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HAL Id: tel-02814388

https://hal.inrae.fr/tel-02814388

Submitted on 6 Jun 2020

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Bénédicte Wenden

To cite this version:

Bénédicte Wenden. La floraison chez le pois par une approche de biologie intégrative : du réseau de gènes à la plante au champ. Sciences du Vivant [q-bio]. Université Paris- Sud, 2008. Français. �tel-02814388�

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UNIVERSITE DE PARIS-SUD U.F.R. SCIENTIFIQUE D’ORSAY

Thèse présentée pour obtenir le grade de

DOCTEUR EN SCIENCES DE L’UNIVERSITE PARIS-SUD XI Discipline : Sciences du Végétal

La floraison chez le pois par une approche de biologie intégrative : du

réseau de gènes à la plante au champ

Flowering time in pea: a systems biology approach from the genetic

network to the field

par Bénédicte WENDEN

Soutenue le 25 novembre 2008 devant le jury composé de :

Evelyne Costes Rapporteur

François Parcy Rapporteur

Catherine Damerval Président du jury

Catherine Giauffret Examinateur

Bruno Moulia Examinateur

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J’espère que [ce prix Nobel] va créer de nouvelles vocations, que ça va stimuler les jeunes chercheurs à s’intéresser à cette recherche d’interface entre la recherche fondamentale et la recherche clinique, à travailler en réseaux multidisciplinaire. Ce qui n’est pas forcément évident pour les jeunes chercheurs, souvent parce que ces réseaux sont représentés par un nombre important de chercheurs et eux, ils ont besoin de publications, et ils se posent toujours la question, est-ce que c’est une bonne idée de travailler comme ça dans un réseau de chercheurs ?

Mon message, c’est oui, il faut le faire parce que ça, c’est la recherche d’aujourd’hui.

Françoise Barré-Sinoussi, prix Nobel de médecine 2008, sur France Inter le 8 octobre 2008

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Accomplissement et soulagement sont les mots qui me viennent à l’esprit au moment de clore ce chapitre. Se lancer dans une thèse pluridisciplinaire était un vrai défi et je suis fière d’avoir pu faire un bout de chemin avec de nombreux chercheurs d’un hémisphère à l’autre.

Mes remerciements vont tout d’abord au département de génétique et d’amélioration des plantes (DGAP), et à la Mission des Relations Internationales (MRI) de l’INRA pour avoir financé mes travaux sans frontières. Un grand merci à Philippe Guerche pour m’avoir accueillie à la Station de Génétique et d’Amélioration des Plantes (SGAP).

Je souhaite remercier les membres de mon jury, Catherine Damerval, Catherine Giauffret, Bruno Moulia et tout particulièrement François Parcy et Evelyne Costes pour avoir accepté d’être rapporteurs de ce manuscrit de thèse.

Je tiens à remercier chaleureusement ma directrice de thèse, Catherine Rameau, qui m’a accueillie dans l’équipe pois, m’a permis de réaliser ce projet de thèse et qui a été un soutien de taille face aux déboires qui découlent forcément d’un pluri-encadrement. C’est avec persévérance qu’elle m’a suivie pour la rédaction de ce mémoire, et je lui suis très reconnaissante pour la disponibilité dont elle a fait preuve, même sous pression.

J’aimerais également exprimer ma gratitude à tous mes co-chefs : Isabelle Lejeune, à Mons, qui m’a refilé des cartons de données plein de poussière (les cartons, et les données), et avec qui j’ai fait chauffer le cerveau sur des discussions éprouvantes pour comprendre le fin mot de l’histoire ; Nathalie Munier-Jolain, à Dijon, qui a pu trouver un peu de place dans son emploi du temps surchargé pour que j’attaque la partie écophysiologie de mon travail.

I would like to thank Christine Beveridge for welcoming me to her lab in Brisbane, and for the many interesting discussions. Also a huge thank to Jim Weller, for giving me the opportunity to come to Hobart, it was a fruitful stay, and I even tasted kangaroo sausage! I thank you both for your comments on my manuscripts; I know I couldn’t have done as well as this without you.

Avoir autant d’encadrants éclectiques et enthousiastes m’a motivée à explorer chaque recoin de mon projet. Il n’y pas de pluridisciplinarité sans réseau.

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rails, pour les domaines qui ne m’étaient pas familiers.

Merci également à Valérie Hecht, pour le travail comme pour l’escalade du Mount Wellington. And to Jim Hanan, thank you for introducing me to computational modelling, my life will never be the same!

Merci à Beate Hoffmann pour avoir été disponible quand j’avais une question bête pour une technique. Merci également à Rosemonde Devaux, Véronique Fontaine et Odile Jaminon qui ont été mes yeux et mes mains pour les expériences réalisées à Mons.

Mes remerciements sont tout particulièrement destinés aux petits pea-mousse, passés et présents, Karine David, Alessandra Maya-Grondard, Magali Rault-Ventroux, Nils Braun, Hélène Proust, Soraya Fermas et Amal Moumene. En quelques mots : piscine, crêperies, blagues à deux balles, Lisbonne, pauses café, pauses thé, re-blagues à deux balles. Je ne pouvais espérer mieux comme coéquipiers. Un mot de plus pour Jean-Paul Pillot qui a commencé en même temps que moi, et qui a tout vu en ce qui concerne mes activités expérimentales (blouse, lunettes et machette dans la jungle léguminesque, entre autres), il lui a fallu beaucoup de courage et de self-control pour travailler avec moi, et je l’en remercie pour ça. I wish to warmly thank my Australian fellows, Liz, Geoff, Brett, Tanya, Alice, Kerry and particularly Julia, for welcoming me to Oz, with long and good coffee breaks! I also want to thank Lim Chee Liew, who was infinitely patient when working with me.

Une pensée particulière pour Johan Gomes, mon premier stagiaire, qui s’en est très bien sorti et m’a donné envie d’encadrer d’autres étudiants pour la suite.

Je remercie du fond du cœur les thésards de l’IJPB, et de la SGAP, Lara, Arnaud, Nicolas, José, Julien, Lien, Lauren, Aurélie, Laure, Thomas, Julie, Manon, Anne… Que des bons souvenirs !

C’est l’histoire de deux thésardes qui ont fait un bout de chemin ensemble, entre rires et larmes. Un grand merci à Amandine, parce qu’on a su cohabiter pendant ces trois ans, et que ça a été une expérience incroyable.

Cette thèse n’aurait jamais vu le jour pour moi sans le soutien de mes parents, qui m’ont encouragée dans la voie que j’avais choisie, m’ont donné les moyens de faire ce que je voulais, et ont toujours été à l’écoute. Enfin, je dédie cette thèse à Antoine, qui m’a soutenue à 200%, et m’a prouvé que je pouvais aller au bout des choses.

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Abstract

Pea (Pisum sativum) represents a valuable model species for systems biology approaches, as it is both a crop and a model species that has been used for decades to investigate developmental processes. In particular, various approaches led to a tremendous amount of data on flowering: (i) genetic and physiological approaches carried out on non-allelic flowering mutants under controlled conditions allowed the development of a descriptive, non-predictive model for the genetic regulation of flowering in pea; (ii) extensive studies on environmental control of flowering led to agroecophysiological models for flowering time prediction, which are not based on genotype and therefore, extrapolation of these results to other genotypes is limited. Additionally, recent molecular advances in pea and the model species

Arabidopsis thaliana improved the knowledge on the regulation of flowering. The

objective of this work was to integrate this wide range of data into a predictive model in which the time of flower initiation has been broken down into two component variables: the node of first open flower (NFI) and the duration between initiation of two nodes (plastochron). I developed a first predictive model for NFI, based on genetic and photoperiodic control of flowering in pea. Furthermore, analyses of lines grown under field conditions allowed a better understanding of NFI and plastochron responses to environmental conditions. These systems biology approaches were complemented by the molecular study of the two pea flowering key genes LATE FLOWERING (LF) and HIGH RESPONSE TO PHOTOPERIOD (HR). In particular, HR was shown to be involved in the light transduction pathway to the circadian clock, which allowed identification of new candidate genes.

These results, together with the new molecular data, lead to a better understanding of the genetic control of flowering and development in pea. This work opens new avenues to modelling approaches for flowering in pea.

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Résumé

Le pois (Pisum sativum), par son double statut d’espèce modèle pour l’étude du développement, et d’espèce agronomique, représente une espèce modèle idéale pour des études intégrées à différentes échelles biologiques. La transition florale est un caractère clé du développement et des approches variées ont conduit à l’obtention de nombreuses données pour la floraison chez le pois : (i) les approches de génétique et physiologie menées en conditions contrôlées sur une large gamme de mutants ont conduit au développement d’un modèle descriptif, mais sans capacité de prédiction, développant les interactions entre les gènes connus contrôlant la floraison ; (ii) l’étude approfondie en conditions de plein champ du contrôle de la floraison a permis de développer des modèles écophysiologiques de la date de floraison en fonction de la photopériode et de la température à forte capacité de prédiction mais qui ne prennent pas en compte le génotype. Plus récemment, les données sur Arabidopsis thaliana permettent d’avoir une compréhension au niveau moléculaire des mécanismes en jeu.

Ce projet est une première approche pour intégrer ce large jeu de données au sein d’un modèle prédictif de la date d’initiation florale, décomposé sous la forme du produit mathématique du premier nœud d’initiation florale (NFI) et du temps nécessaire à l’initiation d’un nouveau nœud à l’apex (plastochrone).

Un premier modèle mathématique pour la régulation génétique du NFI a été développé qui permet de prédire le NFI pour différents génotypes et photopériodes. Les réponses du NFI et du plastochrone aux conditions environnementales et en particulier à la photopériode ont été analysées précisément. Afin de compléter le modèle, je me suis intéressée particulièrement aux deux gènes clés de floraison,

LATE FLOWERING (LF) et HIGH RESPONSE TO PHOTOPERIOD (HR). Des

approches moléculaires pour HR ont permis de montrer que le gène était impliqué dans la voie de transduction de la lumière vers l’horloge circadienne, et de nouveaux gènes candidats ont été proposés.

Ce travail propose des pistes pour exploiter l’approche de modélisation pour la floraison chez le pois à la lumière des nouvelles données moléculaires.

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Table of Contents

Abstract ... i

Résumé ...iii

Table of Contents ... v

List of Figures ... ix

List of Tables ...xiii

List of Boxes... xv

Abbreviations and nomenclature... xvii

1. Introduction ... 1

2. Literature review ... 5

2.1. Introducing Pisum...5

2.1.1. Pisum sativum and its development ... 5

2.1.2. Pea as a model species ...7

2.1.3. Towards a winter pea ideotype...11

2.2. Flowering is a key transition in development ...15

2.2.1. The photoperiod pathway...17

2.2.2. The vernalization response pathway ...25

2.2.3. The autonomous pathway ...27

2.2.4. The gibberellin (GA) pathway ...29

2.2.5. Other pathways?...29

2.2.6. Flowering signals integration: taking the decision to flower ...31

2.2.7. Conservation of the pathways in other species ...33

2.3. Studying flowering in pea: two different approaches ...35

2.3.1. Genetic regulation of flower initiation in pea ...35

2.3.2. The ecophysiological point of view: predicting the time of flowering according to environmental conditions ...40

2.3.3. Attempts to combine genetic and ecophysiological approaches ...43

2.4. How modelling can help with our problematic ...45

2.4.1. Gene network modelling of transition to flowering in Arabidopsis...45

2.4.2. Novel modelling approaches to predict gene-to-phenotype associations...47

3. Aims and strategy ... 49

4. Material and methods... 51

4.1. Plant material and methods ...51

4.1.1. Plant material ...51

4.1.2. Growth conditions...55

4.1.3. Sampling and plant measurement ...57

4.1.4. Plastochron calculation ...59

4.2. Molecular biology...59

4.2.1. Quick DNA extraction ...59

4.2.2. PCR reactions...59

4.2.3. Mapping and marker development...60

4.2.4. Analysis of gene expression...60

4.3. Statistical analyses...63

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5. Computational analysis of flowering in pea (Pisum sativum)... 65

5.1. Note to reader ...66

5.2. Introduction ...66

5.3. Model description...71

5.4. Material and methods ...77

5.5. Results ...85

5.6. Discussion...93

6. Environmental and genetic control of the flowering threshold ... 101

6.1. Introduction ...101

6.2. Plant material was obtained in a homogeneous background ...103

6.3. LF expression at the apex is constant during development...109

6.4. LF expression in different flowering mutants and under different environmental conditions ...109

6.4.1. Temperature has no effect on LF expression ... 111

6.4.2. LF expression in flowering mutants and under two photoperiods ... 113

6.5. LF expression in field cultivars ...115

6.6. Discussion: the control of LF expression...119

6.6.1. The possible effect of differences in harvested leaf tissues on the estimation of LF transcription... 119

6.6.2. Four alleles for LF or a multitude of alleles?... 120

6.6.3. Effect of environmental conditions on LF expression ... 121

6.6.4. Do other loci interact with LF?... 122

7. The pea flowering genes HR, SN and LF regulate plastochron and node of flower initiation in response to environmental conditions... 125

7.1. Note to reader ...125

7.2. Introduction ...127

7.3. Material and methods ...131

7.4. Results ...139

7.4.1. Influence of environmental conditions on plastochron... 139

7.4.2. Influence of genotype on plastochron... 143

7.4.3. A model for plastochron ... 145

7.4.4. Flowering responses to photoperiod in hr and Hr lines... 145

7.4.5. Plastochron and NFI responses in sn mutants... 149

7.5. Discussion...150

7.5.1. Plastochron calculation... 150

7.5.2. Genetic control of NFI and plastochron, and response to photoperiod... 152

7.5.3. Light and photoperiod effects on plastochron... 154

7.5.4. Towards a model to predict time of flowering... 155

7.6. Supplemental results and discussion ...157

7.6.1. Temperature effect on plastochron and NFI under controlled conditions... 157

7.6.2. Role of LF allele on plastochron value ... 157

7.6.3. Combination with a model for the node of flowering... 161

8. Searching for HR: the Holy Grail of flowering studies in pea ... 163

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8.3. HR is involved in light input to the clock: which candidate gene? ...171

9. Discussion and perspectives ... 175 9.1. Relevance and limits of using HR and LF as target genes for the

winter pea breeding strategy...175 9.2. Toward a computational model for flowering time in pea ...177 9.3. New perspectives for the model - Integrate circadian clock

mechanisms...181 9.4. Navigating biological complexity with models: how precise should we

be?...185 10. References... 187 Appendices ...I

Appendix 1. Manipulating flowering time. Wenden et al., 2007. Grain

Legumes n°49. ... I Appendix 2. ‘Quick’ SDS 96 Well Format DNA extraction ... III Appendix 3. Trizol RNA extraction... IV Appendix 4. dCAPS SNP-341marker for polymorphism on LF between

Torsdag and WL1771 (developed by Jérôme Dumur, INRA

Versailles)...V Appendix 5. ClustalW alignment of COLc gene sequences in different pea

genotypes... VI Appendix 6. COLc and other markers scores for Térèse × Champagne

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List of Figures

Figure 1. General view of a (a) vegetative and (b) reproductive pea plant 4 Figure 2. Pea apices observed under Scanning Electron Microscope (SEM) from plants

NGB5839 at various developmental stages 6

Figure 1.1. Dendogram depicting phylogenetic relationship of Papilionoideae legumes,

including haploid genome size 8

Figure 1.2. A simplified consensus map for eight legume species 8

Figure 3. The winter pea strategy 12

Figure 2.1. General scheme of flowering integration in Arabidopsis thaliana 14 Figure 4. The generation of circadian rhythms in Arabidopsis 16 Figure 4.1. A currently consistent molecular model of the Arabidopsis circadian clock 20 Figure 5. The external coincidence model: an example of the photoperiodic flowering

response in long-day (LD) plants 22

Figure 5.1. Circadian expression of key components in the photoperiod pathway 24 Figure 8.1. A model for interaction between FD, FT and TFL1 30 Figure 10.1. Rate of progress from sowing to flowering (1/f, d-1) as affected by

temperature and photoperiod in six cultivars of pea 42

Figure 6. Flowering mutants in different backgrounds used in the experiments, grown

in glasshouse under 16h photoperiod 52

Figure 7. Hr plant carrying different LF alleles, derived from the same WL1771

background, grown in glasshouse under 16h photoperiod, 78 days after sowing 52

Figure 8. Temperature and photoperiod conditions in the growth cabinet for L2

experiment 56

Figure 9. Classic model for regulation of flowering in pea 68 Figure S1. Number of nodes initiated at the apex and open leaves on the main stem

versus thermal time, expressed in cumulative degree-days, from sowing, for NGB5839

plants, grown in glasshouse, under an 11h photoperiod 80

Figure 10. Estimated values of HR genotype parameter βHR, and estimated values of

photoperiod function β(SN, HR)(photoperiod) versus photoperiod (expressed in daily

hours of light) 88

Figure 11. Experimental data versus predicted data for the node of flowering initiation

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Figure 12. Experimental and model-predicted data from the computational model for

the node of flowering initiation (NFI) for different genotypes versus photoperiod

(expressed in daily hours of light) 92

Figure 13. Alternative model for regulation of flowering in pea 98 Figure 14. Description of the different haplotypes identified from the sequence

analysis of PsTFL1c (LF) in several pea lines and their corresponding mutants for LF 102

Figure 15. NFI repartition for plants obtained by the crosses and overview of the steps

for the introgression of Lf-d allele from WL1771 into NGB5839 background 104

Figure 16. dCAPS Marker SNP 341 run for BC2-F1 progenies 106 Figure 17. Node of flower initiation (NFI) for NGB5839 Lf-d plants, transferred from a

glasshouse (16h photoperiod) to a growth room (8h photoperiod) at different times

(Experiment Tr) 106

Figure 18. Relative expression of LF gene in leaf and apices at 7 days after sowing and

in apices during development of WT (NGB5839), grown in a 16h photoperiod in

glasshouse (experiment L1) 108

Figure 19. Relative expression of LF gene, relative to housekeeping gene EF1, in

apices of WT (NGB5839) and sn plants, grown under 16h photoperiod and two

different mean temperatures: 20°C and 15°C 110

Figure 20. LF gene expression, relative to housekeeping gene EF1, for WT plants

(NGB5839) and flowering mutants in NGB5839 background grown in glasshouse

under natural 16h photoperiod (LD) and in growth cabinet under 8h photoperiod (SD) 112

Figure 21. NFI versus LF expression relative to housekeeping gene EF1 in flowering

mutants and cultivated lines. 116

Figure 22. Leaf of Af WT and af mutant plants 118

Figure 23. Number of initiated nodes and expanded leaves according to thermal time

for WL1769 (Hr lf-a), grown in glasshouse under 16h photoperiod (experiment G16) 126

Figure 24. Daily climatic conditions from 1st october 1995 to 31st august 1997 136 Figure 25. Effect of mean temperature on node appearance rate in the field sowing trial 140 Figure 26. Details of plastochron response to mean photoperiod for hr and Hr lines

grown in the field 140

Figure 27. Plastochron response to mean photoperiod in hr and Hr lines grown in

glasshouse and field 142

Figure 28. Plastochron response to mean daily light in hr and Hr lines grown in field 144 Figure 29. Node of floral initiation (NFI) and thermal time between emergence and

flower initiation (TFI) responses to mean photoperiod in hr and Hr lines grown in

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Figure 30. Plastochron and NFI responses to photoperiod for WL1770 (Hr Lf Sn),

NGB5839 (hr Lf Sn) and sn-4 (hr Lf sn) plants grown in glasshouse, under 11 and 20 hr

photoperiods (experiments G11, G20) 148

Figure 31. Plastochron calculated for 16h photoperiod treatments versus LF expression

relative to housekeeping gene EF1 in flowering mutants and cultivated lines 160

Figure 32. Effects of HR on rhythmic expression of clock-related genes 166 Figure 33. Genetic mapping of molecular markers developed for genes on pea LGIII 168 Figure 34. COLc CAPS marker for Térèse × Champagne RIL2 mapping population 170

Figure 35. Mapping of COLc in the region of HR 170

Figure 36. Scheme proposing the possible sites of action of FHY3, TIC, and ELF3 in

the gating of signals from the phytochrome (PHY) and cryptochrome (CRY)

photoreceptors 172

Figure 11.1. Evolution over time of the flowering signal FS, based on the assumption

that FS level is correlated with the number of nodes initiated at the apex 178

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List of Tables

Table 1. Genotype of each line used in experiments 54

Table 2. Experiments overview 54

Table 3. General PCR protocol 58

Table 4. PCR program 58

Table 5. Real-time PCR 62

Table 6. Hypotheses used to build the computational model, based on literature or

present results 74

Table S1. Genotype of each line used in the experiments in this study 78 Table 7. Values of constants and parameters used in the computational model 82 Table S2. Number of preformed nodes observed in seeds for different genotypes and

harvests 84

Table S3. Node of flowering initiation (NFI) for each genotype grown in 11, 16 and

20h photoperiods in a glasshouse 84

Table S4. Node of flowering initiation (NFI) for different genotypes obtained from

previous studies 86

Table S5. Estimation of the number of nodes initiated at the apex when the first true

leaf (node 3) is open 88

Table 8. LF gene expression, relative to housekeeping gene EF1 for pea cultivars, and

flowering mutants in homogeneous backgrounds 114

Table 9. Genotype of each line used in the studies at the flowering loci LF, SN and HR 132 Table 10. Environmental conditions for field experiments 138 Table 11. Plastochrons and first node of flower initiation (NFI) for NGB5839 and sn-4

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List of Boxes

Box 1. Bridging model and crops legumes through comparative genomics 8

Box 2. The plant model, Arabidopsis thaliana 14 Box 3. Photoreceptors and flowering in Arabidopsis 18

Box 4. Molecular mechanisms underlying the central oscillation 20

Box 5. Key components in the photoperiod pathway 24

Box 6. More on the genetic control of vernalization 28

Box 7. Autonomous promotion pathway 28

Box 8. Model for FT signal integration at the apex 30 Box 9. Major flowering pathway genes of Arabidopsis, rice, and barley/wheat 32

Box 10. Linear photothermal flowering model 42

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Abbreviations and nomenclature

CAPS Cleaved Amplified Polymorphic Sequence

cDNA Complementary DNA

dCAPS Derived Cleaved Amplified Polymorphic Sequence

DN Day Neutral

DNA Deoxyribonucleic Acid

FS Flowering Signal

GA Gibberellic Acid

I Flowering Inhibitor

INRA Institut National de la Recherche Agronomique

LD Long Day

LG Liaison Group

mRNA Messenger Ribonucleic Acid NF Node of Flowering

NFI First Node of Flower Initiation

P Mean Photoperiod

PCR Polymerase Chain Reaction QTL Quantitative Trait Loci RIL Recombinant Inbred Lines

RNA Ribonucleic Acid

S Flowering Stimulus

SD Short Day

SEM Scanning Electron Microscope SNP Single Nucleotide Polymorphism RLE Rate of Leaf Expansion

RNI Rate of Node Initiation

T Mean Temperature

Tb Base Temperature TF Time of Flowering

TFI Time of First Flower Initiation WT Wild-Type

Unless indicated, genes, mutants and proteins are written in italic upper case, italic lower case and non-italic upper case, respectively.

For pea genes, loci are written in italic upper case (e.g. HR), dominant alleles are indicated with the first letter in upper case (e.g. Hr) and recessive alleles are written in italic lower case (e.g. hr).

Further information is provided in Boxes that present text, occasionally with Figures. In order to distinguish them from regular Figures, Box Figures are annotated with the number of the Box, followed by a decimal number.

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1. Introduction

The consequences of environmental degradation, including climate change, depletion of water resources and pollution, pose great challenges to modern agriculture. One promising opportunity to combat these effects is through the cultivation of “clean” crops. Due to their nitrogen fixing abilities, legume crops, unlike other cultivated plant species, generally do not need nitrogen fertilisation, thus avoiding soil and water pollution caused by overuse of agricultural fertilisers, and represent an important source of nitrogen in both natural and agricultural ecosystems. Boosting cultivation of environmentally friendly crops such as legumes will allow us to face up to current challenges associated developing sustainable agricultural systems. Unfortunately, cultivation of legumes, including pea and fababean, has declined considerably in Europe in recent years, most notably due to the low productivity of current legume crop varieties. Therefore, extensive research on legume crops, with particular emphasis on improving yield stability, will be essential to the future development of sustainable crop systems. Forage and grain legumes are crop plants belonging to the legume family, which represents the third largest family of flowering plants (more than 650 genera and 18000 species (Polhill and Raven, 1981)). This family, with the botanical name

Fabaceae, is characterised by flowers with five petals (papilionaceous flower with

a butterfly form), dry fruit, that usually dehisces on two sides and is commonly called a pod, and, for nearly all of its members, the ability to host symbiotic nitrogen-fixing bacteria in nodules on the roots of the plant. These bacteria, known as rhizobia, convert nitrogen gas (N2) from the atmosphere to a form of nitrogen (NO3- or NH3) that can be readily absorbed and metabolised by the host plant.

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Forage legume crops are usually raised as feed for livestock, either through grazing or through the production of silage or hay, but are sometimes used for industrial purposes. The clovers (Trifolium spp.) and medics (Medicago spp.), including lucerne (also known as alfalfa), are examples of popular crop forage legumes. Grain legumes are cultivated primarily for their seeds, which are harvested at maturity and are rich in protein and energy. The mature dry seeds of grain legumes are used for animal feed, and for human consumption. More than forty species and countless varieties of grain legumes are cultivated throughout the world. The major species grown in Europe include soybeans (Glycine max), peas (Pisum sativum), faba beans (Vicia faba), lentils (Lens culinaris), beans (Phaseolus), lupins (Lupinus spp.) and chickpeas (Cicer arietinum).

Soybean crops represent three-quarters of the world’s production of grain legumes (185 million tonnes) and are grown primarily in the USA, Brazil, Argentina, Paraguay and Uruguay. Soybeans are popular due to their high fat content (approximatively 20%). In addition, a high-protein meal by-product of soybean production supplements the animal feed industry. Worldwide production of soybeans has increased by 215% in the last 30 years, compared with a less substantial 50% increase in the production of other grain legumes. Global production of grain legume crops other than soybeans amounts to just 57 million tonnes. The European Union with 25 member-states represents only 2% of global grain legume production, 0.5% of global soya production and 9% of the global production share of other grain legumes.

Between 1978 and 1993, grain legume production increased considerably, both in France and throughout the EU. Increases in agricultural productivity of legumes led to market expansion, particularly as livestock feed. However, production area

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and volume eventually levelled out and has even decreased in recent years. For example, pea cultivation increased from 170 000 to 750 000 hectares between 1983 and 1993, and then dwindled to only 250 000 hectares in 2005.

The trend is paradoxical as animal feed currently represents an important potential market for grain legumes in Europe. Nonetheless, issues with legume susceptibility to pathogens, particularly to the oomycete Aphanomyces euteiches, coupled with a progressive decrease in European financial support for growers of legume crops has contributed to the decline in the popularity of legumes amongst growers. As a result breeders will need to increase the quality of current grain legume stocks in order to remain competitive in this market.

While legume crops represent a promising alternative for sustainable agriculture, further research and development is needed to increase the popularity of legumes amongst mainstream commercial growers. This could be achieved through integrated, breeding strategies focused on boosting yields, reducing costs and optimizing rotation strategies.

Further investigations into legume physiology and genomics are also needed in order to realize these improvements. However, investigations of extensive genotype × environment interactions in plants can become extremely cumbersome using traditional methods. New mathematical modelling techniques are now being employed to integrate existing models, experimental data, and hypotheses in order to describe, explain and predict various biological phenomena more efficiently. Pea, as a crop and a model species already characterised by large amounts of experimental data, represents an ideal species for such integrative approaches. Furthermore, breeding strategies, ongoing at Institut National de la Recherche Agronomique (INRA), are set to release winter pea cultivars, which would allow

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a b

Phytomer Apex

Node 3 First node of flower initiation (NFI)

Figure 1. General view of a (a) vegetative and (b) reproductive pea plant. The node 3 corresponds to the first true leaf, as scale leaves are counted as node 1 and 2.

Tendrils Leaflets Stipules

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for stabilisation and improvements in the productivity of the pea crop. Modelling will be important in facilitating the identification of key loci targets for the winter pea breeding strategy by integrating several levels of information relating to various developmental processes.

This study is focused on elucidating regulatory mechanisms governing the onset of flowering in pea. The work has further characterised the phenotypic effects of various flowering genes in pea and their interaction with environmental signals. A major aim of the project was the development of an integrative computer-based modelling tool combining molecular, physiological and ecophysiological data to describe flowering time in pea.

2. Literature review

2.1. Introducing Pisum

2.1.1. Pisum sativum and its development

Pisum sativum (garden or field pea) is an annual legume crop of the Fabaceae

family. The plant is composed of one or more stems with similar structural organization. Each stem is composed of a succession of identical functional units called phytomers (Figure 1a), originating at the apex. Each phytomer consists of:

• An internode, corresponding to the stem between two phytomers.

• A leaf, composed of two stipules, one to three pairs of leaflets, and thin tendrils that coil around any available support.

• An axillary meristem located at the leaf axil, which can produce a branch in the vegetative state. If the phytomer is produced at the apex after floral initiation, the axillary meristem produces one or two flowers (Figure 1b).

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Figure 2. Pea apices observed under Scanning Electron Microscope (SEM) from plants NGB5839 at various developmental stages. Vegetative apex (A) from 7 day-old plant (B); apex in floral transition (C) from 14 day-old plant (D); floral apex (E) from 21 day-old plant (F). m: meristem; p: vegetative primordium; f: floral bud. Primordia and buds are numbered from 0 and counted from the newest primordium produced to the oldest primordium visible on the picture. The flower bud segregates into two flowers, noted as fx and fx’.

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Pea stems are characterized by indeterminate development, where production of successive phytomers occurs at the apex until senescence. The apex is composed of both the apical meristem and a series of primordia nodes decreasing in age (Figure 2). Vegetative primordia, which differentiate into leaves and internodes, are produced at the meristem (Figure 2A) until the apex reaches the stage of floral initiation. The onset of flowering is characterized by the formation of a floral primordium from the meristem (f1 in Figure 2C), located in the axil of the leaf primordium at the same node. The floral primordium later develops into one or two spheres which will produce flowers (f3 and f3’ in Figure 2E).

To evaluate plant development quantitatively at the meristem, we used a plastochron scale, which corresponds to the duration between initiation of two consecutive primordia. In pea, a plastochron can be as brief as 24 h during the vegetative phase (Nougarède and Rondet, 1973). The time elapsing between two fully expanded leaves may be equal or longer than the plastochron, and is defined as the phyllochron (Turc and Lecoeur, 1997).

2.1.2. Pea as a model species

Historically, our understanding of the basis of heredity originated from characterisation of several genes in garden pea. These genes were found to be responsible for natural variation in traits influencing very diverse aspects of plant morphology and development. In 1866, Gregor Mendel firmly established the laws of inheritance through a series of simple breeding experiments using the common garden pea (Mendel, 1866). The traits monitored in his experiments included seed shape (R/r), cotyledon color (I/i), seed and flower color (A/a), pod shape (V/v), pod color (Gp/gp), flower position (Fa/fa), and stem length (Le/le) (Mendel, 1866; White, 1917). Since then, induced mutagenesis has provided a wide range of

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Box 1. Bridging model and crops legumes through comparative genomics.

Recently, two legume species, Medicago truncatula and Lotus japonicus (Figure 1.1) were selected as model legume systems for studying genomics and biology, in particular nodulation and mycorrhization mechanisms (reviewed in (Zhu et al., 2005)). Sequencing and further genomic data provided clues pertaining to the conservation of genome structure among legume species. The mapping of markers in different species allowed for identification of macrosynteny, which refers to conserved gene order between species, as well as microsynteny, among legumes (Figure 1.2). Although the pea genome is approximately 10 times larger than that of M. truncatula and has one less chromosome, the colinearity of genes is remarkably conserved between the two genomes (Choi et al., 2004).

The conserved genome structure between Medicago and crop legumes has allowed, for instance, map-based cloning of genes required for nodulation in crop legumes (for examples, see (Zhu et al., 2005)).

A major challenge for comparative legume genomics is to translate information gained from model species into improvements in crop legumes.

Tribe Genus Species

Genome Size (Mb)

Lens Lens culinaris (lentil) 4116 Vicia Vicia faba (faba bean) 13059 Viceae

Pisum Pisum sativum (garden pea) 4337 Melolitus Melilotus officinalis (sweet clover) 1103 Trifolium Trifolium pratense (red clover) 637

Medicago sativa (alfalfa) 1715

Trifolieae

Medicago

Medicago truncatula (barrel medic) 466

Cicereae Cicer Cicer arietinum (chickpea) 931 Loteae Lotus Lotus japonicus 466 Phaseolus Phaseolus vulgaris (common bean) 588 Vigna Vigna radiata (mung bean) 515 Glycine Glycine max (soybean) 1103 Phaseoleae

Cajanus Cajanus cajan (pigeon pea) 858

Figure 1.2. A simplified consensus map for eight legume species. Mt, M. truncatula; Ms, alfalfa; Lj, L. japonicus; Ps, pea; Ca, chickpea; Vr, mungbean; Pv, common bean; Gm, soybean. S and L denote the short and long arms of each chromosome in M. truncatula. Syntenic blocks are drawn to scale based on genetic distance (Zhu et al., 2005).

Figure 1.1. Dendogram depicting phylogenetic relationship of Papilionoideae legumes, including haploid genome size. Sequencing of the gene rich regions of underlines species is nearing completion (based on (Zhu et al., 2005).

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mutants affecting, for instance, internode length, leaf morphology, seed shape, flowering time and branching (Murfet and Reid, 1993).

Extensive natural variation existing accross pea lines complemented by the availability of an abundance of developmental mutants, ensures that pea is an ideal candidate for investigations into all aspects of plant development. The basic architecture of the plant, with long internodes separating vegetative and reproductive zones and large roots, facilitating xylem sap extraction, enhances the suitability of pea as a candidate for both endogenous and exogenous phytohormone studies at varying developmental stages. Pea is also readily amenable to many different graft unions, allowing the production of genetic chimeras without the complication of adventitious rooting. Therefore, the suitability of pea for investigating developmental traits and long-distance signalling makes it a valuable tool for elucidating the processes underlying the development of complex structures at the whole plant level. However, due to its large genome (Box 1) and the lack of efficient transformation methods for this organism, pea is not suitable for gene cloning.

The candidate gene approach has been extensively employed for gene cloning purposes in pea. The model plant Arabidopsis (Arabidopsis thaliana, Box 2) has aided studies in pea and other important crop species by providing a wealth of information about genes and genetic pathways controlling development processes. The Arabidopsis-based candidate gene approach is facilitated by the conservation of many Arabidopsis genes in model legumes, as has been shown already, for example, in flowering (Hecht et al., 2005) and branching regulation (Sorefan et al., 2003; Johnson et al., 2006).

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With the recent sequencing of Medicago (Medicago truncatula) and Lotus

japonicus, extensive genome conservation was documented through comparative

gene mapping (Box 1). Therefore, map based cloning together with candidate gene approaches can now be considered in pea. Arabidopsis gene homologs can be identified in regions of the Medicago genome that correspond to regions of interest in pea. Pea homolog for the candidate gene can thus be investigated using molecular biological techniques.

In terms of hormonal studies, genetic analysis of gibberellin (GA) biosynthesis is a good example of a case where pea has been particularly well studied with respect to maize and Arabidopsis (Hedden and Proebsting, 1999). The detailed characterization of internode length pea mutants, corresponding to more than 20 loci, has identified 4 loci controlling stem length by altering the level of GA1, which include Mendel’s LE gene. Biochemical analysis of different allelic mutants for this gene has shown that it controls the 3ß-hydroxylation of GA20 to GA1 (Ingram et al., 1984). The cloning of this gene by two groups (Lester et al., 1997; Martin et al., 1997) has provided the molecular basis of the difference of stem length between tall Le and dwarf le lines. Pea has also been employed intensively for the study of the genetic control of branching with Arabidopsis and Petunia (Beveridge, 2006). Grafting experiments between branching mutants and wild-type (WT) plants have highlighted the role of a novel long-distance signal produced in shoot and root (Beveridge et al., 1994; Beveridge et al., 1997) that acts as a branching inhibitor. Easy access to the axillary bud in pea allowed for the design of a simple bioassay to demonstrate that strigolactones were involved in the control of branching (Gomez-Roldan et al., 2008). In these studies, Arabidopsis has been essential to facilitate work at the molecular level, together pea that represents an

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appropriate complementary model species, based on its simple architecture and legume-specific traits.

Dissection of the various pathways controlling plant development is generally carried out in model species such as Arabidopsis. Not only does research on pea benefits from the status of pea as a model species for development, but also an agronomical species. Laboratory and field data can therefore be integrated to build a detailed understanding of pathways controlling interesting traits and new breeding strategies for pea allowing for development of novel ideal phenotypes for crops, termed ideotypes.

2.1.3. Towards a winter pea ideotype

Most pea crops are currently grown in Europe are spring varieties (Figure 3), which are sown after winter frosts. These varieties were selected for traits such as early flowering, one-axis architecture and high grain yield. However, spring pea varieties are characterized by late maturity and less effective radiation use in comparison to a winter crops. This can result in unstable yields between years and sites.

Stabilising and improving the productivity of the pea crops could be achieved through the release of winter cultivars, as has been done, for example, with wheat and canola. Escaping the harmful effects of drought and heat stresses in the spring throughout an earlier flowering period, together with a longer development cycle leading to higher global biomass production, would increase the reliability and productivity of such winter grown pea cultivars (Figure 3). Fall-sown peas are however limited by low temperature together with other stresses associated with winter climatic conditions. For the last 30 years, winter dry pea cultivars have been

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Figure 3. The winter pea strategy: manipulating the dates of floral initiation and flowering in order to escape winter frosts as well as drought and heat stresses in late spring.

Winter pea ideotype

sowing floral initiation Yield maturity Spring pea autumn rains winter

cold stress heat and drought stresses

777

¼

777

¼

777

¼

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bred progressively for better frost tolerance, but until now have never reached the level of tolerance expressed by some forage lines. Given that frost sensitivity increases after floral initiation (Fowler et al., 2001), previous observations have shown that such frost tolerant forage peas are able to escape the main winter freezing periods by delaying their floral initiation under short days (Lejeune-Hénaut et al., 1999).

For winter pea crop, the manipulation of flowering regulatory genes appears to be a promising way to control the key developmental stages, like the dates of floral initiation and flowering. The ideal winter pea should initiate its flower primordia late enough to avoid winter frosts, but flower early enough to escape drought and heat stresses in late spring. A comprehensive understanding of the genetic and physiological basis of the interaction between flowering genes and the environment is essential to the appropriate selection of varieties closely adapted to their specific environments. The breeding strategy chosen by INRA is currently based on two key flowering genes in pea, namely HIGH RESPONSE TO PHOTOPERIOD (HR) and LATE FLOWERING (LF). In particular, the flowering locus HR has recently been shown to colocalize with a major QTL affecting winter frost tolerance in pea (Lejeune-Hénaut et al., 2008). The strategy is to combine the dominant HR allele which confers high response to short days with an appropriate allele of LF which governs the plants’ “inherent lateness”. A good combination of alleles should allow for accurate control of flowering time in the ideal winter pea candidates.

Integrating the knowledge of the genetic control mechanisms governing flowering gained in model species such as Arabidopsis, in combination with what is already known on these genes in pea should help us to better examine and manipulate flowering genes for the development of a successful winter pea strategy.

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Box 2. The plant model, Arabidopsis thaliana.

The genetic approach has been focused mostly on the model species Arabidopsis thaliana, usually grown in controlled conditions,. Arabidopsis is a rosette plant whose flowering is accelerated by long days, vernalization, a rise in ambient temperature (from about 15 to about 25°C), a low red/far-red ratio for incoming light and a low mineral availability. Because it responds similarly to many of the environmental conditions that control flowering in other species, Arabidopsis is an excellent model system. Mechanisms that control the timing of floral initiation have been studied extensively in Arabidopsis through the identification of mutants that flower earlier or later than the wild type but otherwise remain healthy. The study of genes involved in other aspects of plant development such as light perception, hormone metabolism, signal transduction, and floral meristem specification, has shown that they play roles in the regulation of flowering time. Studies have led to the identification of components within individual pathways that affect flowering, and resulting in their positioning within molecular hierarchies.

The flowering response to environmental factors involves an integrated network of pathways which quantitatively control the timing of flowering and involves more than 80 genes (Mouradov et al., 2002; Boss et al., 2004).

So far, four main signalling pathways have been proposed (Mouradov et al., 2002): photoperiod, vernalization, autonomous pathway and gibberellins (Figure 2.1).

Figure 2.1. General scheme of flowering integration in Arabidopsis thaliana. Signalling

pathways control flowering partly through the production, in the leaf, of a mobile flowering signal, also referred as florigen. At the apex, signals are integrated to switch the meristem to flowering, by activating expression of floral meristem identity genes, including LEAFY (LFY) and APETALA1 (AP1). Adapted from (Blazquez, 2005).

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2.2. Flowering is a key transition in development

One of the most crucial developmental transitions in flowering plant is the switch from vegetative to reproductive development. Correct timing of this transition is essential in maximizing reproductive success and thus has a major impact on crop performance. The process involves the quantitative integration of signalling responses to environmental cues with an endogenous developmental program (Simpson and Dean, 2002; Yanovsky and Kay, 2003).

Physiological experiments have shown that multiple regulatory gene pathways exist to promote or repress flowering. These fully integrated pathways act quantitatively prompting the shoot apical meristem to switch from vegetative organ production to flowers formation at a certain threshold (Bernier et al., 1993). Early experiments gave evidence that a long-distance signal was moving from a photoperiod-induced leaf to trigger flowering at the shoot apex (Knott, 1934). Later, it was revealed that this floral stimulus could be transmitted from a flowering partner to a non-flowering partner via a graft union. This “florigen” signal, which is functionally conserved among species, is thought to be part of the flowering integration process from the leaf to the shoot apex.

Genetic dissection of flowering in the model plant Arabidopsis (Box 2) has supported the notion that different pathways are quantitatively regulating the transition to flowering. Researchers have now identified the genes that control these multiple pathways. Floral pathways quantitatively regulate a common set of targets, the floral pathway integrators, which in turn activate the genes involved in reproductive development (Simpson and Dean, 2002). Integration of the various flowering pathways is described in more detail in the following sections, accompanied by a comprehensive description of the photoperiod pathway.

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Figure 4. The generation of circadian rhythms in Arabidopsis. Environmental rhythms in light intensity and/or ambient temperature entrain the central circadian oscillator. The oscillator regulates a range of physiological outputs and maintains these rhythms in an appropriate phase relationship with the entraining environmental cues (adapted from (Hotta et al., 2007)). Central oscillator Input pathways Ouput pathways

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2.2.1. The photoperiod pathway

One of the most important factors controlling flowering time in temperate regions is the duration of the daily light period, or photoperiod. Plants that flower in response to photoperiod are classified as short day (SD plants flower when the daylength is below a threshold level), long day (LD plants flower when the daylength is above a threshold level), or day-neutral (DN plants are able to flower in all photoperiods). Flowering in pea, which is a LD plant, is closely controlled by photoperiod. Some loci involved in the day-length response have been known for decades, and analyses on flowering mostly focus on the photoperiod response. Consequently, the photoperiod pathway in model species will be explicitly described in this literature review.

In Arabidopsis, late-flowering mutants that were delayed in long days led to the identification of “LD pathway” genes, which promote flowering in response to long days. Attempts to explain the underlying mechanisms led to the discovery of fundamental plant processes such as the existence of photoreceptors, systemic signalling from the leaf to the shoot apex for the initiation of flower development, and the role of circadian rhythms as the timekeeping mechanism. The photoperiod flowering pathway can be separated into two functional domains: a circadian clock and a circadian-regulated day-length measurement mechanism.

2.2.1.1. The Arabidopsis circadian clock and photoperiodic flowering

Life on earth is characterized by light and temperature cycles. Therefore selection has occurred throughout evolution for an internal clock that optimizes the plant’s relationship with the environment. Even in the absence of environmental cues, the circadian clock runs within a period close to 24 h (hence its Latin name literally meaning "near a day"), and maintains a relatively constant period within a

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(Thomas, 2006)

Box 3. Photoreceptors and flowering in Arabidopsis

As light represents both an input and a key regulator in the control of flowering via the photoperiod, phytochromes , cryptochromes and potentially novel photoreceptors all appear to play a role.

In Arabidopsis, combinations of the red and far red absorbing photoreceptors phytochromes (PHYs) A to E and the blue light receptors cryptochromes (CRYs) 1 and 2, are involved in the perception of colour, intensity, duration and periodicity of light. Analysis of light-dependent gene pathways in plants suggests that interactions between photoreceptors, light signalling pathways and the circadian clock regulate many aspects of plant germination, growth and development (Putterill, 2001).

Experiments with different wavelengths of constant light show that far red and blue light promote Arabidopsis flowering, while constant red light is inhibitory. PHYA has been shown to be important in low intensity red or blue light reception and to promote flowering whereas PHYB plays an inhibitory role in floral initiation, through high intensity red light reception. CRY1 and CRY2 may have a redundant promotion effect on flowering under LD (Lin, 2000). The role of photoreceptors in flowering was reviewed by (Lin, 2000; Thomas, 2006).

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physiologically relevant temperature range. It is often suggested that the clock provides an adaptive advantage by allowing organisms to anticipate regular changes in the environment and temporally separate incompatible metabolic events (Johnson and Kyriacou, 2005), such as biochemical pathways that are photoinhibited or easily photodamaged by light (Pittendrigh, 1993; Johnson and Kyriacou, 2005).

The circadian clock is a key regulator of metabolism, and thus 10% of the Arabidopsis transcriptome was shown to be under circadian control (Harmer et al., 2000; Michael and McClung, 2003; Covington and Harmer, 2007). The identification of clock-regulated genes may provide insight into the types of processes under circadian influence. Classically, the circadian system was divided into three main components: (i) the input pathways involved in the perception and transmission of environmental signals to synchronize (ii) the central oscillator, which generates and maintains rhythmicity through multiple (iii) output pathways, which connect the oscillator to various physiological and metabolic processes, including cotyledonary movements and stomatal aperture (Figure 4, (Dunlap, 1999)).

To remain synchronized with environmental rhythms, the plant circadian clock possesses a series of input mechanisms that feed environmental information into the oscillator, which adjusts its phase and maintains the circadian clock period close to 24h (Millar, 2004). Entrainment of the circadian clock to cycles of light and darkness involves the action of multiple photoreceptors. In Arabidopsis, at least four of the five existing phytochrome photoreceptors (PHYA, B, D and E) and both of the cryptochromes (CRY1, 2) have been shown to feed biochemical

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Figure 4.1. A currently consistent molecular model of the Arabidopsis circadian clock. (Murakami et al, 2007)

are also important mechanisms. This complex regulation allows the central oscillator to maintain cycles.

In addition to these central clock genes, other components were identified as being clock-associated, including (for instance): LOV KELCH PROTEIN 2 (LKP2), TIME FOR COFFEE (TIC), SENSITIVITY TO RED LIGHT REDUCED 1 (SRR1), and TEJ.

Box 4. Molecular mechanisms underlying the central oscillation.

The core oscillator consists of a negative-feedback loop with morning factors CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) and LATE ELONGATED HYPOCOTYL (LHY) proteins, and evening factors, TIMING OF CAB EXPRESSION 1 (TOC1), EARLY FLOWERING 4 (ELF4) and LUX ARRHYTHMO (LUX) proteins (reviewed in (Gardner et al., 2006; Imaizumi and Kay, 2006)).

Five PRRs belonging to a small family of PSEUDO-RESPONSE REGULATORs are also believed to be another type of component of the central oscillator (Somers et al., 1998; Matsushika et al., 2000; Eriksson et al., 2003; Farre et al., 2005; Nakamichi et al., 2005; Salome and McClung, 2005; Ito et al., 2008) (Figure 4.1). The post-transcriptional regulation of clock proteins such as phosphorylation of CCA1 protein (Daniel et al., 2004) and controlled degradation of TOC1 protein by a clock-associated F-box protein ZEITLUPE (ZTL) (Más et al., 2003)

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information to the clock (Somers et al., 1998; Devlin and Kay, 2000). The role of photoreceptors in flowering is further described in Box 3.

The 24 h circadian period is mediated by a resetting mechanism that shifts the phase of the clock every cycle in response to environmental cues. This phenomenon is referred to as the gating response (reviewed in (Hotta et al., 2007)). In mutants impaired in this pathway gating photoreceptor action, clock-regulated rhythms of leaf movement, hypocotyl elongation and gene transcription become arrhythmic under constant light conditions. One such gating factor is the clock-regulated EARLY FLOWERING 3 (ELF3) protein, which functions as a cyclic repressor of light signalling (McWatters et al., 2000; Covington et al., 2001) (Figure 4.1 in Box 4).

The central core of the circadian clock generates rhythms that maintain a robust near-24 h period through a network of multiple feedback loops with morning and evening factors. Molecular mechanisms controlling the circadian clock are further developed in Box 4.

One of the key characteristics of all circadian rhythms is that they are able to maintain robust rhythms with a period close to 24h over a broad range of physiological temperatures (Edwards et al., 2005). This property is termed temperature compensation. Quantitative genetic and molecular approaches showed

GIGANTEA (GI) plays a critical role in temperature compensation of the clock and

in extending the range of temperatures at which rhythmicity can be maintained (Gould et al., 2006).

The outputs of the central oscillator are pathways that lead to physiological and biochemical rhythms such as photosynthesis, leaf movement, hypocotyl elongation and stomatal movement (for a review, see (Gardner et al., 2006)). The circadian

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Figure 5. The external coincidence model: an example of the photoperiodic flowering response in long-day (LD) plants. The function of the clock-regulated key regulator, which induces the expression of the flowering gene, is regulated by light, therefore, flowering will be accelerated when the late-afternoon expression of the key regulator and the presence of daylight coincide. (Imaizumi and Kay, 2006)

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clock component, GI, partly regulates the flowering response to photoperiod. GI is a key regulator of photoperiodic flowering, which encodes a protein with no domains of known biochemical function but is post-transcriptionally regulated by light and dark (David et al., 2006). GI interacts with SPINDLY (SPY), a protein implicated in gibberellin signalling (Tseng et al., 2004). Together, GI and SPY induce the circadian expression of the CONSTANS (CO) gene, which is the key clock output gene involved in the flowering response to photoperiod.

2.2.1.2. Perception of day-length

Several models have been proposed to explain how the perception of day-length leads to control of developmental responses, such as flowering. The most prominent is the external coincidence model (Pittendrigh and Minis, 1964; Thomas and Vince-Prue, 1997), which proposes that the photoperiodic signal is only generated when a specific external phase (light or dark) coincides with the appropriate internal phase of an internally regulated phase, maintained at the same time each day by the circadian clock (Figure 5). For LD plants, coincidence with dark delays flowering in SD and coincidence with light promotes flowering in LD. According to this model, the photoperiodic response is triggered when the product of an enzymatic reaction reaches a threshold level. The abundance of the substrate of this reaction exhibits a circadian rhythm, and the enzyme that converts the substrate to the product is active in the light but reverts to an inactive state in the dark. Therefore, the photoperiodic response would only occur when exposure to light, which activates the enzyme, coincides with the peak in the circadian rhythm of the substrate (Figure 5).

In this external coincidence model, light has two roles: it entrains the circadian oscillation of light- and dark-sensitive phases and it is directly required for the

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Box 5. Key components in the photoperiod pathway.

CO levels are high between 10h and 12h after dawn (Samach et al., 2000) and the peak of CO mRNA abundance shows an earlier shoulder only under LDs (Suarez-Lopez et al., 2001). This extension of CO expression at the critical point in the photoperiod for LD sensing is produced by the interaction of FLAVIN-BINDING, KELCH REPEAT, F-BOX PROTEIN 1 (FKF1) protein (Imaizumi et al., 2005) and GIGANTEA (GI) under light (Sawa et al., 2007). The FKF1-GI complex might target for degradation a negative regulator of CO transcription and thereby increase CO mRNA levels at the end of the day. FKF1 and GI transcription is regulated by circadian clock, and their mRNA abundance peaks around the time CO transcription rises (Figure 5.1a). The transcription factor CYCLING DOF FACTOR 1 (CDF1) likely plays a part in this negative regulation of CO, by interaction with FKF1 and GI (Imaizumi et al., 2005; Sawa et al., 2007) (Figure 5.1b).

Analysis of protein levels suggested that turnover of CO protein is increased during the night and at the beginning of the day. Experiments ion photoreceptors mutants indicated that PHYTOCHROME B (PhyB) was required for the degradation of the protein early in the day (Valverde et al., 2004) (Figure 5.1c). In contrast, cryptochromes, in particular CRYPTOCHROME 2 (CRY2), as well as PHYTOCHROME A (PhyA), contribute to the stabilization of the protein at the end of the day (Valverde et al., 2004) (Figure 5.1c). Finally, results suggested that the SUPPRESSOR OF PHYA-105 (SPA1, SPA3 and SPA4) proteins, in interaction with the E3 ubiquitin ligase CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1), are important in mediating the degradation of CO protein (Laubinger et al., 2006; Jang et al., 2008) (Figure 5.1c).

(FLOWERING LOCUS T) FT mRNA production is a direct result of CO protein accumulation toward

the end of LDs (Figure 5.1d). Figure 5.1. Circadian expression of key components in the photoperiod pathway. White area: duration of light during LDs and SDs. Yellow area: light in LDs but dark in SDs. Blue area: dark in LDs and SDs. Time in hours from dawn is represented below each diagram. ZT, zeitgeber time; SD, short day; LD, long day (Turck et al., 2008).

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production of the signal (Pittendrigh and Minis, 1964). Recent findings concerning the CONSTANS (CO) gene showed that this gene system had similarities with the output in the external coincidence model. As it was mentioned before, the clock acts to establish a rhythm of the CO gene expression, mediated by interactions described in Box 5. In addition to transcriptional regulation, the observation that expression of CO mRNA during the night under SDs does not promote flowering suggested that exposure to light activates CO function at the post-transcriptional level. This was confirmed by sophisticated manipulation of length and structure of the light/dark cycle to bring peak levels of CO mRNA into the light phase under short days. These experiments promoted flowering of wild-type plants under unfavourable photoperiods and confirmed that coincidence between the peak levels of CO mRNA and light phase was necessary to trigger flowering (Roden et al., 2002).

The combination of these regulatory mechanisms results in accumulation of CO protein specifically under LDs, when it activates the transcription of flowering integrators genes FT (Figure 5.1d) and SOC1 (Kardailsky et al., 1999; Samach et al., 2000; Putterill, 2001; Yanovsky and Kay, 2002).

2.2.2. The vernalization response pathway

When exposed to very low temperatures for several weeks, most plant accelerate flowering, but with variability between species and between varieties of a species. Such a requirement for vernalization, which means promotion of flowering by prolonged exposure to cold, is associated with a winter annual growth habit. Arabidopsis mutants which were still responsive to the photoperiod but impaired in the vernalization response were included in a “vernalization pathway”. In certain plant species, the role of vernalization is to suppress the expression of genes that

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encode repressors of flowering; in Arabidopsis, this suppression is an epigenetic phenomenon which results from the modification of the chromatin of flowering repressors.

Recent studies have demonstrated that variation at one or both loci, FLOWERING

LOCUS C (FLC) and FRIGIDA (FRI), can account for a large portion of the winter

annual habit in Arabidopsis (reviewed in (Amasino, 2005)). FLC encodes a MADS domain protein that acts both in leaves and in the apical meristem to repress downstream floral integrators such as FT and SOC1, thereby acting as a floral repressor to delay flowering (Helliwell et al., 2006; Searle et al., 2006). FLC is considered as the point of convergence of the autonomous and vernalization pathways for FLC mRNA levels are controlled by FRI and other genes in the autonomous pathway (Michaels and Amasino, 1999). FRI encodes a plant-specific protein that elevates FLC expression to a level that effectively represses flowering (Michaels and Amasino, 1999). Vernalization overcomes the effect of FRI by repressing FLC expression at the epigenetic level (Michaels and Amasino, 1999; Sheldon et al., 1999), so that after the cold exposure FLC activity is low, releasing the repression of FT and allowing CO-mediated activation of FT in the long days of spring. This effect overrides other types of regulation such as by the autonomous pathway. The repression of FLC by vernalization occurs because of changes in

FLC chromatin. The low-temperature exposure induces expression of VERNALIZATION INSENSITIVE 3 (VIN3) (Sung and Amasino, 2004), which

interacts with the VERNALIZATION2 (VRN2) polycomblike complex (Wood et

al., 2006). This complex binds to FLC chromatin (Sung and Amasino, 2004),

modifying histone residues also referred as “histone code”, and expression is repressed (Bastow et al., 2004; Sung and Amasino, 2004; Schubert et al., 2006). In

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