather, 1999). The more complex image enhancement techniques, however, generally lose the linearity of the temperature scale, so they are probably best avoided in thermography. Another way to improve the visual impact and apparent temperature discrimination of thermal images is to represent the linear scale in terms of an arbitrary color scale. Although the resulting image is often called a false color image, it is more correctly termed a pseudocolor image. Most of the thermal images presented

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in this chapter have had a pseudocolor enhancement applied using a highcontrast pseudocolor transform. Almost all image analysis programs include a range of continuous pseudocolor transforms or density slices, in which a range of contiguous gray levels are assigned to a specific output color. In addition to the low-pass filters (described in the section on Gaussian noise and cross-talk) to minimize the eVects of noise in the image, there are a number of image-enhancement procedures that can be used to improve image sharpness, based on so-called high-pass filters. The majority of these are based on calculation of the spatial derivatives of pixel intensity and can be either added back to the original image to give a sharpened image, or they can be used on their own to indicate the location of edges (Mather, 1999). Some software packages have been developed specifically for agricultural applications (Ewing and Horton, 1999), although most commercial image analysis programs include sharpening and edge-detection algorithms. 2. Thresholding The most obvious potential advantage of thermal imagery over the use of infrared thermometers, which only give single measurements of temperature, is its ability to obtain estimates of temperatures of a large number of individual surfaces at one time, thus allowing approaches dependent on knowledge of temperature frequency distributions (see Fuchs, 1990). This also greatly increases the potential for replication, a factor that is especially important in applications such as for plant breeding and mutant selection, as discussed in more detail in the section on applications later in the chapter. Related to this is the fact that the usual infrared thermometers have a finite view angle, so it is common for them to have some background (e.g., soil or sky) within the field of view, in addition to plant canopy, thus potentially biasing the readings they obtain. For field experiments this problem is particularly acute early in the life of an annual crop but applies also in many other situations. With thermal imaging several methods become available to eliminate the background. One approach was proposed by Guiliani and Flore (2000), who used an artificial background made of a black polyethylene sheet, which was placed behind the orchard trees they studied so as to avoid interference from other trees, soil, or sky in the background of any image. The black polyethylene had the further advantage that in the sunny conditions used for their experiments its high solar absorptance led to it warming up to well above the temperature of the leaves being studied. This allowed Guiliani and Flore to eliminate all background pixels from the subsequent analysis by using a simple thresholding technique.

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Jones et al. (2002) developed another use of thresholds. This study made use of temperatures of reference surfaces within the image (either a wet surface assumed to be evaporating at the potential rate or a nonevaporating dry surface) as the thresholds. Temperatures of any pixel not falling within the temperature range defined by the references could then be eliminated from the analysis on the assumption that they would not include canopy. Any temperatures below the wet reference were likely to include sky (which may have an apparent radiative temperature below
20 8C), whereas temperatures above the threshold were likely to relate to soil or nontranspiring branches or trunks. Thresholding or the selection of specific density slices is all that can be done with single-channel thermal images, but where additional spectral information is available, many alternative methods of classification become possible, as outlined in the following section. 3. Image Classification A more sophisticated approach to the elimination of irrelevant pixels, and one that is readily applicable to automation, is to use multispectral images and image analysis software to identify pixels corresponding to leaves or to various background materials (see Jones and Leinonen, 2003; Leinonen and Jones, 2004). Although multispectral and hyperspectral imagers including thermal bands are available in remote sensing, for most ecophysiological and physiological applications it is necessary to take separate thermal and digital images. These images are routinely taken with a conventional digital camera closely aligned to the thermal imager and using image analysis software to overlay (coregister) the two images (e.g., Mather, 1999). One can then use the information in the digital image (red, green, blue with a conventional camera or red and infrared with a purpose-built agricultural digital camera (ADC, Dycam, Woodland Hills, CA) to identify leaf material, or even sunlit and shaded leaf material (see Casa, 2003; Jones and Casa, 2001; Jones and Leinonen, 2003). Image classification algorithms are of two main types—unsupervised and supervised classifications. The former use an automated procedure to cluster individual pixels into groups with similar spectral properties; unfortunately, this type of classification does not directly classify them into recognizable objects (e.g., leaf or soil). Of more general use in plantphysiological applications are supervised methods, where specific training areas of an image are identified. The spectral signatures of these areas are then used to identify all similar areas in the same, or other, images. Details of the various methods available may be found in image analysis texts (e.g., Mather, 1999) and the appropriate software manuals. A simple example of the successful application of a supervised classification technique is shown in

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Fig. 1. The frequency distribution of temperatures of grapevine leaves from Plate 2D corresponding to sunlit areas identified as red in Plate 2H (solid bars) and the corresponding of temperatures distribution for shaded areas identified as green in Plate 2H (clear bars).

Fig. 1. In this case one can select only the temperatures of pixels corresponding to leaves for further analysis to obtain either the mean leaf temperature or the temperature distribution of the leaves. Further elaboration of this approach allows us to identify sunlit and shaded leaves separately (Casa and Jones, 2003; Jones and Leinonen, 2003; see Plate 2 and Fig. 1). Image classification software designed for remote-sensing applications such as ENVI (Research Systems, Boulder, CO) provides a powerful tool for achieving the necessary discrimination. 4. Image ratios Where multispectral images are available to supplement the raw thermal image, a particularly powerful tool for the extraction of information and the generation of new derived images is the use of ratios of specific spectral bands. Plant scientists are most often concerned with identifying areas of green leaf or vegetation in the image, and the most useful indices are often based on ratios of reflectances in the red and near infrared regions (see Leprieur et al., 1996; Steven, 1998). Many diVerent ‘‘vegetation indices’’ have been proposed based on these ratios, of which the best known is the normalized diVerence vegetation index (NDVI ¼ (rNIR
rRED)/(rNIR þ rRED), in which rNIR is the reflectance in the near infrared and rRED is the reflectance in the red), or the related soil adjusted vegetation index (SAVI).

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Plate 2. Illustrations of the potential use of image classification (Jones and Leinonen, 2003). The top row of images show (A) a thermal image of Vicia faba beans in a glasshouse, (B) a corresponding red/near infrared image obtained with a Dycam Agricultural Digital Camera, and (C) a classified version of (B) used to extract temperatures of only leaf material in the first image. The second row of images shows (D) a thermal image of a side view of a grapevine canopy, (E) a corresponding color digital image (showing some filter-paper references at the top right of the image), and (F) a corresponding image classified into sunlit leaf (red), shaded leaf (green), and other (blue) areas. The lower panel shows the use of thermography for presymptomatic visualization of hypersensitive reactions and cell death in tobacco (reproduced with permission from Chaerle et al., 1999). (G) and (H) Paired thermal and visible images at 47 hours after TMV infection; and (I) and (J) corresponding images 5 days after infection. (Images courtesy of L. Chaerle; see http://allserv.rug.ac.be/ lchaerle/NBT/NBT.html).

These indices make use of the fact that green vegetation has a very high reflectance in the near infrared (NIR), which contrasts markedly with its high absorptance in the red; high values of the indices indicate complete vegetation cover. Use of a diVerence image enhances the discrimination of plant leaves.

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III. PLANT ENERGY BALANCE AND LEAF TEMPERATURE A. ENERGY BALANCE BASICS

Central to any application of thermal imaging in plant physiology is a good understanding of the basic features of plant and leaf energy balance and its regulation by plant and environmental factors. 1. Mass and heat transfer For a comprehensive description of heat and mass transfer processes, readers are referred to basic texts (e.g., Campbell and Norman, 1998; Jones, 1992; Monteith and Unsworth, 1990); here we restrict ourselves to the essential basics. The transfers of mass (e.g., water vapor in transpiration) and heat from the leaf to the atmosphere occur by a combination of molecular scale processes (diVusion and conduction) and those processes involving mass movement of the medium (convection). In each case the rate of transfer is proportional to the driving force multiplied by a proportionality constant or transfer coeYcient, conventionally termed a conductance in plant physiology. Many workers use resistances (the reciprocal of conductance) to describe the rate control or eYciency of transfer processes. An advantage of resistances is that resistances in series (as are the stomata and the boundary layer) sum linearly; conversely, conductances in parallel sum linearly. In the following we use molar units, even for heat (using the formulation advocated by Campbell and Norman, 1998). As a result the equations presented here are subtly diVerent from many in the literature, which are usually based on mass units. Mole fluxes are readily converted to mass fluxes through multiplication by the molecular mass of the transported gas. For heat the driving force for conduction or convection is the temperature diVerence between the leaf and the bulk atmosphere. One can write for the sensible heat flux (C, W m
2) from a surface at temperature, Ts: C ¼ gaH cp ðTs
Ta Þ ¼ cp ðTs
Ta Þ=rAH
1

ð8Þ
1

in which cp is the molar specific heat of air (¼ 29.3 J mol K ), gaH is the boundary layer conductance to heat transfer (mol m
2 s
1), and raH is the corresponding resistance (m2 s mol
1). For evaporation the driving force is the diVerence in water vapor mole fraction (mol mol
1) between the leaf and the air, so that one can write: E ¼ gw ðesðTleafÞ
ea Þ=pa ¼ gw ðxs
xa Þ

ð9Þ

in which pa is the atmospheric pressure and gW is the overall conductance to water vapor loss (including any stomatal and boundary layer components,

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mol m
2 s
1), and es(Tleaf ) is the saturation vapor pressure of water at leaf temperature and ea is the water vapor pressure of the bulk air, and xs and xa are the corresponding mole fractions. For convenience this equation can be approximated using the Penman approximation (Penman, 1948) as: E ffi gw ðD þ sðTleaf
Ta ÞÞ=pa

ð10Þ

in which D is the atmospheric water vapor pressure deficit (es(Ta)
ea) and s is the slope of the curve relating saturation water vapor pressure to temperature at the mean temperature (145 Pa K
1 at 20 8C). Note that this equation is divided by the atmospheric pressure to convert the driving force to a mole fraction as required for molar units. For energy balance purposes this equation is often expressed in terms of an equivalent heat flux (W m
2), by multiplying through by the molar latent heat of vaporization of water (l, 44.1 kJ mol
1 at 20 8C). DiVusive transfer is the consequence of the thermal movements of individual molecules and is the dominant mechanism in still air (e.g., in the intercellular spaces of plant leaves). For surfaces exposed to the atmosphere, air movements and eddies considerably speed up heat and mass transfer in the process of convection. Where bulk air movements are driven by thermal gradients above the leaf surface, this is termed free convection, with wind causing forced convection. In a fully turbulent flow regime gaH ¼ gaW, whereas in a typical mixed regime over plant leaves gaH ffi 0.92 gaW (Jones, 1992). 2. Energy balance equation All applications of infrared thermography in plant physiology or ecology are based on the study of processes that aVect some aspect of the plant’s energy balance. The energy balance for a leaf can be written: Rn þ M
lE
C ¼ S

ð11Þ

in which Rn is the net radiant flux density absorbed, M is the metabolic heat generated per unit area, lE is the rate of heat loss through the evaporation of water, C is the rate of heat loss by conduction and convection to the environment, and S is the rate of increase of heat content of the tissue. The net radiation term can be expanded to give: Rs þ RLa
RLe þ M
lE
C ¼ S

ð12Þ

in which Rs is the absorbed short-wave radiant flux density, RLa is the absorbed long-wave radiant flux density, and RLe is the emitted long-wave radiant flux density. This equation can be further expanded and expressed in terms of leaf temperature (Tleaf) by substituting from Eqs. 3, 8, and 10:

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Rs þ RLa
esT4leaf þ M
lgw ðD þ sðTleaf
Ta ÞÞ=pa
gaH cp ðTleaf
Ta Þ ¼ ðr cp l  ÞdTleaf =dt:

ð13Þ

in which gW and gaH are the overall conductances to water vapor and heat, and r*, cp*, and l* are, respectively, the density (kg m
3), specific heat (J kg
1 K
1), and thickness (m) of the leaf tissue. In the steady state, where ingoing and outgoing fluxes balance and leaf temperature is stable, dTleaf/dt ¼ 0, so that the right-hand side of this equation disappears (S ¼ 0). This equation can be solved iteratively (Gates and Papain, 1971), or else the net radiation term (which includes the term in T4leaf) can be replaced by introducing what has been termed a net isothermal radiation (Rni ¼ Rs þ RLa
esT4a ), defined as the net radiation that would be absorbed by an identical surface at air temperature, and using a simple linear approximation (see Jones, 1992) to give: Rn ¼ Rs þ RLa
esT4leaf ¼ Rni
esðT4a
T4leaf Þ ffi Rni
cp gr ðTleaf
Ta Þ

ð14Þ

in which gR is a radiative conductance (¼ 4esT4 =cp ). After rearrangement of terms, this allows one to calculate leaf temperature from a knowledge of Ta, D, the transfer resistances gW and gaH, and the net isothermal radiation (M is usually neglected): Tleaf ¼ Ta þ

½Rni þ M
lgW D=pa cp gHR þ l gW s=pa

ð15Þ

in which gHR ¼ gaH + gR. 3. Thermal dynamics In practice the temperature of any plant part varies continuously as a function of changing environment and physiological activity, with the rate depending on the magnitude of the storage term (S) and the heat capacity per unit area of the leaf. Equation 13 can be rewritten to express the rate of change of Tleaf in terms of the equilibrium temperature (Te, defined as the steady state value for the given conditions) as: dTleaf =dt ¼ cp ðTe
Ta Þ gHR þ gW l s=cp aÞ=r cp l 

ð16Þ

The time constant (t) can be derived from this as (Jones 1992, using Campbell and Norman’s [1998] terminology): t ¼ r cp l  =ðcp ½gHR þ gW l s=cp pa Þ

ð17Þ

Because the time constant depends on the boundary layer and stomatal conductances, in principle it is possible to estimate these conductances from time constants if the other constants in this equation are known.

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B. STOMATAL CONDUCTANCE AND EVAPORATION AS FUNCTIONS OF LEAF TEMPERATURE

1. Use of reference surfaces Although stomatal conductance or transpiration rate can be estimated (Jones, 1992; Jones et al., 2004; Omasa and Takayama, 2003) from leaf temperature measurements using rearrangements of the energy balance equations where all the other relevant variables (e.g., Rni, D, Ta, raH) are known for the surface of interest, measurements of all of these variables are not readily obtained for all surfaces in any image. Furthermore, all of these variables are continuously changing in the field. As a result of this substantial temperature variability in the field, and to a lesser extent in the laboratory, early researchers used air temperature as the most obvious comparator against which to normalize measured leaf temperature (e.g., Aston and van Bavel, 1972). Significant elevation of canopy temperature above air temperature was indicative of stomatal closure and crop water stress and provided the first of the so-called ‘‘water stress indices’’ (see the section on crop water stress indices, later in this chapter). An alternative and more convenient approach than measurement of all the relevant variables is to use ‘‘imitation’’ leaf references (Harrison-Murray, 1991; Jones, 1999a; Jones et al., 1997; Qiu et al., 1996) that mimic the leaf of interest in all but its surface conductance to water vapor. The idea is that the use of appropriate reference surfaces allows one to eliminate the need for accurate absolute measurements of leaf temperature and of most of the other variables. Another key advantage of this approach is that the main requirement is only the need for sensitive relative measurements of the temperatures of the leaf and of one or more reference surfaces. The introduction of wet and dry reference surfaces to normalize temperature measurements for current environmental conditions was the next important step (Jones, 1999b; Jones et al., 1997) in deriving absolute estimates of conductance or evaporation, although a simple comparison of the test canopy with a well-watered control has qualitative merit (Gardner et al., 1981). A further refinement is the additional use of surfaces of known conductance to water vapor, such as wet filter paper covered by a calibrated medical dressing of known water vapor permeability (Jones, 1999a). 2. Evaporation rate It has been long recognized (see Jackson, 1982) that E is proportional to (Tdry
Tleaf) with the main diYculty being to estimate the constant of proportionality for any given conditions. Thermographic information can be used in several ways to derive estimates of E. Two of the main approaches

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are outlined here; an extension based on the addition of information on vegetation cover was introduced by Inoue et al. (1994). a. Using boundary-layer conductance. The relevant relationship can be derived as follows. For a dry reference surface (denoted by subscriptdry), such as a nontranspiring leaf, using Eq. 8, one can write for the steady state (there is no soil heat flux, G, for single leaves): Rn;dry ¼ Cdry ¼ cp gaH ðTdry
Ta Þ

ð18Þ

in which Rn is the net radiation absorbed. For a corresponding evaporating surface (denoted by subscriptleaf), we can write the following equation: Rn;leaf ¼ Cleaf þ lE ¼ cp gaH ðTleaf
Ta Þ þ lE:

ð19Þ

Substituting for Rn in these equations using Eq. 14, and subtracting the resulting version of Eq. 18 from Eq. 19 to eliminate Rni gives after rearrangement (Jones, 1992): lE ¼ cp ðgaH þ gR ÞðTdry
Tleaf Þ ¼ cp gHR ðTdry
Tleaf Þ

ð20Þ

This important relationship indicates that evaporation rate from an evaporating surface is directly proportional to the temperature diVerence between the evaporating surface and a similar dry surface. It also follows by extension of the argument used in the preceding paragraphs that the diVerence in evaporation rate from any two similar surfaces is directly proportional to the temperature diVerences between them (e.g., from parts of the same leaf or from diVerent but similarly exposed leaves). From Eq. 20 it also follows that E can be estimated from knowledge of Tleaf and Tdry, as long as an estimate of the boundary layer conductance is available and the air temperature is known (for estimation of gR). Inoue et al. (1990) estimated gaH in this equation directly from measurements of wind speed and leaf size using known relationships from the engineering literature. Other possible approaches for estimation of gaH at a leaf scale include the use of heated and unheated leaves (see Brenner and Jarvis, 1995; Stanghellini, 1987) or dynamic cooling of preheated leaves (see Appendix 8 in Jones, 1992; Ku¨ mmerlen, 1999). Where the net radiation absorbed by the surface is known, gaH can be estimated directly for the dry surface from Eq. 18. Alternatively, if the transpiration rate is measured (e.g., in a gasexchange system), the Eq. 18 can be inverted to estimate the boundary layer conductance (gaH). b. Using net radiation. An alternative formulation of the same energy balance equations allows one to eliminate the requirement for a boundarylayer conductance and use the net radiation instead (Brough et al., 1986; Qiu

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et al., 1996). Eliminating gaH from Eqs. 18 and 19 and substituting isothermal radiation from Eq. 14 gives after some rearrangement: lE ¼ Rni ðTdry
Tleaf Þ=ðTdry
Ta Þ

ð21Þ

Unfortunately, in many situations it is as diYcult to estimate Rni accurately as it is to estimate gaH, so some combination of ancillary information is often used (Jones et al., 2004). 3. Stomatal conductance Rearranging Eq. 15 gives the following steady-state expression for gW as a function of the other variables: gW ¼ ððRni þ MÞ
ðTleaf
Ta Þ cp gHR Þ=ððD þ s ðTleaf
Ta ÞÞl=pa Þ

ð22Þ

M is usually ignored because it is commonly much smaller than the other terms. This equation allows one to calculate an absolute estimate of the water vapor conductance, from which the physiological or stomatal conductance (gs) may be derived as 1/( (1/gW)
(1/gaW)). Again, this equation is diYcult to apply in practice because of the need for accurate estimates of variables such as the boundary layer conductance, net isothermal radiation (which is especially hard to determine accurately), and the correct reference air temperature and vapor pressure deficit. A much more user-friendly approach is to make use of wet and dry reference surfaces to normalize the temperature of the leaf for changing environmental conditions (Jones et al., 1997). Again, assuming that the surface conductance to water vapor (gs) is zero for a dry surface and infinite for a wet surface, and that the reference and transpiring surfaces have the same radiative properties, it is possible to do a similar rearrangement of the energy balance equations (see Jones, 1999b) to give the following (using the Campbell and Norman [1998] units): gs ¼

ðTdry
Tleaf Þ gaH cp gHR  ðTleaf
Twet Þ ðcp gHR þ gaH l s=pa Þ

ð23Þ

Equation 23 gives a simple method of calculating stomatal conductance from thermal data where the temperatures of wet and dry reference surfaces are available. The second term on the right-hand side of this equation depends only on the boundary layer conductance and the temperature. Figure 2 indicates how the relationship between either Tleaf and conductance or the inverse of temperature index in Eq. 23 and conductance vary as functions of gaH. It is clear from Fig. 2 that one can estimate gs without a need to measure environmental variables such as absorbed net radiation or atmospheric humidity if, in addition to Tleaf, Tdry, and Twet, one knows either gaH or the surface

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Fig. 2. Curves relating either (Tleaf
Twet)/(Tdry
Twet) (A) and (B) or surface temperature (Tleaf; 8C) (C) to leaf conductance (on a logarithmic scale in [A]) for diVerent values of the boundary layer conductance (gaH ¼ . 0.444 mol m
2 s
1; 0.888 mol m
2 s
1; 1.25 mol m
2 s
1; 2.51 mol m
2 s
1). Curves calculated using the equations in Jones (1999b). Note that the curves in (A) and (B) are independent of environmental variables such as humidity and absorbed radiation.

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temperature for a surface with a known conductance. As for Eq. 20, the necessary estimate of gaH may be obtained by any of a number of independent methods. The use of reference surfaces with known conductances was proposed by Jones (1999b), who tested a range of microporous materials and medical dressings for this purpose. The approach has been successfully tested further by Diaz-Espejo and Verhoef (2002). In principle an alternative approach to the elimination of the need for an estimate of the boundary layer conductance, or to estimate gaH, would be to supplement the normal (double-sided) wet and dry reference surfaces by a surface that is wet on one side only (Jones, unpublished). Although in theory this should provide the necessary information, an analysis of the sensitivity of the method indicates that it would not normally be accurate enough to give suYciently accurate estimates of gaH in most practical situations (Jones, unpublished).

IV. APPLICATIONS The great majority of applications of thermal imaging to plant physiology and agronomy depend to some degree on alterations in plant-water relations. Although many applications of thermography in the areas of plant response to pollutants, chemicals, pests, or pathogens have been proposed, in almost all cases any observed responses can be traced to alterations in leaf surface conductance (attributable mainly to changes in stomatal aperture), as is outlined in the following sections. Indeed the majority of studies on quantification of the so-called physiological status of plants using thermal imagery (see Inoue, 1986, 1990) also depend on the ability of the approach to quantify changes in the rate of water loss (which is a function of stomatal conductance). In addition to consequences of plant water relations, it has been suggested that such imagery also gives information on diVerences in photosynthetic rate (Inoue, 1990), although this relationship only arises because of the dependence of photosynthetic gas exchange on stomatal conductance and would not detect nonstomatal regulation of photosynthesis (see Jones, 1995). The following sections review in turn each of the main applications of thermal sensing and thermal imagery to plant physiology. A. WATER LOSS, TRANSPIRATION, STOMATAL CONDUCTANCE, AND STRESS INDICES

Thermal imaging has clear advantages over traditional gas-exchange and porometry approaches to the study of stomatal conductance, both because of its nondestructive use with no direct interference with the leaf and because

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of its potential to get many values rapidly, or else to follow dynamic changes without interfering with the leaf ’s natural environment. Although much of the theory in this area has been developed using infrared thermometry, the principles are equally applicable to thermography; therefore applications of both methods will be discussed in some detail. In addition to laboratory and ground-level thermal sensing, there has been substantial interest in the use of airborne- and satellite-based thermal imagery for the study, not only of water stress for irrigation scheduling purposes but also for estimation of evaporation rates from plant canopies. Much of this area of research is outside the scope of this chapter, but some particularly relevant aspects have been reviewed elsewhere (Jones and Archer, 2003; Jones et al., 2004) and specific aspects will be presented where they contribute to plant physiological or ecological studies. 1. Stomatal behavior Thermal imaging is ideally suited to the study of spatial variability of stomatal conductance over and between plant leaves, including stomatal ‘‘patchiness’’ (Omasa and Takayama, 2003; Weyers and Lawson, 1997) and especially for the study of dynamic spatial patterns of stomatal response (Jones, 1999a; Prytz et al., 2003; see Plate 1B). In an elegant study, Prytz et al. (2003) demonstrated both synchronous and asynchronous patterns of stomatal behavior in Avena leaves. During oscillatory transpiration, there was close coupling between stomata in diVerent areas of the leaf but with a clear phase lag between distal and central regions of the leaf. During stable wholeleaf behavior, they provided clear evidence for patchy stomatal conductance, which changed with time. Indeed it is hard to see how such in vivo studies of stomatal patchiness and dynamics could be obtained by any other approach. Although absolute estimates of conductance are useful, much of this work can be achieved without absolute calibration. 2. Crop water stress indices From the outset a major interest in the application of thermal sensing in the field was the development of indices of crop water stress as guidance for irrigation scheduling. These indices are largely assumed to reflect changes in stomatal opening and evaporation rate as water becomes limiting. As outlined previously, raw temperature measurements are of relatively little use as indicators of crop water stress because of their extreme temporal variability, caused by varying environmental conditions. The first useful step in the development of methods for using temperature information in irrigation scheduling was to normalize the canopy temperature with reference to air temperature (see Aston and van Bavel, 1972) and the calculation of what was

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called an SDD (defined as the canopy-air temperature diVerence measured soon after midday, Jackson et al., 1977); significant elevation of canopy temperature above air temperature was indicative of stomatal closure and crop water stress. An alternative approach was to determine the temperature diVerence between the experimental canopy and a comparable well-irrigated crop (Clawson and Blad, 1982; Fuchs and Tanner, 1966) to give what was called a temperature stress day (TSD) (Gardner et al., 1981). Of course, this latter method required a well-watered reference crop, which rather limited application of the approach. Both these methods were still found to be somewhat unsatisfactory because the magnitude of indices such as SDD varied as a function of climatic factors, of which the most important was considered to be atmospheric humidity. In more humid climates, cloudiness also becomes a critical factor. The key step in the development of thermal sensing for irrigation management purposes was the introduction by Idso and colleagues (Idso, 1982; Idso et al., 1981; Jackson et al., 1981) of a crop water stress index (CWSI) that took account of variations in atmospheric humidity. They showed that (Tcanopy
Ta) was linearly related to atmospheric vapor pressure deficit for well-watered crops and defined the CWSI (Fig. 3) as:

Fig. 3. An illustration of Idso’s crop water stress index (CWSI; Idso et al., 1981). A CWSI can be calculated for any observation, x, as the ratio (Tx
Tnws)/ (Tmax
Tnws) at the corresponding vapor pressure deficit. The shaded double arrows represent potential errors and illustrate that the error as a fraction of the potential signal increases as air humidity increases.

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CWSI ¼ ðTcanopy
Tnws Þ=ðTdry
Tnws Þ

ð24Þ

in which Tnws is the temperature expected for a well-watered crop under the same vapor pressure deficit conditions (the non-water-stressed baseline) and Tdry was the temperature of a corresponding nontranspiring crop. Because it was not usually possible to have an actual nonwatered crop adjacent to any field, a standard relationship between (Tcanopy
Tair) and vapor pressure deficit was developed for each crop to give this ‘‘non-water-stressed baseline.’’ Jackson et al. (1981) derived an analytical derivation for CWSI based on the energy balance equations outlined previously, showing that CWSI was proportional to (1-E/Eo), in which Eo is the potential evaporation rate for a well-watered crop. Idso’s CWSI was found to work reasonably well in dry climates but had significant limitations in its application to humid and maritime climates and in environments with substantial climatic variability (Hipps et al., 1985). First, the absolute leaf-air temperature diVerence decreases as atmospheric humidity deficit decreases, and therefore sensitivity decreases. Although this is fully taken into account in the calculation of CWSI, noise becomes an increasing proportion of the signal as humidity deficit (and temperature) decreases (see Fig. 3). Further problems are caused by the fact that the canopy temperature also depends on variations in wind speed, canopy roughness, and net radiation, all of which are more variable in humid than in arid/semi-arid climates. Indeed the cloudless conditions that are assumed for application of the original CWSI are rare in humid climates such as the United Kingdom. As a result, there has been substantial eVort to improve the sensitivity of water stress indices for humid environments (see Jones, 1999b). For example, indices that include net radiation and vapor pressure deficit have been proposed (Jackson et al., 1981; Keener and Kirchner, 1983), whereas de Lorenzi et al. (1993) have proposed modelling the expected temperature of a well-watered crop as a function of environmental conditions. An example of how such a method can provide useful normalization of leaf temperature variation as wind speed and radiation vary is given in Fig. 4. The use of specific reference surfaces within the study area is probably the most powerful method for improving the sensitivity of thermal detection of stomatal closure. An implication of this approach is that canopy temperature measurements are probably best made at the scale of individual leaves. The use of local and simultaneous reference measurements is facilitated greatly by the use of thermography and helps to overcome the shortterm variation of equilibrium temperature. Jones (1999a) and Jones et al. (2002) discuss in some detail the choice of reference material and the need to

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Fig. 4. (A) Typical variation of the temperature of sunlit runner bean leaves (Aug. 10, 1995) over 15-minutes: The continuous line was calculated (using Eq. 15) as the expected temperature of a leaf with open stomata and varies in a rapid and complex manner as a function of changing wind speed and irradiance; . ¼ leaves from well-watered plants; 4 ¼ leaves from an intermediate watering regime;  ¼ leaves from a drought treatment (see Jones, 1999b, for some experimental details). (B) The same data expressed as a diVerence from the theoretical temperature, showing that major variations can be eliminated.

ensure that both the thermal and spectral properties of any reference equate to the leaf being studied. Using single leaf measurements, Jones (1999b) defined a stress index (SICWSI) that is superficially analogous to Idso’s CWSI as: SICWSI ¼ ðTleaf
Twet Þ=ðTdry
Twet Þ

ð25Þ

The main diVerence from CWSI is that this stress index uses a wet surface having an infinite surface conductance rather than the value for a crop transpiring at a well-watered rate (which would have a finite stomatal conductance). Jones (1999b) proposed several alternative formulations of indices based on combinations of Tleaf, Twet, and Tdry. Of particular interest is the index derived from Eq. 23, in which the index given by:

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SIgs ¼ ðTdry
Tleaf Þ=ðTleaf
Twet Þ ¼ a gs

ð26Þ

in which a (¼(cp gHR þ gaH l s/pa)/(gaH cp gHR)) depends only on wind speed and to a lesser extent on air temperature. Particularly as stomata close, this form of the index was found to be more stable than its reciprocal, which relates linearly to the stomatal resistance (Jones, 1999b). 3. Extension of CWSI to mixed pixels A major problem with the original CWSI and any measurements in which the field of view includes both vegetation and background soil, is that the background will aVect the recorded temperature independently of the evaporation rate. For high-resolution studies, image analysis techniques can be used to separate predominantly soil pixels from those that are predominantly vegetation (see the section on image analysis, earlier in this chapter). However, as frequently occurs with remote sensing, where pixels are large and commonly contain a mixture of surfaces, other approaches are necessary. Nemani and Running (1989) made use of the fact that the NDVI (see the section on image ratios) gives a measure of green vegetation cover within the pixel. Using this information, they argued that the slope of the Ts/NDVI relationship gives a measure of stomatal conductance, or evapotranspiration rate. This concept was developed further by Moran et al. (1994; see also Inoue et al., 1994), who derived a trapezoid in NDVI/temperature space, with the vertices representing the temperatures of wet and dry soil at one end and fully transpiring and nontranspiring vegetation at the other end. A waterdeficit index can be derived analogously to CWSI as the ratio (Ts
Twet)/ (Tdry
Twet), in which Tdry and Twet are the intercepts on the dry and wet lines at the observed NDVI. Moran et al. discuss in detail the various assumptions needed in application of this technique for the derivation of this water-deficit index. If the soil is considered dry, the trapezoid reduces to a triangular space, and has led to the calculation of various temperaturevegetation dryness indices (see Sandholt et al., 2002). DiVerences between the indices are largely based on methods for estimating the vertices. 4. Temperature variability As an alternative approach to the development of stress indices, it was suggested (Aston and van Bavel, 1972; Clawson and Blad, 1982) that the variability of canopy temperature within a plot might provide useful information. These early researchers reasoned that soil water depletion might be expected to lead to increased variability across a field because of underlying soil heterogeneity and certain locations drying more rapidly than others. This hypothesis was further confirmed by Gardner et al. (1981),

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who demonstrated that droughted maize crops exhibited a significantly greater temperature variance than did well-irrigated controls. Nevertheless, the temperature distribution for vegetation in the field had been studied relatively little until thermal imagery made this relatively straightforward to accomplish (Inoue, 1986, 1990; Jones et al., 2002). Perhaps the most important theoretical study was that of Fuchs (1990), who demonstrated that the definition of a mean canopy temperature for use in a CWSI is not a trivial problem. For a crop with random leaf orientation he showed that the absolute mean canopy temperature was expected to be relatively insensitive to drought (stomatal closure) as compared with the variance of canopy temperature. By the use of detailed modelling, Fuchs showed that even without variation in soil water content, the range of canopy temperatures (and hence the calculated variance) should increase substantially with decreases in stomatal conductance. For a reduction in stomatal conductance to one sixth of the control value, he predicted that the canopy temperature range would increase from approximately 7.5 K to approximately 17.5 K (Fuchs, 1990). The basis of this prediction is that latent heat loss dominates for wet crops, having a larger influence on temperature than does absorbed shortwave radiation (which changes as a function of leaf angle/exposure to the sun). When stomata close, however, the relative importance of leaf angle increases. A more detailed statistical model of leaf temperature distribution in plant canopies was developed by Dong and Li (1993), which could be adapted to provide a rigorous analysis of canopy temperature variance as a function of stomatal conductance. Data produced by Inoue (1986, 1990), Boissard et al. (1990), Giuliani and Flore (2000), and Jones et al. (2002) support Fuchs’s hypothesis that temperature variance could be a useful indicator of stomatal closure (and hence stress). For example, in Inoue’s experiment (1990) root pruning was used to manipulate stomatal conductance for a corn canopy. In this experiment root pruning had led to a decrease in stomatal conductance from 1.19 cm s
1 to 0.28 cm s
1, with a corresponding mean temperature increase from 33.6 8C to 37.3 8C. The corresponding standard deviation (breadth) of the Gaussian distribution increased approximately 2.8-fold. Interestingly, however, for a similar experiment with wheat, although the mean temperature increased with stress and stomatal closure by a similar amount, there was no clear indication of a corresponding increase of the breadth of the Gaussian distribution. The diVerence in the nature of the responses of these two canopies may reflect a diVerence in the leaf orientation of the two canopies, as Fuchs’s (1990) method only applies for randomly distributed leaves. This requirement for a random leaf orientation was especially apparent when attempting to apply the approach to a grapevine canopy where

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the majority of leaf laminae were oriented in the plane of the rows (Jones et al., 2002). Notwithstanding the imperfect nature of such a canopy for studies of temperature variability in response to stress, clear changes in temperature distributions were apparent in this canopy as a function of water status (Fig. 5). The concept of using the canopy temperature variability has been taken one stage further by Bryant and Moran (1999), who described a simple approach for deriving what they called a histogram-derived crop water stress index (HCWSI). Interestingly, rather than computing the change in variance these researchers defined their stress index in terms of deviation from normality of the temperature frequency distribution, on the assumption that skewness or kurtosis would increase with stress. These preliminary results were promising even though the temperature variation was measured with a pixel size of 2  2 m (significantly larger than the cotton plants studied); there seems to be real scope for a rigorous evaluation of this approach and a comparison with the raw variance of the data for leaf-scale studies. 5. Optimization of measurements a. Resolution and sensitivity to environmental conditions. A critical question when evaluating applications of thermal imaging is the sensitivity of the system for discriminating diVerences in stomatal conductance or evaporation rate. Consideration of the energy balance equations enables one to study the sensitivity to changing environmental conditions (Jones, 1999a, b; Jones et al., 1997). The overall range between the temperatures of a wet and a dry leaf gives a good measure of sensitivity (Jones et al., 2002). For humid, low-light, and high wind speed conditions this range can be as small as a few degrees, whereas under low humidities and high irradiance conditions this temperature range may exceed 15 8C (Fig. 6). Under a range of typical laboratory conditions it was found that a camera with a temperature resolution of 0.1 8C would be able to detect changes in stomatal conductance of as little as 5–15% (Jones, 1999a), though sensitivity generally decreases with extreme conductance values. In general the sensitivity was greatest at lower wind speeds at which leaf temperatures can deviate more from the air temperature than they can at high wind speeds. b. Importance of thermal dynamics. There are a number of problems with the use of thermal measurement in the field related to the fact that in most conditions temperatures change continuously with varying radiation load and varying wind speed (see Jones et al., 1997; Nilsson, 1995). In some cases, however, diVerences in the time response can be of interest. For example,

Fig. 5. Sample temperature frequency histograms for side-on views (as in Plate 2D–F) of grapevine canopies in a partial root zone drying experiment: (A) and (C) cv. Periquita (¼ Castela˜ o) and (B) and (D) cv. Moscatel. The upper figures (A) and (B) are for unirrigated plots and the lower figures (C) and (D) are for fully irrigated plots on July 20, 2002. Full bars are for shaded canopies and gray bars are for sunlit canopies. (Data from the experiment described by Jones et al., 2002.)

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Fig. 6. Variation in (Tdry
Twet) as a function of wind speed and (A) relative humidity ( 80%; 50%: 20%) at a net radiation absorbed of 400 W m
2 and an air temperature of 20 8C, and (B) net radiation absorbed ( 0 W m
2; 100 W m
2; 200 W m
2; 400 W m
2) at a relative humidity of 50% and an air temperature of 20 8C. Calculations as described in Jones (1999b).

Nilsson (1995) noted that both the magnitude of a temperature drop with a gust of wind and the rate of recovery varied with the severity of vascular diseases. Unfortunately, no explanation of these eVects was provided. Although temperature fluctuations are more usually a problem than a benefit in thermal imaging studies, it is theoretically possible in some circumstances to estimate conductances from observed time constraints (Eq. 17).

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One particular feature of thermal dynamics that deserves mention is the need for care when using reference surfaces to calibrate thermal imagery for calculation of stomatal conductance or stress indices (Jones, 1999b). Even if reference surfaces are included in each image so that leaf and reference surface measurements are simultaneous, if they have diVerent time constants than the leaves of interest, they may not be giving truly comparable values. Figure 7 illustrates typical temperature dynamics for some real (runner bean) and model leaves of the same size but with diVerent thermal mass. The greater heat capacity of the model leaves led to their having longer time constants than did the real leaves (see Eq. 17). A consequence was that their temperatures changed more slowly in response to environmental changes than did those real leaves; the resulting asynchrony in behavior led to the real leaf temperature actually exceeding the reference temperature on occasions when temperatures were increasing rapidly, thus leading to large errors in the calculated evaporation rate, conductance, or stress indices (e.g., Eqs. 21, 23, and 25). The time constant for individual leaves is commonly of the order of 30–60 seconds (Gates, 1980; Jones, 1992), though this depends on leaf conductance, leaf size, wind speed, and leaf thickness. c. Importance of spectral properties. A critical aspect of the use of reference surfaces for normalization of thermal image data is the need for the reference surface spectral properties to mimic closely those of the leaves being studied. Any diVerence in short-wave absorptance will aVect their energy balance in such a way as to invalidate any stress indices calculated, though Jones and Archer (2003) present ways in which such diVerences in radiation balance may be corrected. Not only is it important to ensure that short-wave absorptances are similar, but any diVerence in long-wave emissivity may also lead to errors. Because of the diYculties in obtaining artificial references that mimic both the spectral and thermal dynamic properties of real leaves, we have recently recommended the use of wet or dry real leaves as the most generally applicable reference surface (e.g., Jones et al., 2002). Nevertheless, as pointed out in the following paragraph, the problems caused by diVerences in spectral properties can be minimized by concentrating measurements on shaded, rather than sunlit, leaves. d. Sun and view angle. The main impact of view angle in relation to the solar angle is the varying radiant load on (and hence the temperature of ) the imaged leaves. Franc,ois (2002) and others have considered the impact of view angle in some detail, making use of sophisticated canopy radiation transmission models that allow calculation of radiative temperatures taking account of the eVects of leaf and soil temperature separately.

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Fig. 7. (A) Typical thermal dynamics of real and model leaves. The upper solid line is for a dry model constructed of filter paper on a base of a thin aluminium shim, thus conferring a significant thermal mass; the lower solid line is for a corresponding model maintained wet. The thin traces are for two typical real runner bean leaves of similar size to the models, and the dots refer to air temperature. All temperatures were measured using thermocouples. (B) Corresponding variation in short-wave irradiance (top line) and wind speed (bottom line) over the same period as for (A). (See Jones, 1999b, for further information on the experiment.)

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Although some researchers have undertaken their thermal measurements from two or more defined orientations (e.g., east- and west-facing views: Idso et al., 1981; Keener and Kircher, 1983; or from the four cardinal azimuths: Andrews et al., 1992), others have pointed out that the greatest sensitivity of temperature to stomatal conductance is found for leaves oriented toward the sun (highest radiant load) (Fuchs, 1990; Jones et al., 1997). These researchers and others (e.g., Giuliani and Flore, 2000) therefore suggested that viewing from the solar azimuth close to the ‘‘hot spot’’ angle might be optimal. Extensive studies by Jones and colleagues on grapevine (Jones et al., 2002) have considered this question further. They showed for typical conditions with 50% relative humidity for a sunlit leaf (Rn  400 W m
2), the typical range of (Tdry
Twet) would be 10–14 8C, whereas for a shaded leaf (Rn  100 W m
2) it would be only 6–7 8C. This means that the potential sensitivity of the system is greatest for sunlit canopies. It is also worth noting that another reason for confining measurements to sunlit leaves is that the greatest proportion of transpiration may come from these leaves because the stomata of shaded leaves are likely to be more closed, although this may depend somewhat on species. A disadvantage of measurements on sunlit leaves is that one might expect that the temperature variability (e.g., as a function of orientation) would be much greater under direct solar radiation than under diVuse light conditions. This could imply advantages in doing the measurements on shaded leaves. Figure 5 shows an example where the spread of leaf temperatures is indeed greater for the sunlit than for the shaded side of a grapevine canopy. Image analysis techniques can be used to extract only the temperatures of shaded or sunlit leaves from thermal images as required for further analysis (see the section on image analysis, earlier in this chapter). 6. Irrigation scheduling There is extensive literature on the use of thermal infrared information for irrigation scheduling. Because very little of this has made use of thermographic techniques, we will not review this in detail and instead refer the reader to reviews (e.g., Jackson, 1982; Jones, 1999b; Jones et al., 1997). The relevant technical details of the principles have, however, been covered previusly in the discussion of stress indices. Recent developments in imaging and control systems are now starting to open up the possibilities for the development of irrigation control systems based on thermal imaging of crop water stress. Initial trials have been based on the use of simple thermal sensing (Kacira et al., 2001, 2002), although there appear to be real opportunities for the application of thermal imaging. Application to precision irrigation of nonhomogeneous crops would be likely

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to be greatly helped by the combination of thermal imaging with reflectance imagery and appropriate image analysis techniques, as outlined in the section on image classification, earlier in this chapter). A major problem with all infrared methods for irrigation scheduling is that although they may give a sensitive indication of stomatal closure or evaporation rate as an indicator of water deficit, by themselves they give no information on the amount of water needed at any time. It therefore follows that they cannot be used on their own. Two main approaches are envisaged: (1) hand-held thermal imagers may be used as rapid monitoring systems for checking the eVectiveness of existing irrigation scheduling systems on a farm and guiding the application of supplementary irrigation, or (2) imagery may be incorporated into an automated irrigation control system where there is nearly continuous monitoring and the capacity to supply water whenever it is required (e.g., in standard trickleirrigation systems). In this case a standard amount of irrigation would be applied whenever the stress index exceeds a given threshold. The use of expert systems (see Plant et al., 1992) that integrate data from several sources could be modified to combine thermal information with a water budget calculation to derive a robust irrigation schedule, which would potentially adjust the amount of irrigation water applied at any time. B. BIOPHYSICAL AND AERODYNAMIC PROPERTIES

1. Boundary layer transfer Infrared thermography has the potential to provide much biophysical information concerning factors determining the thermal properties of leaves and plants. An important early study investigated the convective transfer processes between leaves and their environment, using thermography of model and real Phaseolus vulgaris leaves in a wind tunnel (Clark and Wigley, 1975). With a turbulent air flow at 2 m
1 across the model leaf blade, they observed approximately a 3 8C temperature diVerence between the leading and trailing edges, whereas for the real leaf in an air flow of 0.7 m s
1, diVerences of approximately 4 8C between the edges and center of the leaf were observed. The observed temperature distributions were used in the calculation of boundary layer conductance. They showed that the observed temperature distribution over the leaf surface led to local deviations from the conventional engineering formulae for calculation of transfer resistances that were based on isothermal surfaces. Their data also provided some of the first evidence for variation in stomatal conductance over the leaf surface. Several other researchers have used thermal images to derive estimates of the variation in boundary layer conductance over the surface of model leaves from rearrangement of Eq. 12 (e.g., Omasa et al., 1984).

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More recently Roth-Nebelsick (2001) used the most up-to-date engineering software to calculate temperature distributions across the surface of diVerentshaped plant leaves as a function of wind speed. Their modelling demonstrated that even small leaves should develop large temperature gradients across their surface at low or zero wind speeds as a result of free convection and buoyancy eVects. These simulations indicated, however, that heat transfer should not be greatly aVected by buoyancy eVects, as has also been found in some experimental studies (de Lorenzi and Jones, 2003; Kitano and Eguchi, 1990). These more detailed calculations still need to be verified experimentally; this should be straightforward using modern thermal imagers. 2. Heat capacity For any studies on the dynamics of leaf temperature it is necessary to determine the heat capacity (r*cp*‘*) of the leaves. Ku¨ mmerlen et al. (1999) describe two methods based on what they term active thermography (studying responses to imposed environmental changes) to determine the spatial distribution over the leaf surface of thermal capacitance: a constant flux approach and a periodic flux method. For the first of these a single step change of incoming irradiance is imposed and the consequent variation in leaf temperature imaged at an appropriate frequency to derive an image of the spatial variation of the time constant (t). The variation in leaf thermal capacity can then be determined by rearranging Eq. 17. These authors used a version of Eq. 20 together with measurements of evaporation rate to derive the necessary estimate of the boundary layer conductance required for substitution into Eq. 16, though this could equally be derived from Eq. 17 for nontranspiring leaves. The periodic flux method involved using Fourier transforms to estimate t from the phase of leaf temperature changes under an imposed periodic heat flux. This method, particularly, led to very clear imaging of the greater heat capacity of the veinal as opposed to the interveinal areas. C. METABOLIC PROCESSES

Any aspect of plant metabolism gives rises to the production of heat, because no metabolic process is fully thermodynamically eYcient. One such process is oxidative respiration, which is used by organisms to generate reducing equivalents and adenosine triphosphate (ATP) and to power metabolic processes. Plants, however, have an additional respiratory pathway where electron transport is not coupled with ATP formation, thus directly releasing a greater portion of the free energy as heat. This alternative respiratory pathway (the alternative oxidase, or AOX, pathway) is resistant to cyanide and is characteristic of plant mitochondria (Vanleberge and McIntosh, 1997). Recently analogues of the animal mitochondrial uncoupling proteins

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that are involved in thermogenesis in animal brown adipose tissue have also been detected in plants (see Jezek et al., 1996; Watanabe et al., 1999) and have been proposed as having a role in regulated thermogenesis during fruit and seed development. Although the metabolic term (M) in the energy balance equation (Eq. 11) is usually neglected because it is thought to be small in relation to other components of the energy balance, there are situations where it may become significant and infrared thermography becomes a valuable tool for its study (see Bermadinger-Stabentheiner and Stabentheiner, 1995; Ito et al., 2003; Lamprecht et al., 2002; Skubatz et al., 1990, 1991). Because of the high heat output of the AOX pathway, there has been particular interest in its eVect on regulation of the thermal balance of plant tissues. The best known example of thermogenesis in plants is the burst of heat generation at flowering, which is observed in several plant species, particularly in the Araceae (including Arum maculatum, Symplocarpus foetidus [skunk cabbage], Philodendron selloum), but also in custard apples, cycads, palms, lotus, RaVelesia, and water lilies (see Ito et al., 2003; Lamprecht et al., 2002). Some of these plants appear to approach a level of homoeothermy (regulated maintenance of a given temperature) for some of the thermogenic period (see Ito et al., 2003; Seymour and Blaylock, 1999). In most of these plants the heat generation appears to be related to volatilization of attractant odors for pollinating insects. The increased rates of respiration in the inflorescences of these thermogenic species have been shown to lead to a temperature rise of 20 8C or higher (Knutson, 1974). Although the use of thermocouples is a perfectly adequate approach for tracking the temperature changes of specific parts of the inflorescence during the metabolic burst, thermography allows ready visualization and quantification of the spatial diVerences in temperature dynamics and was used to particular eVect in the study of the dynamics of thermogenesis in all the various parts of an Arum inflorescence by Bermadinger-Stabentheiner and Stabentheiner (1995). Interestingly, not all phases of thermogenesis appeared to be associated with attraction of insects as the male flowers heated by approximately 7 8C before the inflorescence opened. A disadvantage of thermography for this type of investigation is that without some manipulation of the tissue it is only possible to measure the surface temperatures; Bermadinger-Stabentheiner and Stabentheiner (1995) therefore had to cut away a small section of the floral chamber to obtain information on the thermal dynamics of the male and female flowers. Thermography has also been used to address the question as to whether other tissues, less commonly regarded as thermogenic, can undergo

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thermogenic heating. There is now extensive evidence that salicylic acid plays an important part in the stimulation of the AOX pathway and thermogenesis (Vanleberge and McIntosh, 1997). Therefore applications of salicylic acid might be expected to stimulate thermogenesis in other tissues. Indeed Van Der Straeten et al. (1995) used thermography to show a reproducible temperature increase of 0.5–1.0 8C in tobacco leaves in response to applications of salicylic acid and its analogues. In view of the known stimulation of thermogenic respiration by salicylic acid these authors interpreted the temperature rise as an enhanced thermogenic response. Although they were aware of the importance of evaporation rate to the energy balance and made independent measurements of stomatal conductance, their measurements of stomatal conductance were unfortunately of insuYcient accuracy to rule out the possibility that the observed temperature change had been caused by stomatal closure. In subsequent work, however, they accepted that the temperature change probably resulted from stomatal closure (and hence reduced transpiration) after salicylic acid treatment (Chaerle and Van Der Straeten, 2001). Several studies have reported calorimetric measurements indicating enhanced metabolic heat outputs in response to low temperatures in otherwise nonthermogenic species and have implicated changes in AOX activity (see Breidenbach et al., 1997). In principle, thermography should provide a sensitive tool for determining whether such changes in heat output are reflected in enhanced tissue temperatures. In practice, however, the heat rates quoted for nonthermogenic tissues of approximately 0.68 mW g
1 fresh weight for tobacco and 1.5 mW g
1 fresh weight for wheat (see Nevo et al., 1992; Van Der Straeten et al., 1995) are minute in relation to radiative and evaporative fluxes. Assuming leaves of 0.1 mm thickness, these equate to heat rates of only 1.5 and 0.68 W m
2, respectively (cf. the typical solar irradiance of up to 900 W m
2 [Jones, 1992]). McNulty and Cummins (1987) calculated that the thermogenic temperature increase for Saxifraga cernua would be at most 0.02 8C. In an elegant analysis of the energetics of respiration, Breidenbach et al. (1997) concluded that changes in heat rate were only likely to be very marginally aVected by the nature of the respiratory pathway (with AOX only being expected to generate at most 6% more heat for a given amount of substrate than would the cytochrome oxidase pathway). DiVerences in heat rate must therefore be interpreted in terms of diVerences in overall respiration rates rather than being specifically associated with the AOX pathway. The ecological advantage of the AOX pathway and uncoupling proteins may therefore only be as mechanisms allowing high rates of respiration without a build up of excess ATP.

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H. G. JONES D. DISEASE AND INFECTION

Most plant disease identification and assessment is by subjective visual analysis. This has been supplemented by a range of reflectance and fluorescence imagery approaches (Nilsson, 1995), but what we are primarily concerned with here is the potential of infrared thermometry and thermography to supplement or even replace these methods for the study of plant disease and infection processes. Studies with thermography have been both in the field using airborne or other remote sensing (see Nilsson, 1995, for a review) and in controlled environments and the laboratory (see Chaerle and Van Der Straeten, 2000, 2001) where detailed leaf-scale measurements are possible. The basis of these studies is usually the interference caused by the disease organism to the plant water relations, although there are some cases in which other processes may be implicated in temperature changes (e.g., alterations in radiation interception through altered leaf orientation, altered cuticular waxes, or other reflectance changes as might occur with silver leaf disease in plum). The potential sensitivity of temperature measurements for the study of plant disease was originally demonstrated using infrared thermometry. For example, an early study (Pinter et al., 1979) showed that root infections of sugar beet (by Pythium aphanidermatum) or cotton (by Phymatotrichum omnivorum) could raise leaf temperatures by as much as 3–4 8C above healthy plants, even though visual symptoms were not apparent without digging up the roots. Similar results were also reported for root rots in bean plants (Tu and Tan, 1985) and in soybean (Mengistu et al., 1987). Nilsson (1991) has also undertaken extensive studies of diVerent diseases and crops and demonstrated significant to strong associations in at least 27 crop/disease combinations. Temperature diVerences between diseased and healthy leaves of as much as 6 8C are common in glasshouse conditions (Nilsson, 1995), whereas diVerences of 3–5 8C in flag leaf temperatures of cereals were frequently observed in response to vascular or root rot diseases in the field (Nilsson, 1995). In other cases, however, no temperature diVerence was detected even though symptoms were visible (Duszek, 1987). As a result of the aforementioned studies, it has nevertheless been suggested that screening for root rot resistance could make use of infrared thermography (Tu and Tan, 1985). These field-scale studies have all depended on the disease-induced shoot water deficits, and hence stomatal closure, caused by restrictions to the water supply. Where such gross diVerences occur, aerial infrared thermography can usefully indicate areas of diseased crop, although it is diYcult to distinguish diseased areas from areas with water deficits. Indeed the major problem with using thermography as a diagnostic tool for infection, especially in the field, is the diYculty of separating pathological eVects on the rate of

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water loss from drought-induced stomatal closure. At least on the field scale, there is also the problem, applicable to both water stress and infection, that both stresses reduce green leaf area with the result that infrared sensors see more of the warmer background soil, but this problem can be minimized at the smaller scales by using image analysis methods to eliminate soil pixels (see the section on image ratios, earlier in this chapter), or at the larger scales by using the vegetation index-temperature trapezoid (Moran et al., 1994) as used for their water deficit index. A further problem of temperature measurement in the field as a diagnostic is the influence of gusts of wind, which leads to substantial leaf-leaf and temporal variability (see Fig. 7). As indicated earlier, it is possible that the eVects of some diseases on elevating leaf temperature may result from eVects on other components of the energy balance. For example, some diseases such as rusts and some viruses may alter the short-wave absorptance of the leaves, and this may contribute to temperature changes. It is also worth pointing out that not all diseases cause an increase in leaf temperature; stripe rust on wheat has been reported to reduce stomatal closure and disrupt the cuticle, so that infected leaves may be 0.2–1.0 8C cooler than healthy leaves in the earlier stages of infection (Smith et al., 1986), although leaf temperature increased as infection progressed. In laboratory thermographic studies, Chaerle and colleagues (1999, 2001) have demonstrated that thermal eVects can even be used to predict future cell death events on infection by tobacco mosaic virus (TMV) and in bacterioopsin (bO) transgenic tobacco plants. With TMV inoculation there was close colocation of visible disease symptoms and localized temperature rises (See Plate 2G–J); of particular significance, however, was the observation that clear thermal responses could be detected at least 8 hours before the appearance of visible symptoms of the hypersensitive response (HR) (Chaerle et al., 1999). Although it had been speculated that the build up of salicylic acid in the areas of the developing lesions implied an involvement of the AOX pathway of respiration in a thermogenic temperature rise (see also the previous section on metabolic processes), accurate measurements of transpiration showed that this could fully account for the temperature response. In the bO tobacco plants, cell death was stimulated by temperature shifts; again, thermal changes gave a clear indication of the pattern of future cell death (Chaerle et al., 2001). The actual areas of recently-initiated cell death were cooler than control areas as a result of the loss of water from cells as the cell membranes broke down, whereas the earlier phase of heating reflected stomatal closure (see Plate 2G–J). Interestingly, in contrast to the TMVinduced HR, the Erwinia amylovora harpin-induced HR in Nicotiana sylvestris was shown using thermography (Boccara et al., 2001) to lead to a marked temperature decrease in the harpin-infiltrated zone 3–4 hours after

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elicitation (reaching as much as 2 8C); this was ascribed to stomatal opening. Again, the thermal eVect preceded necrosis symptoms by several hours. To use thermography in any diagnostic sense for plant disease it will almost certainly be necessary to combine thermal information with one or more ancillary measurements using other spectral information (Chaerle and Van Der Straeten, 1999; Nilsson, 1995). This concept has been taken forward very successfully by Chaerle et al. (2003, 2004), who combined thermal imaging with chlorophyll fluorescence imaging (to quantify photosynthesis) and video imagery in a robotized system to study the uptake and early eVects of phenyurea herbicides and their microbial degradation. There remains much scope for combination of thermography with other fluorescence imaging systems operating at other wavelengths and responsive to other physiological processes (Buschmann and Lichtenthaler, 1999). Catena (2003) has reported the use of thermography reveals cavities or rotten tissue in trees, based on the differential thermal behavior of rotten tissue, though analysis is currently rather subjective. E. POLLUTION AND AGRONOMIC EFFECTS

A particular interest for some years has been the potential to use thermal imagery (as well as reflectance and fluorescence imagery) for the diagnosis and study of plants with diVerent agronomic treatments (see Nilsson, 1995) and especially for the study of the eVects of pollution. As with most other thermal studies, any observed responses are mostly based on the principle that any damage will aVect the stomatal conductance, or the rate of water loss through the cuticle, and hence be apparent in a thermal image. Some of the earliest uses of thermal imaging to study the eVects of pollution were those of Omasa and colleagues (1981a, b, 1984). In these studies they demonstrated stomatal closure as a result of exposure to pollutants such as NO2, SO2, and O3. Furthermore, their studies on sunflower leaves also demonstrated that the eVects were uneven over the leaf surface. Such stomatal heterogeneity has been the subject of much more recent physiological interest (e.g., Weyers and Lawson, 1997). The use of thermal imaging in such studies still oVers much potential, though as pointed out by Jones (1999a), there is a limit to the spatial resolution of thermal imaging for the study of stomatal heterogeneity, which is set by the lateral thermal conductivity of the leaf tissue (see the section on spatial resolution, earlier in this chapter). This eVectively restricts the use of thermal imaging to the study of variations in variables such as stomatal conductance to variation at the scale of several mm rather than to the scale of individual stomatal pores.

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The development of hand-held thermal cameras has allowed thermal imaging to be extended to the diagnosis of the physiological status of both woodland and individual urban trees (Catena, 2003; Omasa et al., 1993, 2002). Using a thermal camera carried in a helicopter, Omasa et al. (1993) reported successful diagnosis of physiological activity of individual trees. These studies confirmed the suspicion that both biotic and abiotic stresses cause spatially heterogeneous impairment of the leaves. Currently, adequate information on spatial variation of stomatal conductance for such studies can only be obtained using thermography. Interestingly, these researchers realized in their early work that useful results were most likely to be obtained under cloudy and steady state conditions; this contrasts with the general preconception until then that sunny conditions were optimal for thermal imaging. In other studies it has been shown that nitrogen fertilization also can aVect the canopy temperature of paddy rice (Wakiyama, 2002). In this case higher nitrogen fertilization led to higher leaf blade chlorophyll content, which was shown to be associated with more open stomata (as measured by porometry) and cooler canopy temperatures by 0.85 8C. Several other studies on fertilization eVects on crop temperature were reviewed by Nilsson (1995). As with water stress studies, however, a common problem in canopy-level thermal imaging is the possibility of interference by the background (which increases as a fraction of the image as leaf area index decreases). It is therefore diYcult to distinguish an eVect of nitrogen on stomatal conductance from an eVect on increasing leaf area index. For normal agricultural crops the soil background can be 10–20 8C warmer than the leaves, but even with paddy rice, in which the background is water, the background temperature may be a several degrees higher than the canopy (Matsushima, 2002). As with water stress studies (see the section on extension of CWSI to mixed pixels, earlier in this chapter), the combination of vegetation indices with a thermal stress index (Moran et al., 1994) oVers great potential for improvement of the resolution of thermal imagery in diagnosis at the canopy scale and above. F. GENETIC SCREENING

The ability of thermal imaging to survey a large area within a single image means that it has much potential in plant breeding and genetics for the screening of large numbers of genotypes for diVerences in aspects of their water relations. The first successful use of thermal imaging for plant genetic screening was the isolation of a water-relations mutant by Raskin and Ladyman (1988), who used thermography to identify the ‘‘cool’’ mutant of barley from a screen of 10,000 M2 seedlings. In this case the screen used was to spray the plants with abscisic acid (ABA), which is a growth regulator

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that normally causes stomata to close (and hence raises leaf temperature). Mutants with stomata insensitive to ABA were then identifiable from their lower leaf temperatures as compared with the wild-type response in which leaf temperatures increased. In the initial screening six putative mutants were identified, and an M3 population derived from these. The entire progeny of one of these six plants was nonresponsive to ABA, though all the other progenies had the wild-type behavior. In fact, the temperature of the ‘‘cool’’ mutant leaves was always below that of the wild-type plants, even in the absence of applied ABA. More recently, thermal imaging has also been successfully used to isolate a number of stomatal mutants in Arabidopsis from a total of 85,000 M2 seeds (Merlot et al., 2002; Mustilli et al., 2002). Again, the screen was to search for mutants that appeared cold (stomata remain open) when the plants were subjected to drought and led to the selection of 44 lines with a heritable ‘‘cold’’ phenotype. Two of the mutants found in this type of screen were named open stomata mutants (ost1) and were found to be impaired in the ABA signalling pathway of stomatal closure. Other mutants were found to be deficient in the ABA synthesis pathway and included alleles at ABA1, ABA2, and ABA3 loci (Merlot et al., 2002). In these screens the ost1 mutants were on average approximately 1 8C cooler than the wild-type plants under the conditions of the screen. Although the authors mention that the environmental conditions for the screen were optimized using existing ABA mutants, the selection of experimental conditions appears to have been rather arbitrary. A rigorous analysis of conditions for optimal sensitivity (Jones, 1999b) indicates that the sensitivity (defined as the temperature change for a given percentage change in conductance) would be expected to increase with both increasing vapor pressure deficit (lower humidity) and with increasing net radiation absorbed and to increase non linearly as wind speed decreases (see Fig. 6). This information can be used to provide the best possible conditions for a genetic screen. In another study, Gray et al. (2000) used infrared thermography as one tool to help characterize the hic mutant in Arabidopsis. This mutant produces greatly enhanced numbers of stomata when grown in high CO2 concentrations, although interestingly, there were no obvious phenotypic eVects on stomatal conductance when studied either by conventional methods (porometry) or by infrared thermography. Boccara et al. (2001) have proposed the use of thermography to screen for mutants aVected in elicitor signalling pathways. Infrared thermography has many advantages for this type of screening study: (1) it is completely nondestructive, (2) it does not interfere with the

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normal behavior of the stomata, and (3) it can eVectively sample large numbers of seedlings, especially as it can be readily automated (Chaerle et al., 1999). Another important feature of this application of thermography is that because comparisons are always made against controls, there is no need for accurate calibration of the sensor or of the leaf emissivity. It has also been suggested that thermal imaging could be extended from the controlled-environment studies of the type outlined previously, to the screening of large numbers of genotypes for their stomatal characteristics in the glasshouse or the field, as is required for the mapping of quantitative trait loci (QTLs) (Jones, unpublished data; Price et al., 2002). Indeed Mattsson (1997) successfully used thermography to screen conifer seedlings for transpiration as an indicator of vigor before planting out, finding that an advantage of the technique was its potential to study large numbers of seedlings. Thus far, however, environmental variability in the field (and to a lesser extent in the glasshouse) has made such screening, especially when one is looking at quantitative characters such as stomatal conductance, more diYcult than the laboratory tests previously mentioned. Nevertheless, the inclusion of regularly repeated control genotypes within the array of genotypes growing in the field (Amani et al., 1996) should allow each image to have one or more control lines in it, so that the responses of all other genotypes could be normalized as diVerences from the control. This would largely take account of temporal fluctuations in mean temperature arising from variation in radiation or wind speed. G. FROST TOLERANCE AND DAMAGE

Frost-sensitive species have a limited capacity to tolerate ice formation in their tissues. The control of frost injury in these plants is achieved by avoiding ice formation, in some cases by changes in the temperature to which tissues can supercool. The degree of supercooling is influenced by the presence of plant- and bacterial-derived ice-nucleating agents. Thermography provides a tool for investigating the spatial and temporal distribution of the freezing process in intact plant tissues, because it is possible to detect the initial site of freezing from the release of the latent heat of fusion as water changes from a liquid to a solid phase (the freezing exotherm) during freezing (see Ceccardi et al., 1995). Wisniewski et al. (1997) improved the spatial, thermal, and temporal sensitivity of the technique by the introduction of infrared video thermography. Using this technique the rates of icefront propagation through the plant tissues such as leaves, stems, and flower buds in response to injection of ice-nucleating bacteria could be accurately

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Fig. 8. Video stills of potato plants showing ice nucleation and the spread of freezing following supercooling to
6 8C. The right-hand stem was nucleated at the arrow (A) time ¼ 0 sec; (B) time ¼ 12 sec; (C) time ¼ 15 sec, the right-hand stem full frozen; (D) left-hand stem nucleated at arrow, time ¼ 1 min 40 sec; (E) time ¼ 1 min 43 sec; (F) whole plant fully frozen, time ¼ 1 min 44 sec. (Reproduced from Fuller and Wisniewski, 1998, Copyright (1998), with permission from Elsevier.)

followed, with rates as fast as 1 mm s
1 being observed (Fig. 8). The radiometer was shown to be able to detect freezing in droplets as small as 0.5 ml. Many other studies have extended this approach to investigation of other factors influencing the pattern and rate of freezing in species such as potato and cauliflower (e.g., Fuller and Wisniewski, 1998), grasses and cereals (Pearce and Fuller, 2001; Stier et al., 2003), evergreen Eucalyptus leaves (Ball et al., 2002), tomato (Wisniewski et al., 2002), black currant (Carter et al., 2001), cranberry (Workmaster et al., 1999), and grapevine (Hamed et al., 2000). An interesting alternative imaging technique for the study of freezing in plant tissues is the use of NMR imaging, which detects the mobility of water in diVerent tissues (Ishikawa et al., 1997).

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V. CONCLUDING COMMENTS Thermography provides a very powerful tool for the study of spatial variation in plant and canopy temperatures with many potential applications in plant physiology and ecology. Its prime value is where there is interest in spatial variability of temperature in any scene; while in combination with other imaging techniques (e.g., visible reflectance or fluorescence) it may provide a powerful tool in diagnosis. There are, however, many situations in which somewhat simpler techniques may be the methods of choice. For example, in many situations, monitoring of temperature dynamics may be adequately achieved using single–channel, infrared thermometers (e.g., Bugbee et al., 1999) or even thermocouples, whereas the direct output of handheld infrared thermometers may have advantages for some rapid sampling applications. Other possibilities that would use much simpler technologies to provide similar spatial information could include the use of visible imaging of heat-sensitive paint based on liquid-crystal temperature sensors (Neville, 1994; Smith et al., 2004) or the use of thermofilms spread over the objects of interest (e.g., Lamprecht et al., 2002). Unfortunately, although these may have much potential in some situations, the need to paint these onto the surface or to attach them can interfere with the plant water relations and the radiation balance.

ACKNOWLEDGMENTS I am grateful to various agencies including the European Union (Projects ICA3-CT-1999-00008, EVKI-2000-22061, HPRN-CT-2002-00254) for funding aspects of the work presented in this chapter.

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Sequences and Phylogenies of Plant Pararetroviruses, Viruses, and Transposable Elements

CELIA HANSEN AND J. S. HESLOP-HARRISON

Department of Biology, University of Leicester, Leicester LE1 7RH, United Kingdom

I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Plant Genome Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Retroelements in the Genome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Reverse Transcriptase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Viruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Retroelements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Viral Retroelements—Retrovirales. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Nonviral Retroelements—Retrales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Viral and Nonviral Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Between Retrotransposon and Virus—the Envelope Gene . . . . . . . . . . B. Pararetroviruses in Plant Genomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Relationships between Retroelements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Conserved Regions of Retroelement Genes . . . . . . . . . . . . . . . . . . . . . . . . V. Interaction Between the Plant Genome and Retroelements. . . . . . . . . . . . . A. Amplification and Host Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Retroelements As Markers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Sequence Motifs and Horizontal Transfer . . . . . . . . . . . . . . . . . . . . . . . . . D. Silencing and Resistance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Advances in Botanical Research, Vol. 41 Incorporating Advances in Plant Pathology 0065-2296/04 $35.00

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ABSTRACT DNA elements found within cells that include the enzyme reverse transcriptase can be brought together within a unified taxonomic framework. The framework includes retroelements that are components of the nuclear genome, and recognized viruses where nuclear integration is unknown, occurs occasionally or is frequent. The classification probably has a natural basis and reflects aspects of the evolution and phylogeny of the elements. Complete retroelements and retroviruses include two or more open reading frames (ORFs) that encode single proteins or polyproteins. The order of the genes in the elements varies. In recent years it has been shown that pararetroviruses can be integrated in the plant genome, and evidence indicates they can be transcribed to give infectious virus. In this chapter, we show scale alignments of genomes from the six taxonomic families of reverse-transcribing viruses, gypsy and copia-like retroelements and long interspersed nuclear elements (LINEs), and also the enzyme telomerase, to show the lengths of the elements and the order of genes. We also show amino-acid alignments and key conserved residues or domains within the reverse transcriptase (RT), RNase H (RH), integrase (INT), and aspartic protease (PR) genes and in a conserved cysteine-histidine (CH) zinc-finger-like domain. A unified classification of reverse-transcribing elements is useful for phylogenetic and taxonomic purposes and understanding their contribution to plant genome function and evolution.

I. INTRODUCTION A. PLANT GENOME ORGANIZATION

It has long been established that genomes contain, in addition to genes and regulatory sequences, various classes of tandemly or dispersed repetitive DNA sequences, each with characteristic chromosomal locations (Schmidt and Heslop-Harrison, 1998). Some of this repetitive DNA is structural, such as the repeat motifs found at the telomeres, centromeres, and secondary constrictions. Other significant components of the genome are the transposable elements present in all species of bacteria, animals, fungi, and plants, with a copy number of hundreds to millions in most species (Flavell et al., 1997). Transposable elements can be divided into two major types: (1) the class (sometimes type) I transposable elements, retroelements including long terminal repeat (LTR) and non-LTR retrotransposons, and (2) the DNA transposable elements, DNA transposons, or class II transposable elements, which have the capacity to excise themselves and reintegrate elsewhere in the genome. The retroelements replicate via an RNA intermediate using reverse transcriptase (RT), leaving the original element in the genome, and have the potential to insert a new copy of the element in the DNA. These properties of mobility and replication allow transposable elements (typically 2–15 kb in length) to be one of the most dynamic and rapidly evolving components of the genome, often being dispersed over much of the chromosome’s length.

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These class I transposable elements are closely related to some viruses, many having viral properties. As suggested by Hull (1999a, 2001), the retroelements can be integrated into a common taxonomic system with a basis in evolutionary phylogeny. This chapter does not repeat the comprehensive reviews of Kumar and Bennetzen (1999) or Kunze et al. (1997), which describe the diversity and nature of DNA transposons and retroelements. Instead it focuses on the understanding of the relationship between retroelements and their similarities with virus sequences, reviewing key conserved domains and amino-acid residues, and also discussing the presence of integrated sequences of the family of plant pararetroviruses, an emerging subject. B. RETROELEMENTS IN THE GENOME

To many researchers the proportion of the genome represented by transposable elements, and their recognizable but degenerate derivatives, has come as a surprise. In the human genome sequence (some 3000 Mbp long) transposable elements account for 45% of the genomic DNA (International Human Genome Consortium, 2001), and in the mouse (2500 Mbp) they account for 38% of the genome (Mouse Genome Sequencing Consortium, 2002). In the small genome plant species sequenced to date, Arabidopsis thaliana (145 Mbp) and rice (430 Mbp), the transposable elements account for between 10% (The Arabidopsis Genome Initiative, 2000) and 18% (Feng et al., 2002; Sasaki et al., 2002). These plant species are not representative for plants in general because the proportion of transposable elements is higher in species with larger genomes. Complete genome sequencing projects have diYculties tackling large intergenic or repeat regions where retroelements may be abundant (Barakat et al., 1997; Brandes et al. 1997; The Arabidopsis Genome Initiative, 2000; Tikhonov et al., 1999). In the rice draft genome, assembled contigs (361 Mbp) had 16% transposable elements, whereas fully masked reads that could not be integrated into the complete sequence (78 Mbp) included 59% transposable elements ( Yu et al., 2002). It is notable that in the unassembled fully masked reads 97% of transposable elements are retroelements, whereas another group of retroelement-related sequences, miniature inverted-repeat tandem elements (MITEs), represented only 1%; in contrast, these repeat classes accounted for 42% and 40%, respectively, in the assembled contigs. Because much single-copy rich DNA is in the assembled regions, the diVerence indicates that retroelements are mostly in intergenic heterochromatic regions and that MITEs insert preferentially near genes ( Yu et al., 2002). The same may be the case for the number of diVerent transposable elements found in the Arabidopsis genome.

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In mammals and plants the largest portion of the transposable elements in the genome are usually retrotransposons; in mammals, non-LTR LINEs are most abundant, whereas in the plants the largest portion is made up of LTR copia and gypsy elements (Feng et al., 2002; International Human Genome Sequencing Consortium, 2001; Sasaki et al., 2002; The Arabidopsis Genome Initiative, 2000). The genome-integrated retrotransposons have been recognized as a major evolutionary force in host genomes, which can be very diverse organisms from bacteria and yeasts to plants and animals, both because of their abundance and the eVects of insertion in or near to genes and regulatory sequences. Nevertheless, most retroelements have no known phenotypic eVect on the host, although their insertion into the genome can disrupt gene expression, and transcribed copies in the form of viruses give severe illnesses in mammals, including diseases caused by retroviruses (e.g., human immunodeficiency virus [HIV]) and pararetroviruses (e.g., hepatitis B virus [HBV]). C. REVERSE TRANSCRIPTASE

Retroelements are characterized by the presence of a gene encoding reverse transcriptase, RNA-directed DNA polymerase, which is capable of using an RNA template to make a complementary DNA molecule, thus allowing their autonomous amplification via an RNA intermediate transcribed from the DNA form using cellular RNA polymerase. RT was discovered by Baltimore (1970) and Temin and Mizutani (1970), and is considered to be an ancient and widespread enzyme. Support for its early origin comes from the similarity of extant RT enzymes across viruses, prokaryotes, and eukaryotes. RT has domains in common with the RNA-directed RNA polymerase of RNA viruses, suggesting that they share an ancient common ancestor (Xiong and Eickbush, 1990). The RNA viruses are believed to be at least as old as retroelements because they have a greater diversity and are present in many prokaryotes and eukaryotes. The history of retroelements may well coincide with the origin of a DNA-based life form some 3.5 billion years ago. At the earliest stages of life it is likely that RNA genes were converted to DNA, which then provided the basis for subsequent evolution. This suggests that an RNA-dependent DNA polymerase—reverse transcriptase—was an early and critical enzyme in the origin of DNA-based organisms (see HeslopHarrison, 2000). Although widespread distribution of retroelements is most likely to be explained by their single origin and evolutionary descent into virtually all modern organisms (both prokaryotes and eukaryotes), it is probable that the cross-species transfer of sequences, either as DNA or RNA (horizontal transfer), and perhaps the convergent evolution of

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sequences has contributed to the extant distribution of diVerent retroelement types (Brown, 2003). D. VIRUSES

Viruses have well-recognized properties of gene expression and replication in host cells, but there is no concise, universally accepted definition of a virus. The International Committee on Taxonomy of Viruses (ICTV) describes a virus as ‘‘an elementary biosystem that possesses some of the properties of living systems such as having a genome and being able to adapt to changing environments. However, viruses cannot capture and store free energy and they are not functionally active outside their host cells’’ (from Hull, 2001). Until the 1990s the viruses were named individually using physical and biological properties based on the symptoms they cause, their host range, replication strategy, particle structure and, to some extent, biochemical composition. The nature of the viral genome—whether DNA or RNA, single or double stranded—has been used to categorize viruses for many years, and both the size of the genomic nucleic acid and its sequence are now important characters in classification that have allowed some grouping of individuals. Now viral taxonomy is stabilizing, with most viruses fitting into larger taxonomic groupings having a natural basis related to phylogeny (Buchen-Osmond, 2003; Hull, 1999a, 2001, 2002; ICTV, 2003), at least at levels that have been named as family and suborder levels. As pointed out by Hull (2001), genomic retroelements fit the ICTV definition of a virus, and based on their common features can be fitted into the structure of a natural phylogeny.

II. RETROELEMENTS Nuclear DNA elements that include the gene RT can be regarded as retroelements, and Hull (1999a, 2001) proposed a unified classification for reverse transcribing elements that includes viruses and transposable elements with RT. The elements can be divided into retroviruses, pararetroviruses, and the abundant group of nuclear sequences, the retrotransposons, including the LTR retrotransposons, non-LTR retroposons, and group II mitochondrial introns (Hull, 1999a, 2001; Fig. 1). Another important enzyme, telomerase (Blackburn, 1992), which adds the telomere sequences to most eukaryotic chromosomes, also incorporates a RT function (Lingner et al., 1997) and can be aligned with other sequences (refer to Fig. 3, later in the chapter).

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Fig. 1. A classification of retroelements and related viruses (after Hull, 1999a, 2001; ICTV, 2003). BSV, Banana streak badnavirus; CaMV, Cauliflower mosaic caulimovirus; CsVMV, Cassava vein mosaic cavemovirus; PVCV, Petunia vein clearing petuvirus; RTBV, Rice tungro bacilliform tungrovirus; SbCMV, Soybean chlorotic mottle soymovirus. The taxonomic endings follow the ICTV nomenclature—order: virales; suborder (after Hull): ineae; family: viridae; subfamily (not shown): virinae; genus: virus.

A. VIRAL RETROELEMENTS—RETROVIRALES

The group of ‘‘DNA and RNA reverse transcribing viruses’’ (Pringle, 1999) includes the Retrovirales (Hull, 2001) and consists of elements potentially capable of infection, such as the retroviruses (RNA genome), the pararetroviruses (DNA genome) (see Fig. 1), and those with no known infectivity such as copia and gypsy retrotransposons. The vertebrate retroviruses of the suborder Orthoretrovirineae have an RNA genome in the infective form and are transcribed into DNA by RT, which is then integrated into the nuclear genome of the host with the assistance of the encoded integrase (Table I). The suborder Pararetrovirineae (pararetroviruses), found both in plant and in animal kingdoms, encapsulates a double-stranded (ds) DNA genome and replicates through an RNA intermediate; no integrase function is detected in their genome and integration is not an obligatory part of their replication, infection, and transmission cycle (Hull and Covey, 1996). In Hull’s (1999a) classification two families of pararetroviruses are given, the animal viruses of the Hepadnaviridae (two genera) and the plant viruses of the Caulimoviridae, including six genera: Badnavirus, Caulimovirus, and four genera represented by a small number of individual viruses, Tungrovirus (Rice tungro bacilliform tungrovirus [RTBV]); Petuvirus (Petunia vein clearing petuvirus [PVCV]); Soymovirus (Soybean chlorotic mottle soymovirus [SbCMV]); and Cavemovirus

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(Cassava vein mosaic cavemovirus [CsVMV]; Tobacco vein clearing cavemovirus [TVCV]) (ICTV, 2003; Pringle, 1999). No retrovirus senso stricto has yet been found in plants, although certain characteristic genes, putative envelope or transit proteins (see ‘‘Between Retrotransposon and Virus—the Envelope Gene,’’ in the next section), have been identified in some gypsy-like and copia-like elements such that they have characteristics of retroviruses (Kumar, 1998; Laten et al., 1998; Petropoulos, 1997; Wright and Voytas, 2002). In the classification (see Fig. 1) the suborder Retrotransposineae, including the Pseudoviridae (Ty1-copia group) and the Metaviridae (Ty3-gypsy group), has been placed under Retrovirales. These elements can form viruslike particles, although they have no known viral infectivity. Retrotransposineae may have one or two open reading frames (ORFs) containing gag and pol genes bordered by LTRs, sometimes with a third ORF present. Structurally, copia and gypsy diVer in the order of encoded genes (see Table I and Fig. 3). Retrotransposineae have a replication strategy similar to that of Orthoretrovirineae, in which integration of a new copy into DNA is an obligatory part of the replication cycle, although they have no encoded features enabling them to move from cell to cell. B. NONVIRAL RETROELEMENTS—RETRALES

The order Retrales, with the suborder of Retroposineae, has fewer similarities with infective viruses than the Retrotransposineae suborder, although some genes and the organization of the genes have relationships (Figs. 1–3). The Retroposineae includes the non-LTR elements LINEs and their truncated (short) derivatives SINEs (see Figs. 1 and 3; Table I): LINEs are simpler structures than Retrotransposineae but contain many common functional properties, including gag and pol, and an endonuclease function. Included also is the suborder Retronineae, which contains the group II mitochondrial introns.

III. VIRAL AND NONVIRAL ELEMENTS A. BETWEEN RETROTRANSPOSON AND VIRUS—THE ENVELOPE GENE

The envelope gene (env), or the related coding sequence known as the movement protein (MP) or transit peptide, gives a transcribed DNA element the ability to move with a high frequency between cells and become infective. Although the envelope gene is not well conserved in primary sequence, both viral and putative retrotransposon envelope proteins share structural

TABLE I Genes and Other Components of Retroelements Gene or component ORF

Full name

Position

Function

References

Open reading frame Long terminal repeat

—

Sequence capable of translation into a protein.

—

Flanking retrotransposineae

Petropoulos, 1997

PBS

Primer binding site

Gag

Groupspecific antigen

Approx. 18 nt at the end of the 50 LTR Usually one of the first ORFs

CP Cys-His or C-H

Coat protein CysteineHistidine repeat motif

Regions of several hundred base pairs (250–4000) containing regulatory sequences for gene expression: Enhancer, promoter, transcription initiation (capping), transcription terminator and polyadenylation signal. The 30 LTR is not normally functional as a promoter, although it has exactly the same sequence arrangement as the 50 LTR. Instead the 30 LTR acts in transcription termination and polyadenylation. As a consequence of the replication mechanism of the elements, the two LTRs are identical at the time of integration. Binding site for a specific tRNA that functions as the primer for reverse transcriptase to initiate synthesis of the minus () strand of viral DNA. The gag precursor is cleaved by the viral protease (encoded by pol) into three mature products: the matrix (MA), the capsid (CA), and the nucleocapsid (NC), together forming the ‘‘capsid’’ that surrounds the genome; this complex is the virus core. It is equivalent to the coat or transit protein. Equivalent to gag. RNA or DNA binding site of the coat protein or gag.

LTR

— C-terminal of gag

Petropoulos, 1997 Lecellier and Saı¨b, 2000

— de Kochko et al., 1998

GR box Pol

Polyprotein

PR

Aspartic protease

RT RH INT

Env

MP TAV PPT

C-terminal of gag in certain retroelements — pol

Contains three glysine/arginine basic sequences—functionally Lecellier and Saı¨b, 2000 equivalent to C-H? Contains aspartic protease, reverse transcriptase, and RNase H. and in some cases integrase. Cleaves the full length mRNA.

PR has a significant role in the processing of the polyprotein precursor into the mature form. Reverse pol RNA dependant DNA polymerase—translates RNA transcriptase to DNA. Ribonuclease pol RNase H is an enzyme that specifically degrades RNA H/RNase H hybridized to DNA. Integrase pol Enzyme responsible for removing two bases from the end of the LTR and inserting of the linear, double-stranded DNA copy of the retroelement genome into the host cell DNA. Envelope genes mediate the binding of virus particles to Envelope After pol, but their cellular receptors enabling virus entry, the first step gene not in in a new replication cycle. Thus the envelope genes give pararetrovirus retroelements the ability to spread between cells and if MP ¼ env individuals (e.g., infectivity). Contain the proteins SU (surface) and TM (transmembrane). Movement — Cell-to-cell movement, maybe equivalent to env. protein Transactivator — Regulating translation of the polycistronic mRNA. Polypurine 7–18 nt just The PPT produce the RNA primer for the synthesis of the tract upstream of plus (þ) strand of viral DNA. the 30 LTR

— Ono et al., 1986

— Petropoulos, 1997 Petropoulos, 1997

Lo¨ wer et al., 1996

Hull, 2002 de Kochko et al., 1998 Petropoulos, 1997

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Fig. 2. The relationship and origin of retroelements based on alignment of the reverse transcriptase (RT) region (after Xiong and Eickbush, 1990).

similarities. They are typically translated from spliced mRNAs, and the primary product encodes a signal peptide and a transmembrane domain near the C terminus (Chavanne et al., 1998; Vicient et al., 2001; Wright and Voytas, 1998, 2002). Malik et al. (2000) proposed that a nonviral ancestor to errantiviruses (Metaviridae, Drosophila specific gypsy-like virus) acquired the envelope gene from another family of double-stranded DNA insect virus, the Baculoviridae, because the envelope gene from these two insect viruses was found to share sequence features. Furthermore, baculoviruses were found to harbor inserts of LTR retrotransposons, which could be a step in the acquisition of an envelope gene by the latter. There are at least eight cases of envelope-like gene acquisition in the broad group of retroelements: Sire1 from the copia group; Athila, Cyclops, Osvaldo, Cer, Tas, and errantiviruses from the gypsy group. Vertebrate retroviruses and the family of plant caulimoviruses with envelope genes may also have arisen from groups without the gene, perhaps acquiring it by fusion of an LTR-retrotransposable element with a plant virus (Chavanne et al., 1998; Malik et al., 2000). Alternatively, transposable elements could be remnants of infectious viruses that have lost most of the envelope gene; perhaps the gene is less useful in plants compared to animals because cell walls might be an obstacle to membrane-membrane fusions, allowing a virus to enter a cell (Bennetzen, 2000).

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B. PARARETROVIRUSES IN PLANT GENOMES

Vertebrate retroviruses have long been known to integrate into the nuclear genome of their host at a stage of their replication cycle (Be´ nit et al., 1999; Herniou et al., 1998; Lo¨ wer, 1999). For example, the mammalian HBV is spontaneously and illegitimately integrated into nuclei in cancerous and precancerous liver cells (see Pineau et al., 1996; Tagieva et al., 1995; Wang et al., 2001). However, until recently, plant pararetroviruses have been considered as independent particles in the host genome. The ds DNA form of plant pararetroviruses are infective, causing a range of vein chlorosis, ring-spot, mosaic, and mottling symptoms. Most pararetroviruses have a narrow host range: CaMV (Cauliflower mosaic caulimovirus) rarely infects species outside the Brassicaceae, and the genera of caulimoviruses in general infect dicotyledons. Caulimoviruses infect most tissues in the host plant, but badnaviruses may be restricted to the vascular tissue (Hohn and Fu¨ tterer, 1997). The pararetrovirus particle moves between cells via plasmodesmata and between individual plants by insect transmission: usually Caulimovirus by aphids and Badnavirus by mealy bugs (Hull and Covey, 1996). In the mid-1990s, analysis of the epidemiology of some plant virus diseases revealed an unexplained spread, previously not noticed or explained by asymptomatic and low levels of chronic infection. Bananas (Musa) can be infected by Banana streak badnavirus (BSV) (see Dahal et al., 1998; Harper and Hull, 1998; Harper et al., 1999), which causes disease throughout tropical regions. However, the appearance of symptoms did not always correlate with the presence of infected plants or insect vectors in the field and infection was pronounced in plants that had been stressed. In particular, plants from tissue culture of certain varieties (Dahal et al., 2000), and plants exposed to low night temperatures showed symptoms. Polymerase chain reaction (PCR) amplification using primers from within the sequence of BSV, in situ hybridization to nuclear chromosomes of the Musa accessions using BSV fragments as probes (Harper et al., 1999), and genomic library screening (Ndowora et al., 1999) indicated that there was a sequence homologous to BSV integrated in the nuclear DNA of these Musa varieties. In situ hybridization to nuclear DNA stretched to its full molecular length showed that the integrated BSV sequence was repeated in two diVerent structures of 150 and 50 kb, respectively (Harper et al., 1999). It is believed that sexual hybridization, tissue culture, and other stress can generate episomal viruses by recombination of the integrated sequence. Geering et al. (2000, 2001) found that there was variability in the type of BSV-like sequence integrated in the genome of Musa and remnants of other BSV sequences are found in both A and B genome Musa. As a consequence of the integration of BSV

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sequences into the Musa genome, consideration and care is needed regarding the safe movement of germplasm and methods of plant breeding and tissue culture (Harper and Hull, 1998). Evidence from epidemiology and molecular biology suggests that, as in Musa, there is a possibility that other plant species also include viral sequences that can be expressed and give rise to episomal viruses and infection. Integrated PVCV sequences have been detected in Petunia hybrida (Richert-Po¨ ggeler et al., 1996), and in situ hybridization indicates that the sequences are concentrated at relatively few sites (Richert-Po¨ ggeler et al., 2003). There is evidence that a complete PVCV genome is present in one Petunia cultivar and that at least part of the viral genome is present in many cultivars (Harper et al., 2002). The presence of the integrated virus sequence is correlated with the appearance of disease symptoms and virus particles in some P. hybrida varieties, again under particular environmental conditions. The allohexaploid Nicotiana edwardsonii was formed by the hybridization between N. clevelandii (female, 4x) and N. glutinosa (male, 2x). In N. edwardsonii the spontaneous appearance of episomal virus particles (TVCV) was discovered under certain environmental conditions. Southern hybridization of TVCV sequences to genomic DNA of N. edwardsonii and N. glutinosa showed that TVCV was integrated in the nuclear DNA (Lockhart et al., 2000). It is possible that the expression of episomal TVCV in N. edwardsonii was triggered by the rearrangement of otherwise deficient integrants during the interspecific hybridization and the subsequent chromosome doubling. In a study of DNA-flanking transgenes, Jakowitsch et al. (1999) sequenced regions of nuclear DNA with high homology to a pararetrovirus from N. tabacum. From these fragments it was possible to assemble a hypothetical 7981-bp pararetrovirus-like (PRV-L) genome called tprv here. (Although named TPV for tobacco pararetrovirus by the authors of this

Fig. 3. Alignment of retroelements including a LINE, copia and gypsy elements, pararetroviruses, and retroviruses, with telomerase, another enzyme with reverse transcriptase (RT) activity. A scale in base pairs is shown. The alignment is manually optimized around the amino acids DD, key aspartate residues at the active site of the RT. For the abbreviations of genes and other components see Table I. Domain code: DD site of RT; RNase domain (RH); integrase domain (INT); Cysteine-Histidine motif (C-H); protease domain (PR); envelope/movement protein domain (ENV/MP). See Figs. 4 to 8 for alignments of the genes and other components. References are given in descriptions of the individual elements. Web appendix at www.molcyt.com includes a detailed description of the domains of retroelements and telomerase in Fig. 3. Individual elements include TY1, Boeke et al., 1998; copia, Emori et al., 1985; CaMV, Franck et al., 1980; BARE, Mannienen and Schulman, 1993; telomerase, Nakamura et al., 1997, Xia et al., 2000; BAGY, Panstruga et al., 1998; SFV, Renne et al., 1992; HBV, Seeger, 1999, Takahashi et al., 1998.

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book, ICTV recognizes TPV as the geminivirus Texas pepper virus.) The genome tprv is most closely related to TVCV (75%) and CsVMV (42%) at the nucleotide level and has the same genomic structure as TVCV. In this case no expression of a virus has been detected. Within the same plant family, Solanaceae, pararetrovirus-like sequences with nuclear integration have also been found in tomato and potato. Budiman et al. (2000), as part of a tomato genome sequencing project, generated a sequence-tagged connector (STC) framework from a bacterial artificial chromosome (BAC) library of Lycopersicon esculentum. As might be expected, copia- and gypsy-like retrotransposon sequences were abundant, but PRV-L sequences were also detected in the sequence data. Hansen et al. (2003) detected several families of a PRV-L sequence in potato (Solanum tuberosum) using PCR primers designed to pararetrovirus consensus sequences, isolating genomic DNA fragments and characterizing by sequencing, Southern and in situ hybridization. Genomic sequence data indicate the presence of PRV-L sequences in the rice genome. RTBV is widespread in Southeast Asia and causes substantial losses in rice production. The genome is approximately 8000 bp long, with four ORFs (Hull, 1999b). In the genomic rice sequence (Sasaki et al., 2002), fragments related to all four ORFs are found, with sequences of the RTRNase H region and part of the MP (EMBL sequence number AP000559, Sasaki et al., 2002, unpublished data). Another survey of rice detected three PRV-L fragments related to RTBV (Mao et al., 2000).

IV. RELATIONSHIPS BETWEEN RETROELEMENTS Whether infectious or not, sequences classified as retroelements have common features that can be used to analyze evolutionary relationships. The characteristic shared RT region, as a defining feature, can be used to align the sequences, while the presence, order, and sequence of other conserved regions allows further comparison. Lerat et al. (1999) have discussed the possible ‘‘modular’’ evolution of the conserved functional blocks with retroelements. Xiong and Eickbush (1990) analyzed the full RT region of a wide selection of retroelements, identifying seven common peptide regions (domains 1–7) containing 178 amino acids with chemically similar residues within the majority of the 82 RT sequences analyzed. They rooted their phylogenetic tree with the RNA-directed RNA polymerase from RNA viruses (see Fig. 2). Based on the analysis it was suggested that the ancestral retrotransposable element had a gag gene and a pol gene, either as two separate ORFs or one large ORF and no LTRs. Hepadnaviruses and non-LTR retrotransposons

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become the first branches on the tree (see Fig. 2), and branches of the Hepadnaviruses and Caulimoviruses include a fragment of pol gene containing the RT-RNase H domain. The retroviruses may represent a retroelement, which acquired an envelope gene, making it possible to be transmitted between cells. For the retroelements of bacteria and organelles they considered the possibility that the RT region was captured by functional bacterial introns, or organelle genomes or plasmids (Xiong and Eickbush, 1990). Figure 3 illustrates the structures of representative elements from the five families of viruses shown in Fig. 1, the suborder Retroposineae, and the nuclear-encoded enzyme telomerase, also containing a RT gene. Sequences are drawn to scale and boxes emphasize key coding regions that are conserved between the elements. The elements are aligned through two completely conserved amino-acid residues, aspartic acid (DD) in the RT region (Xiong and Eickbush, 1990). Most of the retroelements have their ORFs designated as gag, pol, and, where present, envelope. The gag gene is equivalent to the coat protein in viruses, and the envelope gene has an equivalent function to the MP of plant pararetroviruses. The Cysteine-Histidine (C-H) motif (see Table I) always precedes the protease, which is before the RT, and the RNase H is located immediately after the RT. The integrase domain is situated after RNase H in gypsy elements and retroviruses and between the protease and RT in copia elements. Pararetroviruses do not have an integrase domain. The envelope gene is situated as the last ORF in gypsy and retroviruses and before RT in pararetroviruses as a movement domain or function. A. CONSERVED REGIONS OF RETROELEMENT GENES

In addition to the outline of complete retroelements in Fig. 3, detailed structures of the most conserved domains, including the RT, RNase H, CH motif, integrase, and aspartic protease, are shown. The RT domain is highly conserved, and Fig. 4 aligns sequences corresponding to domains 3–7 in Xiong and Eickbush (1990). RNase H, part of the polyprotein ORF, degrades RNA in RNA/DNA hybrids. Malik and Eickbush (2001) aligned RNase H within retroelements and highlighted some single amino acids believed to be important in the catalytic reaction of the protein: D, E, D, D.* For the elements shown the structure can be written as DX27–48EX18–33DX29–54D (where X represents any amino acid and the subscript shows the number between conserved residues; *These single-letter codes are used are used in this section to designate amino-acid residues: C ¼ cysteine; D ¼ aspartate; E ¼ glutamic acid; G¼ glycine; H ¼ histidine; K ¼ lysine; L ¼ leucine; S ¼ serine; X ¼ any amino acid.

Fig. 4. Alignment of the conserved reverse transcriptase (RT) region of the retroelements in Fig. 3. The sequences cover domains 3–7 from Xiong and Eickbush (1990). The telomerase (Eap123), BLIN, and HBV have longer sequences than the others, and a group of amino acids has been removed and replaced with the corresponding number, 103 for Eap123, 27 for BLIN, and 58 for HBV, all at the start of the alignment. Each dot () represents three amino acids from the sequences.

Fig. 5. Alignment of the RNase H (RH) region of the retroelements in Fig. 3. The arrows above the alignment point to amino-acid residues believed to be important for the catalytic mechanisms of RNase H; D, E, D, (H), D. Each dot () represents three amino acids from the sequences.

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some authors show only the number without X) (Fig. 5). The non-LTR retrotransposons and retroviruses have an H between the last two Ds. A DXS motif can be detected in many of the sequences, including several other single or multiple amino acids, many of which are found in the retroelements in Fig. 3. The integrase is also part of the polyprotein and mediates the integration of an element into nuclear DNA. The integrase domain contains both a wellconserved zinc finger (HHCC) and a DDX35E motif (Fayet et al., 1990; Khan et al., 1991). These two motifs were found in the copia, gypsy, and retrovirus elements in Fig. 3. In these elements the zinc finger motif is HX3–6X20–33 CX2C (Fig. 6). The two amino acids KD are conserved between Cyclops, Athila, human endogenous retrovirus (HeRV-K), and Simian foamy virus (SFV). The DDX35E motif is some 26–32 amino acids downstream of the last C in the zinc finger. In Fig. 6 the general motif is DX52–64DX32–36E, excluding Cyclops, which has a very long sequence between the two Ds: 111 amino acids. Capy et al. (1996) found no similarities to the integrase domain of LTR retrotransposons in LINEs. The C-H motif at the C-terminal of gag or in the coat protein is very well conserved (Covey, 1986) and is found in LINE, copia and gypsy elements, in pararetroviruses, and a retrovirus (Fig. 7). The protein may bind genomic RNA or DNA to assist in packaging of virus particles and perhaps other processing. It consists of a short sequence with a characteristic pattern of cysteine and histidine amino acids, making up a zinc finger. The motifs in the LINE, copia, gypsy, and retrovirus elements are very similar, having the amino acid sequence CX2CX3–4HX4C, whereas the pararetroviruses have an additional CX with the motif CXCX2CX4HX4C. The third LINE CH motif has longer intervals between the C and H than the two other LINE motifs, CX4CX5HX6C. The second C-H motif in BSV is rather diVerent from the others, having six Cs and an H, CX2CX7HX3CX2CX4CX2C. Aspartic protease, also part of the polyprotein, cleaves full-length mRNA. The protease region is poorly conserved, the best homology being an LX0– 4DXG motif, with a few widely spaced conserved amino acids (Fig. 8; see also McClure, 1991).

V. INTERACTION BETWEEN THE PLANT GENOME AND RETROELEMENTS Retroelements represent a major fraction of genomic DNA, and their maintenance and replication impacts on the organism where they are present. Insertion of retroelements causes changes in the host genome such as

Fig. 6. Alignment of conserved regions from the integrase (INT) region of 10 sequences from Fig. 3. The first motif (H-H-C-C) is a zinc finger; the next motif is D-D-E. Part of the Cyclops sequence has been replaced with the number (71) of amino acids removed to show alignment. Each dot () represents three amino acids from the sequences.

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Fig. 7. Alignment of the Cysteine-Histidine (C-H) motif with 10 sequences from Fig. 3. The motif includes a zinc finger with the amino acids C-C-H-C.

insertional mutation, chromosome breakage, chromosome rearrangement, altered gene regulation, and sequence amplification. Even remnants of integrated viruses have been shown to have promoter/enhancer activity in the LTRs, active splice sites, ORFs, or RT activity and to have the ability to be retrotransposed by complete elements (Lo¨ wer, 1999). Various forms of viruses are integrated in the nuclear genomes of eukaryotes, and some are active in transcription and making of episomal virus particles. They could be under active selection (e.g., cosegregation) because of integration proximal to an allele. Alternatively, the integration could alter the expression of neighboring host genes in a useful, regulatory manner or give virus resistance by antisense expression (Bejarano et al., 1996; Mette et al., 2002) or other silencing mechanisms. The retroviruses and pathogenic pararetroviruses cause disease that will often be detrimental to their host, although cases of cross-protection are known in which one infection gives protection against subsequent infection by another virus. Hence retrotransposons and inserted RT viruses may have protective consequences for their host. A. AMPLIFICATION AND HOST CONTROL

Evolution of copy number in retroelements can be interpreted as showing periods of low and high amplification and insertional activity, with evidence that this is related to the development or environment of the host (Grandbastien, 1998; Kalendar et al., 2000). Investigation of barley centromeres revealed the presence of a family of gypsy-like elements (cereba), whereas other gypsy elements showed a contrasting distribution (Vershinin et al., 2002). Thus there is control of element location, suggesting host genome-insert interactions are involved. Unusually high activity or unexpected appearance of retroelements is often found in connection with stress events such as tissue culture and

Fig. 8.

Alignment of the aspartic protease (PR) region of retroelements in Fig. 3. Conserved amino acids are P-L-D-G-G-G.

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wide hybridization (Dahal et al., 2000; Lockhart et al., 2000; Mhiri et al., 1997). Thus evolution and amplification of retroelements can be suggested to occur in sudden steps (in contrast to operating as a molecular clock), and the periods of activity of retroelements would be diYcult to estimate from extant data because they can behave diVerently from the genic and other DNA. SanMiguel et al. (1998) show that retrotransposon activity is recent in maize, with virtually all elements inserting within the last six million years and most in the last three million years. Both Athila and Tat1 gypsy retrotransposons have high sequence degeneracy in the coding regions, whereas they have near sequence identity of their 50 and 30 LTRs (more than 95%; see Fig. 3). The similarity of LTRs suggests that these elements integrated relatively recently or that transcripts from defective elements were acted upon in trans to generate the insertions (Wright and Voytas, 1998). B. RETROELEMENTS AS MARKERS

Retroelements are important both to evolutionary studies and as tools in molecular studies. Because of their abundance, mode of amplification, and insertion in the genome throughout much of its length, the features of retroelements can be used as a source of polymorphic markers for discrimination of plant species or genotypes. In particular, pairs of outward-facing primers from the LTRs of retrotransposons are proving valuable for PCR amplification of DNA lying between retroelements, hence giving interretrotransposon amplified polymorphic (IRAP) markers (Kalendar et al., 1999). C. SEQUENCE MOTIFS AND HORIZONTAL TRANSFER

The motifs in Figs. 4 to 8 show conservation of key amino-acid residues, presumably a consequence of common evolutionary origin. How did sequences come to have their current widespread distribution? In many cases, vertical transmission by descent from a common ancestor can be proposed as the distribution mechanism. However, viruses, including pararetroviruses, spread from cell to cell and to new organisms independently of inheritance of nuclear DNA. This horizontal transfer can be proposed for some groups of retroelements (or gene components), with evidence coming from high similarity between elements from distantly related species and inconsistencies between the phylogeny of the element and that of the hosts (Capy et al., 1994). The best example of horizontal transfer is that of the P element in Drosophila (Daniels et al., 1990). In plants it can be envisaged that retroelements may be transmitted directly as DNA or RNA, or after packaging with other viral DNA sequences. Sugimoto et al. (1994) showed how a

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virus could package and transfer a transposable element from maize into rice. Genomic DNA sequences unrelated to retroelements (or from other elements) might be transferred by evolutionary mechanisms such as unequal crossing over. Such changes are suggested by the retroviruses in Fig. 3, where the distance between RT and RNase H is larger than for the other elements. Malik and Eickbush (2001) propose that an early lineage of retroviruses replaced their existing RNase H domain with one from a LINE-like element, which were placed after the original RNase H: the two share the amino acid H (see Fig. 5, second arrow from right). The integrase component is placed diVerently in the copia group compared to the gypsy and retrovirus groups (see Fig. 3), whereas it is missing from the pararetroviruses, showing the flexibility of this motif and changes during evolution. Capy et al. (1996, 1997) suggest that the integrase domain with the DDE motif of LTR-retrotransposons and retroviruses originated from the transposases of some DNA transposable elements. D. SILENCING AND RESISTANCE

In many plant systems the RNA interference phenomenon leads to small pieces of RNA guiding de novo methylation of homologous DNA sequences. Methylation is eVectively targeted against the promoters of transposable elements (see Martienssen and Colot, 2001). Apart from this short-term protection, methylation also provides a potential mechanism for long-term protection by driving a C to T mutation of the element sequence (Bestor, 1999). For many years, plant expression of viral coat proteins has been known to confer resistance to viral infection. More recently Matzke et al. (2001; see also Waterhouse et al., 2001) discussed how RNA interference might operate, because transcription of retroelement RNA could drive degradation and interfere with replication of viral and other RNA species. Mette et al. (2002) investigated whether integrated viruslike sequences exhibit features that would be compatible with a potentially new type of homology dependant virus resistance. It was believed that stably methylated sequences have supplied long-term viral immunity, perhaps accompanied by weakening or extinction of the related exogenous virus.

ACKNOWLEDGMENTS We are most grateful to Dr. Glyn Harper, John Innes Centre, Norwich, United Kingdom, for help with the manuscript and our work described in this chapter. C. Hansen thanks the John Innes Foundation for award of a

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research studentship. We thank the European Union for support through grant QLRT-2001-02098, ‘‘Pararetroviruses: Disease, Integration and Genomes.’’ Some concepts were developed within the IAEA Coordinated Research Programme, ‘‘Physical mapping technologies.’’

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Role of Plasmodesmata Regulation in Plant Development

ARNAUD COMPLAINVILLE AND MARTIN CRESPI

Institut des Sciences du Ve´ge´tal, Centre National de la Recherche Scientifique, 91198 Gif sur Yvette, France

I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Structure, Biogenesis, and Permeability of Plasmodesmata . . . . . . . . . . . . . A. Structure of Plasmodesmata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Biogenesis of Plasmodesmata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Permeability of Plasmodesmata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Regulation of Plasmodesmata Permeability . . . . . . . . . . . . . . . . . . . . . . . . III. Physiological Regulation of PD Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Central Role of the SE-CC Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Phloem Loading in Source Leaves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Transport of Solutes through the Phloem . . . . . . . . . . . . . . . . . . . . . . . . . D. Phloem Unloading into Sinks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Source/Sink Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Plasmodesmata–Mediated TraYcking of Macromolecules and Manipulation of PD Function by Macromolecules . . . . . . . . . . . . . . . . . . . . A. Passive and Active Transport of Macromolecules through PD. . . . . . B. Macromolecular Transport in the Phloem . . . . . . . . . . . . . . . . . . . . . . . . . C. Mechanisms of Macromolecular TraYcking . . . . . . . . . . . . . . . . . . . . . . . D. Evidence for the Influence of Macromolecular TraYcking in Developmental Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Integrative Approach: Regulation of Symplasmic Domains in Plant Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Vegetative Shoot Apical Meristem: A Model for the Integration of Developmental Signaling in Symplasmic Domains. . . . . . . . . . . . . . . B. Symplasmic Domains and Organogenesis. . . . . . . . . . . . . . . . . . . . . . . . . . VI. Conclusion and Future Prospects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advances in Botanical Research, Vol. 41 Incorporating Advances in Plant Pathology 0065-2296/04 $35.00

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ABSTRACT The cytoplasms of neighboring plant cells are linked by channels spanning the cell walls called plasmodesmata (PD). Extensive communication occurs through these channels that can allow the passage of solutes and macromolecules in a highly regulated fashion. The permeability of plasmodesmata has been shown to be regulated during plant development by many physiological and stress conditions and a growing number of endogenous macromolecules have been shown to regulate PD permeability. This regulation seems plays a role in inter-organ communication at the whole plant level. Moreover, plant can be divided into symplasmic fields or symplasmic domains, which are groups of cells interconnected by functional plasmodesmata. Symplasmic domains are completely isolated from surrounding tissues whereas symplasmic fields retain a certain level of communication with surrounding tissues. This organization is thought to ensure the coordination of development by allowing diffusion of signal molecules like transcription factors within the cells of a symplasmic field or domain.

I. INTRODUCTION During the evolution of multicellular organisms, two major routes have diverged, separating animals from plants and fungi. Unlike animal cells, plant cells are surrounded by a cell wall that prevents direct contact between the cytoplasmic membranes of neighboring cells. Another major diVerence resides in the control of cell fate, which in plants is governed by positional information instead of cell lineage. Therefore constant exchange of signals between plant cells, notably in meristems, is crucial for plant development. Although examples of cell-to-cell communication processes involving the secretion of molecules into the extracellular space, and their subsequent perception by membrane receptors, have been described in plants (Clark et al., 1996), these organisms rely extensively on communication via channels called plasmodesmata (PD), spanning the cell walls and connecting the cytoplasms of adjacent cells. PD were originally seen as static structures controlling the passage of molecules, according to their sizes, by a simple steric eVect. However, over the last decade, especially during the last few years, numerous data have been accumulated, showing that PD are highly regulated structures, both by a variety of molecules and during developmental processes and in stress conditions. In contrast to the previous idea that only molecules smaller that 1 kilodalton (kDa) could move across PD, it has been shown that a diversity of molecules was able to move through these structures, including metabolites, proteins, and RNAs. TraYcking through plasmodesmata also plays a critical role in whole plant physiology because it regulates phloem loading and delivery of metabolites into sink organs. Indeed plants can be seen as dynamic mosaics of symplasmic domains—

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groups of cells between which molecules can freely diVuse across PD. The regulation of these symplasmic domains might be critical for the coordination of developmental processes, for example, by allowing the diVusion of transcription factors within a group of cells sharing the same fate. Herein we will discuss the molecular, physiological, and developmental regulations of PD function, as well as their consequences in plant development.

II. STRUCTURE, BIOGENESIS, AND PERMEABILITY OF PLASMODESMATA A. STRUCTURE OF PLASMODESMATA

The structure of plasmodesmata has been well described by classical studies using transmission electron microscopy (reviewed in Overall and Blackman, 1996). PD appear as complex, coaxial, membranous conduits spanning the cell wall, containing a tubular structure called the desmotubule, which is constituted by a modified endoplasmic reticulum contiguous with the cortical reticulum of the two cells. The pore of PD has an average diameter between 20 and 50 nm and the diameter of desmotubule is approximately 15 nm. The desmotubule not only stabilizes the internal structure of PD but also limits their lumen and porosity. The inner structures of PD are sensitive to detergents and proteases (Turner et al., 1994), which suggests that the desmotubule is associated to proteins that maintain the structure of PD. Globular structures likely corresponding to these proteins are arranged in two antiparallel helices present on the periphery of the cavity (the cytoplasmic membrane) and on the desmotubule (Overall and Blackman, 1996). The putative globular proteins present on the desmotubule and the plasma membrane are connected by spokelike extensions whose network constitutes microchannels having a diameter between 1.5 and 2.5 nm and through which traYcking occurs (Ding et al., 1992). The two ends of PD are tightened by a neck partly composed of callose, a 2 -1,3-glucan. A ring termed ‘‘sphincter’’ was also observed around PD, and it could regulate the opening of PD by contractile proteins. This ‘‘static’’ view of plasmodesmata comes from electron microscopy studies conducted on fixed material. Hence the dynamic aspects of these structures are still unclear. Because of the diYculty to extract intact PD from the cell walls without subcellular contaminants, the composition of these structures is still poorly known. Several putative PD-associated proteins have been characterized in maize (Epel, 1994). In particular a 41-kDa protein was immunolocalized to PD and Golgi membranes, whereas a protein called PAP26, immunolocalized to PD, was identified as crossreacting with antibodies raised against animal connexin43. This protein was

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also found in gap junctions, the functional animal equivalent of PD. Using direct immunolocalization techniques, several cytoskeleton proteins have been shown to be associated with PD (Aaziz et al., 2001). In particular, actinlike and myosin-like molecules were localized to PD in diverse plant species (Radford and White, 1998; White et al., 1994). Interestingly, a plant specific form of myosin (myosin VIII) was shown to be present in the PD of the postcytokinetic cell wall in Arabidopsis thaliana (Reichelt et al., 1999). Moreover, a centrin-like protein (a calcium-binding contractile protein), calreticulin (a calcium-sequestering protein), and a calcium-dependent protein kinase (CDPK) have also been localized in PD, the centrin-like protein in the neck region and calreticulin in the endoplasmic reticulum associated with PD (Baluska et al., 1999; Blackman et al., 1999; Yahalom et al., 1998). The presence of cytoskeletal and contractile proteins, as well as calciumregulated proteins, is consistent with a regulation of PD gating by contractile proteins regulated by calcium signalling. B. BIOGENESIS OF PLASMODESMATA

Plasmodesmata are classified into two types according to their mode of formation (Fig. 1). Primary plasmodesmata form at the cell plate during cytokinesis, when the endoplasmic reticulum is trapped between Golgi vesicles, bringing the material necessary for the formation of the new cell wall. The membranes of the vesicles form the cytoplasmic membrane, delimiting PD, and the trapped reticulum becomes highly compressed and gives rise to the desmotubule (Ehlers and Kollmann, 2001). Conversely, secondary PD appear de novo across a preexisting cell wall. The cell wall between the two adjacent cells gets thinner and disappears locally to allow the fusion of cytoplasmic membranes, and portions of the endoplasmic reticulum are trapped before the reconstruction of the cell wall. Therefore primary PD are typical of new cell walls and secondary PD are added to them or replace them as the cell wall matures. Moreover, in contrast to primary PD, secondary PD allow the diVusion of molecules between nonclonally related cells. Another distinction concerns the shape of PD. They can appear as single channels spanning the cell wall (termed simple PD) or as complex channels composed of several branches (termed branched PD). Branched PD can be more or less complex, ranging from a mere ‘‘Y’’ or ‘‘H’’ shape to a complex structure formed by numerous branches and a central cavity. Primary PD usually appear as simple PD and can further diVerentiate into branched PD, presumably by the coalescence of several simple PD. Secondary PD can appear directly as branched PD, but they can also be simple. Simple PD are thought to be a more primitive form of PD because they are the first

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Fig. 1. Structure and biogenesis of plasmodesmata (Haywood et al., 2002) Formation of primary and secondary PD. Primary PD are formed during the cell plate assembly by entrapment of endoplasmic reticulum between vesicles whereas secondary PD are formed on existing cell walls by local disappearance of the cell wall and fusion of the cytoplasmic membranes.

ones to be deposited after the formation of the cell plate. Moreover, simple PD predominate in primitive plant groups such bryophytes and pteridophytes (Cook et al., 1997; Tilney et al., 1990). Moreover, branched PD are usually more selective for the passage of macromolecules and solutes than simple PD (Oparka et al., 1999); therefore they are thought to have appeared later in evolution to allow controlled symplasmic communication between cells. C. PERMEABILITY OF PLASMODESMATA

The permeability of PD was initially explored using microinjection of inert fluorescent molecules of diVerent sizes into plant cells and monitoring their movement to adjacent cells. The upper limit of the size of molecules that can traYc through PD is defined as the ‘‘size exclusion limit’’ (SEL), which is used

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as an estimation of PD permeability. The majority of these experiments have been conducted on tobacco leaf mesophyll cells. When injected with fluorescent dextrans, mesophyll cells from mature tobacco leaves were shown to have a basal SEL of approximately 1 kDa. This size corresponds to a 0.75 nm Stokes radius, which is consistent with the observed diameter of the microchannels observed in electron microscopic studies (i.e., 1.5–2.5 nm) (Wolf et al., 1989). However, the basal SEL has been shown to vary greatly depending on tissue types and age. For instance, PD connecting the sieve elements and the companion cells of the phloem have been shown to have a SEL 10 times greater than that of mesophyll cells (Kempers and Van Bel, 1997). The measurement of PD SEL by microinjection has the disadvantage of wounding the injecting cells, which might induce changes in the permeability of PD, and therefore is mainly applicable to superficial tissues, because deep tissues are not easily accessible to microinjection without damaging the surrounding tissues. Recently, alternative techniques to measure the SEL of PD have been developed to overcome the limitations of microinjection. Expression of translational fusions of green fluorescent protein (GFP) to proteins of diVerent sizes was used to overcome the problem of wounding (Oparka et al., 1999). A transformed cell able to express the translational fusion is alive, and the measurement of the SEL might be closer to the real SEL of the PD in that cell. The GFP fusion can be expressed transiently in superficial tissues by particle bombardment, or it can be expressed from cellspecific promoters, allowing it to drive the expression of the reporter construct in deeper tissues not easily accessible to microinjection (Oparka et al., 1999). When GFP fusions are expressed transiently by particle bombardment, the targeted cells is wounded by the particles, but the cell has time to repair the damage and resume a normal physiological state, because movement is observed at least several hours after bombardment, in contrast to microinjection experiments in which movement is observed minutes after injection. However, one has to be careful when comparing the results from fluorescent dextran microinjection and protein fusion expression, because the time scales of the experiments are very diVerent (i.e., minutes for microinjection and hours for bombardment). Moreover, the Stokes radius of proteins is not directly comparable to that of dextrans of similar molecular weight, depending on the shape of the protein. Furthermore, it is possible that some proteins could unfold before traYcking through PD. Alternatively, noninvasive imaging of the unloading of fluorescent dyes previously loaded into the phloem has also been extensively used (Gisel et al., 1999, 2002; Oparka et al., 1994, 1995; Roberts et al., 1997; Ruan et al., 2001; Zhu et al., 1998). This technique has the advantage of being fast and noninvasive but only allows theanalysis of PD from cells symplasmically connected to the phloem.

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D. REGULATION OF PLASMODESMATA PERMEABILITY

These studies have shown that PD exist in three basic conformations: closed, open, and dilated (Crawford and Zambryski, 2000; Zambryski and Crawford, 2000). The exact mechanisms underlying this regulation have not been precisely determined, although some data are consistent with predictions based on the nature of the PD-associated proteins identified so far. As expected from the presence of centrin, calreticulin, and a CDPK in PD, an increase in intracellular calcium causes immediate closure of PD (Holdaway-Clarke et al., 2000). Furthermore, mastoparan-induced activation of heterotrimeric G-proteins induces cytoplasmic calcium waves, resulting in the closure of PD (Tucker and Boss, 1996). In addition, polyphosphoinositols (IP2 and IP3), which are known to control calcium signaling in other plant systems (Franklin-Tong et al., 1996), are able to induce the closure of PD in staminal hairs of Setcreasea purpurea (Tucker, 1988). Intracellular calcium waves could reduce the aperture of PD by inducing the contraction of PD-associated contractile proteins, either directly for centrin or indirectly through the action of calreticulin and the CDPK for actinlike and myosin-like proteins. In particular, myosin VIII and calreticulin were found to be enriched at ‘‘sink’’ PD (PD present in highly communicating tissues), compared to PD in noncommunicating tissues, and were therefore hypothesized to be molecular determinants of the dilation of PD (Baluska et al., 2001). Similarly, a decrease in adenosine triphosphate (ATP) concentration in conditions of anaerobiosis allows opening of PD (Cleland et al., 1994). The ATP may be needed for the contraction of the cytoskeletal proteins. Furthermore, disruption or stabilization of actin with cytochalasin D or phalloidin causes a dilation or a constriction of the ends of PD, respectively, accompanied by drastic modifications of the SEL (Ding et al., 1996). Another cellular aspect shown to play a role in PD regulation is the nature of the cell wall microdomains surrounding PD. Cell walls show a particular composition in these microdomains: cellulose is depleted and callose and pectins are more abundant (Baluska et al., 2000; Radford and White, 1998). Recent studies have shown that the deposition of callose is a very fast phenomenon in response to wounding, stress, or pathogen attack and could greatly decrease the permeability of PD by forming plugs that restrict PD-mediated movement (Radford and White, 1998, 2001). PD permeability was shown to be reduced by aluminium-induced callose deposition (Sivaguru et al., 2000) and in transgenic plants deficient in a-1,3-glucanase showing enhanced callose deposition (Iglesias and Meins, 2000). Similarly, pectins are abundant around plasmodesmata (Orfila and Knox, 2000), and it has been reported that pectin methyl esterase, an enzyme responsible for

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deesterification of secreted pectins, localizes preferentially around PD (Morvan et al., 1998). Moreover, interaction of pectin methyl esterase with the viral movement protein (MP) of the Tobacco Mosaic Virus (TMV), which is known to gate PD, is essential for this PD-gating activity (Chen et al., 2000; Dorokhov et al., 1999). Modification of the cell wall composition in microdomains surrounding PD could aVect the architecture of PD and thus modify their permeability.

III. PHYSIOLOGICAL REGULATION OF PD FUNCTION Unlike xylem elements in which water transport is driven by a tensional gradient mainly generated by transpiration, translocation in the phloem occurs via mass flow. Sugars are synthesized in photosynthetically active organs, termed sources (e.g., mature leaves), loaded into the phloem through which they are transported, and unloaded into heterotrophic organs, termed sinks. In most plants the major form under which carbon is transported is sucrose. The driving force for phloem translocation is thought to have two origins: phloem loading of sugars and water transfer from the xylem in source organs; and phloem unloading into sinks, followed by the removal of sucrose in these organs. PD-mediated traYcking plays a critical role at various stages of this process. It is involved both in phloem loading and in unloading, and the PD allowing the exchange between sieve elements and companion cells of the phloem are unusual (as described in the following paragraphs) and play a central role in this transport. A. CENTRAL ROLE OF THE SE-CC COMPLEX

Unlike xylem circulatory elements, namely tracheids and vessels, which are composed of dead empty cells, phloem circulating elements are composed of living cells. Primitive phloem circulatory elements called leptoids are found in certain erect mosses. These leptoids still contain a degenerate nucleus and small vacuoles. However, in more highly evolved plant groups a degeneration of the cytoplasmic content, including the disappearance of the nucleus, is observed in sieve elements (Van Bel et al., 2002). Consequently, sieve elements (SEs) became increasingly dependent on adjacent parenchyma cells for their survival and function. The specialization of these cells culminated in angiosperms with the appearance of a highly specialized physiological unit, the sieve element–companion cell (SE-CC) complex. The central role of this complex in phloem transport has been reviewed in Oparka and Turgeon (1999) and in Van Bel et al. (2002). Sieve elements and companion

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cells (CCs) originate from the same mother cells, but they are very diVerent in appearance and physiology once diVerentiated. Mature SEs appear as almost ‘‘empty’’ cells deprived of nucleus, vacuoles, ribosomes, Golgi stacks, and cytoskeleton. Mitochondria are scarce and dilated, and a few diVerentiated plastids are present. A highly modified endoplasmic reticulum and filamentous phloem proteins (P-proteins) are also present. All these remnants of a cellular content are located at the periphery of the cell and form the so-called parietal layer. The end cell walls, between two consecutive sieve elements, is characterized by enlarged symplasmic channels called sieve plate pores, thus creating a symplasmic continuum between all sieve elements. In contrast, companion cells have a very dense cytoplasm full of ribosomes, a large nucleus, and many active mitochondria. These features are typical of a cell with a very active metabolism. The SE is genetically and metabolically dependent on the companion cell, which provides it with all the elements that it is unable to synthesize. This dependence implies the need for a highly regulated traYcking between the two partners. Indeed PD between SEs and CCs are numerous and specialized. They comprise one broad, pore-like channel on the SE side and several branches on the CC side. Hence they were termed pore-plasmodesmata units (PPUs). PPUs are characterized by an unusually high SEL. Whereas the SEL of PD interconnecting mature leaf mesophyll cells is approximately 1 kDa, the SEL of PPUs was found to be between 10 and 40 kDa (Kempers and Van Bel, 1997), allowing the diVusion of proteins such as the 27 kDa GFP (Imlau et al., 1999). Sieve elements respond to wounding by obstructing the sieve plate pores. The observation in vivo of translocating sieve elements allowed to dissect their responses to wounding by a microcapillary (Knoblauch and Van Bel, 1998). Limited damage led to the detachment from the plasma membrane of P-proteins that will form a plug on sieve plate pores, whereas further damage also induced the explosion of plastids and the release of their content in the sap, further contributing to the obstruction of the sieve plate pores. During their diVerentiation, the SE-CC complexes undergo a symplasmic isolation from the adjacent parenchyma in the transport phloem, although PD are present between the SE-CC complexes and the surrounding phloem parenchyma cells (Van Bel and Van Rijen, 1994). In contrast, SEs and CCc remain symplasmically interconnected after their diVerentiation. Plasmodesmata play a central role in the function of the SE-CC complex. Phloem loading in source leaves and phloem unloading into sinks depend on the regulation of PD permeability between the SE-CC complex and surrounding tissues, and phloem sap translocation is dependent on the regulation of PPU permeability.

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Phloem loading in source leaves occurs in minor veins, as revealed by a number of classical studies in which source leaves of plants that had been exposed to radiolabelled 14CO2 displayed accumulation of radioactivity specifically in minor veins (Van Bel, 1993). Sucrose synthesized in mature photosynthetic leaves is transported through the mesophyll, toward the phloem parenchyma, and across plasmodesmata. A mutant of maize named sxd1 (sucrose export defective) shows defects in the export capacity of photosynthates from the tips of source leaves that displayed an abnormal accumulation of starch and anthocyanin, as well as distorted vascular tissue in the minor veins. This phenotype was correlated to a structural modification of PD at the bundle sheath-vascular parenchyma interface in the tips of source leaves, thus providing evidence that sucrose is transported symplasmically to the phloem parenchyma (Russin et al., 1996). This mutant was later found to be aVected in a novel chloroplastic protein involved in a chloroplast to nucleus signalling pathway necessary for proper late-stage diVerentiation of bundle sheath cells, including the developmentally regulated modification of PD (Provencher et al., 2001). Whereas sucrose is transported from mesophyll cells to phloem parenchyma by mere diVusion across PD, the actual phloem-loading process occurs at the critical interface between phloem parenchyma and the SE-CC complex. At this interface, sucrose has to be transported against a concentration gradient, the concentration of sugars being much higher in the SE-CC complexes of the minor veins of source leaves than in surrounding cell types in most plant species (Geiger et al., 1973). This active transport generates the osmotic gradient leading to the entry of water from the xylem into the phloem and generates the high turgor that drives the flow of sugars and other compounds in the phloem. This loading against a concentration gradient explains the autoradiography pattern showing accumulation of radioactivity in minor veins of the source leaves of plants exposed to 14CO2 (Gamalei, 1989) This loading can be achieved by two diVerent ways, and two types of plants—apoplasmic and symplasmic loaders—can be distinguished on the basis of their phloem mechanisms. Plants belonging to the first category (especially cucurbits) are usually restricted to the tropics and subtropics, whereas most temperate crops are apoplasmic loaders. In the case of apoplasmic-loading plant species, plasmodesmata are scarce between the SE-CC complexes of the minor veins of source leaves and surrounding leaf tissues. Hence loading is thought to occur via sugar transporters. Sucrose is first transported from phloem parenchyma cells into the apoplasmic space, possibly via an eZux carrier, and then actively loaded

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from the apoplasmic space into the SE-CC complex by an Hþ-sucrose cotransporter, the energy necessary for this transport being provided by an Hþ-ATPase present in the plasma membrane of the companion cell. This unloading can occur directly in the SE or in the CC. In certain cases, companion cells present specific cell wall ingrowths, increasing the exchange surface and thus facilitating sucrose uptake from the apoplasm. These cells are called transfer cells. In the case of apoplasmic loaders, phloem loading can be blocked by p-(chloromercuri) benzene sulfonic acid (PCMBS), an eVective inhibitor of the Hþ-sucrose transporter (Van Bel, 1993), and expression of a heterologous invertase (an enzyme responsible for the cleavage of sucrose) in the apoplasm interrupts phloem loading (von Schaewen et al., 1990). The sucrose transporters involved in phloem loading have been detected in companion cells for certain species (Stadler and Sauer, 1996; Stadler et al., 1995) or in sieve elements for others (Ku¨ hn et al., 1997). Inhibition of the expression of the sucrose transporter SUT1 in potato and tobacco by antisense strategies showed that it was essential for phloem loading and long-distance transport. In eVect, potato plants with drastically lowered expression of SUT1 have a reduced tuber yield and accumulate sugars in their mature leaves because of impaired export, which was confirmed in tobacco (Bu¨rkle, 1998; Ku¨ hn, 1996; Riesmeier, 1994). In the case of symplasmic loaders, phloem loading is not aVected by PCMBS (Van Bel, 1993) and the density of PD connecting bundle sheath cells to companion cells of the minor veins is particularly high and was used as a criterion to tentatively assign a plant to one category or another (Gamalei, 1989). These specialized companion cells with a high PD density are termed intermediary cells. PD connecting intermediary cells to bundle sheath cells are highly branched, and their branches on the intermediary cell side are more numerous and narrow than those on the bundle sheath cell side. Assuming that the transport of solutes through PD is bidirectional and occurs by passive diVusion, the question arises as to how a diVusion mechanism can lead to the translocation of sugars against a concentration gradient and allow phloem loading. It seems that these plants have developed a physiological answer to this problem, in the form of the ‘‘polymer trap model.’’ Indeed most symplasmic loaders translocate the raYnose family oligosaccharides (RFOs), especially the tetrasaccharide stachyose. These RFOs are synthesized by transfer of galactose residues from galactinol (a dimer of galactose and myoinositol) into sucrose. The enzymes responsible for the synthesis of RFOs are located in the intermediary cells of minor veins (Beebe and Turgeon, 1992; Holtaus and Schmitz, 1991). It has thus been proposed that sucrose diVuses into intermediary cells through PD and is used there to synthesize RFOs, which then accumulate in the SE-CC

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complex because they are too large to diVuse back into bundle sheath cells. However, as the permeability of PPUs connecting intermediary cells and sieve elements is particularly high, they can freely diVuse into the translocation stream (Turgeon, 1996). In the case of symplasmic phloem loading the energy necessary to allow sugar loading against a concentration gradient is not used by a sucrose transporter but by the metabolism of RFOs. RFO synthesis depletes the SE-CC complex from sucrose, thus allowing it to enter by diVusion, and creates the RFO tension gradient in the translocation stream, allowing mass flow to occur. Although RFOs are used as transport sugars in these species, sucrose was also detected at significant levels in the translocation stream in some species. Although it is diYcult to estimate the proportion of sugars in the phloem sap reliably, the possibility remains that both apoplasmic and symplasmic loading mechanisms coexist to some extent in the same species. Alternatively, there could be downstream processing of RFOs into sucrose (Turgeon, 1996). Some species, including many temperate trees, classified as being symplasmic loaders on the basis of the ultrastructure of intermediary cells have been shown not to translocate RFOs or other sugar polymers that could be trapped in the intermediary cells. Autoradiography studies conducted after exposure to radiolabelled CO2 reveals that source leaves did not show a typical loading pattern in minor veins. Furthermore, the solute content of the minor vein SE-CC complexes is lower than that of the mesophyll. Hence it is thought that these species do not perform phloem loading in the proper sense of the term, namely an active transport against a concentration gradient. Rather, photosynthates simply diVuse from the mesophyll cells to the translocation stream in these species (Turgeon, 1996). C. TRANSPORT OF SOLUTES THROUGH THE PHLOEM

Translocation through the phloem is a result of a pressure gradient between source tissues and sink tissues. High osmotic pressure is generated in source leaves by phloem loading and drives the transport of relatively pure water from the xylem into the phloem. For this translocation to occur, the SE-CC complex of the transport phloem was thought to be an impermeable conduit from which no solute could diVuse, which is consistent with the observation that the SE-CC complex of the transport phloem is symplasmically isolated (Oparka and Turgeon, 1999; Van Bel and Van Rijen, 1994). Indeed this conduit is not as impermeable as was originally thought. Another intriguing issue is how sucrose and other transported compounds can move by mass flow between the CC and the SE without washing out the low molecular weight metabolites needed for the metabolism of CC. For example, galactinol, the

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precursor of RFOs in symplasmic loaders, is found in the companion cells but is absent from sieve elements. If RFOs diVuse from the CC to the SE through PPUs, galactinol should also diVuse in the SE, its molecular weight being inferior to that of RFOs (Turgeon, 1996). In a recent elegant study, Ayre et al. (2003) addressed the question of solute retention in companion cell and in the phloem translocation stream. They analyzed the transport of sucrose, galactinol, RFOs, and octopine both in wild-type Coleus blumei, a symplasmic loader, and in transgenic tobacco, expressing the enzymes responsible for the synthesis of galactinol and octopine specifically in companion cells. They were able to show that solutes had diVerent behaviours according to their size and charge. All solutes were eYciently transported from the CC to the SE. Octopine and RFOs were eYciently translocated in the phloem with minimal leakage, as a result of their ionized state and size, respectively. In contrast, galactinol leakage from the phloem symplasm was much faster, which explains its low concentration in the translocation stream of symplasmic loaders. Similarly, sucrose leakage from the SE-CC complex is substantial, but it is constantly retrieved from the apoplasm by sucrose transporters to maintain the pressure gradient within the translocation stream. The AtSUC2 and StSUT1 sucrose transporters are expressed along the transport phloem and are thought to play a role in sucrose retrieval because of their expression pattern (Ku¨hn et al., 1997; Truernit and Sauer, 1995). This leakage is necessary to supply ‘‘sinks’’ adjacent to the transport phloem, but plants must control the balance between phloem leakage of photosynthates and their retrieval to maintain the pressure gradient. This seems to be achieved either by sugar transporter-mediated retrieval or by the synthesis high molecular weight oligosaccharides being eYciently retained in the phloem stream. Although SE-CC complexes in the transport phloem are symplasmically isolated, this isolation is reversible and PD between SE-CC complexes and surrounding tissues are held shut by an energy-dependent process. In eVect, Wright and Oparka (1997) showed that application of metabolic inhibitors restored the symplasmic continuity between the phloem and surrounding tissues in Arabidopsis roots. Moreover, Itaya et al. (2002) showed that a viral MP was able to move from the symplasmically isolated stem transport phloem to surrounding tissues, which indicates that the transport phloem is not isolated from surrounding tissues because PD are absent or irreversibly closed, because they can be opened by an MP. Hence the plant regulates eZux and retrieval of sucrose from the transport phloem by a dynamic and active process. Some issues still remain unclear; for example, how are small metabolic intermediates ATP and adenosine diphosphate (ADP) not depleted from the companion cells by the translocation stream? The answer might be of two types: whether the metabolism of the companion cell is somehow

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compartmentalized, which renders metabolic intermediates unavailable for diVusion through PPUs, or alternatively, the metabolism of the companion cell is particularly fast to compensate for the permanent loss of intermediates in the translocation stream. D. PHLOEM UNLOADING INTO SINKS

Phloem unloading into carbon sinks can occur symplasmically via plasmodesmata or apoplasmically via sugar transporters, depending on the type of organ and its developmental stage (Table I). After export of sucrose from the phloem into the apoplasm, apoplasmic unloading can involve a disaccharide or a monosaccharide transporter. In the first case, sucrose can be transported directly by the transporter, whereas in the second case, sucrose has to be cleaved by an invertase located in the cell wall before its uptake by a hexose transporter into the sink cells. Fourteen putative monosaccharide transporters and seven disaccharide transporters have been reported in Arabidopsis. Sugar transporters not only have a role in phloem unloading, but they also serve as sugar sensors likely used as starting points of signalling pathway regulating photosynthate allocation at the whole plant level. The diversity and role of the sugar transporters have been reviewed in Williams et al. (2000). In the case of symplasmic unloading an increase of PD permeability is usually observed in sink tissues. This has been reported in various sink tissues such as the root tip and lateral root primordia of Arabidopsis (Oparka et al., 1994, 1995), potato tubers (Viola et al., 2001), Rhizobium-induced nodule primordia in legumes (Complainville et al., 2003), and sink leaves of tobacco and barley (Haupt et al., 2001; Roberts et al., 1997). In most cases the diVerences in PD permeability between actively growing sink organs and source tissues or weak sink organs corresponds to a switch between symplasmic isolation to symplasmic continuity. In continuously growing meristematic tissues such as the root tip of Arabidopsis the phloem is symplasmically connected to surrounding tissues; this continuity is lost when root cells diVerentiate. This is associated with a decrease in PD numbers and permeability (Zhu et al., 1998). In contrast, in tissues where diVerentiated cells have to dediVerentiate to allow the formation of a new organ such as lateral root primordia, potato tubers, or nodule primordia, symplasmically isolated cells restore their symplasmic connection with the phloem during de novo organogenesis. The permeability of PD in sink tissues has been described to be very high. In an elegant study Imlau et al. (1999) expressed the 27-kDa GFP under the companion cell-specific promoter AtSUC2 in Arabidopsis. They were able to

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detect unloading of GFP in various sink organs (e.g., root apices and lateral root primordia, reproductive organs, sink leaves), whereas it stayed confined to the phloem in source leaves and in the transport phloem. Oparka et al. (1999) further showed that a GFP fusion as large as 50 kDa could move across PD in sink leaves of tobacco. Although the size of proteins is not directly comparable to the stokes radius of dextrans, these experiments demonstrated that the SEL of PD in sink tissues was much higher than originally thought. To overcome the limitation of measuring the SEL based on the movement of proteins, Fisher and Cash-Clark (2000) used severed aphid stylets to inject fluorescent tracers into sieve tubes of wheat grains. Thus they showed that 16 kDa dextran, having a Stokes radius of 2.6 nm, was eYciently unloaded from the SE-CC complex in grains. Surprisingly, very large molecules such as 400 kDa Ficoll, having a Stokes radius of 11 nm, were also unloaded from the SE-CC complex but in an irregular pattern, and they did not show any postphloem movement. It is possible, however, that such large molecules damage the PD of the SE-CC complex, enlarging them in some way. Baluska et al. (2001) observed that ‘‘sink’’ PD were enriched in calreticulin and myosin VIII and proposed that these two molecules were molecular determinants of sink strength, playing a role in the dilation of PD in sink tissues. Phloem unloading mechanisms in various organs are summarized in Table I. Two examples represent two kinds of situations. In the first example, phloem unloading was studied in detail during potato tuberization using a combination of biochemical analysis, autoradiography, and fluorescent dye unloading (Viola et al., 2001). Tuberization was shown to involve a switch from an apoplasmic-unloading mechanism in non-swelling stolons to a symplasmic unloading in tuberizing stolons. This switch was associated with a diminution of soluble invertase activity, to an increase in sucrose synthase activity, and to an increase in the ratio of sucrose to fructose, pointing out that the switch to symplasmic unloading is associated with a switch from an invertase-sucrolytic pathway to a sucrose synthase-sucrolytic pathway. Conversely, in the second example, nematode-induced syncytia in the Arabidopsis root are symplasmically isolated from the phloem, although they are strong carbon sinks. Juergensen et al. (2003) have identified among all the sugar transporters in Arabidopsis at least one sucrose transporter (AtSUC2) expressed in those cells and likely involved in apoplasmic sucrose unloading into those cells. Although the situations described in these previous two examples seem to be well supported, it is not always the case. For instance, calculations based on the conductance of plasmodesmata and cytoplasm indicate that symplasmic unloading of sucrose cannot satisfy the carbon demands in the root tip of maize (Bret-Harte and Silk, 1995), although the root tip is well known to

TABLE I Phloem Unloading Mechanisms in Various Plant Organsa Plant

Organ

A. thaliana

Root apex

Medicago ssp.

Root apex

A. thaliana

Lateral root primodium

A. thaliana N. tabacum Barley Potato N. benthamiana

Sink leaves Tuber Agrobacterium tumefaciensinduced tumor

Name and type of Unloading mechanism sugar transporter Symplasmic (þapoplasmic?) Symplasmic (þapoplasmic?) Apoplasmic followed by symplasmic Symplasmic Symplasmic Symplasmic

Cucurbita maxima Ricinus communis Medicago ssp. Sinorhizobium-induced nodule Symplasmic primordium

Reference

AtSTP4 (MST)H Oparka et al., 1994; Truernit et al., 1996 MtST1 (MST) Complainville et al., 2003; Harrisson, 1996 Oparka et al., 1995 Imlau et al., 1999 Oparka et al., 1999 Haupt et al., 2001 Viola et al., 2001 Pradel et al., 1999

Complainville et al., 2003

M. truncatula

Vicia faba

Nitrogen fixation zone of mature Rhizobium-induced nodules Root cortical cells infected with mycorrhizal fungi Nematode-induced syncytium Storage parenchyma of tap roots Developing embryo

Apoplasmic (þsymplasmic?) Apoplasmic

Petunia

Germinating pollen

Apoplasmic

M. truncatula A. thaliana Carrot

Apoplasmic?

?

Complainville et al., unpublished results

Apoplasmic (þsymplasmic?) Apoplasmic

MtST1

Harrisson, 1996

AtSUC2 (DST)

Juergensen et al., 2003

DcSUT2 (DST)

Shakya and Sturm, 1998

A. thaliana N. tabacum a The name and type of sugar transporters involved are cited when they are known. DST ¼ disaccharide transporter; MST ¼ monosaccharide transporter.

VfSTP1 (MST) VfSUT1 (DST) PhPMT1 (MST) AtSTP4 (MST) AtSUC1 (DST) NtSUT3 (DST)

Weber et al., 1997 Lemoine et al., 1999; Stadler et al., 1999; Truernit et al., 1996; Ylstra et al., 1998

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be subject to extensive symplasmic unloading. This suggests that both unloading mechanisms could coexist in the root tip. Indeed monosaccharide transporters have been shown to be expressed in the root tips of Arabidopsis (AtSTP4) and Medicago truncatula (MsST1) (Harrison, 1996; Truernit et al., 1996). This situation might be general in other sink organs. E. SOURCE/SINK RELATIONSHIPS

A critical event in source/sink relationships is the sink-source transition in leaves. Emerging leaves still import carbon compounds from mature leaves. As they develop and start active photosynthesis, the balance reverses to a net carbon-exporting state. This transition starts at the tip of the leaf and progresses toward its base. Oparka et al. (1999) showed that this transition was associated with drastic changes in PD function and structure in tobacco. By expressing GFP under the control of the companion cell-specific promoter AtSUC2, Oparka et al. observed that GFP traYcked symplasmically out of the phloem into mesophyll cells of sink leaves, whereas it stayed confined to the phloem in source leaves, as described previously in Arabidopsis (Imlau et al., 1999). This marker of phloem unloading allowed them to follow the progression of the sink-source transition in a single tobacco leaf and to take samples from the same leaf for ultrastructural analysis at diVerent locations relative to the position of the barrier between sink and source tissues. This study enabled them to show that the sink/source transition was associated to a switch from simple to branched PD and that the latter had a greatly reduced permeability compared to the former not only in the phloem but also in mesophyll and epidermal cells. Roberts et al. (2001) further described the progression of this transition in all cell types of the leaf using a combination of electron microscopy and a translational fusion between the GFP and the MP of the TMV as a marker of PD branching. They proposed a model describing the start of PD branching during the transition in trichomes and its extension to the leaf epidermis, vascular bundles, and later to mesophyll cells. Itaya et al. (1998) had previously used similar approaches to demonstrate modifications in the structure and function of PD during leaf development but without relating them directly to changes in their permeability. Recently, Wright et al. (2003) used the AtSUC2 promoter to drive the expression of free or endoplasmic reticulum-bound GFP in companion cells of the phloem to profit from GFP fluorescence as a marker of vein maturation and function in tobacco leaves. Endoplasmic reticulum-bound GFP was unable to traYc from cell to cell. Veins of tobacco leaves are classified from class I to class V based on their degree of branching from the main vein (class I) (Fig. 2A and B). As described previously (Van Bel, 1996), phloem unloading in sink leaves

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Fig. 2. Structure and function of the veinal network in tobacco leaves (Roberts et al., 1997) (A) and (B) show diagrammatic representations of vein classes in the tobacco leaf. (A) The midrib (class 1) gives rise at regular intervals to class II veins. The class III veinal network, a branched veinal system that forms discrete islands on the lamina, lies between the class II veins. (B) Detail of the boxed region in (A) showing the position of minor veins (classes IV and V) within the islands of the class III veinal network. (C) Schematic model of the sink/source transition in a developing tobacco leaf. At the top, the source part of the leaf (blue) and the source part at the bottom (yellow). Phloem loading in the source part occurs apoplasmically in minor veins. Phloem unloading in the sink part occurs from the class III veinal network. In this part the minor veins are still immature (green) and receive assimilates directly from the mesophyll by cell-to-cell movement.

involved class III veins (‘‘release’’ phloem), whereas phloem loading in source leaves involved minor veins of classes IV and V (‘‘collection’’ phloem) and class I and II veins are referred to as the ‘‘transport’’ phloem (Fig. 2C). AtSUC2-GFP-ER expression in all minor veins was up-regulated during the sink-source transition, whereas GFP unloading from class III veins ceased in AtSUC2-GFP plants. Minor veins were not functional before the sinksource transition because they did not express the AtSUC2 promoter-driven GFP and their functional diVerentiation (expression of AtSUC2-GFP) was dependent on light during the sink-source transition despite beingstructurally mature. In contrast, expression of AtSUC2-GFP in higher order veins was unaVected by the sink-source transition, suggesting that a diVerential regulation is involved in the maturation of minor and major veins, further stressing the specialization of the diVerent vein classes. Studies using fluorescently labeled viruses (PVX [potato virus X] and TMV) showed that the systemic spread of virus infection strictly followed the pattern of unloading described for solutes (Cheng et al., 2000; Roberts

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et al., 1997). Viruses escaped from the phloem, preferentially in sink parts of leaves undergoing the sink/source transition, and were unloaded into sink leaves from class III veins. These observations show that this transport pathway is not restricted to solutes but reflects a general translocation stream used by a wide variety of molecules. There is still debate on the mechanisms that plants use to control assimilate allocation at the whole plant level. The most trivial answer would be that assimilate allocation is regulated on one side of the translocation stream by the strength of the diVerent sink organs and on the other side by the synthesis of assimilates. The sink strength of an organ could be determined by several factors including conductivity of PD or sugar transporters, cytoplasmic streaming, and the actual consumption of assimilates. In this context it is interesting to ask whether the activity of sucrose-mobilizing enzymes is a major determinant of sink strength. For example, in potato tubers, sucrose synthase activity eventually leads to the conversion of sugars into an insoluble polymer such as starch, thus creating a sucrose concentration gradient and driving the influx of sucrose into the tuber. The activity of these enzymes does not always seem to be a limiting factor for sink growth (Stitt, 1996; Sturm and Tang, 1999). For instance, although sucrose synthase plays a major role in the uptake of sucrose in potato tubers, its expression can be reduced by approximately two thirds without aVecting tuber yield and starch content (Zrenner et al., 1995). In contrast to potato tubers, reduction of only 15% of sucrose synthase activity inhibits cotton fiber initiation and elongation, indicating that the activity of this enzyme is limiting in that case (Ruan et al., 2003). Many classical experiments have shown that the growth of a sink can be increased by the removal of competing sinks such as other potato tubers. Similarly, in legumes a phenomenon called autoregulation limits the number of Rhizobium-induced nodule initiations systemically as a function of the energetic resources available (Caetano-Anolle´ s and Bauer, 1988) and is abolished if already formed nodules are removed. This mechanism could act by a regulation of resource allocation. However, a number of mutants aVected in autoregulation have been identified recently, several of which were shown to be aVected in receptor kinases called NARKs (nodule autoregulation receptor-like kinases), which are expressed in the aerial parts of plants. Furthermore, the hypernodulation phenotype resulting in their mutation was graft transmissible and dependent on the genotype of the shoot, indicating that a diVusible signal transported from the aerial part to the root induced this phenomenon (Szczyglowski and Amyot, 2003). These results point out that the regulation of assimilate allocation to diVerent sinks could occur via a cross-talk between sources and sinks by long-distance signalling rather than a direct response to the availability of assimilates. Further support for this

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hypothesis comes from the observation that plants expressing viral MPs display alterations in carbohydrate metabolism and partitioning. In tobacco plants expressing the TMV-MP, soluble sugar and starch unexpectedly accumulated in source leaves, as a result of a reduced export of sugars from source leaves (Lucas et al., 1996; Olesinski et al., 1995). In potato the eVect of the expression of TMV MP was dependent on its site of expression. When expressed in the phloem, it induced sugar accumulation in leaves and decreased export from source leaves, whereas it induced an increase in sucrose export when expressed in green tissues (Almon et al., 1997; Olesinski et al., 1996). Furthermore, (Balachandran et al., 1995) demonstrated by a structurefunction analysis that the eVect of the TMV MP on carbon partitioning could be separated from its action on PD permeability. This eVect was not restricted to the MPof TMV and was also observed with the viral MPs of the Cucumber Mosaic Virus (CMV) (Shalitin et al., 2002) and the potato leafroll virus (Hofius et al., 2001). The varying eVects of MPs and the uncoupling of the PD dilatation function from its function on assimilate partitioning have led to the interpretation that these eVects are caused by the interaction of MPs with a supracellular signalling pathway controlling assimilate metabolism and allocation. For example, MPs could compete with regulatory host proteins controlling assimilate traYcking or perturb their movement at the whole plant level, thus aVecting the putative cross-talk between sources and distant sinks regulating assimilate allocation (Lucas and Wolf, 1999). Such signalling molecules could be macromolecules (proteins or RNAs) transported through the phloem to coordinate photosynthesis and carbohydrate supply to sinks.

IV. PLASMODESMATA–MEDIATED TRAFFICKING OF MACROMOLECULES AND MANIPULATION OF PD FUNCTION BY MACROMOLECULES The capacity of macromolecules to traYc through PD and to modify their permeability has been the subject of many recent reviews (Haywood et al., 2002; Heinlein, 2002; Wu et al., 2002). Evidence for this feature has accumulated over the last decade, starting with the studies on virus movement and continuing with an ever-increasing number of endogenous macromolecules that also seem to be able to traYc through PD and modify their permeability. A. PASSIVE AND ACTIVE TRANSPORT OF MACROMOLECULES THROUGH PD

Two types of molecular traYcking through PD can be distinguished: passive and active movement, also termed non-targeted and targeted movement (Crawford and Zambryski, 2001). Non-targeted movement refers to

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nonspecific diVusion through PD of proteins having a Stokes radius smaller than the SEL of PD. This type of movement is usually encountered in tissues such as sink organs, in which the SEL of PD is high due to environmental conditions or the physiological status. In contrast, targeted movement refers to the traYcking of proteins with a Stokes radius usually larger than the SEL of PD but able to ‘‘gate’’ PD to allow their own movement and/or the movement of other macromolecules. Non-targeted movement was observed in several sink organs. Imlau et al. (1999) expressed the GFP (27 kDa) under the companion cell-specific promoter AtSUC2. They observed that GFP was restricted to the phloem in source leaves and the transport phloem of roots and shoots, whereas it was unloaded into sink organs. Oparka et al. (1999) further demonstrated that macromolecules as large as 50 kDa could move freely across PD in the epidermis of sink leaves and that a virus defective for its movement protein was capable of cell-to-cell traYcking in sink leaves. Complainville et al. (2003) also demonstrated that GFP could traYc into Rhizobium-induced nodule primordia. These observations lead to the hypothesis that macromolecular traYcking was indeed the default situation in sink organs. The first evidence of targeted cell-to-cell traYcking came from the study of virus movement. Viral MPs were originally identified as being essential for virus movement (Deom et al., 1993). Moreover, MPs are able to move from cell to cell when injected into mesophyll cells, although they are much larger than the measured SEL of PD in this tissue. MPs are not only capable of inducing their own movement, but they are also able to increase drastically the SEL of PD (from 800 Da to 15 kDa) when purified proteins are microinjected or when MP transgenes are expressed in transgenic plants. They are also able to induce the cell-to-cell movement of RNAs (Wolf et al., 1989). The observation that mutant viruses defective in their MPs are unable to achieve their cell-to-cell communication function suggested that it is dependent on a specific interaction with cellular components. Crawford and Zambrysi (2001) compared targeted movement induced by the MP of the TMV (TMV-MP) and non-targeted movement in diVerent physiological states. Although non-targeted movement was highly influenced by the age and physiological status of the leaf, targeted protein movement was not. Indeed the TMV-MP was able to increase the SEL of PD irrespective of the age of the leaf. Moreover, although the SEL of PD in sink leaves was higher than the SEL of source leaves PD ‘‘gated’’ by the TMV-MP, viral RNA was unable to move across PD in sink leaves (Oparka et al., 1999), whereas it was able to do so in source leaves in the presence of the MP (Wolf et al., 1989). This suggests that the mechanisms of PD

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permeability regulation by physiological and environmental conditions and the gating of PD by macromolecules are diVerent. The PD-gating capacity is not restricted to movement proteins. The first evidence that endogenous proteins could act in a similar way than movement proteins came from studies with the transcription factor KN1. A dominant mutation of kn1 aVected cell fate in the maize leaf. Movement of this transcription factor was suggested because the domain where the KN1 protein was detected (the L1, L2, and L3 layers of the shoot apical meristem [SAM]) was larger than the domain of its RNA expression (the L2 and L3 layers of the SAM) (Jackson et al., 1994). Because of the diYculty in accessing the shoot apical meristem by microinjection, cell-to-cell movement studies were performed on tobacco leaf mesophyll cells. KN1, like movement proteins, was able to induce its own movement through PD to increase the SEL of PD and induce the movement of its own RNA, whereas point mutations in the KN1 protein aVected this activity (Lucas et al., 1995). Later studies confirmed that a KN1-GFP protein fusion was able to move from cell to cell in the SAM of Arabidopsis. When expressed specifically in the layers L1 and L2 of the shoot apical meristem, this fusion was also detected in the L3 layer, in contrast to a smaller yellow fluorescent protein (YFP)GFP fusion or to a fusion of GFP to a mutant form of KN1 unable of cell-to-cell traYcking in microinjection assays (Kim et al., 2002). Similar cellto-cell traYcking capacities have been reported for the transcription factors FLO, LFY, GLO, and DEF, either by microinjection studies or the generation of genetic chimeras (Table II). The functional significance of the traYcking of transcription factors in the SAM was further shown by studies conducted on LFY (Sessions et al., 2000). Although lfy RNA is normally detected in all layers of the young floral buds of the inflorescence meristem, a mutant phenotype could be reverted to wild type by driving the expression of lfy RNA only in the L1 layer of the SAM, showing that LFY can traYc from the L1 layer to the L2 and L3 layers and that it retains its biological activity after its movement. An increasing number of endogenous macromolecules, not only transcription factors, have been reported to have the capacity of moving across PD and/or gating PD (see Table II). This stresses the importance of the possible non-cell autonomous activity of macromolecules in plant development. B. MACROMOLECULAR TRANSPORT IN THE PHLOEM

Macromolecular traYcking through the phloem may play a major role in plant development because it is the basis of interorgan communication. Despite the diYculty in accessing the phloem sap for analysis, its collection

TABLE II Endogenous Proteins Capable of Movement through PD Protein

Type

Endogenous tissue

Tissue where movement was shown

Reference

KN1

Transcription factor

Shoot apical meristem

FLO LFY GLO DEF SHR PP2 RPP13-1 CmPP16

Transcription factor Transcription factor Transcription factor Transcription factor Transcription factor Phloem lectin Thioredoxin h Paralog to movement protein Cytochrome b5 reductase

Shoot apical meristem Shoot apical meristem Shoot apical meristem Shoot apical meristem Root apical meristem SE-CC complex SE-CC complex SE-CC complex

Leaf mesophyll Shoot apical meristem Shoot apical meristem Shoot apical meristem Leaf mesophyll Shoot apical meristem Root apical meristem Cotyledon mesophyll Leaf mesophyll Leaf mesophyll

SE-CC complex

Cotyledon mesophyll

Chaperone

Phloem

Leaf mesophyll

Lucas et al., 1995 Kim et al., 2002 Mezitt and Lucas, 1996 Sessions et al., 2000 Kragler et al., 1998 Perbal et al., 1996 Nakajima et al., 2001 Balachandran et al., 1997 Ishiwatari et al., 1998 Xoconostle-Cazares et al., 1999 Xoconostle-Cazares et al., 2000 Aoki et al., 2002

Sucrose transporter

SE-CC complex

SE-CC complex

Ku¨ hn et al., 1997

CmPP36 CmHSC70-1 CmHSC70-2 SUT1 (mRNA and protein?)

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from species (e.g., cucurbits) that readily exudate phloem sap after sectioning or from severed aphid stylets inserted in the phloem allowed it to identify a number of proteins in the 20–60 kDa range present in the translocation stream (Sjolund, 1997). Because SEs do not have the machinery necessary for protein synthesis, these proteins are likely synthesized in the CCs and, subsequently, are translocated into the SEs. A number of SE-localized endogenous proteins have been shown to be synthesized in CCs and move into SEs through PPUs (Balachandran et al., 1997; Bostwick et al., 1992; Clark et al., 1997; Golecki et al., 1999; Ishiwatari et al., 1998; XoconostleCazares et al., 1999, 2000). Intriguingly, the RNA of a sucrose transporter involved in phloem loading and localized in the SE membrane (SUT1) was detected in both the CC and SE, whereas its transcription occurs in the CC. This suggests the possibility that translation of sut1 RNA could occur in Ses, although electron microscopy studies have proved that SEs are deprived of ribosomes. These experiments prove that RNA can traYc between CCs and SEs across PPUs, although the role of the presence of sut1 RNA in SEs is not clear. The high values reported for the SEL of PPUs, together with the observation that GFP can freely diVuse from CC to SE (Imlau et al., 1999), raises the possibility that translocation from CC to SE is the default pathway. However, a number of phloem proteins have been shown to be able to gate PD when microinjected in heterologous tissue (usually mesophyll) (Balachandran et al., 1997; Ishiwatari et al., 1998; Xoconostle-Cazares et al., 1999, 2000). This apparently contradicts the hypothesis of traYcking between SE and CC as a default pathway because it suggests that many phloem proteins can achieve active transport through PD. It is possible, however that the constant traYcking of these proteins between CCs and SEs constantly keeps PPUs in a dilated state, thus allowing the passive traYcking of other proteins such as the GFP and explaining why the SEL of PPUs is always high. Interestingly, when an endoplasmic reticulum-associated form of GFP was expressed in companion cells under the control of the AtSUC2 promoter, it stayed confined to companion cells (Wright et al., 2003), indicating that the subcellular compartmentalization of proteins in the CCs is a critical determinant of their availability for transport in the translocation stream. A good number of the macromolecules synthesized in the CC and entering the translocation pathway are likely to be structural components necessary for the function of SE, although some of them might serve as long-distance signalling molecules. The movement of macromolecules entering the translocation stream is not limited to the immediate vicinity of the CCs where they were synthesized. Grafting experiments have shown that GFP expressed in the companion cells of the stock could be translocated into

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the phloem of the scion and accumulate in CCs of the scion transport phloem before being unloaded into sink organs (Complainville et al., 2003; Imlau et al., 1999; unpublished data). Furthermore, heterografting studies revealed that the scion sap contained proteins synthesized in the stock phloem (Golecki et al., 1999; Tiedemann and Cartens-Berhens, 1994). This likely reflects a long-distance macromolecular translocation pathway used by signalling molecules to be transmitted between distant organs. Indeed it seems that macromolecules follow the same pathway as assimilates; namely they are ‘‘loaded’’ into the translocation stream from CCs in the collection phloem, translocated in the transport phloem, and unloaded into sinks, as Fisher et al. (1992) proposed. Along the translocation pathway, protein synthesis, modification, and turnover also occur in the transport phloem. The exit of macromolecules from the phloem also seems to follow the flow of assimilate and to occur mainly in-sink organs, thus providing a way to achieve communication between source and sink organs (Imlau et al., 1999). However, interorgan communication is likely not to be restricted to sink organs, and the long-distance movement of a reporter protein such as GFP is likely not to reflect all long-distance macromolecular movement occurring in the plant. Indeed although SE-CC complexes of the stem transport phloem are described as symplasmically isolated, Itaya et al. (2002) reported the exit of a fusion between the GFP and an MP from these cells into surrounding tissues. This observation proves that SE-CC complexes of the transport phloem are not isolated by the absence of PD at the boundary with surrounding tissue, but by their reversible closure. If an MP is able to achieve active movement through these closed PD, it is reasonable to think that endogenous proteins might have similar capacities, thus providing a way to achieve interorgan macromolecular communication with organs symplasmically isolated from the phloem. Macromolecular translocation in the phloem is not restricted to proteins and also concerns RNAs. It has been known for a long time that viruses use the phloem as a transport pathway to vehicle their RNA genome, and the spread of systemic silencing, thought to be mediated by small RNA species, has been shown to occur through the phloem pathway (Palauqui et al., 1997). Ruiz-Medrano et al. (1999) reported the long-distance movement of the CmNACP RNA and its delivery into sink tissues, as detected by heterografting studies. The entry of RNA in the translocation pathway and its exit are likely to involve its association with proteins, because passive movement of RNA alone has not been reported so far, even in sink tissues (Oparka et al., 1999). It seems that RNAs have to be associated with MPs or proteins able to gate PD, such as KN1, to move from cell to cell. Indeed a paralog to the movement protein of the RCNMV has been identified in the phloem sap

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of Cucurbita maxima (CmPP16) (Xoconostle-Cazares et al., 1999). Similar to an MP protein, CmPP16 is able to gate PD and mediate its own cellto-cell movement, as well as bind to RNA nonspecifically and induce cell-tocell movement of RNA. The presence of such proteins in the phloem could be the basis of long-distance traYcking of signalling RNA through the phloem. C. MECHANISMS OF MACROMOLECULAR TRAFFICKING

It was initially thought that macromolecules could diVuse across plasmodesmata when their size was smaller than that of the channel, and that certain proteins possessed the property to open this channel to induce communication between cells for specific purposes. Although this vision could probably be accurate for passive transport (e.g., in sink organs), the situation seems to be more complex and regulated for active transrport. New insight into the mechanisms of transport through PD came from the study of Kragler et al. (1998). The authors first demonstrated that the passage of macromolecules such as KN1 microinjected in leaf mesophyll cells involved the unfolding of the protein, because cross-linked KN1 (incapable of unfolding) was unable to move from cell to cell. They further showed that the gating of PD by KN1 was independent of the passage of KN1 across PD. Cross-linked KN1 and KN1 bound to gold particles, both incapable of cellto-cell movement because of their inability to unfold or size, respectively, induced an increase in the SEL of PD. Furthermore, KN1 bound to gold particles inhibited the cell-to-cell movement of free KN1, likely caused by competition for a putative PD-localized receptor. Similar results were also obtained with a viral MP. These results suggest that PD-mediated traYcking of macromolecules involves the interaction with a putative receptor, resulting in physical changes both in the conformation of the protein and in the size of the microchannel. In a later study aimed at characterizing the putative PD receptor the same authors used a modified phage display to identify peptides binding to proteins from a PD-enriched subcellular fraction, and whose binding was competed by the KN1 protein (Kragler et al., 2000). Small peptides showing homology to a motif of KN1 had the capacity to inhibit the KN1-induced SEL increase of PD in microinjection experiments, without aVecting the cell-to-cell movement of KN1. This study provided further evidence of the uncoupling of the PD dilation function and the active macromolecular traYcking. Putative components of the PD-mediated traYcking pathway have been identified by the same group. First, chaperones of the heat shock cognate-70 (hsc70) class identified in the phloem sap were shown to be

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capable of increasing the SEL and inducing their own cell-to-cell movement and that this capacity was dependent on the presence of specific motifs (Aoki et al., 2002). It has been proposed that these chaperones could play a role in the unfolding and/or refolding of non-cell autonomous proteins during and after their passage through PD. It has also been proposed that these proteins could facilitate the movement of RNAs through PD, because other chaperones of this family have been shown to bind RNA. Second, the chaperones have identified a protein called NtCAPP1 by aYnity purification against CmPP16 (Lee et al., 2003). This protein is localized in the endoplasmic reticulum of the cell periphery, and a form of NtCAPP1 deleted in an Nterminal transmembrane domain had the capacity to block the cell-to-cell movement of the non-cell autonomous proteins CmPP16 and the TMV movement protein but not that of KN1 and the CMV movement protein, when the truncated form of NtCAPP1 was coinjected or expressed in transgenic tobacco plants. Furthermore, transgenic tobacco plants expressing this truncated protein or silenced in the Ntcap1 gene resulted in abnormal leaf and floral development. These observations have led Lee and Aoki et al. to hypothesize that NtCAPP1 is part of the pathway involved in the active translocation of macromolecules and in particular, that it could act to shuttle specific noncell autonomous proteins to the PD microchannel. However, most of their evidence comes from the eVects of a truncated form of NtCAPP1, which they have shown to have a diVerent subcellular localization than the wild-type form (the nucleus instead of the endoplasmic reticulum), and little is shown on the eVects of the wild-type form of the protein in translocation. In addition to unfolding of proteins, proteolytic processing has also been shown to be necessary for the cell-to-cell movement of CmPP36, a cytochrome b5 reductase expressed in CC, as well as for its capacity to increase the SEL of PD (Xoconostle-Cazares et al., 2000). Proteolytic processing could also be needed for the translocation of other proteins. In contrast to proteins, RNAs have not been reported so far to be able to gate PD alone, nor to move from cell to cell without being associated to a protein in a ribonucleoprotein complex. Oparka et al. (1999) have shown that although the SEL of PD in sink leaves is particularly high and allows the free passage of GFP fusions up to 50 kDa, viral RNA cannot diVuse from cell to cell without the help of an MP. Like MPs, some endogenous proteins are able to potentiate the movement of their own RNA. However, certain proteins induce the movement of RNA in specific manner, such as KN1, which is able to induce the movement of its sense but not antisense RNA (Lucas et al., 1995), whereas others (e.g., CmPP16) induce the movement of RNA in a non–sequence-specific manner (Xoconostle-Cazares et al.,

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1999). It is likely to assume that the latter proteins allow the entrance of a wide variety of RNA molecules into the translocation stream. D. EVIDENCE FOR THE INFLUENCE OF MACROMOLECULAR TRAFFICKING IN DEVELOPMENTAL PROCESSES

Although the cell-to-cell movement of a good number of endogenous proteins, including transcription factors, have been described, the role of this movement in plant development is not clear for most of them. Recent results obtained by Nakajima et al. (2001) indicate that the movement of a transcription factor could provide positional information in the root apical meristem of Arabidopsis. The SHORT ROOT protein (SHR) is essential for endodermis specification, whereas the shr RNA is only detected in the stele. Nakajima and others first showed that a fusion between SHR and the GFP could move into the endodermis when expressed in the stele under the control of the shr promoter. Furthermore, they drove the expression of shr in the endodermis under the control of the scr promoter. This resulted in the diVerentiation of supernumary endodermis layers by an autocatalytic process, in which the movement of SHR in the adjacent cell layer triggered the diVerentiation of this layer into endodermal cells. These cells will in turn produce the SHR driven by the endodermis-specific scr promoter and the produced SHR protein will traYc to the adjacent cell layer. In this way a self-expanding layer of endodermal cells will be generated. Moreover, the authors also showed that SHR was both cytoplasmic and nuclear in the stele, whereas it was strictly nuclear in the endodermis. In the wild-type situation it is possible that the nuclear localization of SHR in the endodermis prevents it from traYcking into adjacent cell layers once it has entered the endodermis cells because it would be rapidly sequestered in the nucleus after translocation. However, when produced directly in the cytoplasm of endodermal cells (under the control of the SCR promoter), it might traYc to the next cell layer before entering the nucleus of the endodermal cell. It is unclear what determines the diVerential subcellular localization of SHR in the stele and in the endodermis. One explanation could be that the transport of SHR across PD would induce some modifications in the protein, inducing its nuclear localization and preventing it from moving into adjacent cell layers. This study provides a clear example of the significance of protein movement in development, because a protein produced in a cell layer can influence the fate of an adjacent cell layer. The traYcking of RNAs can also play a major role in development. For example, the spreading of systemic post-transcriptional gene silencing (PTGS) is thought to be caused by to cell-to-cell movement of small RNA species (siRNA). These RNA species are thought to move across PD, and

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particularly in the phloem, to be unloaded into sink organs, following the same pathway as assimilates and viruses, as shown by heterografting studies (Palauqui et al., 1997; Voinnet et al., 1998). Interestingly, the silencing signal is excluded from the symplasmically isolated guard cells in mature leaves, consistent with the idea that the spreading of silencing occurs by cell-to-cell movement through PD. The spreading of this silencing signal can induce long-distance developmental modifications when a development gene is aVected. The mechanism of the movement of the silencing signal is still unknown. An intriguing question, however, comes from the observation that the shoot apical meristem is usually not aVected by PTGS, although RNAs have been shown to be unloaded from the phloem into the SAM (Ruiz-Medrano et al., 1999). This suggests the existence of a surveillance system controlling the entry of RNAs from the phloem into the SAM, as evidenced by Foster et al. (2002), who showed that expression of the TGBp1 movement protein from White Clover Mosaic Virus (WCIMV) resulted in the loss of leaf polarity and abolished this surveillance, allowing both viruses and PTGS signals to enter the SAM. In addition to small RNA species involved in PTGS, a new class of regulatory micro-RNAs (miRNA) is now emerging and appears to be conserved between animal groups and to be present in Arabidopsis (Llave et al., 2002). In C. elegans, two of these miRNA act as transcriptional repressors regulating the timing of development (Lau et al., 2001; Lee and Ambros, 2001). The production of these miRNA is thought to involve similar mechanisms than that of siRNA involved in PTGS (amplification by an RNA-dependent RNA polymerase and cleavage of the dsRNA by a DICER-like molecule ). It is plausible to think that these miRNA could be transported across short or long distances through PD and through the phloem in plants via a pathway equivalent to that used for siRNA to act as regulatory molecules. Another evidence of the role of RNA traYcking in development came from the study of a natural tomato-dominant mutation caused by the fusion between a gene of encoding a glycolytic enzyme, the pyrophosphate-dependent phosphofructokinase (PFP), and a KN1-like homeobox gene, LeT6 (Kim et al., 2001). This mutation conferred an abnormality of leaf morphology and was graft transmissible. In situ reverse transcriptase-polymerase chain reaction (RT-PCR) experiments, allowing to follow the fate of the fusion transcript, revealed its translocation from mutant stock to the SAM of wild-type scions. Furthermore, the accumulation pattern of the fusion transcript in the SAM of the grafted wild-type scion was similar to that in mutant plants, which diVers from the pattern of the wild-type LeT6 RNA in wild-type SAMs. This supports that the pattern of accumulation of the mutant RNA in the SAM is dependent on the RNA itself and not on its

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promoter, thus stressing a new paradigm in gene expression: the RNA per se can be more important than the gene to specify the pattern of gene expression. These observations provide support for the hypothesis that RNA can act as a signalling molecule to coordinate the development of distant organs.

V. INTEGRATIVE APPROACH: REGULATION OF SYMPLASMIC DOMAINS IN PLANT DEVELOPMENT After their division, plant cells are generally symplasmically connected to their sister cells by primary plasmodesmata. When they diVerentiate, they can either maintain this symplasmic connection or become symplasmically isolated. In addition, during development these connections can be reestablished or newly formed with clonally unrelated cells by the formation of secondary PD. The establishment of communication between certain cells and their isolation from others creates groups of cells symplasmically connected to each other. These communication units are either called symplasmic domains when their isolation with surrounding tissues is complete, or symplasmic fields when a limited connection with surrounding tissues is retained. For example, the cells of the Arabidopsis embryo are initially symplasmically connected and therefore form a symplasmic domain. However, after germination this symplasm is partitioned into diVerent symplasmic domains. For example, although root apical cells close to the meristem are symplasmically coupled, root epidermal cells rapidly become isolated during their diVerentiation process, followed by other root cell types (Duckett et al., 1994; Oparka et al., 1994), although the root apex remains connected to the phloem. Indeed plants can be seen as dynamic mosaics of symplasmic domains and fields. This supracellular organization might be a critical feature of the plasticity of plant development that is essentially postembryonic, in contrast to animal development. It is thought to allow the integration of developmental and physiological signals, thus coordinating the common development of groups of cells. This view is well exemplified by the functioning of the SAM of angiosperms. A. VEGETATIVE SHOOT APICAL MERISTEM: A MODEL FOR THE INTEGRATION OF DEVELOPMENTAL SIGNALING IN SYMPLASMIC DOMAINS

Initial studies have separated the shoot apical meristem of angiosperms into diVerent layers and zones according to the rate and the direction of cell divisions. The SAM has first been divided generally into three diVerent layers, termed L1 to L3, from the surface of the meristem to its inner tissues (in Arabidopsis). The cells from the L1 and L2 layers divide in a single plane,

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whereas the cells of the L3 layer have diVerent division orientations. L1 and L2 layers were also grouped under the term tunica; L3 is also termed corpus (Fig. 3A). Additional layers can also be present in the tunica in certain plant species. Studies using genetic chimeras revealed that cells within a layer were normally clonally related (Sussex, 1989) and that the diVerent layers give rise to diVerent tissues in mature leaf and stem organs, the most external layers (tunica) giving rise to superficial tissues, whereas the corpus gives rise to deeper tissues. Consequently, the cells within a layer are mainly connected by primary PD appearing during the formation of the cell plate, whereas cells from diVerent layers are connected by secondary PD. The second level of organization used to describe the meristem in addition to the layered

Fig. 3. Organization of the shoot apical meristem (SAM) of angiosperms (A) Organization the SAM in layers (Arabidopsis). The cells in the bilayered tunica divide anticlinally, whereas cells in the corpus divide in both orientations. (B) Organization of the SAM in domains. Within the tunica and the corpus, the diVerent zones correspond to diVerent symplasmic domains. (C) Progression of one cell from one domain (central zone) to the other (peripheral zone) during the enlargement of the meristem. This progression involves closure of certain connections with neighboring cells and establishment of others.

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organization was the zonated organization. The meristem can roughly be divided into two main zones according to the metabolic activity of their cells: the central zone (containing the initial cells) is characterized by a low division rate and a low metabolic activity and the peripheral zone has a much higher division rate and metabolic activity, which gives rise to leaf primordia (Fig. 3B). This organization based on histological studies was correlated with the patterns of expression of homeotic genes involved in meristem activity (Fletcher and Meyerowitz, 2000; Nakajima and Benfey, 2002). The symplasmic connectivity of the SAM was initially explored using microinjection of fluorescent symplasmic tracers (Rinne and van der Schoot, 1998; van der Schoot and Rinne, 1999). They revealed that the SAM can be divided into three distinct symplasmic fields: one composed of the tunica layer in the central zone, another one composed of the corpus layer in the central zone, and a third one composed of the cells in the tunica in the peripheral zone, thus forming a symplasmic ring. Therefore the organization of the meristem previously described on the basis of histological studies turned out to reflect a genuine functional symplasmic compartmentalization of the meristem. Interestingly, a transient connection between the central zone and a minor segment of the peripheral zone field has been observed when the SAM was relatively small (Rinne and van der Schoot, 1998). This transient continuity could serve for the exchange of morphogenetic signal molecules between the two fields, possibly leading to the initiation of a new leaf primordium. These studies were completed by tracer loading experiments in which the fluorescent tracer had been loaded in leaves of Arabidopsis, thereby assessing the connectivity of the SAM to the phloem (Gisel et al., 1999). This revealed that the tunica and corpus were functionally diVerent in the sense that tracers could be loaded from the phloem into the tunica but not in the corpus, which was symplasmically isolated from the phloem, forming a genuine symplasmic domain. However, tracer unloading into the tunica was subject to developmental changes because the number of apices reached by the tracer was dependent on the age of the plant and larger tracers such as 3 kDa fluorescein isothiocyanate (FITC)-dextrans do not move into the SAM. Hence this connectivity seems to be limited in contrast to most other sink organs (e.g., root apices and sink leaves). Van der Schoot and Rinne (1999) proposed an interesting model to explain the establishment and the functional significance of the zonated connectivity of the SAM. They postulate that the traYcking between clonally related cells and cells from a diVerent clonal origin diVers; the first one involves primary PD, whereas the second one involves secondary PD, which are thought to have diVerent traYcking capacities. Consequently, traYcking between the tunica and the corpus would diVer from traYcking inside of the tunica because the former

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relies exclusively on secondary PD. Similarly, as the meristem of angiosperms usually contains several initial cells in each layer, each layer can be divided into several clonally related groups of cells interconnected by primary PD. Secondary PD connects each of these groups of cells to the other clonally related subdomains. This model points to the regulation of meristem function when one assumes the existence of two types of mobile developmental factors: those traYcking only across primary PD and those also traYcking across secondary PD. The transcription factor KN1, for example, would belong to the second category because it is able to move from the corpus to the tunica (Jackson et al., 1994). Thus the cells within one subfield of the meristem would have a coordinated fate as a result of the diVusion of factors of the first type moving across primary PD, whereas the movement of factors of the second type would act to coordinate development of the diVerent subfields. The restricted diVusion of factors of the first type between clonally related cells of a single layer could act to regulate phyllotactic patterning and delimit organ boundaries. This model alone does not fully explain why the central zone and the peripheral zone of the tunica are symplasmically isolated one from another, because they are clonally related. The dynamic delimitation of these symplasmic fields must involve the successive position-dependent closure and reopening of PD because cells derived from the central zone progress toward the peripheral zone and become part of another symplasmic unit (Fig. 3C). The regulation of cellcycling times within a field (slow in the central zones and fast in the peripheral zones) and metabolic activity could result from the diVusion of cytoplasmic factors acting as regulators of cyclins, as well as the sharing of metabolites. The non-cell autonomy of factors such as KN1, whose domain where the protein is detected extends further than its RNA expression domain, allows the action of the transcription factor in cells in which it is not expressed. However, a question that has been subject to debate over the last few years concerns the interest for a plant to express non-cell autonomous transcription factors in all cells of a domain in which it can move. This seems to be the case, for instance, for LFY and DEF, which have the capacity to move from cell to cell but for which no diVerences in the patterns of RNA and protein localization have been detected. In addition to their expression pattern in the whole domain where the protein has to act, the cellto-cell traYcking capacity of such proteins could represent a redundant ‘‘coordination insurance,’’ ensuring that all cells in a specific domain coordinately adopt the same fate. In addition, it could also homogenize the concentration of the transcription factor between all the cells of the domain, thus correcting potential cell-to-cell variations in the expression level. For example, if a cell within a domain is mutated in one of these mobile

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transcription factors, it is likely not to aVect the developmental program because this cell will still receive the transcription factor from surrounding cells of the domain. The non-cell autonomy of such transcription factors might be an adaptation to the high mutation frequency of meristematic cells because of their high division rate. B. SYMPLASMIC DOMAINS AND ORGANOGENESIS

When cells enter their diVerentiation program, the loss of connectivity with surrounding tissues is common and has, for example, been described in root epidermis cells (Duckett et al., 1994), SE-CC complexes of the transport phloem cells (Van Bel and Van Rijen, 1994). An extreme example of symplasmic isolation during development is the case of stomatal guard cells, in which PD become truncated and nonfunctional, eventually leading to a complete isolation from any symplasmic transport, including the spreading of viruses and PTGS (Oparka and Roberts, 2001). In contrast, some cells regain their connectivity with surrounding tissues during developmental processes. During plant development, new symplasmic fields and domains are created while others disappear, but a relevant issue is whether these symplasmic domains are connected to the phloem. True symplasmic domains completely isolated from the phloem could be characterized by an autonomous developmental program, whereas symplasmic fields able to communicate with the phloem have access to long-distance signalling molecules, allowing the coordination of their development with the physiology of the whole plant. Moreover, developing organs generally have a high demand in carbohydrates and other metabolites. Thus tissues symplasmically connected to the phloem can have access to symplasmic unloading, whereas symplasmically isolated tissues are provisioned by apoplasmic unloading, which involves the expression of sugar transporter genes. If one postulates that apoplasmic unloading might be less eYcient than symplasmic unloading, the symplasmic isolation of certain developing organs has to be explained by issues concerning developmental signaling; for example, the necessity to isolate the organ from certain signal molecules. The formation of many new organs involves the establishment of continuity between the phloem and the cells involved in the organogenesis. The root apical meristem and its recently produced cell tissues have been shown to be in symplasmic continuity with the phloem, in contrast to the root cap, which constitutes another symplasmic domain (Oparka et al., 1994; Zhu et al., 1998). However, as root cells diVerentiate, this symplasmic continuity is broken and PD numbers decrease in the mature zone of the root (Zhu et al., 1998). Lateral roots take their origin in the root pericycle. When the

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pericycle of Arabidopsis roots starts to divide, it remains initially symplasmically isolated from the phloem of the primary root, behaving as an isolated symplasmic domain. However, symplasmic continuity is rapidly established concomitantly with the diVerentiation of the phloem connector elements. In contrast, Rhizobium-induced nodule primordia originate in cortex cells of the root hair extension zone of legume roots. This organogenesis involves the de-diVerentiation of three diVerent cell types: the pericycle, the endodermis, and the cortex (Complainville et al., 2003; Timmers et al., 1999). Before nodule initiation, these cells are symplasmically isolated from the phloem. Symplasmic continuity with the phloem is established as soon as these cells start to divide, and the extension of the created symplasmic field precedes the division of adjacent cells. Because nodule initiation involves the de-diVerentiation of cells from several layers, it involves the formation of both primary (on the new cell walls) and secondary PD (on preexisting cell walls), as shown by Complainville et al. (2003). The creation of a symplasmic continuity with the phloem has also been described in the organogenesis of potato tubers (Viola et al., 2001) and Agrobacterium-induced tumors (Pradel et al., 1999). In such cases the connectivity with the phloem could play a role in the unloading of metabolites necessary for the construction of the new organ as well as the unloading of signalling molecules such as proteins (in particular, transcription factors), regulatory RNAs, or phytohormones. In addition, unloaded sugars do not necessarily have a metabolic role but can also act as signalling molecules, because sugars can have a regulatory role in a variety of developmental processes such as cell division (Rolland et al., 2002). In contrast, other developmental programs involve the permanent or temporary isolation of cells involved in the organogenesis. When the SAM of Arabidopsis turns into an inflorescence meristem, its connectivity with the phloem is transiently shut down before being reestablished at later floral development stages (Gisel et al., 1999, 2002). An expansion of the central symplasmic field of the tunica layer has also been observed in the SAM of Sinapis alba, as well as a reduction in the frequency of PD during its floral transition (Ormenese et al., 2000, 2002). The temporary isolation of the SAM during floral transition can seem surprising at first if one assumes that a florigenic inductive signal has to be transmitted from the leaf to the shoot apex to induce the floral transition. However, this signal could have reached the SAM just before its isolation, and the temporary restriction of communication with the phloem might reinforce its inductive action because it would be sequestered in the SAM. Alternatively, the temporary isolation of the SAM could reflect a restriction of the movement of a floral repressor into the SAM.

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Ruan et al. (2001) presented an elegant explanation of the role of the temporary loss of symplasmic continuity during the elongation of cotton fiber. They monitored parallel symplasmic continuity with surrounding tissues revealed by the import capacity of carboxyfluorescein, the expression of sucrose and Kþ transporters, and the expression of expansin, a cell wall loosening protein. They were able to identify three phases in the elongation of the cotton fibre. Until 9 days after anthesis (DAA), the PD were open to carboxyfluorescein transport, expansin expression was high, and sucrose and Kþ transporters expression was low. Consequently, during this phase the turgor pressure was the same as that of surrounding tissues as a result of the equilibration of solute concentrations through PD, and cell elongation was moderate. Between 10 and 15 DAA, PD closed; this closure was associated with a switch from simple to branched PD, as seen in tobacco leaves undergoing the sink-source transition (Oparka et al., 1999). Expansin expression decreased and the expression of sucrose and Kþ transporters was maximal, contributing to the build up of the turgor pressure and leading to a high rate of elongation. Eventually, after 16 DAA, PD reopened and the expression of sucrose and Kþ transporters decreased, whereas expansin expression was minimized. During this last stage the fibre stopped elongating, presumably because of the high rigidity of the cell wall and the low turgor pressure. This model provides an example that stresses the role of symplasmic isolation on development is not necessarily related to the regulation of the traYcking of signal molecules but can also be explained by biophysical forces required during development. The various roles of symplasmic isolation in plant development still remain to be explored and it is likely that such processes can be related to many diVerent functions ranging from isolation from nonautonomous signal-transduction pathways to physiological constraints. Similar for gap junctions in animals, symplasmic isolation might also be related to the regulation of the transmission of electrical signal. Although the regulation of symplasmic continuity and isolation during development is often seen as a switch between two discrete states (communicating and noncommunicating) because of the methods of study, the situation is not always so Manichean, and it is likely that simple reductions in cytoplasmic continuity without a complete isolation will be described in future studies. One such example has been provided by the study of Arabidopsis embryogenesis (Kim et al., 2002). The authors monitored the uptake of symplasmic tracers of diVerent sizes by developing embryos. They identified a transition occurring at the ‘‘torpedo’’ stage of embryo development corresponding to a reduction in the SEL of PD, because they stopped to allow the transport of 10 kDa tracers, whereas they still allowed the import of small tracers (500 Da). The authors further used this essay as a screen to

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identify embryo-defective mutants maintaining dilated PD at this stage of their development. Two mutants called ise1 and ise2 (increased size exclusion limit of PD) were thus identified. The mutant embryos displayed a normal but retarded embryonic morphogenesis at the mid-torpedo stage. Mutant seeds can germinate to give rise to very small and stunted plants showing defects in root hair patterning, presumably caused by the lack of symplasmic isolation of epidermal root cells (Kim et al., 2002).

VI. CONCLUSION AND FUTURE PROSPECTS Animal cells can be in symplasmic continuity with each other through gap junctions. PD have often been compared with gap junctions, and some epitopes have even be found in common (Yahalom et al., 1991). The permeability of gap junctions is also regulated; however, the maximum SEL of gap junctions usually only allows the passage of small molecules up to 1.5 kDa. Therefore they seem to have a role in electric coupling, for example in the heart muscle, or in the diVusion of small metabolites or small signalling molecules such as second messengers. Cell-to-cell communication during animal development is usually achieved by cell-to-cell contact. However, there are some examples of the formation of a syncitium like in the Drosophila embryo. The existence of this unique cytoplasm in which the nuclei divide allows the creation of gradients of transcription factors and RNAs involved in embryo patterning. Unlike plant cells, animal cells rapidly acquire their identity and become independent from interaction with surrounding cells to achieve their developmental program. For example, the E cell (the gut precursor cell) of the 8-cell C. elegans embryo can be isolated and still develop into a gut (Leung et al., 1999). In contrast, plant cells constantly depend on cell-to-cell communication throughout development, because diVerentiated cells can reenter the division cycle and acquire a new identity in response to various signals. The cell wall represents a barrier to the cellto-cell diVusion of signal molecules between plant cells; hence PD represent their major communication route between neighboring cells because they can allow the traYcking of a variety of macromolecules and be ‘‘gated’’ by some of them for specific purposes. The dynamic grouping of plant cells destined to a common fate during development into symplasmic fields and domains facilitates the coordination of developmental programs because a number of regulatory molecules can diVuse between those cells. Moreover, the diVusion of such factors likely creates gradients of morphogens playing a major role in positional signalling. These regulatory molecules include phytohormones, metabolites playing both physiological and signalling roles,

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second messengers, and eventually macromolecules, including proteins and RNAs. Similarly to gap junctions, electric signals could also be transmitted through PD. A number of new regulatory molecules are currently being discovered in plants. For example, small RNA species such as siRNA and miRNA have recently emerged in plants as specific regulators of RNA translation or stability. Although little is known so far about the mechanisms of the transport of such RNA molecules, it is reasonable to think that they will turn out to be subject to a regulated or a non-regulated transport through PD. The diVusion of signal molecules within the cells of a symplasmic field allows a coordination of development, even if this is redundant for transcription factors that are expressed in the entire zone in which they can traYc. In those cases macromolecular traYcking capacity is likely to reflect a mechanism to ensure that the concentration of this factor will be homogenous in the whole field. Moreover, traYcking via PD also allows long-distance communication between remote organs via the phloem. This communication could be simply physiological in nature by regulating the source-sink interactions, for example, or could involve the long-distance traYcking of information molecules between distant organs. Despite the recent progress in the identification of PD-regulating molecules and the description of PD regulation during development, this field is still wide open and a lot of questions remain unanswered. For example, the structure of PD is still elusive because the actual structural components of PD have not been identified precisely yet because of the poor accessibility of these structures to biochemical approaches. Moreover, the mechanisms regulating ‘‘active’’ PD opening by macromolecules are still poorly understood and those regulating ‘‘passive’’ regulation of their SEL, for example in sink organs, are even less documented. This passive regulation is likely to involve a combination of individual PD SEL modulation and a turnover of PD, resulting in the disappearance of old ones and the appearance of new ones. For example, it is still not known whether the switch from simple to branched PD in the sink-source transition of tobacco leaves is caused by the coalescence of simple PD to form branched ones or the disappearance of the former, followed by the appearance of the latter. Little is known about PD turnover during development because PD have been mainly observed by electron microscopy on fixed tissues in which all dynamic aspects are lost. So far, most markers of PD allowing live imaging are not fully specific or allow only the visualization of a certain type of PD (movement proteins only label branched PD). The discovery of better PD-localized markers could facilitate the study of PD dynamics. So far, research in the PD field has been limited by the lack of genetic information about PD structure and regulation. It is likely that mutants

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aVected in PD permeability have not been identified so far because these mutants would turn out to be lethal if PD permeability is drastically aVected. However, artificial alteration of PD permeability by expression of PD regulatory proteins does not seem to give severe phenotypes and the low number of mutants aVected in PD function could simply be caused by the fact that the determination of this kind of phenotype has been diYcult until recently. Moreover, these phenotypes might also be masked by other phenotypes. To overcome the limitation of the potential lethality of such mutations, Gisel et al. (2002) identified PD SEL mutants out of embryo-defective mutants. Attempts are also being made to identify mutants aVected in macromolecular phloem unloading; for example, looking for mutants on an AtSUC2-GFP background that do not unload GFP into sink tissues. Such mutants have been found and are being characterized (Michelle Cillia, Kristen Aliano, and David Jackson, personal communication). However, it is likely that such mutants might also be aVected in a number of other processes that the PD SEL eVect would be the consequence of, making it diYcult to separate the cause and eVect. In the future, reverse genetics approaches could also be envisioned. For this to occur, it would be of great interest to identify more proteins associated with PD. Cutler et al. (2000) fused a cDNA bank of Arabidopsis to GFP to analyze systematically the localization of proteins. More recently, Medina Escobar et al. (2003) used a TMV-based expression system to perform high-throughput screening of subcellular localizations of such random cDNA-GFP fusion. Although no PD-targeting motifs have been clearly characterized, this approach allowed the identification of 12 PD-associated proteins. Eventually the modification of the expression of the corresponding genes could bring new information on the regulation of PD function. The alternative to genetic approaches could also come from biochemical studies aiming at identifying the targets of molecules known to regulate PD function. The use of yeast two-hybrid screening of protein partners of PD-regulatory proteins and the identification of peptide ligands might lead to significant progress in this field. It is likely that most answers will come from a combination of these approaches.

ACKNOWLEDGMENTS We thank Adam Kondorosi for careful reading of the manuscript and Karl Oparka and Shmuel Wolf for helpful discussions. AC was supported by the Ministe`re de l’Education Nationale, de la Recherche et de la Technologie (MENRT).

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AUTHOR INDEX

Numbers in bold refer to pages on which full references are listed.

A Aaziz, R., 198, 235 Abbott, J. C., 90, 91, 94, 98 Abe, Y., 146, 147, 159 Abola, P., 24, 48 Abul-Hajj, Y. J., 34, 51 Adamek, C., 35, 43 Aguilar, J. M. M., 16, 42 Ahlborn, B., 16, 18, 56 Ahrer, W., 13, 51 Aiga, I., 110, 113, 145, 151, 162 Aikman, D. A., 109, 127, 128, 129, 135, 138, 140, 141, 143, 144, 160 Aizawa, S., 24, 53 Akahane, Y., 192 Akashi, T., 7, 19, 42 Akin, D. E., 92, 96 Alkhimova, A. G., 184, 192 Allan, A. C., 201, 237 Almon, E., 215, 235, 239 Ambros, V., 224, 238 Ames, P., 16, 42, 50 Ampe, F., 24, 48 Amrhein, N., 9, 42, 62 Amyot, L., 214, 241 Andersen, J., 136, 162 Andersson, A., 77, 98 Andrews, P. K., 143, 155 Andrews, W. H., 191 Angell, S., 224, 242 Antanassova, R., 85, 86, 95 Anterola, A. M., 68, 69, 77, 78, 80, 83, 84, 94, 95, 96 Anxolabe´he`re, D., 186, 188 Aoki, K., 218, 222, 235 Aoki, T., 7, 19, 42 Appelbaum, E. R., 17, 18, 20, 35, 38, 52 Arata, Y., 155, 159 Archer, N. A. L., 113, 114, 115, 127, 129, 132, 143, 159, 160 Argillier, O., 85, 96 Arguello-Astorga, G. R., 220, 224, 241 Armitage, J. P., 16, 43 Arunachalam, M., 34, 43 Arunakumari, A., 35, 39, 55 Asamizu, E., 23, 51 Ashby, A. M., 16, 42

Ashford, D. A., 73, 101 Ashrar, G., 134, 158 Ashworth, E. N., 154, 163 Askari, B. M., 90, 98 Aspeborg, H., 77, 98, 99 Aston, A. R., 127, 136, 155 Atabekov, J. G., 201, 236 Atanassova, R., 90, 92, 95 Atkinson, E. M., 5, 28, 47 Aufsatz, W., 184, 187, 191 Aurelle, H., 26, 46 Auriac, M. C., 5, 60, 230, 242 Avramova, Z., 167, 192 Awano, T., 71, 102 Axtell, J. D., 92, 96 Ayabe, S., 7, 19, 42 Ayre, B. G., 207, 235 B Baba, T., 167, 168, 178, 192 Badelt, K., 198, 242 Badgett, A., 29, 30, 57 Baggett, B. R., 13, 43 Bahrami, A. R., 152, 158 Bailey, P., 76, 99 Bairoch, A., 23, 30, 48 Baker, L., 217, 218, 222, 239 Bakhuizen, R., 5, 60 Balachandran, S., 215, 218, 219, 235, 239 Balatti, P. A., 22, 32, 43, 55 Baldauf, S., 73, 101 Ball, M. C., 154, 156 Baltimore, D., 168, 188 Baluska, F., 198, 201, 209, 235, 240, 241 Banfalvi, Z., 17, 43 Bani, D., 32, 49 Bao, Y., 212, 238 Barakat, A., 167, 188 Barakate, A., 66, 87, 90, 91, 94, 96, 98 Barbour, W. M., 16, 17, 20, 31, 32, 43, 59 Barella, S., 200, 227, 230, 234, 237 Barkei, J., 17, 20, 52 Barlow, P. W., 201, 235 Barloy-Hubler, F., 24, 48 Barnett, M. J., 24, 48 Barriere, Y., 85, 87, 92, 95, 96, 97, 98

246

AUTHOR INDEX

Barss, H. D., 149, 162 Bartel, D. P., 224, 238 Bartholic, J. F., 110, 156 Bartsev, A. V., 25, 43 Barz, W., 35, 43 Bassam, B. J., 9, 18, 58 Bate, N. J., 82, 95, 99, 101 Batley, M., 9, 17, 18, 20, 46, 57, 58 Batut, J., 24, 48 Baucher, M., 68, 69, 70, 80, 85, 86, 87, 88, 89, 90, 92, 93, 95, 104, 105 Baucom, C., 80, 100 Bauer, D. W., 23, 44 Bauer, W. D., 6, 16, 44, 46, 54, 214, 235 Baulcombe, D. C., 224, 242 Bauman, L. F., 89, 96 Baumberger, I. C., 30, 32, 43 Bazin, C., 178, 182, 187, 188, 190 Beachy, R. N., 172, 173, 189, 197, 200, 216, 236, 243 Bechtold, N., 85, 98 Beck, C., 32, 49 Becker, A., 6, 24, 43, 48 Becker, F., 117, 161 Becquart de Kozak, I., 29, 43 Bedgar, D. L., 70, 97 Bedmar, E. J., 39, 54 Beebe, D. U., 205, 235 Beer, S. V., 23, 44 Beger, R. D., 35, 51 Begum, A. A., 18, 43 Beiles, A., 147, 161 Bejarano, E. R., 184, 188 Bell, J. N., 11, 58 Bellato, C. M., 32, 43 Bell-Lelong, D. A., 76, 87, 95, 102 Ben Amor, B., 6, 43 Bender, G. L., 19, 53 Bendich, A. J., 146, 162 Benfey, P. N., 218, 223, 227, 239 Be´ nit, L., 175, 188 Bennetzen, J. L., 167, 174, 186, 188, 190, 192 Bergman, K., 16, 42, 50 Berlin, J., 35, 43 Bermadinger-Stabentheiner, E., 146, 147, 156 Bernardi, G., 167, 188 Bernard-Vailhe, M. A., 88, 89, 90, 92, 95 Bernier, G., 230, 240 Besl, L., 17, 43 Besle, J. M., 88, 89, 90, 92, 95 Bestor, T. H., 187, 188 Bevan, M. W., 65, 76, 96, 98, 103, 104 Bhalerao, R., 77, 98 Bhat, T. K., 34, 35, 39, 43 Bhattacharya, I., 7, 44 Bidne, K. G., 148, 160 Biggs, D. R., 16, 55

Binder, A., 88, 103 Binns, A. N., 66, 95, 105 Birringer, M., 35, 59 Bisseling, T., 5, 6, 26, 28, 49, 53, 59, 61 Bjourson, A. J., 5, 28, 45, 56 Blackburn, E. H., 169, 188 Blackman, L. M., 197, 198, 199, 235, 240 Blad, B. L., 110, 128, 133, 137, 157, 158 Bladergroen, M. R., 27, 44 Blanco, L., 115, 146, 155, 160 Blaut, M., 35, 59 Blaylock, A. J., 146, 162 Blomqvist, K., 77, 98 Blount, J. W., 76, 78, 79, 82, 84, 85, 86, 90, 91, 95, 96, 98, 100, 103, 104 Boboye, B. E. A., 30, 50 Boccara, A. C., 149, 152, 156 Boccara, M., 149, 152, 156 Boeke, J. D., 188 Boerjan, W., 68, 69, 70, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 93, 95, 98, 100, 102, 103, 104, 105 Boerjan, W. A., 76, 96 Boevink, P., 199, 200, 208, 210, 212, 216, 220, 222, 231, 234, 239, 240 Bogdanove, A. J., 23, 44 Bohlool, B. B., 7, 44 Boissard, P., 137, 156 Boistard, P., 24, 48 Bokkenheuser, V. D., 35, 61 Bolanos-Vasquez, M. C., 9, 12, 17, 18, 21, 44 Bolwell, G. P., 71, 72, 78, 96, 105 Bonn, G. K., 13, 51 Bonnet, D., 5, 45 Bono, J.-J., 6, 45, 49 Booker, F. L., 88, 101 Bookland, R., 17, 20, 52 Boon, J. J., 88, 89, 90, 98, 103 Boot, K. J. M., 5, 44, 60 Borevitz, J. O., 76, 96 Boss, W. F., 201, 242 Bostwick, D. E., 219, 235, 236 Botha, C. E. J., 199, 236 Bothe, G., 24, 48 Botterman, J., 88, 89, 92, 95 Bouche-Pillon, S., 217, 218, 222, 239 Boudet, A. M., 66, 71, 76, 80, 87, 88, 89, 90, 91, 93, 96, 98, 100, 102, 103, 104, 105, 106 Boue, C., 149, 152, 156 Boukli, N. M., 5, 25, 28, 43, 49 Boundy-Mills, K. L., 22, 44 Bout, S., 85, 96 Boutry, M., 24, 48 Bowen, B. A., 18, 35, 38, 52 Bowles, D. J., 73, 101 Bowman, J. L., 65, 101, 224, 237 Bowra, B. J., 16, 44

AUTHOR INDEX Bowser, L., 24, 48 Boyd, D. R., 10, 35, 36, 39, 57 Brakke, T. W., 116, 161 Brandes, A., 167, 188 Brandner, J., 205, 241 Brandt, S., 17, 18, 20, 38, 52 Braune, A., 35, 59 Breda, C., 29, 43 Breidenbach, R. W., 147, 156 Brennan, R., 154, 156 Brenner, A. J., 128, 156 Brenner, S., 28, 56 Bret-Harte, M. S., 209, 235 Brewin, N. J., 6, 30, 52, 55, 60 Briggs, S. P., 204, 241 Brocard, L., 208, 210, 211, 215, 220, 230, 236 Brough, D. W., 128, 156 Broughton, W. J., 2, 3, 5, 10, 11, 23, 24, 25, 28, 30, 31, 33, 41, 43, 44, 47, 48, 49, 52, 53, 54, 56, 57, 59, 61 Brown, H. T., 108, 156 Brown, I. R., 24, 44 Brown, J. R., 169, 188 Brown, S., 11, 58 Brunet, F., 178, 190 Bryant, M. P., 35, 53 Bryant, R. B., 137, 156 Buchberger, W., 13, 51 Buchen-Osmond, C., 169, 188 Budiman, M. A., 176, 188, 191 Buffard, D., 29, 43 Bugbee, B., 155, 156 Bugos, R. C., 85, 86, 97 Buhrmester, J., 24, 48 Burggraeve, B., 76, 78, 79, 84, 85, 96, 102 Bu¨ rkle, L., 205, 235 Burlat, V., 70, 87, 92, 97 Burn, J. E., 33, 50 Bu¨ schges, R., 191 Buschmann, C., 150, 156 Busson, R., 78, 79, 84, 85, 102 Buttner, D., 84, 97 C Cadeddu, M., 116, 161 Cadieu, D., 24, 48 Caetano-Anolles, G., 6, 16, 44, 54, 214, 235 Cairns, J. E., 153, 162 Campalans, A., 72, 104 Campbell, G. S., 112, 114, 116, 124, 126, 129, 156 Campbell, M. M., 65, 66, 76, 88, 89, 90, 98, 101, 102, 103 Campbell, W. H., 85, 86, 97 Canio, W., 224, 238 Canny, M., 154, 156 Cano-Delgado, A. I., 65, 96

247

Canter-Cremers, H., 17, 51 Capela, D., 24, 48 Capellades, M., 85, 92, 105 Capy, P., 176, 182, 186, 187, 188, 190 Carcamo, R., 172, 173, 189 Ca´ rdenas, L., 5, 44 Carels, N., 167, 188 Carlile, M. J., 16, 48 Carlson, J. E., 73, 97 Carlson, R. W., 6, 17, 20, 29, 30, 31, 32, 44, 57, 58, 59 Carper, J., 13, 43 Carraway, D. T., 68, 78, 102 Carrington, J. C., 224, 239 Cartens-Berhens, U., 220, 241 Carter, J., 154, 156 Carter, S. A., 213, 236 Casa, R., 112, 113, 121, 156, 158, 159 Casacuberta, J. M., 186, 191 Casella, J.-F., 175, 188 Cash-Clark, C. E., 209, 237 Caspar, R., 177, 192 Casper-Lindley, C., 24, 44 Castrillon, D., 188 Catley, M., 65, 96 Catoira, R., 5, 61 Cech, T. R., 169, 191 Ceddardi, T. L., 153, 157 Cedergren, R. A., 30, 44 Cerff, R., 174, 188 Chabannes, M., 66, 87, 91, 96, 103 Chabbert, B., 85, 86, 87, 88, 89, 90, 92, 93, 95, 96, 98, 99, 105 Chaerle, L., 123, 147, 148, 150, 153, 157, 163 Chain, P., 24, 48 Chalmers, D. J., 143, 155 Chandler, M., 182, 189 Chang, C. F., 66, 105 Chang, H. S., 76, 98 Chang, W. Z., 34, 50 Chapman, K. B., 191 Chapman, S., 234, 239 Chapple, C., 66, 68, 69, 76, 80, 83, 84, 86, 87, 89, 92, 93, 95, 97, 99, 100, 101, 103, 104 Chapple, C. C. S., 87, 96, 102 Charon, C., 11, 54 Chavanne, F., 172, 188 Chaves, M. M., 116, 121, 123, 136, 137, 138, 139, 140, 141, 143, 159 Chellapan, P., 34, 43 Chen, C. Y., 76, 78, 79, 84, 85, 96, 102 Chen, F., 68, 69, 71, 72, 79, 82, 83, 84, 85, 86, 90, 91, 92, 96, 97, 98, 100, 101, 103 Chen, H. C., 29, 45 Chen, M. H., 201, 235 Chen, R. H., 66, 95

248

AUTHOR INDEX

Chen, X. C., 34, 47 Chen, Y. C., 17, 53 Chen, Y. P., 39, 45 Cheng, N. H., 213, 236 Cheng, X. F., 69, 73, 83, 87, 89, 91, 101 Cheng, Z. K., 167, 168, 178, 192 Cherney, D. J. R., 92, 96 Cherney, J. H., 92, 96 Chiang, V. L., 68, 69, 73, 78, 83, 85, 86, 87, 89, 91, 97, 99, 100, 101, 102, 105 Chino, M., 218, 219, 238 Chiu, J.-H., 175, 193 Cho, M. J., 9, 45 Chognot, E., 85, 86, 105 Choi, Y. J., 9, 53 Chojecki, J., 88, 89, 92, 98 Chovnick, A., 186, 189 Christensen, J. H., 76, 80, 81, 82, 84, 103 Chudek, J. A., 92, 99 Citovsky, V., 201, 235 Clark, A. M., 219, 236 Clark, E. T., 24, 44 Clark, J. A., 110, 144, 157 Clark, S. E., 196, 236 Clarke, T. R., 135, 136, 149, 151, 160 Clawson, K. L., 133, 136, 157 Cleland, R. E., 201, 236 Clergeot, P. H., 18, 62 Clouse, S. D., 11, 58 Cochrane, F. C., 80, 96 Colenbrander, V. L., 89, 96 Collins, R. E., 80, 96 Collmer, A., 24, 48 Colot, V., 187, 191 Cominelli, E., 76, 99 Complainville, A., 208, 210, 211, 216, 220, 230, 236 Connelly, P. S., 199, 241 Cook, D., 5, 6, 43, 45, 61 Cook, J., 175, 190 Cook, M. E., 199, 236 Cooke, T. J., 199, 241 Cooper, J. D., 13, 43 Cooper, J. E., 5, 10, 12, 14, 15, 19, 28, 35, 36, 37, 38, 39, 40, 45, 56, 57, 60 Cornelis, G. R., 19, 45 Cornu, A., 90, 92, 95 Cornu, D., 85, 86, 88, 90, 93, 95, 103, 105 Coronado, C., 10, 45 Costa, A. D. T., 146, 159 Costa, M. A., 70, 80, 96, 97 Cote, F., 29, 58 Couderc, F., 6, 29, 30, 48 Courtenay, A., 76, 103 Coutos-Thevenot, P., 76, 100 Covey, S. N., 170, 175, 182, 189, 190 Cowie, A., 24, 48 Cramer, C. L., 78, 96 Crawford, K. M., 200, 215, 216, 236, 243

Cregan, P. B., 33, 58 Crespi, M., 11, 54, 208, 210, 211, 216, 220, 230, 236 Criddle, R. S., 147, 156 Crist-Estes, D. K., 16, 44 Crockard, M. A., 5, 45 Crowell, A. L., 70, 97 Cruz, S. S., 200, 208, 213, 240 Cubo, M. T., 31, 45 Culianez-Macia, F. A., 66, 76, 104, 105 Cullimore, J. V., 6, 45 Cunningham, S., 18, 33, 45 Currier, W. W., 16, 45 Cusumano, J. C., 69, 76, 83, 84, 86, 87, 95, 97, 102, 104 Cutler, S. R., 234, 236 Cvrckova, F., 201, 209, 235 Czichi, U., 72, 97 D Dahal, G., 175, 177, 186, 189, 191 Dahlbeck, D., 24, 44 Dai, L., 167, 193 Dakora, F. D., 9, 10, 17, 18, 21, 45, 53, 56 Daniels, S. B., 186, 189 Dannenhoffer, J. M., 219, 235, 236 Danoun, S., 66, 87, 91, 96 D’Arcy-Lameta, A., 17, 46 Das, H. R., 7, 44 Datla, R. S. S., 85, 86, 97 Dauwe, S., 128, 145, 160 Davies, A. E., 26, 28, 47 Davila, G., 29, 49 Davin, L. B., 70, 77, 78, 80, 95, 96, 97 Davis, R. W., 24, 48 Dax, E., 208, 210, 211, 216, 220, 230, 236 Day, R. B., 7, 47 Dazzo, F. B., 5, 7, 9, 18, 45, 46, 58 Deakin, W. J., 23, 24, 25, 43, 54 Dean, J. F. D., 70, 97 Debelle, F., 3, 26, 46 de Billy, F., 5, 60 De Boer, M., 22, 23, 24, 25, 52 De Boever, F., 149, 157 De Bruyn, A., 78, 79, 84, 85, 102 De Cooman, L., 10, 40, 57 Dejean, A., 175, 192 de Kochko, A., 172, 173, 189 de La Rue, M., 38, 49 Del Greco, A., 23, 24, 61 de Lorenzi, F., 134, 145, 157 Deltour, R., 230, 240 de Maagd, R. A., 25, 46 Demont, N., 26, 46 de Nadai, V., 85, 86, 89, 90, 93, 99, 100

AUTHOR INDEX Denarie, J., 3, 5, 6, 26, 43, 46, 53, 61 Denault, J. W., 66, 69, 83, 84, 92, 97 Deng, Y. J., 167, 193 Deom, C., 199, 216, 236, 243 De Paepe, R., 149, 152, 156 de Rudder, K. E. E., 30, 46 De Rycke, R. M., 76, 96 Desai, A., 29, 55 de Sousa, C., 116, 121, 122, 134, 137, 138, 139, 140, 141, 143, 159 De Vleesschauwer, V., 76, 96 Devreese, B., 78, 79, 84, 85, 102 D’ Haeze, W., 31, 46 Dharmatilake, A. J., 16, 46 Dharmawardhana, D. P., 73, 74, 97, 104 Diaz-Espejo, A., 131, 157 Dietrich, A., 76, 101 Dietzgen, R. G., 175, 189 Dilworth, M. J., 16, 39, 44, 45, 62 Dinant, S., 197, 235 Ding, B., 197, 201, 207, 212, 217, 218, 220, 222, 236, 238, 239 Dinkova-Kostova, A. T., 70, 97 Ditta, G. S., 65, 101 Dixon, R. A., 7, 11, 46, 55, 69, 71, 72, 76, 78, 79, 82, 83, 84, 85, 86, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104 Djordjevic, M. A., 5, 9, 10, 11, 17, 18, 19, 20, 28, 29, 45, 46, 50, 51, 53, 54, 57, 58 Doerfel, A., 23, 52 Doerner, P. W., 82, 95 Dolan, L., 225, 229, 236 Dombrecht, B., 3, 55 Dominguez, J., 5, 44 Dong, A. M., 146, 162 Dong, G., 137, 157 Dore, J., 35, 51 Dorokhov, Y. L., 201, 236 Doudrick, R. L., 167, 188 Douglas, C. J., 72, 73, 74, 76, 83, 84, 97, 98, 100, 104 Dow, J. M., 66, 105 Dowell, V. R., 35, 61 Dowling, D. N., 3, 53 Downie, J. A., 5, 6, 17, 25, 26, 28, 30, 31, 33, 45, 46, 47, 50, 52, 58, 59, 61 Dreano, S., 24, 48 Dreyer, D., 5, 45 Drobak, B. K., 201, 237 Droter, M., 155, 156 Druka, A., 184, 192 Duckett, C. M., 200, 208, 210, 225, 229, 236, 239 Ducrey, B., 12, 46 Duda, E., 33, 34, 52 Dudits, D., 11, 58

249

Dudley, M. W., 66, 105 Duelli, D. M., 6, 30, 46, 55 Duncan, G. H., 208, 210, 237 Dunn, M. F., 29, 47 Duran, A. L., 79, 96 Durst, F., 72, 97 Duszek, L. J., 148, 157 Dwivedi, U. N., 85, 86, 97 E Eberhard, A., 6, 54 Ebinuma, H., 76, 100 Economou, A., 25, 26, 28, 30, 31, 45, 47, 58, 59 Edwards, K., 88, 89, 92, 98 Edwards, R., 78, 82, 97, 102 Eguchi, H., 145, 160 Ehlers, K., 199, 202, 236, 242 Ehlting, J., 84, 97 Ehrhardt, D. W., 5, 28, 47, 234, 236 Eichinger, D., 188 Eickbush, T. H., 168, 174, 178, 179, 180, 187, 191, 193 Elkan, G. H., 33, 49 Elkind, Y., 82, 95, 97, 99, 101 Elliott, R. L., 115, 162 Ellis, B. E., 72, 73, 74, 87, 96, 97, 104 Ellis, D., 93, 99 Elmayan, R., 220, 224, 240 El Turk, J., 33, 59 Emerson, S. J., 224, 237 Emori, Y., 189 Endre, G., 6, 47 Engle, F. E., 36, 59 Engler, G. J., 76, 96 Ennos, A. R., 66, 87, 99 Epel, B. L., 197, 199, 200, 208, 210, 212, 216, 220, 222, 231, 232, 235, 236, 239, 243 Epple, P., 210, 211, 212, 242 Epstein, A. H., 148, 160 Eriksson, K. E. L., 70, 97 Eriksson, M. E., 77, 99 Erlandsson, R., 77, 98 Escombe, F., 108, 156 Eshed, H. Y., 65, 101 Esnault, R., 10, 29, 33, 43, 45, 59 Estabrook, E. M., 11, 47 Estrada-Garcia, M. T., 5, 57 Etzler, M. E., 7, 47, 51 Eudes, A., 80, 81, 82, 84, 88, 89, 90, 98, 105 Evans, W. C., 36, 51 Everaert, E. S. W., 10, 40, 57 Evert, R. F., 204, 241 Ewing, N. N., 7, 47 Ewing, R. P., 120, 157

250

AUTHOR INDEX

F Faktor, O., 78, 101 Farrand, S. K., 2, 47 Faucher, C., 5, 53 Fauquet, C., 172, 173, 189 Favet, N., 85, 86, 95 Fayet, O., 182, 189 Federspiel, N. A., 24, 48 Fellay, R., 23, 28, 30, 33, 41, 47, 48, 56 Felle, H. H., 5, 6, 28, 47, 57 Fellows, R. J., 204, 237 Feng, J., 34, 47 Feng, Q., 167, 168, 189 Fenzi, F., 152, 160 Ferdinando, D., 71, 72, 105 Ferran, J., 38, 50 Ferret, V., 85, 86, 87, 89, 90, 92, 93, 97, 100 Filiault, D. L., 154, 163 Finan, T. M., 24, 48 Fink, G. R., 188 Firmin, J. L., 17, 18, 20, 48 Fisher, D. B., 200, 208, 209, 210, 220, 225, 229, 237, 239 Fisher, R. F., 24, 31, 33, 34, 48 Flavell, A. J., 166, 189 Fleps, U., 192 Fletcher, J. C., 227, 237 Flore, J. A., 120, 137, 143, 158 Forkmann, G., 7, 48 Forsberg, L. S., 6, 29, 44, 48 Forster, B., 88, 103 Forster, R. L., 224, 237 Foster, T. M., 224, 237 Foster, V. E., 19, 58 Foxon, G. A., 88, 89, 90, 92, 98 Frafel, N., 30, 32, 43 Franceschi, V. R., 205, 207, 218, 219, 221, 222, 238, 243 Franck, A., 189 Franc,ois, C., 116, 142, 157 Franke, R., 66, 69, 80, 83, 84, 86, 87, 89, 92, 97, 103 Franken, C., 6, 53 Franklin-Tong, V. E., 201, 237 Fraysse, N., 6, 29, 30, 48 Freeman, J. P., 35, 51 Freiberg, C., 23, 28, 30, 33, 47, 48, 56 Frey, J., 33, 47 Friedl, E., 192 Frisch, D., 178, 191 Fritig, B., 85, 86, 95 Frolova, O. Y., 201, 236 Frommer, W. B., 205, 207, 218, 238 Frost, J. W., 17, 20, 56 Fu, G., 167, 168, 189 Fuchs, M., 109, 117, 120, 133, 137, 138, 143, 158, 161 Fujishige, N. A., 7, 61

Fujita, M., 70, 71, 72, 97, 102, 105 Fujiwara, T., 201, 218, 219, 236, 238, 241 Fukushima, K., 68, 96 Fuller, M. P., 153, 154, 158, 161, 163 Furbank, R. T., 200, 214, 231, 240, 241 Futsaether, C. M., 132, 162 Fu¨ tterer, J., 175, 190 G Gabriac, B., 72, 97 Gadella, T. W. J., 6, 49 Gagnon, H., 19, 21, 48 Gahrtz, M., 205, 241 Gajendiran, N., 35, 48 Galan, J. E., 24, 48, 53 Galera, C., 5, 61 Galibert, F., 24, 48 Gallagher, A., 16, 43 Gal-On, A., 215, 241 Galun, E., 215, 239 Gamalei, Y. V., 204, 205, 237 Gang, D. R., 70, 97 Gao, M. S., 6, 54 Garcia, M., 32, 53 Gardner, B. F., 127, 133, 136, 158 Garmier, M., 149, 152, 156 Gates, D. M., 119, 126, 141, 158 Gatineau, M., 87, 104 Gaucher, E. A., 171, 190 Gaut, B. S., 186, 192 Gautam, H., 7, 44 Gaworzewska, E. T., 16, 48 Gazzani, S., 208, 209, 210, 230, 242 Geelen, D., 17, 21, 55 Geering, A. D. W., 175, 189 Gehring, C. A., 5, 28, 49 Geiger, D. R., 204, 237 Geiger, O., 28, 30, 41, 46, 49, 54, 59, 60 Gellerstedt, G., 70, 102 Genty, B., 151, 152, 160 Geoffroy, P., 69, 84, 85, 90, 99, 103 Gerhold, D., 33, 58 Geurts, R., 6, 53 Ghelani, S., 25, 59 Giaquinta, R. T., 204, 237 Gilbert, M., 93, 99 Gillette, W. K., 33, 49 Girard, L., 29, 49 Giraudat, J., 151, 152, 160 Girffiths, H., 153, 162 Gisel, A., 200, 227, 230, 234, 237 Gizatullin, R. Z., 175, 192 Glenn, A. R., 39, 45, 62, 565 Glenn, D. M., 154, 163 Glenn, S. A., 6, 54 Gloudemans, T., 5, 59

AUTHOR INDEX Gloux, S., 24, 48 Glushka, J., 5, 19, 28, 30, 36, 41, 49, 54, 60 Godeke, K. H., 9, 42 Godrie, T., 24, 48 Goedhart, J., 6, 49 Goepfert, S., 69, 104 Goethals, K., 31, 33, 49 Goff, S., 178, 191 Goffeau, A., 24, 48 Goffner, D., 92, 95 Goldberg, R. B., 7, 61 Golding, B., 24, 48 Goldmann, A., 38, 49 Golecki, B., 219, 220, 237 Golinowski, W., 5, 23, 24, 25, 33, 47, 54, 57, 61 Gomez-Ospina, N., 222, 238 Gonzalez, J. E., 6, 54 Gorenstein, N. M., 167, 192 Go¨ ttfert, M., 23, 30, 32, 33, 43, 49, 52, 58 Go¨ tz, R., 16, 49 Gough, C., 5, 6, 43, 61 Goujon, T., 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93, 97, 98, 99, 105 Gouzy, J., 24, 48 Grace, J., 128, 156 Graham, L. E., 199, 236 Graham, T. L., 12, 49 Grand, C., 72, 98 Grandbastien, M.-A., 184, 186, 189, 191 Granier, F., 88, 89, 90, 105 Grant, O. M., 116, 121, 123, 136, 137, 138, 139, 140, 141, 143, 159 Gray, J. E., 152, 158 Gray, K. M., 30, 50 Grayer, R. J., 12, 52 Greenberg, E. P., 30, 50 Grenet, E., 90, 92, 95 Griffitts, J. S., 234, 236 Grima-Pettenati, J., 66, 71, 76, 87, 88, 90, 91, 93, 96, 100, 102, 103, 104 Grimes, D. W., 144, 161 Grob, P., 32, 49, 58 Grob, T., 186, 190 Grønlund, M., 6, 57 Grundler, F. M., 209, 211, 238 Guerreiro, N., 28, 29, 50 Guerrier, D., 38, 50 Guevara-Gonzalez, R. G., 220, 224, 241 Guez, C., 76, 100 Guiliani, R., 120, 137, 143, 158 Guillet, C., 92, 95 Guilley, H., 189 Guiney, E., 86, 93, 103 Gulash, M., 16, 50 Guo, D. J., 69, 71, 72, 79, 83, 84, 85, 86, 90, 91, 92, 97, 98, 101, 103 Gurjal, M., 24, 48

251

Gurni, A. A., 19, 61 Guyot, G., 137, 156 Gyuris, J., 33, 34, 52 H Haggblom, M. M., 35, 61 Hahlbrock, K., 76, 101 Haigh, J., 88, 105 Hake, S., 65, 77, 102, 217, 218, 222, 228, 238, 239 Halpin, C., 66, 68, 80, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 102, 103 Hamblin, J., 7, 50 Hamdi, Y. A., 39, 51 Hamed, F., 154, 158 Hamilton, J. T. G., 10, 35, 36, 39, 57 Hamilton, W. D. O., 25, 47 Han, B., 76, 98 Hancock, R. D., 208, 209, 210, 230, 242 Handa, A. K., 201, 235 Hanin, M., 33, 47 Hansen, C., 178, 189 Hansen, L. D., 147, 156 Hao, P., 167, 168, 189 Hara, S., 93, 100 Harding, S. A., 69, 73, 83, 89, 99, 101 Hardman, A., 30, 58 Harley, C. B., 191 Harmer, S. L., 76, 98 Harper, G., 175, 177, 178, 189, 190, 191, 192 Harper, J. D., 197, 235 Harper, J. E., 9, 45 Harrison, M. J., 210, 211, 212, 237 Harrison-Murray, R. S., 127, 158 Hartwig, U. A., 7, 9, 16, 17, 20, 32, 50, 54 Harwood, C. S., 39, 50 Hashimoto, Y., 110, 113, 144, 150, 158, 161 Hata, S., 5, 52 Hatfield, J. D., 148, 160 Hatfield, J. L., 109, 132, 142, 158 Hatfield, R., 70, 98 Hatfield, R. D., 80, 87, 89, 101, 102, 103 Hattermann, D. R., 16, 43 Hatton, D., 76, 98 Haudenshield, J. S., 197, 236 Hauffe, K. D., 76, 98 Haughn, G., 218, 240 Haupt, S., 208, 209, 210, 230, 234, 237, 239, 242 Havelange, A., 230, 240 Hawes, M. C., 7, 62 Hawkins, F. K. L., 31, 47 Hawkins, S., 71, 104 Hayashi, H., 218, 219, 238 Haywood, V., 198, 215, 237 He, S. Y., 19, 24, 50, 58 Head, J. F., 115, 162 Heath, R. L., 153, 157

252

AUTHOR INDEX

Hedrick, S. A., 11, 58, 82, 97 Hehn, A., 69, 104 Heidmann, T., 175, 188 Heilman, J. L., 110, 158 Heinlein, M., 215, 237 Heinze, T. M., 35, 51 Heller, W., 7, 48 Hemm, M. R., 66, 68, 69, 80, 83, 84, 89, 92, 97, 99, 103 Hempel, F. D., 200, 217, 218, 227, 230, 231, 232, 234, 237, 238 Henikoff, S., 174, 191 Hennecke, H., 30, 32, 33, 43, 49, 58 Henriksson, G., 70, 102 Hepler, P. K., 5, 44, 201, 237 Herbers, K., 215, 237 Herdewijn, P., 78, 79, 84, 85, 102 Heritage, A. D., 149, 161 Hermann, G. J., 232, 239 Hermans, C., 150, 157 Hernandez-Lucas, I., 24, 48 Herniou, E., 175, 190 Herrera-Estrella, L. R., 220, 224, 241 Hertzberg, M., 77, 98, 99 Heslop-Harrison, J. S., 166, 167, 168, 175, 178, 184, 188, 189, 190, 192 Hess, P., 209, 211, 238 Hetherington, A. M., 152, 158 Hibino, T., 88, 89, 99 Higgins, J., 29, 45 Higuchi, T., 88, 89, 99 Higuet, D., 182, 187, 188 Himmelsbach, D. S., 84, 85, 106 Hind, G., 201, 235 Hino, K., 192 Hipps, L. E., 134, 158 Hirai, A., 145, 162 Hirochika, H., 186, 192 Hirsch, A. M., 7, 11, 50, 54, 55, 61, 62 Hirth, L., 189 Hishiyama, S., 83, 99 Hodge, T. P., 197, 240 Hoffmann, B., 33, 34, 52 Hoffmann, L., 69, 84, 85, 90, 99, 103 Hofius, D., 215, 237 Hofmann, M., 154, 156 Hofte, M., 150, 157 Hogan, E. T., 13, 43 Hogenesch, L. B., 76, 98 Hohn, T., 175, 177, 190, 192 Holdaway-Clarke, T. L., 201, 237 Hollingsworth, R. I., 30, 44 Holroyd, G. H., 152, 158 Holsters, M., 17, 21, 31, 33, 46, 49, 55 Holt, K., 66, 87, 88, 89, 90, 92, 93, 98, 102 Holtaus, U., 205, 237 Holzberg, S., 208, 210, 237 Holzhauser, D., 32, 49 Hong, A., 24, 48

Hong, G. F., 25, 33, 34, 47, 50, 59 Hopkins, D. W., 86, 92, 93, 99, 103 Hopper, W., 35, 39, 50 Horowitz, M., 215, 235 Horrocks, R. D., 144, 161 Horton, P., 153, 162 Horton, R., 120, 157 Horvath, B., 5, 31, 58, 59 Hostettmann, K., 12, 13, 46, 50 Howell, M., 5, 45 Howles, P. A., 82, 99, 104 Hrazdina, G., 72, 105 Hu, H. L., 34, 47, 50 Hu, W. J., 83, 85, 86, 99, 105 Hu, X., 167, 168, 189 Huang, Y. C., 167, 168, 189 Hubac, C., 38, 50 Hubbard, K., 80, 100 Hubbell, H. D., 7, 46 Huber, B., 108, 158 Huck, C. W., 13, 51 Hueck, C. J., 19, 51 Hughes, D., 154, 156 Hughes, J. d’A., 175, 186, 189 Hughes, T. R., 169, 191 Hui, E. K.-W., 175, 193 Huizar, L., 24, 48 Hull, R., 167, 169, 170, 173, 175, 177, 178, 189, 190, 191 Hull, R. J., 197, 215, 235, 236 Hulsen, K., 150, 157 Humphreys, J. M., 66, 68, 69, 83, 84, 92, 97, 99 Hungria, M., 17, 18, 21, 32, 51 Huntley, S. K., 93, 99 Hur, H. G., 35, 51 Hussein, Y. A., 39, 51 Husson, H. P., 10, 18, 45, 62 Hyman, R. W., 24, 48 Hynes, M. F., 29, 45 I Ibrahim, A. R. S., 34, 51 Ibrahim, R. K., 19, 21, 48 Ide, H., 154, 159 Idesawa, K., 23, 51 Idso, S. B., 109, 112, 133, 134, 135, 143, 149, 158, 159, 161 Iglesias, V. A., 201, 237 Iiyama, K., 85, 105 Illig, J., 210, 211, 212, 242 Imlau, A., 199, 200, 203, 208, 210, 212, 216, 219, 220, 222, 231, 238, 239 Immonen, S., 184, 190 Innes, R. W., 17, 20, 51, 57 Ino, T., 109, 158 Inoue, K., 72, 76, 79, 84, 85, 86, 90, 91, 96, 98, 100

AUTHOR INDEX Inoue, Y., 128, 132, 136, 137, 138, 149, 159, 161 Inouye, S., 189 International Committee on Taxonomy of Virology (ICTV), 169, 170, 171, 190 International Human Genome Sequencing Consortium, 167, 168, 190 Inze, D., 85, 86, 88, 90, 93, 95, 105 Irving, H. R., 5, 28, 49 Ishifuji, M., 93, 100 Ishikawa, A., 23, 51 Ishikawa, M., 154, 159 Ishiwatari, Y., 218, 219, 238 Israelsson, M., 77, 99 Italis, R., 182, 187, 188 Itaya, A., 207, 212, 220, 238 Ito, K., 146, 147, 159 Ito, M., 71, 105 Ito, T., 89, 99 Iwanov, L., 119, 159 J Jabbouri, S., 30, 31, 33, 44, 47, 48 Jackson, D. P., 217, 218, 222, 228, 238, 239 Jackson, R., 73, 101 Jackson, R. D., 108, 109, 127, 132, 133, 134, 136, 142, 143, 148, 156, 158, 161 Jacobs, M., 5, 51 Jacobsen, K. R., 219, 236 Jacobsen, S. E., 196, 236 Jain, V., 7, 8, 17, 18, 51 Jakowitsch, J., 177, 184, 187, 190, 191 Jameel, H., 90, 93, 101 Jarvis, P. G., 128, 156 Jauneau, A., 76, 87, 96, 100, 201, 239 Jay, M., 16, 53 Jeffrey, A. M., 36, 51 Jenkins, A. D., 148, 161 Jeon, J. H., 77, 78, 95 Jerina, D. M., 36, 51 Jezek, P., 146, 159 Jiang, G. Q., 22, 23, 24, 25, 52 Jin, H. L., 76, 99 John, M., 33, 34, 52 Johnson, A. F., 80, 100 Johnsson, A., 132, 162 Johnston, A. W. B., 16, 17, 18, 20, 25, 26, 28, 31, 32, 33, 43, 45, 47, 48, 50, 51, 58, 59 Jonard, G., 189 Jones, A. D., 19, 57 Jones, H. G., 109, 110, 113, 114, 115, 116, 118, 119, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 135, 136, 137,

253

138, 139, 140, 141, 142, 143, 144, 145, 148, 151, 153, 156, 157, 159, 160, 162 Jones, J., 76, 99 Jones, L., 66, 87, 99 Jones, R. J., 92, 103 Jones, T., 24, 48 Jones, W. T., 19, 56 Joseleau, J. P., 87, 91, 92, 96, 97, 103 Joseph, C. M., 3, 9, 10, 16, 17, 18, 19, 20, 21, 32, 45, 50, 51, 53, 54, 56, 57, 60 Joshi, C. P., 68, 78, 102 Joshi, R. S., 18, 35, 38, 52 Jouanin, L., 80, 85, 86, 87, 88, 89, 90, 92, 93, 95, 97, 98, 99, 100, 103, 104, 105 Joulanin, L., 80, 81, 82, 84, 98 Juda, L., 65, 66, 102 Juergensen, K., 209, 211, 238 Jung, H. G., 82, 92, 104 Jung, S. H., 9, 53 K Kabbara, A. A., 5, 28, 49 Kacira, M., 143, 160 Kade, M., 19, 61 Kahn, D., 24, 48 Kahn, M. L., 24, 48 Kajita, S., 83, 93, 99, 100 Kalendar, R., 174, 184, 186, 190, 192 Kalkkinen, N., 24, 58, 201, 236 Kalman, S., 24, 48 Kalo, P., 6, 47 Kalsi, G., 7, 47, 51 Kamm, A., 167, 188 Kanaya, S., 189 Kaneko, T., 6, 23, 51, 54, 60 Kanemasu, E. T., 109, 133, 158 Kannenberg, E. L., 6, 44, 52 Kanno, T., 184, 187, 191 Kape, R., 16, 17, 18, 20, 38, 52 Kasschau, K. D., 224, 239 Katayama, Y., 83, 93, 99, 100 Katayose, Y., 167, 168, 178, 192 Kato, T., 23, 51 Katz, A., 197, 243 Katz, R. A., 182, 190 Kawabata, H., 93, 100 Kawaoka, A., 76, 100 Kawashima, K., 23, 51 Kawazu, T., 88, 99 Kay, S. A., 76, 98 Kays, S. J., 66, 106 Keating, D. H., 5, 24, 48, 61 Keener, M. E., 134, 143, 160 Keller, F., 206, 235 Kempers, R., 200, 203, 238 Kendrick-Jones, J., 197, 201, 209, 235, 240 Kennedy, E. P., 28, 41, 60

254

AUTHOR INDEX

Kent, S. P., 7, 50 Kereszt, A., 6, 47 Kerr, A., 2, 62 Kerr, Y. H., 122, 160 Kersey, R., 72, 100 Kessler, S., 224, 238 Kevei, Z., 6, 47 Keyser, H. H., 33, 58 Khan, E., 182, 190 Khashoggi, A., 184, 188 Kidwell, M. G., 186, 189 Kijne, J. W., 5, 9, 10, 11, 44, 57, 60 Kim, H., 80, 89, 100, 103 Kim, I., 217, 218, 231, 232, 238 Kim, J. S., 29, 30, 43, 57, 58 Kim, K. Y., 22, 23, 24, 25, 52 Kim, M., 224, 238 Kim, W. S., 22, 23, 24, 25, 52 Kimes, D. S., 116, 160 Kimura, T., 23, 51 Kindl, H., 72, 97 Kirchner, P. L., 134, 143, 160 Kirshnan, H. B., 5, 57 Kirst, M., 80, 100 Kishida, Y., 23, 51 Kiss, E., 24, 48 Kiss, G. B., 6, 47 Kisselev, L. L., 175, 192 Kistner, C., 6, 60 Kitano, M., 145, 160 Kite, G. C., 12, 52 Kiyokawa, C., 23, 51 Kleinhofs, A., 184, 192 Knight, A. E., 197, 240 Knight, C. D., 33, 52 Knight, M., 36, 51, 92, 95 Knight, M. E., 88, 89, 90, 98 Knoblauch, M., 202, 203, 238, 242 Knox, J. P., 201, 240 Knutson, R. M., 146, 160 Kohara, M., 23, 51 Kollmann, R., 199, 236 Kollmeyer, W. D., 18, 33, 45 Kombrink, E., 84, 97 Komp, C., 24, 48 Kondorosi, A., 5, 10, 11, 18, 28, 29, 31, 33, 34, 38, 43, 45, 47, 50, 52, 54, 58, 59, 62, 208, 210, 211, 216, 220, 230, 236 Kondorosi, E., 5, 11, 28, 33, 34, 47, 52, 58, 59 Kooter, J. M., 187, 191 Kopcinska, J., 5, 23, 24, 25, 54, 57 Korth, K. L., 78, 82, 95, 100 Kosslak, R. M., 17, 18, 19, 20, 22, 35, 38, 44, 52, 58 Kotlizky, G., 199, 200, 208, 210, 212, 216, 220, 222, 231, 239 Kouchi, H., 5, 52

Kragler, F., 198, 215, 218, 221, 222, 235, 237, 238 Kramer, P. J., 110, 158 Krause, A., 11, 23, 52 Kreps, J. A., 76, 98 Krishnan, H. B., 22, 23, 24, 25, 29, 32, 34, 43, 47, 52, 54, 55 Krumholz, L. R., 35, 53 Ku, M. S. B., 220, 237 Kubis, S., 167, 188 Kubori, T., 24, 53 Kuc, J., 89, 100 Kuempel, P. L., 17, 20, 51, 57 Ku¨ hn, C., 205, 207, 218, 238 Kuleck, G., 7, 62 Kulkosky, J., 182, 190 Kumar, A., 166, 167, 171, 189, 190 Ku¨ mmerlen, B., 128, 145, 160 Kundig, C., 32, 49 Kunze, R., 167, 190 Kuo, C. I., 23, 52 Kurth, R., 173, 191 Kuykendall, L. D., 2, 59, 62 Kwon, M. O., 201, 236 L Lacombe, E., 76, 100 Ladyman, J. A. R., 151, 162 LaFleur, D., 175, 191 Laird, D. W., 232, 243 Lakshmi, V., 116, 161 Lalaure, V., 24, 48 Lallemand, J.-B., 175, 188 Lam, T. B. T., 85, 105 Lamb, C. J., 11, 58, 76, 78, 82, 95, 96, 97, 99, 100, 101, 102, 104 Lambers, H., 123, 146, 149, 150, 157, 163 Lameta, A. D., 16, 53 Lamprecht, I., 115, 146, 155, 160 Lan, V. T. T., 11, 52 Langin, T., 182, 186, 187, 188 Lapidot, M., 216, 236 Lapierre, C., 66, 78, 79, 80, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 102, 103, 104, 105 Lara-Tejero, M., 24, 53 Larkins, B. A., 219, 235 Larosiliere, R. C., 16, 50 Laten, H. M., 171, 190 Latha, S., 35, 53 Lau, N. C., 224, 238 Lauvergeat, V., 71, 76, 100, 104 Lavin, C. A., 199, 236 Lawson, C. G. R., 10, 11, 53 Lawson, T., 132, 150, 163 Lay, J. O., 35, 51 Lecellier, C.-H., 172, 173, 190 Lechtenberg, L. F., 89, 96

AUTHOR INDEX Lecouer, I., 38, 49 Lee, C. A., 24, 53 Lee, D., 83, 100 Lee, J. G., 30, 44 Lee, J. Y., 222, 238 Lee, R. C., 224, 238 Lee, R. H., 224, 237 Lee, S. H., 9, 53 Lee, S. P., 76, 98 Lefebvre, V., 151, 152, 160 Legrand, M., 69, 84, 85, 86, 87, 90, 91, 92, 94, 95, 99, 103 Leibovitch, S., 18, 43 Leinonen, I., 121, 122, 123, 159 Lemoine, R., 205, 207, 208, 211, 218, 238, 239, 243 Leo´ n-Barrios, M., 18, 53 Leple, J.-C., 85, 86, 89, 90, 93, 100, 103, 105 Leprieur, C., 122, 160 Lerat, E., 178, 190 Lerouge, P., 5, 53 Leshkevich, J., 69, 73, 89, 101 Le Strange, K. K., 19, 53 Leung, B., 232, 239 Levin, J. Z., 196, 236 Lewin, A., 3, 53 Lewis, N. G., 68, 69, 70, 77, 78, 80, 83, 84, 94, 95, 96, 97, 100 Leyna, A., 76, 100 Li, L. G., 68, 69, 73, 78, 83, 87, 89, 91, 100, 101, 102 Li, Q., 34, 47 Li, S. G., 167, 193 Li, Y., 73, 101, 167, 168, 189 Li, Z., 137, 157 Liang, G., 207, 220, 238 Liang, X. O., 78, 102 Liang, X. W., 11, 58, 76, 100 Liaud, M.-F., 174, 188 Lichtenstein, C., 184, 188 Lichtenthaler, H. K., 150, 156 Liljegren, S. J., 65, 101 Lim, E. K., 73, 101 Lim, L. P., 224, 238 Lim, P. O., 7, 61 Limmer, N., 16, 49 Limpens, E., 6, 53 Lin, C. C., 17, 53 Lin, L. P., 17, 53 Lindow, S. E., 153, 163 Lindstrom, K., 17, 21, 60 Ling, P. P., 143, 160 Lingner, J., 169, 191 Lister, R. M., 219, 235 Lithgow, J. K., 30, 58 Liu, B., 165, 191 Liu, S. T., 34, 50 Liu, Y. L., 167, 168, 189 Llave, C., 224, 239

255

Llewellyn, D. J., 200, 214, 231, 240, 241 Lo, S. J., 173, 191 Loake, G. J., 16, 42, 78, 101 Lockhart, B. E., 175, 177, 186, 189, 191 Lockhart, B. E. L., 175, 186, 189 Loh, J., 32, 53 Lohrke, S., 22, 44 Lois, R., 76, 101 Long, S. R., 5, 6, 16, 17, 18, 20, 24, 25, 28, 29, 31, 33, 34, 43, 47, 48, 55, 56, 59, 60, 61, 62 Lo¨ nning, W.-E., 167, 190 Lo´ pez-Lara, I. M., 5, 19, 30, 36, 54, 59 Lorio, J., 22–25, 52 Lorite, M. J., 39, 54 Lough, T., 187, 193 Lough, T. J., 224, 237 Lo¨ wer, J., 173, 191 Lo¨ wer, R., 173, 175, 184, 191 Lu, D. Y., 92, 103 Lu, F. C., 80, 85, 89, 98, 100, 101, 103 Lucas, H., 186, 191 Lucas, W. J., 197, 198, 199, 200, 201, 204, 208, 215, 216, 217, 218, 219, 221, 222, 224, 229, 235, 236, 237, 238, 239, 240, 243 Lue, N. F., 193 Lugtenberg, B. J. J., 5, 9, 10, 11, 17, 19, 20, 25, 28, 30, 33, 34, 36, 39, 41, 46, 49, 54, 55, 57, 59, 60, 62 Lundblad, V., 169, 191 Lundeberg, J., 77, 98 Lung, J., 83, 99 Luukkainen, R., 17, 21, 60 Luyten, E., 3, 25, 26, 28, 55, 61 Lynn, D. G., 66, 95, 105 M Ma, F., 207, 220, 238 Maba, B., 85, 87, 98, 104 Machado, D., 34, 54 Machray, G. C., 208, 209, 210, 230, 242 Mack, J. P. G., 182, 190 MacKay, J., 80, 81, 82, 84, 88, 89, 90, 93, 98, 101, 103 Mackay, S., 66, 76, 105 Madsen, E. B., 6, 54, 57 Madsen, L. H., 6, 54, 57 Maeda, T., 201, 241 Magyar, Z., 11, 58 Mah, N., 72, 74, 104 Mahadevan, A., 34, 35, 39, 43, 48, 50, 53, 55 Maher, E. A., 82, 101 Maillard, M., 12, 13, 50 Maillet, F., 5, 6, 43, 53 Maillot, M. P., 90, 92, 95 Majumdar, A., 171, 190 Makinen, K., 201, 236

256

AUTHOR INDEX

Malik, H. S., 174, 179, 187, 191 Manen, J. F., 3, 53 Mann, M., 169, 191 Manners, J. M., 92, 103 Manninen, I., 191 Mansfield, J. W., 24, 44 Mansfield, S. D., 73, 74, 76, 93, 99, 103, 104 Mao, L., 178, 188, 191 Marchio, A., 175, 192 Mareck, A., 201, 239 Marie, C., 23–25, 54 Marita, J. M., 66, 69, 78, 79, 80, 83, 84, 85, 86, 87, 89, 91, 96, 101, 102, 103 Marketon, M. M., 6, 54 Marmiroli, N., 208, 209, 210, 230, 242 Marque, C., 88, 90, 105, 106 Marston, A., 12, 13, 46, 50 Marstrop, H. G., 86, 93, 103 Martens, H. J., 212, 243 Martin, C., 66, 76, 99, 102, 104, 105 Martin, D. M., 70, 97 Martin, J., 175, 190 Martin, M. R., 85, 86, 93, 99 Martin, W., 70, 97 Martinez-Romero, E., 2, 62 Marty, R., 32, 49 Martz, F., 69, 84, 85, 86, 90, 92, 95, 99 Masoud, S. A., 78, 82, 95, 104 Mastienssen, R. A., 187, 191 Masuki, A., 151, 161 Masuta, C., 212, 238 Masuy, D., 24, 48 Mather, P., 115, 119, 120, 121, 160 Mathesius, U., 5, 6, 11, 54 Matsuda, Y., 207, 220, 238 Matsumoto, H., 201, 241 Matsumoto, M., 23, 51 Matsumoto, T., 167, 168, 178, 192 Matsuno, A., 23, 51 Matsushima, D., 151, 160 Matsushima, Y., 24, 53 Mattei, M.-G., 175, 192 Mattsson, A., 153, 160 Matzke, A. J. M., 177, 184, 187, 190, 191 Matzke, M. A., 177, 184, 187, 190, 191 Maurer, P., 187, 188 Maury, S., 69, 84, 85, 90, 99, 103 Mavandad, M., 78, 82, 97, 102 Maxwell, C. A., 7, 9, 17, 19, 20, 32, 50, 54, 56 McBurney, T., 109, 126, 127, 129, 134, 138, 139, 141, 142, 143, 159 McClure, M. A., 182, 191 McDougall, G. J., 70, 88, 102, 104, 105 McFarland, K. C., 218, 219, 221, 222, 238, 243 McInnis, S., 76, 103 McIntosh, L., 145, 147, 163 McIntyre, C. L., 92, 103

McKhann, H. I., 11, 54, 55 McMichael, C. M., 86, 87, 97 McMichael, L. A., 175, 189 McNulty, A. K., 147, 160 Medina Escobar, N., 234, 239 Meeuse, B. J. D., 146, 162 Mehrtens, F., 76, 99 Meinhardt, L. W., 22, 32, 55 Meins, F., Jr., 201, 237 Mele, G., 65, 77, 102 Melzer, M., 215, 237 Mengistu, A., 148, 160 Menke, J., 177, 186, 191 Merida, A., 66, 76, 105 Merits, A., 201, 236 Merlot, S., 151, 152, 160 Meromi, A., 82, 95 Message, B., 38, 49 Messens, E., 17, 21, 55, 78, 79, 84, 85, 102, 123, 153, 157 Mette, M. F., 177, 184, 187, 190, 191 Metzlaff, K., 65, 96 Meyer, D., 69, 104 Meyer, H., 3, 53 Meyer, K., 76, 83, 86, 87, 95, 97, 100, 102, 104 Meyermans, H., 76, 78, 79, 84, 85, 96, 102 Meyerowitz, E. M., 196, 227, 236, 237 Mezitt, L. A., 218, 239 Mhiri, C., 186, 191 Mian, I. S., 193 Miao, L., 204, 240 Michiels, J., 3, 55 Mielke, M. R., 85, 86, 105 Migne, C., 90, 92, 95 Migner, P., 18, 43 Mihacea, S., 6, 47 Mila, I., 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 97, 98, 99, 103, 104, 105 Miller, K., 175, 190 Mishiro, S., 192 Mithofer, A., 6, 55 Mizutani, S., 168, 192 Mochizuki, Y., 23, 51, 52 Mohan, N., 34, 43 Mohan, R., 35, 59 Momii, K., 127, 128, 162 Monje, O., 154, 156 Monsalve-Fonnegra, Z., 220, 224, 241 Monteith, J. L., 124, 160 Monties, B., 85, 86, 88, 89, 90, 92, 93, 95, 96, 98, 105 Montorzi, G., 33, 47 Monzer, J., 218, 219, 221, 222, 238, 243 Moore, B., 230, 240 Moore, L. H., 35, 61 Moran, M. S., 136, 137, 138, 149, 151, 156, 160

AUTHOR INDEX Moran, S. M., 127, 136, 158 Morant, M., 69, 104 Morel, J.-B., 186, 191 Moremong, M., 143, 155 Mori, T., 201, 241 Morin, G. B., 191 Moritz, T., 77, 99 Morohoshi, N., 93, 100 Morreel, K., 78, 79, 84, 85, 102 Morris, P., 19, 55, 58 Morrison, I. M., 70, 102 Morrison, W. H., 84, 85, 90, 91, 106 Morvan, C., 201, 239 Morvan, O., 201, 239 Mouse Genome Sequencing Consortium, 167, 191 Moyano, E., 76, 104 Mulder, L., 6, 60 Mulder, M. M., 88, 103 Mulders, I. H. M., 25, 46 Mulders, S., 6, 54 Mulligan, J. T., 17, 25, 28, 55, 60 Munoz, S., 32, 58 Mur, L. A., 11, 57 Murphy, G., 31, 45 Murphy, J. B., 7, 47 Mustilli, A. C., 151, 152, 160 Muthukumar, G., 35, 39, 55 Myton, K., 66, 87, 88, 90, 103, 105, 106 N Nadlerhassar, T., 82, 95 Nagamura, Y., 167, 168, 178, 192 Nainawatee, H. S., 7, 8, 17, 18, 51 Nakajima, K., 218, 223, 227, 239 Nakajima, Y., 167, 186, 192 Nakamura, D., 24, 53 Nakamura, T. M., 191 Nakamura, Y., 6, 23, 51, 57 Nakashima, J., 71, 102 Nakayama, S., 23, 51, 52 Nakazaki, N., 23, 51, 52 Nakazono, M., 146, 163 Namken, L. N., 110, 156 Napier, R., 197, 235 Natera, S., 29, 45 Navot, N., 215, 239 Nawy, T., 218, 223, 239 Naylow, A. W., 110, 158 Ndowora, T., 175, 191 Negrel, J., 84, 85, 88, 90, 91, 106 Nelson, L., 3, 53 Nelson, O. E., 89, 100 Nelson, R. S., 212, 213, 236, 238 Nelson, T. A., 146, 162 Nemani, R. R., 136, 161 Nemoto, K., 218, 219, 238 Neumann-Haefelin, D., 192

257

Neville, C., 155, 161 Nevo, E., 147, 161, 184, 190 Newman, L. J., 65, 66, 76, 102, 103 Nguyen, L., 217, 218, 222, 239 Ni, W. T., 82, 85, 86, 92, 95, 101, 102, 104 Nicotra, A. B., 154, 156 Niehaus, K., 6, 43 Nieuwkoop, A., 17, 43 Niimura, Y., 167, 168, 178, 192 Nilsson, H. E., 139, 140, 148, 149, 150, 151, 161 Nilsson, P., 77, 98, 99 Noel, K. D., 6, 30, 46, 55, 60 Nogales, J., 32, 58 Nony, E., 5, 45 Norman, H. E., 116, 160 Norman, J. M., 111, 114, 116, 123, 126, 129, 156 North, H., 151, 152, 160 Nurmiaho-Lasslia, E. L., 24, 58 Nussaume, L., 85, 98 O Ober, K., 16, 49 O’Connell, A., 66, 87, 88, 90, 93, 102, 103 Ogawa, J., 29, 55 Ogawa, K., 151, 161 Oger, P., 2, 47 Okker, R. J. H., 17, 25, 33, 34, 39, 46, 55, 59, 62 Olbryt, M., 6, 54 Oldroyd, G. E. D., 5, 6, 43, 55, 61 Olesinski, A. A., 215, 239 Olivares, J., 32, 39, 54, 58 Oliver, D., 88, 89, 92, 98 Olson, E. R., 19, 58 Olszewski, N. E., 175, 177, 186, 189, 191 O’Malley, D. M., 80, 88, 89, 90, 93, 101, 103 Omasa, K., 110, 113, 115, 144, 150, 161 Omid, A., 215, 237, 241 Omori, S., 83, 99 Onda, Y., 146, 147, 159 ¨ nnerud, H., 70, 102 O Ono, M., 173, 191 Oparka, K. J., 199, 200, 202, 206, 207, 208, 209, 210, 211, 212, 213, 216, 219, 220, 222, 225, 229, 230, 231, 234, 236, 237, 239, 240, 242, 243 Opsomer, C., 88, 89, 92, 95 Ordentlich, A., 147, 161 Oresnik, I. J., 29, 45 Orfila, C., 201, 240 Ori, N., 65, 77, 102

258

AUTHOR INDEX

Ormenese, S., 230, 240 Ornston, L. N., 16, 35, 39, 56 Orr, J., 66, 82, 95, 105 Ortiz, R., 186, 189 Osakabe, K., 68, 78, 102 Osawa, H., 201, 241 Osuji, J. O., 175, 190 Otha, Y., 192 Otsuki, Y., 186, 192 Otterman, J., 116, 161 Ottle´ , C., 116, 157 Ougiya, H., 93, 100 Overall, R. L., 196, 197, 198, 201, 235, 237, 240, 242 P Paaren, H. E., 17, 18, 20, 35, 38, 52 Pacios Bras, C., 26, 27, 28, 31, 55 Paiva, N. L., 11, 13, 43, 55, 82, 85, 86, 99, 102 Palacios, R., 29, 49 Palaqui, J. C., 220, 224, 240 Palm, C., 24, 48 Palta, J. P., 154, 163 Pandya, S., 29, 55 Pankhurst, C. E., 16, 19, 55, 56 Panstruga, R., 191 Papain, L. E., 119, 126, 141, 158 Parales, R. E., 39, 50 Parish, R. W., 5, 28, 49 Park, H. L., 9, 53 Park, J., 215, 239 Parke, D., 16, 35, 39, 56 Parniske, M., 6, 16, 17, 18, 20, 38, 52, 56, 60 Parr, A., 66, 73, 76, 99, 101, 105 Parvathi, K., 69, 71, 83, 86, 97, 103 Patzlaff, A., 76, 103 Paule, C., 80, 100 Paz-Ares, J., 76, 102 Pean, M., 66, 80, 87, 91, 96, 103 Pearce, R. S., 154, 161 Pearce, S. R., 166, 189 Pearson, J. P., 30, 50 Peck, M. C., 24, 34, 48, 62 Pees, E., 9, 11, 25, 46, 60 Penfield, S., 65, 96 Peng, Y., 191 Penman, H. L., 125, 161 Penmetsa, R. V., 5, 6, 43, 61 Perazza, D. E., 65, 66, 102 Perbal, M.-C., 218, 240 Perez, H., 5, 44 Perl, A., 215, 239 Perret, X., 2, 5, 23, 24, 25, 28, 30, 31, 44, 48, 54, 56, 57, 61 Peters, N. K., 16, 17, 18, 20, 56 Peterson, K. R., 186, 189 Peterson, M., 92, 98

Petit-Conil, M., 66, 68, 80, 85, 86, 87, 88, 89, 90, 93, 95, 99, 100, 102, 103, 105 Petropoulos, C. J., 171, 172, 173, 192 Pfuger, J., 217, 218, 231, 232, 238 Philippe, H., 175, 188 Phillips, D. A., 3, 7, 9, 10, 16, 17, 18, 19, 20, 21, 31, 32, 33, 34, 45, 50, 51, 53, 54, 56, 57, 59, 60 Pichon, J. M., 122, 160 Pichon, M., 92, 95 Pierre, M., 11, 58 Piffanelli, P., 191 Pilate, G., 80, 85, 86, 87, 88, 89, 90, 93, 95, 100, 103, 104, 105 Pillai, B. V. S., 35, 57 Pillonel, C., 88, 103 Pinc,on, G., 84, 85, 87, 90, 91, 94, 103 Pineau, P., 173, 190 Pinter, P. J., 109, 132, 142, 158 Pinter, P. J., Jr., 109, 127, 132, 133, 134, 135, 148, 158, 161 Pintortoro, J. A., 76, 100 Piquemal, J., 66, 80, 87, 88, 90, 93, 102, 103 Pittacco, A., 134, 157 Plant, R. E., 144, 161 Plaskitt, K., 66, 105 Plazinski, J., 17, 51 Podila, G. K., 85, 86, 97, 105 Pohl, T. M., 24, 48 Poinsot, V., 6, 29, 30, 48 Polard, P., 180, 187 Pollet, B., 66, 78, 79, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 97, 98, 99, 100, 102, 103, 104, 105 Pollien, J. M., 220, 224, 240 Poole, F. L., 84, 85, 106 Popko, J. L., 68, 78, 83, 85, 86, 99, 100, 102, 105 Portetelle, D., 24, 48 Pradel, K. S., 199, 200, 208, 210, 212, 216, 220, 222, 230, 231, 239, 240 Pre`re, M. F., 182, 189 Presnell, T., 88, 90, 93, 101 Presting, G., 178, 191 Pre´ vot, L., 116, 157 Price, A. H., 153, 162 Price, N. P. J., 6, 30, 57 Price, W. S., 153, 159 Priess, J. R., 232, 239 Pringle, C. R., 170, 171, 192 Prior, D., 200, 208, 213, 225, 229, 236, 240 Prior, D. A. M., 200, 208, 210, 225, 229, 239 Prome´ , J.-C., 3, 5, 26, 46, 53 Provencher, L. M., 204, 240 Prytz, G., 132, 162

AUTHOR INDEX Pueppke, S. G., 3, 5, 22, 23, 24, 25, 29, 32, 34, 43, 44, 47, 52, 54, 55, 57 Pu¨ hler, A., 6, 24, 43, 48 Puigdomenech, P., 72, 85, 92, 104, 105 Purnelle, B., 24, 48 Puvanesarajah, V., 17, 20, 31, 32, 59 Q Qi, H., 115, 162 Qi, Y., 207, 220, 238 Qiu, G. Y., 127, 128, 162 Quentin, M., 201, 239 Quinto, C., 5, 44 Quirion, J. C., 10, 18, 45, 62 R Radford, J. E., 197, 201, 240 Radutoiu, S., 6, 54, 57 Rae, A. L., 92, 103 Raes, J., 76, 80, 81, 82, 84, 103 Rafii, F., 35, 51 Rakwalska, M., 6, 54 Ralph, J., 66, 68, 69, 70, 78, 79, 80, 83, 84, 85, 87, 89, 91, 95, 96, 98, 99, 100, 101, 102, 103 Ralph, R. D., 84, 85, 86, 101 Ralph, S., 80, 89, 103 Ramond, P., 182, 189 Ramsperger, U., 24, 48 Ranjeva, R., 6, 45 Rao, J. R., 10, 19, 35, 36, 37, 38, 39, 40, 45, 57 Raschke, K., 108, 162 Raskin, I., 147, 151, 161, 162 Rasmussen, K., 136, 162 Rasmussen, S., 71, 72, 78, 82, 95, 100, 104 Ratet, P., 10, 18, 33, 45, 59, 62 Rech, P., 76, 100 Recourt, K., 9, 10, 11, 57, 60 Rector, M. A., 224, 239 Redmond, J. W., 9, 17, 18, 19, 20, 28, 46, 50, 53, 57, 58 Rees, W. G., 112, 162 Regina, M., 186, 190 Reginato, R. J., 109, 132, 133, 134, 142, 148, 158, 161 Reichel, C., 212, 240 Reichelt, S., 197, 240 Reinhold, V. N., 28, 41, 60 Relic, B., 5, 41, 47, 57 Renne, R., 192 Rensing, K. H., 73, 74, 104 Retzel, E., 80, 100 Reuhs, B. L., 6, 29, 43, 44, 48, 57, 58 Revel, J. P., 232, 243 Revital, K., 197, 243

259

Ribak, O., 82, 97 Ricco, R. A., 19, 61 Richards, A. J. M., 16, 42 Richards, K., 189 Richardson, A. E., 9, 18, 58, 70, 104 Richert-Po¨ ggeler, K. R., 177, 192 Riesmeier, J. W., 205, 240 Rigau, J., 72, 85, 92, 104, 105 Rinne, P. L., 227, 240, 242 Ripperger, A., 65, 106 Rivelli, M., 16, 56 Rivera-Bustamante, R. F., 220, 224, 241 Rivero, M., 19, 61 Ro, D. K., 72, 74, 104 Robbins, M. P., 19, 55, 58 Roberts, A. G., 199, 200, 208, 209, 210, 212, 213, 216, 220, 222, 229, 230, 231, 239, 240, 242, 243 Roberts, I. M., 199, 200, 208, 210, 211, 212, 213, 216, 220, 222, 230, 231, 236, 239, 240 Roberts, K., 66, 76, 105, 197, 225, 229, 236, 242 Roberts, N. J., 7, 47 Robertson, J. G., 33, 52 Roche, P., 5, 53 Rochepeau, P., 41, 47 Rodelas, B., 30, 58 Rodgers, M. W., 71, 72, 105 Rodriguez-Quinones, F., 33, 58 Rohde, A., 76, 80, 81, 82, 84, 103 Roine, E., 24, 44, 58 Rojas, M. R., 222, 238 Rolfe, B. G., 5, 6, 9, 10, 11, 17, 18, 19, 20, 28, 29, 45, 46, 50, 51, 53, 54, 57, 58 Rolland, F., 230, 240 Romantschuk, M., 24, 44, 58 Romero, C. M., 115, 146, 155, 160 Romero, D., 29, 49 Romero, J., 85, 86, 89, 90, 93, 100 Roos, C., 17, 21, 60 Rosenberg, C., 3, 53 Rosenberg, N. J., 114, 159 Rosenthal, A., 23, 28, 30, 48, 56 Ross, K. L., 30, 44 Rossen, L., 17, 18, 20, 33, 48, 52, 58 Rost, T. L., 200, 208, 229, 243 Rostas, K., 31, 58 Rotenberg, E., 112, 113, 114, 126, 128, 131, 159 Rothlisberger, S., 32, 49 Roth-Nebelsick, A., 108, 145, 162 Ruan, Y., 200, 214, 231, 240 Rubery, P. H., 5, 51 Ruegger, M. O., 66, 69, 83, 84, 92, 97, 104 Ruel, K., 87, 91, 96, 103 Ruelland, E., 72, 104 Ruiz-Medrano, R., 218, 219, 220, 221, 222, 224, 241, 243

260

AUTHOR INDEX

Ruiz-Sainz, J. E., 25, 34, 46, 54 Running, S. W., 136, 161 Russin, W. A., 204, 241 Ryan, M. D., 90, 98 Ryder, T. B., 11, 58 Rynne, F., 39, 56 S Saarinen, J., 201, 236 Saarma, M., 201, 236 Sablowski, R., 76, 98 Sablowski, R. W. M., 76, 104 Sadowsky, M. J., 19, 22, 33, 44, 58 Saedler, H., 167, 190, 218, 240 Sai¨b, A., 172, 173, 190 Saigo, K., 189 Saiki, H., 71, 102 Saji, S., 186, 192 Sakata, K., 167, 168, 178, 192 Salanoubat, M., 214, 243 Sallaud, C., 10, 33, 45, 59 Samaj, J., 71, 104, 197, 201, 235, 240, 241 Samuels, A. L., 73, 74, 104 Sanchez, F., 5, 44 Sandal, N., 6, 54, 57, 60 Sandberg, G., 77, 98 Sande, E., 17, 56 Sandholt, I., 136, 162 Sanjua´ n, J., 32, 39, 54, 58 Sanjua´ n-Pinilla, J. M., 32, 58 SanMiguel, P. J., 167, 186, 192 Santa Cruz, S., 199, 200, 208, 210, 212, 216, 220, 222, 230, 231, 239, 240 Santos, T., 116, 121, 122, 135, 136, 137, 138, 139, 141, 143, 159 Sargent, C. L., 9, 18, 58 Sarkanen, S., 70, 97 Sasaki, T., 167, 168, 178, 192 Sasamoto, S., 23, 51 Sasinowski, M., 178, 191 Sato, S., 6, 54, 57, 60 Sato, T., 71, 146, 147, 159 Sato, Y., 65, 77, 102 Sauer, N., 199, 200, 203, 205, 207, 208, 209, 210, 211, 212, 216, 219, 220, 222, 230, 231, 236, 238, 239, 240, 241, 242, 243 Sautter, C., 5, 54 Savidge, B., 65, 101 Savidge, R., 70, 104 Savoure´ , A., 11, 33, 58, 59 Sawada, H., 2, 59, 62 Saxton, M. J., 147, 156 Scheidemann, P., 13, 59 Schell, J., 33, 34, 52 Schell, M., 17, 43 Schell, M. A., 31, 59 Scheres, B., 5, 59

Scheu, A. K., 25, 59 Schlaman, H. R. M., 5, 10, 18, 31, 33, 34, 39, 54, 55, 59 Schluederberg, S. A., 16, 42 Schmid, J., 210, 211, 212, 242 Schmidt, E. L., 7, 44 Schmidt, J., 33, 34, 52 Schmidt, P. E., 10, 11, 59 Schmidt, R., 205, 242 Schmidt, T., 166, 167, 188, 192 Schmitt, R., 16, 49 Schmitz, K., 205, 237 Schmolz, E., 115, 146, 155, 160 Schmundt, D., 128, 145, 160 Schobert, C., 218, 219, 235 Schoch, G., 69, 104 Schoefer, L., 35, 59 Scholz-Starke, J., 209, 211, 238 Schoonerjans, E., 38, 49 Schrader, J., 77, 98 Schripsema, J., 10, 11, 20, 57, 62 Schroeder, B. P., 66, 106 Schubert, K. R., 72, 100 Schuch, W., 66, 76, 78, 86, 87, 88, 89, 90, 91, 93, 94, 96, 98, 102, 103, 104, 152, 158 Schulman, A. H., 174, 184, 186, 190, 191, 192 Schultz, E., 36, 59 Schultze, M., 5, 11, 28, 31, 33, 47, 58, 59 Schulz, A., 205, 207, 218, 219, 220, 237, 238, 241 Schulz, W., 76, 101 Schulze-Lefert, P., 191 Schurr, U., 128, 145, 160 Schwarzacher, T., 177, 192 Schwarz-Sommer, Z., 218, 240 Schweizer, M., 192 Scott, J. T., 80, 89, 103 Sederoff, R., 73, 76, 80, 88, 89, 90, 93, 100, 101, 103, 105 Seeger, C., 192 Seguin, A., 88, 89, 90, 105 Self, R., 36, 51 Selman, S., 92, 98 Selman-Housein, G., 72, 104 Sena, G., 218, 223, 239 Sengupta-Gopalan, C., 11, 47 Seo, D. H., 9, 53 Sessions, A., 217, 218, 241 Sever, N., 208, 210, 211, 216, 220, 230, 236 Sewalt, V. J. H., 82, 92, 99, 104 Seymour, R. S., 146, 162 Sha, K., 217, 218, 231, 232, 238 Shakya, R., 211, 241 Shalitin, D., 215, 241 Sharkey, T. D., 204, 241 Sharkov, G., 146, 148, 163

AUTHOR INDEX Sharma, A., 33, 58 Sharma, N. D., 10, 35, 36, 39, 57 Sharma, O. P., 34, 35, 39, 43 Shash, K., 218, 221, 238 Shaw, C. H., 16, 42 Shaw, S. L., 5, 6, 43, 59 Shearman, C. A., 17, 33, 58 Sheeley, D. M., 28, 41, 60 Sheen, J., 230, 240 Sheng, J., 201, 235 Shepard, R. J., 177, 192 Shevchenko, A., 169, 191 Shiba, T., 189 Shibata, D., 88, 89, 99 Shimizu, H., 150, 161 Shimpo, S., 23, 51, 52 Shirley, A. M., 86, 87, 97, 102 Shirley, B. W., 7, 59 Short, T. H., 143, 160 Sibout, R., 80, 81, 82, 84, 85, 87, 88, 89, 90, 98, 104, 105 Sijmons, P. C., 152, 158 Silk, W. K., 209, 235 Simmonds, M. S. J., 12, 52 Simoncsits, A., 31, 58 Simpson, R. J., 9, 18, 58 Singh, B., 34, 35, 39, 43 Sinha, N., 204, 224, 238, 240 Sivaguru, M., 201, 241 Sjoland, R. D., 219, 241 Skaggs, M. I., 219, 235, 236 Skalka, A. M., 182, 190 Skubatz, H., 146, 162 Smeltzer, R. H., 68, 78, 102 Smit, G., 17, 20, 31, 32, 59 Smith, C., 65, 76, 96, 98, 103 Smith, C. G., 71, 72, 105 Smith, J. T., 13, 43 Smith, P., 6, 53 Smith, R. C. G., 149, 162 Snyder, W. E., 115, 162 Soaink, H. P., 6, 31, 60 Sohlenkamp, C., 30, 46, 54, 59 Somerville, C. R., 87, 96, 102, 234, 236 Somssich, I. E., 84, 97 Song, S. C., 17, 53 Sonnewald, U., 205, 214, 215, 237, 242, 243 Sotta, B., 151, 152, 160 Sovonick, S. A., 204, 237 Spaink, H. P., 5, 9, 11, 17, 19, 25, 26, 27, 28, 30, 31, 34, 36, 41, 44, 46, 49, 54, 55, 59, 60, 62 Stabentheiner, A., 146, 147, 156 Stacey, G., 16, 17, 18, 20, 31, 32, 33, 43, 45, 53, 58, 59 Stacey, N., 66, 105 Stadler, R., 205, 211, 241 Staehelin, C., 2, 25, 31, 33, 43, 47, 56

261

Staehelin, L. A., 222, 238 Stafford, H. A., 8, 12, 60 Staggs, R., 80, 100 Stanghellini, C., 128, 134, 157, 162 Stanghellini, M. E., 148, 161 Stanley, J., 3, 53 Stapper, M., 149, 162 Staskawicz, B. J., 24, 44 Stecher, G., 13, 51 Steele, C. L., 7, 46 Steele, H. L., 10, 12, 14, 15, 19, 38, 60 Steenackers, M., 76, 96 Steven, M. D., 122, 162 Stewart, D., 70, 88, 102, 104, 105 Stier, J. C., 154, 163 Stitt, M., 205, 214, 241, 242 Stoggl, W. M., 13, 51 Stokke, D. D., 83, 99 Stoll, M., 115, 120, 122, 135, 136, 137, 138, 139, 140, 142, 159 Stougaard, J., 6, 54, 57, 60 Stracke, S., 6, 60 Strain, B. R., 110, 158 Strasser, R., 150, 157 Straume, M., 76, 98 Strausbaugh, L. D., 186, 189 Streit, W. R., 3, 60 Strittmatter, C. D., 19, 61 Strolbel, G. A., 16, 45 Sturm, A., 211, 214, 241 Stuurman, N., 26, 27, 28, 31, 55 Su, C. L., 213, 236 Subramaniam, R., 76, 98 Sugadev, R., 34, 43 Sugimoto, K., 184, 190 Sugimoto, M., 23, 51, 52 Sukhan, A., 24, 53 Sumner, L. W., 79, 96 Sun, J. Y., 69, 83, 87, 91, 101 Sundberg, B., 77, 98 Suominen, L., 17, 21, 60 Suoniema, A., 186, 190 Surin, B. P., 26, 46 Surman, A., 76, 103 Surzycki, R., 24, 48 Sussex, I. M., 226, 241 Sutherland, R. A., 112, 163 Sutton, J. M., 25, 28, 60 Suzuki, S., 85, 105 Swarup, S., 35, 57 Szczyglowski, K., 6, 54, 60, 214, 241 T Tabata, S., 6, 23, 51, 52, 54, 57, 60 Tachibana, H., 148, 160 Tacke, E., 215, 237 Tagieva, N. E., 175, 192 Taira, S., 24, 44

262

AUTHOR INDEX

Tak, T., 5, 9, 11, 28, 41, 44, 60 Takabe, K., 71, 72, 88, 99, 102, 105 Takahashi, K., 192 Takeuchi, C., 23, 51, 52 Takeuchi, M., 71, 72, 105 Tamagnone, L., 66, 76, 105 Tan, C. S., 148, 163 Taneda, H., 90, 93, 101 Tang, G. Q., 214, 241 Tanner, B., 154, 156 Tanner, C. B., 109, 112, 133, 158, 163 Tanskanen, J., 184, 190 Tao, H., 30, 55, 60 Taylor, N., 172, 173, 189 Teeri, T. T., 77, 98 Telli, G., 154, 158 Temin, H. M., 168, 192 Tenkouano, A., 186, 189 Tepfer, D., 38, 49 Teplitski, M., 6, 54 Terris, B., 175, 192 Teuber, L. R., 19, 57 Teutonico, R. A., 66, 105 Teutsch, H., 72, 97 Tewfik, M. S., 39, 51 The Arabidopsis Genome Initiative, 167, 168, 192 Thebault, P., 24, 48 Thomas, B., 19, 58 Thomas, J. E., 175, 189 Thomas-Oates, J. E., 5, 19, 28, 30, 36, 49, 54 Thompson, G. A., 218, 219, 220, 235, 236, 237 Thottappilly, G., 175, 186, 189 Thow, G., 234, 239 Tiedemann, R., 220, 241 Tikhonov, A. P., 167, 186, 192 Tilney, L. G., 199, 241 Tilney, M. S., 199, 241 Timmers, A. C. J., 5, 60, 230, 242 Ting, I. P., 153, 157 Tiollais, P., 173, 190 Tollier, M.-T., 85, 86, 88, 89, 90, 93, 95, 96, 98, 105 Tomimura, Y., 83, 99 Tomkins, J., 178, 191 Tonelli, C., 76, 99 Torres, M. A., 85, 92, 105 Toval, G., 85, 86, 89, 90, 93, 100 Traub, O., 232, 243 Treilhou, M., 30, 48 Tremolieres, A., 38, 50 Trewavas, A. J., 201, 237 Tristem, M., 175, 190 Truchet, G., 3, 5, 46, 53, 60, 230, 242 Truernit, E., 203, 207, 208, 210, 211, 212, 216, 219, 220, 238, 242 Tsai, C. J., 83, 85, 86, 99, 105 Tsao, C. C., 68, 78, 102

Tsutsumi, N., 146, 163 Tu, J. C., 148, 163 Tu, Z.-X., 175, 192 Tucker, E. B., 201, 242 Tukey, J. W., 115, 163 Tully, R. E., 22, 44 Turek, R., 192 Turgeon, R., 200, 202, 205, 206, 208, 213, 235, 239, 240, 242 Turner, A., 197, 242 Turner, S. R., 66, 87, 99 U Udagama-Randeniya, P., 70, 104 Uemura, M., 146, 147, 159 Uhlen, M., 77, 98 Ullmann, P., 69, 104 Ullrich, C. I., 210, 230, 240 Ulrich, E., 80, 100 Umehara, Y., 6, 57 Umezawa, T., 68, 69, 73, 78, 89, 99, 100, 101, 102 Unsworth, M. H., 124, 160 Uralil, J., 24, 53 V Vaadia, Y., 215, 235 Vain, P., 224, 242 Valcke, R., 149, 157 Valderrama, B., 29, 49 Vallet, C., 85, 86, 93, 99 van Bavel, C. H. M., 127, 136, 155 Van Beeumen, J., 78, 79, 84, 85, 102 Van Bel, A. J., 200, 202, 203, 205, 206, 209, 211, 212, 229, 238, 242 van Berkum, P. B., 2, 47 van Brussel, A. A. N., 5, 9, 10, 11, 17, 20, 28, 41, 44, 57, 60, 62 Van Caeneghem, W., 123, 149, 157 Vance, C. P., 26, 61 van den Berg, J. D. J., 5, 19, 36, 54 Vandenbol, M., 24, 48 Van den Bosch, K., 5, 45 Van de Peer, Y., 76, 80, 81, 82, 84, 103 van der Lee, F. M., 152, 158 Vanderleyden, J., 3, 25, 26, 28, 55, 61 van der Schoot, C., 227, 230, 240, 242 Van Der Straeten, D., 122, 147, 148, 150, 153, 157, 163 Vanderveer, P. J., 204, 241 van der Winden, J., 177, 184, 187, 190, 191 Vandewiel, C., 5, 59 Van Doorsselaere, J., 76, 80, 85, 86, 87, 88, 89, 90, 93, 95, 100, 103, 104, 105 Van Eck, H., 5, 59 Van Gijsegem, F., 19, 45 van Kammen, A., 5, 59

AUTHOR INDEX Vanleberge, G. C., 145, 147, 163 Van Montagu, M., 17, 21, 31, 33, 49, 55, 76, 78, 79, 84, 85, 86, 88, 89, 90, 92, 93, 95, 96, 102, 105, 122, 147, 148, 150, 157, 163 van Rhijn, P., 7, 25, 26, 28, 61 Van Rijen, H. V. M., 203, 206, 229, 242 Van Spronsen, P. C., 5, 60 van Tunen, A. J., 11, 57 van Vliet, T. B., 5, 60 Vaucheret, H., 220, 224, 240 Vavasseur, A., 151, 152, 160 Veit, B., 217, 228, 238 Veitch, N. C., 12, 52 Vela, S., 35, 61 Velasco, L., 39, 54 Vercesi, A. E., 146, 159 Verdaguer, B., 172, 173, 189 Verhoef, A., 131, 157 Verma, D. P. S., 19, 58 Vermeiren, N., 3, 55 Vermerris, W., 70, 85, 89, 96, 98, 102 Vernhettes, S., 186, 191 Verreth, C., 26, 28, 61 Vershinin, A. V., 184, 192 Vesk, M., 197, 242 Vicient, C. M., 174, 192 Vidal, A., 136, 137, 149, 151, 160 Vidali, L., 5, 44 Vignols, F., 85, 92, 105 Vinardel, J. M., 34, 54 Viola, R., 208, 209, 210, 230, 242 Viprey, V., 5, 23, 24, 25, 54, 61 Vlassak, K. M., 25, 26, 28, 61 Vogt, T., 87, 96 Voinnet, O., 224, 242 Volkmann, D., 197, 201, 209, 235, 240, 241 von Schaewen, A., 205, 242 Vorholter, F. J., 24, 48 Voytas, D. F., 171, 174, 186, 193 Vuylsteke, D., 186, 189 W Wagner, G. J., 72, 105 Wagner, M. L., 19, 61 Wais, R., 5, 28, 47 Wais, R. J., 5, 61 Wakiyama, Y., 151, 163 Walker, N. A., 201, 237 Walker, S. A., 5, 28, 61 Wang, C. X., 115, 162 Wang, H. B., 70, 97 Wang, H. L., 215, 218, 219, 221, 222, 235, 243 Wang, J., 167, 193 Wang, M.-B., 187, 192 Wang, P.-C., 175, 192 Wang, Q., 84, 97

263

Wang, S. Y., 167, 168, 189 Wang, X., 76, 98 Wang, Y., 215, 241 Warmbrodt, R. D., 232, 243 Warnberg, L., 201, 236 Watanabe, A., 23, 51, 146, 163 Waterhouse, P. M., 187, 193 Watkins, P., 201, 237 Watson, M. D., 16, 42 Watts, D. G., 127, 133, 136, 158 Weber, H., 211, 242 Webster, E. A., 86, 92, 93, 99, 103 Wei, W. S., 24, 58 Weidner, S., 24, 48 Weigel, D., 215, 217, 218, 241, 243 Weinman, J. J., 9, 11, 18, 29, 45, 53, 58 Weinrich, S. L., 191 Weinstein, E. G., 224, 238 Weisshaar, B., 76, 99 Wells, B., 33, 52, 197, 242 Wells, D. H., 18, 24, 48, 61 Werck-Reichhart, D., 69, 72, 97, 104 Werner, D., 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 38, 44, 52, 56, 59, 60 Wery, J., 19, 57 Wetzel, A., 13, 59 Weyers, J. D. B., 132, 150, 163 Wheeler, J., 92, 98 Whetten, R., 73, 76, 80, 88, 89, 100, 101, 103, 105 White, R. G., 197, 201, 240, 242 Wiegand, C. L., 110, 156 Wigge, P. A., 215, 243 Wigley, G., 110, 144, 157 Wijffelman, C. A., 9, 10, 11, 17, 20, 25, 46, 60, 62 Wijfjes, A. H. M., 5, 25, 46, 60 Wilkinson, A., 30, 58 Wilkinson, M., 175, 190 Willecke, K., 232, 243 Willemse, J., 6, 53 Williams, L. E., 208, 243 Williams, M. N. V., 29, 58 Williams, P., 30, 58 Willmitzer, L., 205, 214, 242, 243 Wilson, K. E., 17, 18, 20, 48 Winder, J., 92, 98 Wing, R. A., 178, 188 Winkel-Shirley, B., 71, 105 Winter, J., 35, 61 Wisniewski, M., 153, 154, 156, 158, 163 Wisniewski-Dye, F., 6, 30, 58, 61 Witty, M., 184, 188 Wolf, S., 197, 199, 208, 211, 215, 216, 220, 230, 235, 236, 237, 239, 241, 243 Wolfe, J., 154, 156 Wolfender, J.-L., 12, 13, 46, 50 Wolf-Watz, H., 19, 45 Wolters, A. M., 5, 59

264

AUTHOR INDEX

Wong, C. H., 3, 53 Wong, C. M., 39, 62 Wong, G. K. S., 167, 193 Wong, K., 24, 48 Woo, H.-H., 7, 62 Woo, S.-S., 178, 191 Woo, Y. M., 212, 238 Wood, H. N., 66, 95 Wood, J. M., 36, 59 Wood, T. C., 178, 188, 191 Woodward, F. I., 152, 158 Workmaster, B. A. A., 154, 163 Wright, D. A., 171, 174, 186, 193 Wright, K. M., 200, 207, 208, 210, 212, 219, 239, 243 Wu, J. Z., 167, 168, 178, 192 Wu, X., 215, 243 Wu, Y., 220, 237 X Xi, C.-W., 3, 55 Xia, J., 193 Xia, Y. J., 76, 96 Xiang, Y., 218, 219, 221, 222, 235, 243 Xiong, Y., 168, 174, 178, 179, 180, 193 Xoconostle-Cazares, B., 218, 219, 221, 222, 235, 238, 243 Y Yahalom, A., 197, 232, 243 Yahiaoui, N., 80, 87, 88, 89, 90, 103, 105, 106 Yamada, M., 23, 51, 52 Yamamoto, K., 167, 168, 178, 192 Yang, Y., 34, 50 Yang, Z., 201, 241 Yano, T., 127, 128, 162 Yanofsky, M. F., 65, 101, 217, 218, 241

Yasuda, S., 68, 96 Ye, Z. H., 65, 66, 84, 85, 90, 91, 106 Yeh, K. C., 24, 34, 48, 62 Ylstra, B., 209, 241 Yoo, B. C., 218, 219, 221, 222, 238, 243 York, W. S., 28, 41, 60 Yoshida, S., 6, 60 Yoshinaga, A., 85, 86, 87, 93, 96, 99 Young, J. M., 2, 59, 62 Young, L. Y., 35, 61 Yu, J., 85, 86, 97, 167, 193 Yu, Y., 178, 191 Yuan, J., 24, 58 Yuki, S., 189 Yung, M. H., 76, 98 Z Zaat, S. A. J., 10, 17, 20, 62 Zakharyev, V. M., 175, 192 Zalensky, A., 5, 59 Zambryski, P. C., 200, 215, 216, 217, 218, 227, 230, 231, 232, 234, 236, 237, 238, 243 Zamski, E., 215, 235 Zelinski, L. J., 144, 161 Zhang, D.-X., 174, 188 Zhang, F., 18, 43 Zhang, L. M., 70, 102 Zhang, X. Q., 167, 193 Zhang, Y. J., 167, 168, 189 Zhong, R. Q., 65, 66, 84, 85, 90, 91, 106 Zhou, Y. H., 69, 83, 87, 91, 101, 167, 193 Zhu, J. J., 167, 168, 189 Zhu, T., 76, 98, 200, 208, 229, 243 Zhu, Y., 207, 220, 238 Zimmerlin, A., 71, 72, 105 Zon, J., 9, 17, 56, 62 Zrenner, R., 214, 243 Zuanazzi, J. A. S., 10, 18, 33, 45, 59, 62 Zwartkruis, F., 5, 59

SUBJECT INDEX

A A. thaliana. See Arabidopsis thaliana AHL. See N-acyl homoserine lactone Arabidopsis genes in, 80–90 CAD with, 88–90 CCoAOMT with, 84–5 CCR with, 87 C3H with, 84 C4H with, 83 COMT with, 85 F5H with, 86–7 4CL with, 83–4 HCT with, 84 PAL with, 82 lignification in development of, 65–6 lignin biosynthesis in, 65–6, 72–6, 80–90, 92 PD regulation in, 197, 207, 208, 209, 212, 215, 223, 224, 225, 226, 227, 230, 231, 234 thermal imaging applications with, 151–3 transcription regulation in, 75–6 Arabidopsis thaliana myosin in, 198 phloem unloading mechanisms in, 210–11 Arum maculatum thermogenesis in, 146 Azorhizobium caulinodans legume root infection with, 2 B B. japonicum. See Bradyrhizobium japonicum Banana streak badnavirus (BSV) pararetroviruses phylogenies with, 168, 175, 182 Barley phloem unloading mechanisms in, 210 Bradyrhizobium japonicum flavonoid synthesis/release in, 10–11 legume root infection with, 2, 10–11, 16, 18, 23, 24, 30, 31–2, 35, 36, 37, 38–9 BSV. See Banana streak badnavirus

C CAD. See Cinnamyl alcohol dehydrogenase Caffeic acid O-methyltransferase (COMT) lignin biosynthesis with, 68, 71, 72, 78, 79, 85–6, 90–3 monolignol biosynthetic pathway with, 68 Caffeoyl-COA O-methyltransferase (CCoAOMT) lignin biosynthesis with, 68, 71, 72, 76, 77, 78, 79, 84–5, 90 monolignol biosynthetic pathway with, 68 Calcium-dependent protein kinase (CDPK) PD with, 198, 201 CaMV. See Cauliflower mosaic caulimovirus Capillary electrophoresis coupled to mass spectrometry (CE-MS) flavonoid identification with, 13 Capillary zone electrophoresis (CZE) flavonoid identification with, 13, 14 Capsular polysaccharides. See K-antigen polysaccharides Carrot phloem unloading mechanisms in, 211 Cassava vein mosaic cavemovirus (CsVMV) pararetroviruses phylogenies with, 168, 169, 175 Cauliflower mosaic caulimovirus (CaMV) pararetroviruses phylogenies with, 168, 175 CCoAOMT. See Caffeoyl-COA O-methyltransferase CCR. See Cinnamoyl-COA reductase CDPK. See Calcium-dependent protein kinase CE-MS. See Capillary electrophoresis coupled to mass spectrometry CH. See Cysteine-histidine C3H. See Cinnamate 3-hydroxylase C4H. See Cinnamic acid 4-hydroxylase Cinnamate 3-hydroxylase (C3H) lignin biosynthesis with, 68, 69, 72, 74, 75, 77, 78, 84, 92 monolignol biosynthetic pathway with, 68 Cinnamic acid 4-hydroxylase (C4H) lignin biosynthesis with, 68, 72, 74, 75, 76, 78, 81, 82–3

266

SUBJECT INDEX

Cinnamic acid 4-hydroxylase (C4H) (cont. )

monolignol biosynthetic pathway with, 68 Cinnamoyl-COA reductase (CCR) lignin biosynthesis with, 68, 71, 72, 76, 77, 78, 87–8, 91, 92, 93, 94 monolignol biosynthetic pathway with, 68 Cinnamyl alcohol dehydrogenase (CAD) lignin biosynthesis with, 68, 69, 71–2, 76, 77, 78, 81, 88–90, 91–94 monolignol biosynthetic pathway with, 68 Coat protein (CP) retroelement of, 173 COMT. See Caffeic acid O-methyltransferase Coniferyl alcohol lignin polymer with, 66, 67, 69, 70, 73, 89 CP. See Coat protein Crop water stress indices (CWSI) mixed pixels and, 136 thermal imaging application of, 132–6 CsVMV. See Cassava vein mosaic cavemovirus Cucurbita maxima phloem unloading mechanisms in, 208, 219 CWSI. See Crop water stress indices Cysteine-histidine (CH) retroelement of, 166, 172, 179, 182, 184 CZE. See Capillary zone electrophoresis D DEF movement through PD of, 217, 220, 228 E Envelope gene viral/nonviral element phylogenies with, 171–174 EPS. See Extracellular polysaccharides Erwinia amylovora, 149 EST. See Expressed sequence tags Expressed sequence tags (EST) lignin biosynthesis with, 77 Extracellular polysaccharides (EPS) legume-Rhizobia interactions with, 4, 6, 29, 42 F Ferulate 5-hydroxylase (F5H) lignin biosynthesis with, 68, 72, 74, 75, 81, 86–7, 91, 93, 94

monolignol biosynthetic pathway with, 68 F5H. See Ferulate 5-hydroxylase Flavonoids legume-Rhizobia interactions with 1–42 chemoattractants in, 16–17 conclusion to, 41–2 diversity of, 14–31, 20–21, 22, 26, 27 early signaling events in, 4 flavonoid-dependent gene expression in, 28–31 flavonoid identification in, 12–14, 15 flavonoid reception in, 26, 27, 31–41 flavonoid synthesis/release in, 7–12 functions of, 14–31, 20–21, 22, 26, 27 growth stimulators in, 16–17 introduction to, 1–3 metabolism in, 34–41 nod factor synthesis in, 17–19, 20–1, 22 nodulation gene inducers in, 17–19, 20–1, 22 overview of, 3–7 regulatory NodD protein interaction in, 20–1, 26, 27, 31–4 TTSS induction in, 19–28, 26, 27 FLO movement through PD of, 217, 220 4-coumarate COA ligase (4CL) lignin biosynthesis with, 68, 70, 71, 76, 77, 78, 81, 83–4, 91, 93, 94 monolignol biosynthetic pathway with, 68 Fourier-transform infrared spectroscopy (FT-IR) lignin polymer analyzed with, 80 G G. max. See Glycine max Gas-chromatography-mass spectrometry (GC-MS) flavonoid identification with, 12, 14 GLO movement through PD of, 217, 220 Glycine max legume-Rhizobia interactions with, 11, 17, 20, 32 H HCT. See Hydroxycinnamoyl-COA shikimate/quinate hydroxycinnamoyl transferase HCWSI. See Histogram-derived crop water stress indices Hepadnaviridae, 170

SUBJECT INDEX High-performance chromatography (HPLC) flavonoid identification with, 12 High-performance thin-layer chromatography (HPTLC-UV) flavonoid identification with, 13, 14 Histogram-derived crop water stress indices (HCWSI) thermal imaging application of, 138, 139 HPLC. See High-performance chromatography HPTLC-UV. See High-performance thinlayer chromatography HR. See Hypersensitive response Hydroxycinnamoyl-COA shikimate/quinate hydroxycinnamoyl transferase (HCT) lignin biosynthesis with, 69, 81, 84 Hypersensitive response (HR) thermal imaging applications with, 149 I ICTV. See International Committee on Taxonomy of Viruses Infrared sensing applications of aerodynamic properties in, 144–5 agronomic effects in, 150–1 Arabidopsis in, 152 biophysical properties in, 144–5 CWSI in, 132–6 disease in, 148–50 environmental sensitivity/resolution in, 138, 140 frost tolerance/damage in, 153–4 genetic screening in, 151–3 heat capacity in, 145 infection in, 148–50 irrigation scheduling in, 143–4 measurement optimization in, 138–43 metabolic processes in, 145–7 pollution in, 150–1 QTL mapping in, 153 spectral properties with, 141 stomatal behavior in, 132 stomatal conductance in, 131–44 stress indices in, 131–44 sun/view angle with, 137, 141–3 temperature variability in, 136–8 thermal dynamics with, 138, 141 TMV in, 149 transpiration in, 131–44 water loss in, 130, 131–44 plant energy balance with, 124–31 basics of, 124–6 boundary-layer conductance in, 128 energy balance equation in, 125–6 evaporation in, 127–31

267

isothermal radiation in, 126 mass/heat transfer in, 124–5 net radiation in, 128–9 reference surfaces used in, 127 stomatal conductance in, 124–5, 127–131 thermal dynamics in, 126 plant physiology/ecophysiology with, 108–55, 142, 154 black body radiation in, 111 concluding comments on, 155 image analysis in, 119–23 image classification in, 121–2, 123 image enhancement in, 119–20 image ratios in, 122–3 introduction to, 108–10 leaf temperature in, 108–10 Phaseolus vulgaris in, 117, 118, 144 remote temperature measurement in, 111–13 SDD in, 109, 133 sensor technology improvements in, 110 thermal emissivities in, 112 thermal radiation in, 111–23 thresholding in, 120–1 temperature estimation errors with, 113–19 background temperature and, 113–15 cross-talk in, 115–16 emissivity in, 113–15 Gaussian noise in, 115–16 radiometric v. aerodynamic temperature in, 116–17 spatial calibration errors in, 116 spatial resolution in, 117–18 temporal resolution in, 118–19 INT. See Integrase Integrase (INT) retroelement of, 166, 173, 183 International Committee on Taxonomy of Viruses (ICTV), 169, 170, 171, 178 Interretrotransposon amplified polymorphic (IRAP) retroelements phylogenies with, 184 IRAP. See Interretrotransposon amplified polymorphic K K-antigen polysaccharides (KPS) legume-Rhizobia interactions with, 4, 6, 29, 42

268

SUBJECT INDEX

KN1 movement through PD of, 217, 220, 222–24, 228 KPS. See K-antigen polysaccharides L LC-MS. See Liquid-chromatography-mass spectrometry Leaf temperature heat capacity with, 145 plant energy balance and, 124–31 plant physiology with, 108–10 stomatal conductance as function of, 127–31 thermal imaging and, 108–10, 113–15, 121–2, 124–31, 135, 136–7, 138–40, 143, 144–5, 149, 151 Legume root infection Rhizobia in, 1–42 chemoattractants with, 16–17 conclusion to, 41–2 diversity of, 14–31, 20–1, 22, 26, 27 early signaling events with, 4 flavonoid-dependent gene expression in, 28–31 flavonoid identification with, 12–14, 15 flavonoid reception by, 26, 27, 31–41 flavonoid synthesis/release with, 7–12 functions of, 14–31 growth stimulators with, 16–17 introduction to, 1–3 metabolism with, 34–41 nod factor synthesis with, 17–19, 20–1, 22 nodulation gene inducers with, 17–19, 20–1, 22 overview of, 3–7 regulatory NodD protein interaction with, 20–1, 26, 27, 31–4 TTSS induction with, 19–28 LFY movement through PD of, 217, 220, 228 Lignin biosynthesis cell biology of, 70–5 alternative models in, 74–5 enzyme immunolocalization in, 70–2 metabolite channelling in, 72 monolignols exported to cell wall in, 73–4 commercial application with, 92–4 composition analysis of, 79–80 conclusions on, 94 content analysis of, 79–80 genes involved in, 80–1 introduction to, 64–5 lignin polymer structure and, 67 metabolic regulation in, 78–9

monolignol biosynthetic pathway with, 68–70 basic pathway in, 68 different models of, 69–70 recent revisions in, 68–9 monolignol polymerization with, 70 perspectives on, 94 plant development with, 65–7 plants with modified expression of, 81–92 CAD in, 88–90 CCoAOMT in, 84–5 CCR in, 87–88 C3H in, 84 C4H in, 82–3 COMT in, 85–6 F5H in, 86–7 4CL in, 83–4 HCT in, 84 multiple gene manipulation in, 90–2 PAL in, 82 structure analysis of, 79–80 transcriptional regulation in, 75–7 factors with, 75–7 microarray transcript profiling with, 77 regulatory elements with, 75–7 Lipopolysaccharides (LPS) legume-Rhizobia interactions with, 4, 6, 29–30, 42 Liquid-chromatography-mass spectrometry (LC-MS) flavonoid identification with, 12, 13 Long terminal repeat (LTR) retroelement of, 166, 168, 169, 171, 172–3, 174, 178, 182, 184, 187, 188 Lotus japonicus legume-Rhizobia interactions with, 6 LPS. See Lipopolysaccharides LTR. See Long terminal repeat M M. loti. See Mesorhizobium loti M. sativa. See Medicago sativa M. truncatula. See Medicago truncatula Mass spectrometry (MS) flavonoid identification with, 12 Medicago sativa legume-Rhizobia interactions with, 3, 10, 11, 16, 17, 20 Medicago truncatula legume-Rhizobia interactions with, 5, 6 phloem unloading mechanisms in, 208–9, 210 MEKC. See Micellar electrokinetic capillary chromatography Mesorhizobium loti legume root infection with, 2, 11, 19, 23, 35, 36, 38–9

SUBJECT INDEX Micellar electrokinetic capillary chromatography (MEKC) flavonoid identification with, 13 Miniature inverted-repeat tandem elements (MITE) retroelement of, 167 MITE. See Miniature inverted-repeat tandem elements Movement protein (MP) viral/nonviral element phylogenies with, 171, 173, 178, 179 MP. See Movement protein MS. See Mass spectrometry N N. benthamiana. See Nicotiana benthamiana N. edwardsonii. See Nicotiana edwardsonii N. tabacum. See Nicotiana tabacum N-acyl homoserine lactone (AHL) legume-Rhizobia interactions with, 4, 6, 30 Nicotiana benthamiana phloem unloading mechanisms in, 208 Nicotiana edwardsonii, 177 Nicotiana sylvestris, 149 Nicotiana tabacum phloem unloading mechanisms in, 208–9 NMR. See Nuclear magnetic resonance Nod factors genes required for synthesis of, 17–19 legume-Rhizobia interactions with, 4–7, 10–11, 17–19, 26, 28, 30, 31, 33, 41 Nuclear magnetic resonance (NMR) flavonoid identification with, 12 lignin polymer analyzed with, 80 O Open reading frames (ORF) retroelement of, 166, 171, 172, 178–9, 184 ORF. See Open reading frames Orthoretrovirineae, 170–1 P P. hybrida. See Petunia hybrida P. sativum. See Pisum sativum P. vulgaris. See Phaseolus vulgaris P-coumarate 3 hydroxylase lignin biosynthesis with, 84 PAL. See Phenylalanine ammonia lyase Para-coumaryl alcohol lignin polymer with, 67, 84 Pararetrovirineae, 170 Pararetroviruses phylogenies of, 165–88 BSV in, 170, 175, 182 BVS in, 170, 175, 182

269

CaMV in, 170, 175 genome retroelements with, 167–8 introduction to, 166–9 plant genome organization with, 166–7 RT with, 168–9 PBS. See Primer binding site PD. See Plasmodesmata PDU. See Pore-plasmodesmata units Petunia hybrida, 177 Petunia vein clearing cavemovirus (PVCV) pararetroviruses phylogenies with, 170, 177 Phaseolus vulgaris legume-Rhizobia interactions with, 9, 11, 12, 17, 21 thermal imaging with, 117, 118, 144 Phenylalanine ammonia lyase (PAL) lignin biosynthesis with, 68, 71–2, 76, 77, 78, 81, 82, 92 monolignol biosynthetic pathway with, 68 Philodendron selloum thermogenesis in, 145 Phylogenies ICTV and, 169, 170, 171, 177 pararetroviruses in, 165–87 abstract of, 166 genome retroelements with, 167–8 introduction to, 166–169 plant genome organization with, 166–7 RT with, 168–9 retroelements in, 167–8, 169–71 amplification/host control with, 184–6 Badnavirus with, 170, 175 Caulimovirus with, 170, 175, 179 classification of, 170 conserved regions of, 176, 177–80, 179–82, 183, 184, 185 Hepadnaviridae with, 170 horizontal transfer with, 176, 179, 181, 183, 184, 185, 186–7 IRAP with, 186 markers as, 186 Orthoretrovirineae with, 170–1 Pararetrovirineae with, 170 plant genome interaction with, 182–7 relationships between, 170, 174, 176, 178–82 resistance with, 187 Retrales as, 170, 171 Retrovirales as, 170–1 RH alignment with, 181 RT alignment with, 180 sequence motifs with, 176, 180, 181, 183, 184, 185, 186–7 silencing with, 187

270

SUBJECT INDEX

Phylogenies (cont. )

viral/nonviral elements of, 171–78 envelope gene with, 171–74 MP with, 171, 173, 178, 179 pararetroviruses with, 175–7 Pinus taeda lignin biosynthesis with, 76, 77, 78 Pisum sativum legume-Rhizobia interactions with, 17, 20 Plasmodesmata (PD) Arabidopsis with, 198, 207, 208, 209, 212, 217, 223, 224, 225, 226, 227, 230, 231, 234 biogenesis of, 199–200 conclusions on, 232–4 electron microscopy of, 197, 200, 202, 212, 219, 233 future prospects with, 231–3 introduction to, 196 macromolecule manipulation of, 215–25, 218 developmental process influenced by, 229–30 mechanisms of, 221–22 passive/active transport in, 215–17, 219 PTGS with, 223–4, 229 RT-PCR with, 225 SAM in, 217, 224 SHR with, 218, 223 transcription factors in, 217, 218 transport in phloem with, 219–24 mediated trafficking of, 213–23 myosin in, 198, 199, 201, 209 permeability in structure of, 199–200 permeability regulation in, 200–1 CDPK in, 201 SEL in, 199–200 physiological regulation of, 202–16 PDU in, 201, 203, 204, 205, 217 phloem unloading into sinks in, 208–212, 210–211 RFO with, 205–7 SE-CC in, 202–7, 209, 218, 220, 229 solute transport with, 206–7 source leaf phloem loading with, 203–6 source/sink relationships in, 212–15 plant development through regulation of, 196–233 SEL of, 199–201, 203, 209, 216–17, 219, 221, 222, 231, 232–4 macromolecule transport with, 216–17, 219, 221, 222 structure of, 196–7 CDPK in, 197 symplasmic domain regulation with, 225–32

developmental signaling with, 225–9 organogenesis with, 229–32 SAM in, 225–9 Pore-plasmodesmata units (PDU) PD regulation with, 203, 205, 206, 207, 219 Post-transcriptional gene silencing (PTGS) macromolecule trafficking of PD with, 223–4, 229 Potato phloem unloading mechanisms in, 210 Primer binding site (PBS) retroelement of, 172 PTGS. See Post-transcriptional gene silencing PVCV. See Petunia vein clearing cavemovirus Pyrolysis gas chromatograph-mass spectrometry (Pyrolysis GC-MS) lignin structure analyzed with, 80 Q QTL. See Quantitative trait loci Quantitative trait loci (QTL) thermal imaging applications of, 152–3 R R. etli. See Rhizobium etli R. galegae. See Rhizobium galegae R. leguminosarum. See Rhizobium leguminosarum R. lupini. See Rhizobium lupini R. tropici. See Rhizobium tropici Raffinose family oligosaccharides (RFO) PD regulation with, 205–7 Retrales retroelement phylogeny of, 169, 170 Retroelements phylogenies of, 167–8, 169–71 amplification/host control with, 184–6 Badnavirus with, 170, 175 Caulimovirus with, 170, 175, 179 classification of, 170 conserved regions of, 176, 179–82, 183, 184, 185 Hepadnaviridae with, 170 horizontal transfer with, 176, 180, 181, 183, 184, 185, 186–7 IRAP with, 186 markers as, 186 Orthoretrovirineae with, 170–1 Pararetrovirineae with, 170 plant genome interaction with, 182–7 relationships between, 170, 174, 176, 178–82

SUBJECT INDEX resistance with, 187 Retrales as, 170, 171 Retrovirales as, 170–1 RH alignment with, 181 RT alignment with, 180 sequence motifs with, 176, 180, 181, 183, 184, 185, 186–7 silencing with, 187 Retrovirales retroelement phylogeny of, 170–1 Reverse transcriptase-polymerase chain reaction (RT-PCR) macromolecule trafficking of PD with, 224 Reverse transcriptase (RT) retroelement of, 166, 168–9, 170, 173, 174, 178–9, 180, 184, 187 RFO. See Raffinose family oligosaccharides RH. See RNase H Rhizobia legume root infection with, 1–42 chemoattractants in, 16–17 conclusion to, 41–2 diversity of, 14–31 early signaling events in, 4 flavonoid-dependent gene expression in, 28–31 flavonoid identification in, 12–14, 15 flavonoid reception in, 26, 27, 31–41 flavonoid synthesis/release in, 7–12 functions of, 14–31 growth stimulators in, 16–17 introduction to, 1–3 metabolism in, 34–41 nod factor synthesis in, 17–19, 20–1, 22 nodulation gene inducers in, 17–19, 20–1, 22 overview of, 3–7 regulatory NodD protein interaction in, 20–1, 26, 27, 31–4 TTSS induction in, 19–28, 26, 27 Rhizobium etli legume root infection with, 2, 23, 30, 31, 32 Rhizobium galegae legume root infection with, 2 Rhizobium leguminosarum flavonoid synthesis/release in, 7–11 growth stimulators in, 16–17 legume root infection with, 2, 9–11, 16–18, 24–6, 28, 30, 31, 33–4, 38–40, 42 TTSS induction in, 24–26 Rhizobium lupini legume root infection with, 2, 16, 19

271

Rhizobium tropici legume root infection with, 2, 31 Rice tungro bacilliform tungrovirus (RTBT) pararetroviruses phylogenies with, 170, 178 Ricinus communis phloem unloading mechanisms in, 208 RNase H (RH) retroelement of, 166, 173, 179, 181, 187 RT. See Reverse transcriptase RTBT. See Rice tungro bacilliform tungrovirus S S. fredii. See Simorhizobium meliloti S. meliloti. See Simorhizobium meliloti SAM. See Shoot apical meristem SbCMV. See Soybean chlorotic mottle soymovirus SDD. See Stress degree day SE-CC. See Sieve element-companion cell SEL. See Size exclusion limit Shoot apical meristem (SAM) developmental signaling modeled with, 225–9, 230 macromolecule manipulation with, 217, 224 organization of, 225–7 PD with, 217, 224, 225–9, 230 transcription factors in, 217, 218 Short root protein (SHR) movement through PD of, 218, 223 SHR. See Short root protein Sieve element-companion cell (SE-CC) PD regulation with, 202–7, 209, 218, 220, 229 Simorhizobium fredii legume root infection with, 2, 17, 19, 22–5, 28, 29, 32 Simorhizobium meliloti flavonoid-dependent gene expression in, 29–31 flavonoid synthesis/release in, 9–11 growth stimulators in, 16–17 legume root infection with, 2, 3, 6, 9–11, 16–18, 24, 29–32, 34, 36, 38, 39 Sinapyl alcohol lignin polymer with, 67, 69, 70, 73, 89 Size exclusion limit (SEL) PD with, 199–201, 203, 209, 216–17, 219, 221, 222, 231, 232–4 definition of, 201 macromolecule transport through, 216–17, 219, 221, 222 permeability with, 199–201

272

SUBJECT INDEX

Soybean chlorotic mottle soymovirus (SbCMV) pararetroviruses phylogenies with, 170 Stomatal conductance boundary-layer conductance with, 128–9 CWSI and, 132–6 definition of, 123 environmental sensitivity/resolution with, 138, 140 evaporation with, 127–31 measurement optimization with, 138–43 net radiation with, 128–9 reference surfaces used with, 127 spectral properties with, 141 sun/view angle with, 137, 141–3 temperature variability in, 136–8 thermal dynamics with, 126, 138–41, 142 thermal imaging with, 124–5, 127–31, 131–44, 146–7, 150–3 Stress degree day (SDD) thermal sensing with, 109, 133 Symbiosis receptor-like kinase (SYMRK) legume-Rhizobia interactions with, 6 Symplocarpus foetidus thermogenesis in, 146 SYMRK. See Symbiosis receptor-like kinase T T. repens. See Trifolium repens T. subterraneum. See Trifolium subterraneum Taxonomy. See Phylogenies Texas pepper virus (TVP) pararetroviruses phylogenies with, 175 Thermal imaging applications of aerodynamic properties in, 144–5 agronomic effects in, 150–1 Arabidopsis in, 152–3 biophysical properties in, 144–5 CWSI in, 132–6 disease in, 148–50 environmental sensitivity/resolution in, 138, 140 frost tolerance/damage in, 153–4 genetic screening in, 151–3 heat capacity in, 145 infection in, 148–50 irrigation scheduling in, 143–4

measurement optimization in, 138–43 metabolic processes in, 145–7 pollution in, 150–1 QTL mapping in, 153 spectral properties with, 141 stomatal behavior in, 132 stomatal conductance in, 131–44 stress indices in, 131–44 sun/view angle with, 136, 141–3 temperature variability in, 136–8 thermal dynamics with, 138–41, 142 TMV in, 149 transpiration in, 131–44 water loss in, 130, 131–44 plant energy balance with, 124–31 basics of, 124–6 boundary-layer conductance in, 128 energy balance equation in, 125–6 evaporation in, 127–31 isothermal radiation in, 126 mass/heat transfer in, 124–5 net radiation in, 128–9 reference surfaces used in, 127 stomatal conductance in, 124–5, 127–31 thermal dynamics in, 126 plant physiology/ecophysiology with, 108–55, 112, 130, 133, 135, 138, 140, 142, 154 black body radiation in, 111 concluding comments on, 155 image analysis in, 119–23 image classification in, 121–2, 123 image enhancement in, 119–20 image ratios in, 122–3 introduction to, 108–10 leaf temperature in, 108–10 Phaseolus vulgaris in, 117, 118, 144 remote temperature measurement in, 111–13 SDD in, 109, 133 sensor technology improvements in, 110 thermal emissivities in, 112 thermal radiation in, 111–23 thresholding in, 120–1 temperature estimation errors with, 113–19 background temperature and, 113–15 cross-talk in, 115 emissivity in, 113–15

SUBJECT INDEX Gaussian noise in, 115 radiometric v. aerodynamic temperature in, 116–17 spatial calibration errors in, 116 spatial resolution in, 117–18 temporal resolution in, 118–9 TMV. See Tobacco mosaic virus Tobacco mosaic virus (TMV) PD with, 201, 212, 213, 215–16, 222, 234 thermal imaging applications with, 149 Tobacco vein clearing cavemovirus (TVCV) pararetroviruses phylogenies with, 171, 177–8 Trifolium repens legume-Rhizobia interactions with, 17, 20

273

Trifolium subterraneum legume-Rhizobia interactions with, 10, 11 TTSS. See Type III secretion system TVCV. See Tobacco vein clearing cavemovirus TVP. See Texas pepper virus Type III secretion system (TTSS) legume-Rhizobia interactions with, 19–28 V V. sativa. See Vicia sativa Vicia faba phloem unloading mechanisms in, 211 thermal imaging of, 123 Vicia sativa legume-Rhizobia interactions with, 10, 11, 17, 20