There Is One Great Truth on This Planet: Whoever You Are, Or Whatever It Is That You Do, When You Really Want Something, It

To my dearest and beloved one, my daughter Sana

‘ There is one great truth on this planet: whoever you are, or whatever it is that you do, when you really want something, it's because that desire originated in the soul of the universe. The soul of the world is nourished by people's happiness. When you want something, all the universe conspires in helping you to achieve it, And no heart has ever suffered when it goes in search of its dream. ’

Paulo Coelho

Promoters:

Prof. dr. ir. Marie-Christine Van Labeke Department of Plant Production, Ghent University, Ghent, Belgium

Dr. ir. Jan De Riek Institute for Agricultural and Fisheries Research, Unit Plant (ILVO), Melle, Belgium

Members of the examination board:

Prof. dr. ir. Stefaan De Smet (Chair) Ghent university, Ghent, Belgium

Prof. dr. Kevin Folta University of Florida, Gainesville, USA

Prof. dr. Els Van Damme Ghent Universty, Ghent, Belgium

Prof. dr. ir. Kathy Steppe Ghent Universty, Ghent, Belgium

Prof. dr. ir. Wannes Keulemans KULeuven, Leuven, Belgium

Dean: Prof. dr. ir. Guido Van Huylenbroeck

Rector: Prof. dr. Paul Van Cauwenberge

Farzaneh RAZAVI

Molecular & Physiological Responses to Drought Stress in Fragaria sp.

Thesis submitted in fulfilment of the requirements for the degree of Doctor (PhD) in Applied Biological Sciences

Nederlandse vertaling van de titel van dit proefschrift:

Moleculaire en Fysiologische Reacties op Droogtestress bij Aardbei

Razavi F. (2012). Molecular & Physiological Responses to Drought Stress in Fragaria sp.

ISBN-number: 978-90-5989-570-6

The author and the PhD promoters give the authorisation to consult and to copy parts of this work for personal use only. Every other use is subject to the copyright laws. Permission to reproduce any material contained in this work should be obtained from the author

ACKNOWLEDGEMENT

‘ The teacher gives not of his wisdom, but rather of his faith and lovingness’ ‘Khalil Gibran’

First of all, I am grateful to my promoter Prof. dr. ir. Marie Chrisitine Van Labeke and I would like to give my sincere thanks to her support and for her great enthusiasm, inspiration, and efforts to support my doctoral research. I acknowledge all that I have learnt from Prof. Van Labeke during my PhD and I deeply thank her for providing many opportunities for my research progress, with her great expertise I could realize the production of my thesis. I would also like to thank my co-promoter, dr. ir. Jan De Riek, who advised my research steps in the best way at ILVO (Plant Science Unit). He provided me with all the facilities that I needed in my molecular research and I am deeply thankful to Jan and his team in this institute for their great expertise and contribution to my research. I am pleased to thank everyone who supported me in ILVO, Dr. ir. Johan Van Huylenbroeck, Dr. ir. Kristiaan Van Laecke, Dr. ir. Ellen De Keyser, Dr. ir. Emmy Dhooghe and Prof. dr. ir. Erik Van Bockstaele. Also I will never forget the help I received from Mrs. Veerle Bouyssens and Mrs. Laurence Desmet for their all technical support in ILVO. I am grateful to all members of my examination committee for the time they spent for the evaluation of my thesis and for their thoughtful comments. I want to take all those who have contributed in any way in my thesis. Thank all my colleagues in the laboratory of In Vitro Biology and Horticulture in the Department of Plant Production in Ghent University. Machteld, Thea, Christophe, Mieke, Patricia, Ellen, ir. Linda Zamariola, ir. Annelies Christiaens, ir. Katrien De Dauw and ir. Lien De Hauwere, thanks to you all not only for your kind helps and support, but also for being so nice and friendly. I would like to express gratitude to Prof. Kevin Folta from The University of Florida for his support during my internship in his lab and thanks to his colleagues for providing a stimulating environment in which to learn. Prof. Folta, you taught me to be a positive and brave researcher. I would also like to show my appreciation to Prof. Wannes Keulemans from KULeuven for his kind support in helping me to fulfil all procedures for starting my education in Belgium, and also allowing access and use of their lab facilities in my pre-doctoral program. I should mention that this PhD could not be accomplished without the financial support of Iranian Ministry of Science and Ministry of Agriculture, BOF and FWO grants from Ghent University. Special thanks to the academic staff of Iranian Ministry of Agriculture, Seed and Plants Improvement Institute (SPII), who supported me, particularly thanks to my colleague Dr. Hamid Abdollahi for all his support and also to all my other colleagues in the research institute of Chahar-Mahal province. And I will never forget to thank my friends : Lina M.A., Maryam H.H., Nasrin G.H., and Sheida K.A., and all my other dear friends in Belgium. Thanks to them all.

Above all I would like to highlight that I would not be here without the support of my beloved family. My parents and my sisters, Fereshteh, Samira and Mahsa, I cannot say how much I miss you and how much I wish you were here with me. Thanks to my husband Reza for his patience, support and for encouraging me during the hard moments. And finally I have the most special thanks to my dearest, my daughter Sana. My honey Sana, I am greatly indebted to you and I will do my best to be a great mother with more time for you from now on.

Farzaneh RAZAVI Gent, December 2012

TABLE OF CONTENTS

List of abbreviations & acronyms…………………………………………………………………...... i Thesis outline.………………………………………………………………………………………….vii

CHAPTER 1: Water deficit stress and strawberry (Fragaria) response to drought ...... 1

1.1 Introduction ...... 3 1.2 The genus Fragaria ...... 4 1.3 Economic importance and nutritional value of strawberries ...... 4 1.4 Water deficit and plant responses to drought stress ...... 5 1.4.1 Introduction ...... 5

1.4.2 Effect of water deficit on plant physiology, growth characteristics and cellular structure .... 7

1.4.3 Effect of water deficit on plant metabolites...... 8

1.4.4 Signalling during drought stress ...... 17

1.5 Epigenetic control of plant response to drought stress; non-coding RNA (miRNA and siRNA) and plant drought tolerance ...... 33

1.6 Genotype variation in plant response to water deficit ...... 34 1.7 Osmotic stress (dehydration and salt stress) in strawberry (Fragaria spp.): an overview ...... 34 1.8 Recommended genomics and genetics approaches for studying of drought tolerance in Fragaria ...... 36

1.8.1 Introduction ...... 36

1.8.2 Structural and functional genomics ...... 37

1.8.3 Functional molecular biology ...... 37

1.8.4 Transgenic system and genetic engineering ...... 38

1.9 Conclusions ...... 39

CHAPTER 2: Evaluation of chlorophyll fluorescence as a probe for drought stress in strawberry ...... 41

2.1 Introduction ...... 43 2.2 Materials and Methods ...... 44 2.2.1 Plant material, water treatments and environmental conditions ...... 44

2.2.2 Leaf water potential ...... 45

2.2.3 Measurement of chlorophyll fluorescence ...... 45

2.2.4 Measurement of morphological traits ...... 45

2.2.5 Statistical analysis ...... 46

2.3 Results ...... 46 2.3.1 Climatic data...... 46

2.3.2 Volumetric moisture content (θv) of the substrate and leaf water potential ...... 46

2.3.3 Effect of drought stress on Chl a fluorescence ...... 47

2.3.4 Effect of drought stress on morphological parameters ...... 47

2.4 Discussion ...... 49 2.4.1 Drought stress and leaf water potential ...... 49

2.4.2 Drought stress and sensitivity of fluorescence parameters in strawberry ...... 50

2.4.3 Drought effects on morphological traits ...... 51

2.5 Conclusion ...... 51 CHAPTER 3: Osmotic solute content and antioxidant defence in strawberry under drought stress ...... 53

3.1 Introduction ...... 55 3.2 Materials and methods...... 56 3.2.1 Plant material, drought treatment and environmental conditions ...... 56

3.2.2 Leaf water potential (Ψw) ...... 56

3.2.3 Carbohydrate analysis ...... 57

3.2.4 Proline content ...... 57

3.2.5 Protein concentration ...... 57

3.2.6 Total Antioxidant Capacity (TAC) ...... 58

3.2.7 Catalase (CAT) activity ...... 58

3.2.8 Ascorbate peroxidase (APX) activity ...... 58

3.2.9 SOD activity ...... 58

3.2.10 Statistical analysis ...... 59

3.3 Results ...... 59 3.3.1 Climatic data, volumetric substrate moisture content and leaf water potential ...... 59

3.3.2 Carbohydrates ...... 59

3.3.3 Proline content ...... 61

3.3.4 Total antioxidant content (TAC) ...... 62

TABLE OF CONTENTS

3.3.5 Protein content and the activity of antioxidant enzymes: CAT, APX and SOD ...... 62

3.4 Discussion ...... 63 3.4.1 Plant water relations under drought stress ...... 63

3.4.2 Osmotic adjustment ...... 64

3.4.3 Protein content ...... 65

3.4.4 Total antioxidant capacity (TAC) and antioxidant enzymatic activities ...... 65

3.5 Conclusion ...... 67 CHAPTER 4: Isolation of candidate genes ...... 69

4.1 Introduction ...... 71 4.2 Materials and methods...... 72 4.2.1 Selection of candidate genes ...... 72

4.2.2 In silico identification of candidate genes in Fragaria ...... 72

4.2.3 Primer development and PCR amplification ...... 72

4.2.4 Cloning of PCR products and verification of selected gene amplicons for RT-qPCR ...... 73

4.3 Results and discussion ...... 75 4.3.1 Characteristics of isolated Fragaria sequences ...... 75

4.4 Conclusion ...... 87 CHAPTER 5: Genotype variation in drought tolerance in Fragaria & associated AFLP and EST candidate gene markers ...... 89

5.1 Introduction ...... 91 5.2 Materials and Methods ...... 92 5.2.1 Plant material ...... 92

5.2.2 RWC and WLR measurements and dehydration condition ...... 96

5.2.3 DNA isolation and AFLP amplification ...... 96

5.2.4 EST marker development and amplification ...... 97

5.2.5 Statistical analysis ...... 97

5.3 Results ...... 97 5.3.1 RWC and WLR measurements ...... 97

5.3.2 Molecular marker characteristics ...... 100

5.3.3 Genetic relationship as revealed by AFLP and EST markers ...... 102

5.3.4 Association testing of DNA markers with physiological traits ...... 104

5.4 Discussion ...... 113 5.4.1 Phylogenetic relationship ...... 113

5.4.2 Plant water relations in response to drought stress at the physiological and molecular level ...... 114

5.4.3 Candidate genes from known metabolic pathways behind drought tolerance, EST markers and association with the eco-physiological parameters ...... 117

5.5 Conclusion ...... 118 CHAPTER 6: Expression profiling and characterization of candidate genes under water deficit by RT-qPCR ...... 121

6.1 Introduction ...... 123 6.2 Materials and methods...... 124 6.2.1 Plant material ...... 124

6.2.2 Drought stress treatments ...... 124

6.2.3 Relative water content (RWC) and leaf water potential ...... 125

6.2.4 Carbohydrates ...... 125

6.2.5 Proline ...... 126

6.2.6 Total Antioxidant Capacity (TAC) ...... 126

6.2.7 Protein concentration and catalase (CAT) activity ...... 126

6.2.8 Superoxide dismutase (SOD) activity ...... 127

6.2.9 Malondialdehyde (MDA) content ...... 127

6.2.10 RNA isolation and cDNA synthesis ...... 128

6.2.11 Construction of standard curves for the RT-qPCR assay ...... 129

6.2.12 RT-qPCR assay ...... 129

6.2.13 Statistical analysis ...... 130

6.3 Results ...... 130 6.3.1 Plant water balance ...... 130

6.3.2 Metabolites and Enzymes ...... 133

6.3.3 Gene expression analysis ...... 142

6.4 Discussion ...... 160

TABLE OF CONTENTS

6.4.1 Plant water balance under water deficit in Fragaria ...... 160

6.4.2 Metabolite and gene transcript regulation under water deficit in Fragaria ...... 161

6.5 Conclusion ...... 174 CHAPTER 7: Genetic transformation of Fragaria with candidate genes involved in plant response to drought stress ...... 177

7.1 Introduction ...... 179 7.2 Materials and methods...... 179 7.2.1 Construction of RNAi and over- expression vector ...... 179

7.2.2 Bacterial Strain ...... 180

7.2.3 Plant materials, transformation, tissue culture and regeneration ...... 180

7.2.4 Molecular characterization of transformants ...... 182

7.3 Results ...... 182 7.4 Discussion ...... 188 7.5 Conclusion ...... 189 CHAPTER 8: General discussion and conclusions ...... 191

Summary…………………………………………………………………………………………...... 203 Samenvatting…………………………………………………………………………………...... 207 Bibliography ……………………………………………………………………………………….211 Curriculum Vitae....……………………………………………………………………………...... 255

LIST OF ABBREVIATIONS & ACRONYMS

ABA - abscicic acid ABRE - ABA-Responsive Element AFLP - Amplified Fragment Length Polymorphism AIV- acid invertase AKR - aldo-keto reductase ALDH - aldehyde dehydrogenases ANOVA - analysis of variance APX - ascorbte peroxidase AsA - reduced ascorbate, Ascorbic Acid ASC - ascorbate ATP - adenosine triphosphate bp - base pair BSA - bovine serum albumin CAPS - Cleaved Amplified Polymorphic Sequence Catalase - CAT cAPX - cytosolic APX cDNA - complementary DNA CDPK-calcium-dependent protein kinase Chl (a) fluorescence - Chlorophyll fluorescence chlAPX - chloroplast APX

Cq - quantification cycle CSD - Cu-ZnSOD DAS - days after drought stress settlement DHA – dehydroascorbate DHAR - dehydroascorbate reductase DHN - dehydrin DNA - deoxyribonuclec acid DRE - dehydration -responsive element DREB2A - dehydration-responsive element (DRE) - binding protein 2A EST - Expressed Sequence Tag

Fm - maximum fluorescence yield

Fv - variable fluorescence

Fv/Fm - the maximum quantum efficiency of Photosystem II Fru - fructose FW - fresh weight

LIST OF ABBREVIATIONS & ACRONYMS

GA - gibberellic acid GalUR - D-galacturonic acid reductase GalLDH - l-galactno-1, 4-lactone dehydrogenase GB - glycine betaine GFP - green-fluorescent protein Glc - glucose GO - gene annotation GOI - genes of interest GOPX - guaicol peroxidase GPX - glutathione peroxidase GR - glutathione reductase GSH - reduced glutathione GSSG - glutathione disulfide GST- glutathione-S-transferase • HO 2 - hydroperoxyl radical HO• - hydroxyl radical

H2O2 - hydrogen peroxide HSP - heat shock protein HXK - hexokinase IBA - indol-3-butyric acid Inv - invertase Inv-CW - cell wall/extracellular invertase Inv-N - neutral invertase Inv-V- vacuolar invertase ISSR - Inter Simple Sequence Repeat JA - jasmonic acid LEA - late embryogenesis abundant protein LD - linkage disequilibrium mapping LPO - lipid peroxidation MAS - marker assisted selection MAPK - mitogen-activated protein kinase MDA - malondialdehyde MDA - monodehydroascorbate MDAR - monodehdroascorbate reductase MJ - methyl jasmonate

LIST OF ABBREVIATIONS & ACRONYMS

NCED3 - 9-cis-epoxycarotenoid dioxygenase noRT - no reverse transcriptase samples NRQ - normalized relative quantity NTC - no template control 1 O2 - singlet oxygen •- O2 - superoxide radical OA - osmotic adjustment OLP - osmotin-like proteins OX - over expression PA - phaseic acid PA - polyamine PAR - photosynthetic active radiation PCR - polymerase chain reaction PCD - programmed cell death PC - primer combination P5CS - ∆1-pyrroline-5-carboxylate synthase P5CDH - ∆1-pyrroline-5-carboxylate dehydrogenase P5CR - ∆1-pyrroline-5-carboxylate reductase PDH - proline dehydrogenase PM - plasma membrane POX - peroxidases ProDH - proline dehydrogenase PSI - photosystem I PSII - photosystem II

QA - primary quinine acceptor of electrons in PSII

QB - secondary quinine acceptor of electrons in PSII qN - non-photochemical quenching of chlorophyll fluorescence qP - photochemical quenching of chlorophyll fluorescence QTL- Quantitative Trait Loci R - reference genes RAPD - Random Amplification of Polymorphic DNA RFLP - Restriction Fragment Length Polymorphism RFO - raffinose family oligosaccharides RNAi - RNA interference ROS - reactive oxygen species

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LIST OF ABBREVIATIONS & ACRONYMS

RT-qPCR - quantitative reverse transcription polymerase chain reaction RWC - relative water content SA - salysilic acid SNP - Single Nucleotide Polymorphism SOD - superoxide dismutase SPS - sucrose phosphate synthase SSR - Simple Sequence Repeats STS - Sequence Tagged Site SUC - sucrose SUS/SuSy - sucrose synthase TAC - total antioxidant capacity T-DNA - transfer DNA TSC - total soluble sugar WLR - water losing rate WUE - water use efficiency

Ψw - leaf water potential

ΦPSII - effective quantum yield of PSII electron transport

THESIS OUTLINE

Plant environmental (abiotic) stress constitutes a major limitation to agricultural production and the farmer's livelihood. Crop production is hardly ever free of environmental stress. The major plant environmental stresses of contemporary economic importance worldwide are drought, cold (chilling and freezing), heat, salinity, soil mineral deficiency and soil mineral toxicity. Interest in drought resistance, in its basic or applied aspects, has been growing recently. Concerns about climate change and water scarcity in agriculture and horticulture are important reasons for expanding this research area (Blum 2011).

Plants must adapt under drought stress to survive and/or maintain an efficient growth and production. Drought stress triggers various physiological and biochemical pathways in plants including stomatal closure, suppression of photosynthesis and growth, adjustment of metabolites mainly by their sugar and amino acid metabolism, synthesis of different protective proteins, generation of reactive oxygen species (ROS) and induction of antioxidant defence system (Fulda et al. 2011; Seki et al. 2007; Bartels and Sunkar 2005). Numerous studies have demonstrated the key role of abscisic acid (ABA) in plant adaptation to drought stress; both ABA-dependent and -independent regulatory systems as well as ROS and sugar signalling pathways are involved in plant adaptation to water deficit (Huang et al. 2012). Plant adaptation under water deficit is a dynamic and multifaceted mechanism that is anchored in the regulation of different cellular and molecular pathways (Krasensky and Jonak 2012; Seki et al. 2007; Bartels and Sunkar 2005). Numerous drought-responsive genes were identified using whole- genome oligonucleotide microarrays and these were mainly related to metabolite adjustment under water deficit (Huang et al. 2008; Seki et al. 2007; Bartels and Sunkar 2005). Cultivated strawberry, Fragaria × ananassa, an herbaceous fruit-bearing species belonging to the Rosaceae is very susceptible to water deficit. Strawberry cultivation relies on irrigation as fruit production and plant growth is hampered under water limitation (Grant et al. 2012; Vij and Tyagi 2007). The characterization of the response of Fragaria to water deficit is necessary for developing drought-tolerant lines that can be used in breeding programs. The main question is how the regulation of different drought defence mechanisms in Fragaria works. To date, the knowledge of molecular control mechanisms of abiotic stress tolerance especially drought in Fragaria is still limited. The primary objectives of this study were to identify and characterize the plant defence mechanisms to drought stress in Fragaria and to classify available Fragaria genotypes based on their drought tolerance capacity. As a first step we evaluated physiological and metabolic changes in Fragaria under water deficit and screened different Fragaria genotypes based on physiological traits linked to the plant water balance. To unravel the molecular control mechanisms of drought tolerance we used a candidate gene approach. Changes observed at the metabolic level under water deficit were the ‘key’ for a literature search for candidate genes that might be involved in these changes both as functional

vii

genes in different metabolic pathways in plant drought adaptation or as regulators (transcription factors). Candidate genes were selected from pathways such as ABA biosynthesis, sugar and proline metabolism, antioxidant defence system, transcription factors, etc. Firstly, by association analysis it was tested if allelic variance at these candidate genes could better predict drought tolerance in a relatively large set of 23 Fragaria genotypes compared to random DNA-based markers that are generated from anonymous genomic DNA sequences. The next step was to study differentially expressed genes along with analysis of plant biochemical responses in a selected set of 4 Fragaria species and cultivars with different drought susceptibility. Finally, we started gene silencing (RNAi)/overexpression approaches to be able to start a further functional analysis of selected genes in Fragaria response to water deficit.

In chapter 1, an introduction on strawberry and a review on drought stress and plant adaptation mechanism under water deficit is presented. Important progress has been made in understanding plant response to water deficit in different plant species. In this chapter, we explain the function of ABA (an important regulator in the crosstalk of different plant acclimation responses) and introduce the main effects of drought stress on physiological traits and metabolic pathways. In addition, drought – inducible ABA–dependent and –independent regulatory networks together with ROS, and sugar signalling pathways, regulatory elements such as transcription factors and target genes and the integration between drought stress with other environmental stresses and plant growth regulators are discussed.

Chapter 2 and 3 focus on physiological and biochemical adaptations of strawberry plants under drought stress. In chapter 2, we perform a progressive drought stress and study chlorophyll a fluorescence as a non-destructive tool for early detection of photosynthetic dysfunctions under drought stress in the cultivar ‘Elsanta’.

In chapter 3, the osmotic adjustment by proline and sugar metabolism together with enzymatic and non-enzymatic antioxidant defence response in ‘Elsanta’ under progressive water deficit will be discussed.

The selection of putative candidate genes involved in plant response to drought stress and their isolation in Fragaria sp. is described in chapter 4. Isolated putative sequences will be later applied for development of EST markers associated to drought tolerance (chapter 5) and for quantitative expression analysis by RT-qPCR under water deficit in Fragaria (chapter 6). The selection of candidate reference genes for RT-qPCR and isolation from Fragaria is also described in chapter 4. The assessment of genetic control of drought tolerance in Fragaria by using Amplified Fragment Length Polymorphism (AFLP) and functional Expressed Sequence Tag (EST) molecular markers is described in chapter 5. In this study, we establish DNA fingerprints for a set of 23 Fragaria genotypes using AFLP and EST markers, characterize their response to drought stress by measurement of

viii

parameters associated to the leaf water status, examine the correlation between specific DNA markers and leaf water relation and drought tolerance and test the feasibility of association mapping in a small set of accessions. In total 21 strawberry cultivars are used in this study and two species, including one European (F. vesca) and one American species (F. chiloensis) with a known differential response to drought.

In chapter 6, RT-qPCR (quantitative reverse transcription polymerase chain reaction) is developed for expression analysis of candidate genes in Fragaria under water deficit. In a set of 4 drought-sensitive and -tolerant Fragaria genotypes, the expression level of selected candidate genes is analysed by RT- qPCR together with selected metabolites. We also evaluate the expression stability of different candidate reference genes under established experimental conditions to verify appropriate reference genes for normalization of real-time PCR data in Fragaria under water deficit.

In chapter 7, we start the next step for further functional analysis of some up/down regulated genes under water deficit in Fragaria by gene silencing, RNAi and over-expression approaches. We construct RNAi suppression and CaMV 35S over-expression constructs for CAT, P5CS and FaAIV and transfer RNAi and over-expression constructs into different Fragaria genotypes (species and cultivars). Moreover, we expand our knowledge about differences in transformation and shoot regeneration efficiency between different genotypes and constructs.

Finally, in chapter 8, we discuss our overall results, conclude about the characterized mechanisms involved in Fragaria adaptation under drought stress, and give recommendations for further research.

Chapter 1 Water deficit stress and Strawberry (Fragaria) response to drought

Chapter 1

Water deficit stress

1.1 Introduction

Crop production is worldwide subjected to increasing environmental severity and abiotic stresses, especially drought and salinity. The potential impact of climate change will result in unreliable precipitation patterns and endanger the available quality and quantity of irrigation water. Drought stress and its effects on crop productivity in horticultural systems are receiving increasing interest. Long-term adaptive solutions will rely heavily on breeding of more tolerant crops. Yet a short-term alternative includes the selection of drought tolerant cultivars next to the development of more sustainable irrigation techniques. Plants survive the limited availability of water by different adaptation mechanisms. Understanding these mechanisms underlying the plant’s tolerance to drought stress is therefore critical for the development of drought tolerant cultivars and is one of the major research subjects in plant biology and physiology today. Drought tolerance is a complex phenomenon, comprising a number of physiological and biochemical adaptations both at the cellular and plant level (Rampino et al. 2006; Bartels and Sunkar 2005; Suprunova et al. 2004). Complex traits such as tolerance to abiotic stresses like drought stress are multifaceted processes including many metabolites and metabolic pathways controlled by numerous genes at several locations of the genome (Krasensky and Jonak 2012; Shinozaki & Yamaguchi-Shinozaki 2007). Although a number of metabolic pathways and genes have been identified in drought stress response, drought tolerance is still poorly understood. Tolerant crops were already developed through several classical breeding programs mainly by introducing traits from stress-adapted wild relatives, but due to the multigenic nature of the trait, selection is difficult (Bartels and Sunkar 2005). Hence, the study of plant drought tolerance needs different approaches such as genetics, genomics, molecular biology, physiology as well as a breeding program to improve crop production under water deficit. Strawberry plants in their natural environment are exposed to different abiotic stresses such as water deficit, cool temperature during flowering, and frost during bloom, winter cold, high temperature and salinity that can all potentially reduce the yield of the crop by more than 50% (Vij and Tyagi 2007). Commercial strawberry cultivation relies on water provided by irrigation (Grant et al. 2012), this not only in greenhouse production but also in field production under a limited seasonal rainfall. The shallow root system (50-90% of the roots in 0-15 cm zone), large leaf area and high water content of the fruit (Fragaria ×ananassa) make this species very susceptible even to short periods of water deficit (Klamkowski and Treder 2006). Under increasing global warming and limitation of water resources, development of high quality strawberry cultivars with improved water use efficiency (WUE) will be necessary. A better understanding of the mechanisms by which strawberry responds to water deficit might be helpful in developing drought-tolerant strawberry genetic lines that can be used in breeding programs.

Chapter 1

In addition to being an important horticultural crop, strawberry (Fragaria spp.) is a good candidate for studying the principles of drought tolerance in the Rosaceae which contains economically important fruit, nut, ornamental and wood-bearing species from different subfamilies such as peach, cherry and almond (Amygyloideae), apple and pear (Maloideae), blackberry, raspberry and rose (Rosoideae), etc. Furthermore, Fragaria is a unique species in this family because it is a rapidly growing herbaceous perennial with a small genome, it can be handled easily under glasshouse and laboratory settings to study abiotic stress and transformation and regeneration protocols are available. Therefore, strawberry has been considered as the translational genomics model for studying plant response to different abiotic stresses in the Rosaceae family (Folta and Dhingra 2006).

1.2 The genus Fragaria

The genus Fragaria L. belongs to the family Rosaceae, sub-family. The Rosaceae is an ancient plant family containing many genera and includes many well-known species of economic importance particularly edible temperate zone fruits, ornamentals, timber crops and medicinal or nutraceutical plants (Hummer and Hancock 2009; Janick 2005). The genus Fragaria L. includes 21 European, American and Asian species distributed in the temperate and Holarctic zones (Staudt 2005, 2003, 1999 a, b, 1989; Staudt and Dickore 2001, Rousseau-Gueutin et al. 2008). The genus Fragaria L. has a basic chromosome number of seven (x=7) (Ichijima 1926); it includes species with different ploidy levels: diploids, tetraploids, pentaploids, hexaploid (single species) and octoploids. The most common wild species is F. vesca (2n=2x=14) which is considered the model species for the genus (Oosumi et al. 2006). F. vesca is cultivated to a limited extent in North America and Europe. The cultivated strawberry Fragaria × ananassa (2n=8x=56), is an accidental hybrid of two octoploid species and arose in the mid-1700 in France where plants of F. chiloensis imported from Chile were placed near F. virginiana originating from eastern North America. Subsequent breeding efforts have produced large sized and high quality fruit both for field cultivation and for protected culture (Darrow 1966). Now Fragaria × ananassa is the most important strawberry cultivated worldwide. Many strawberry cultivars have been released through different classical breeding programs with different aims in America, Europe and Asia (Japan) (Hummer and Hancock 2009).

1.3 Economic importance and nutritional value of strawberries

Strawberry is the most important soft fruit worldwide and is cultivated in the northern hemisphere (98%), although no genetic or climatic barriers prevent its production in the south. More than 75 countries produce strawberries (FAOSTAT 2010). Most of the crop is grown for fresh fruit, but a small portion is frozen or used to make preserves (jam, jelly, conserve) (Hummer and Hancock 2009; Janick 2005). The USA is the most important producing nation with approximately 25% of the world’s crop followed by China, Turkey, Spain, Egypt, Republic of Korea, Mexico, Japan, Poland, Russian

Water deficit stress

Federation, Germany and Italy (FAOSTAT 2010). The total world production has been estimated at 4- 5 million tonnes (FAOSTAT 2010). Ripe strawberries are composed of approximately 90% water and 10% total soluble solids and contain many important nutritional components (Hemphill and Martin 1992). Glucose and fructose are the main fruit sugars: over 80% of the total sugars and 40% of the total dry weight (Wrolstad and Shallenberger 1981). The main organic acid is citric acid that composes 88% of total acids (Green 1971), the fruit further contains a considerable level of ellagic acid which is a natural anti-proliferative phenol antioxidant (Maas et al. 1991). The red color of strawberry results from the production of anthocyanins mainly pelargonidin-3-glucosidase (Wrolstad et al. 1970; Kalt et al. 1993). Strawberry has a rich vitamin content; 10 fruits of strawberry supplying 95% of the recommended dietary requirements for vitamin C (Mass et al. 1996; Hemphill and Martin 1992). Strawberry has a delicate flavor and aroma. Strawberry flavor is a complex combination of sweetness, acidity and aroma; the most intensely flavored fruits usually have high levels of both titratable acidity and soluble solids (Kader 1991). The primary component of strawberry aroma is thought to originate from a complex mixture of esters, alcohols, aldehydes and sulfur compounds (Dirinck et al. 1981; Pérez et al. 1996). Concentrations of volatile esters vary extensively between cultivars and create great variations in aroma quality (Pérez et al. 1996, 1997). The aroma content also differs across species and the wild species like F. vesca and F. virginiana have a much stronger aroma than the cultivated genotypes (Hirvi and Honkanen 1982).

1.4 Water deficit and plant responses to drought stress

1.4.1 Introduction

The amount of water available for plant growth is mainly influenced by water resource limitation which depends on climate and soil conditions. Any water content of plant tissues or cells that is below the highest water content at the fully hydrated state is defined as water deficit or osmotic stress and plants with higher water-use efficiency are expected to better tolerate water deficit conditions. The reduction of the cytosolic and vacuolar volumes by induction of osmotic stress and removal of water from the cytoplasm into the extracellular space activates the cellular dehydration under drought stress (Taiz and Zeiger 2006; Bartels and Sunkar 2005; Larcher 2003). Depending on the severity and duration, water deficit enforces an osmotic stress that leads to turgor loss, disorganization in membrane integrity, denaturing or deactivation of proteins, generation of reactive oxygen species (ROS) and oxidative damage. These effects consequently result in repression of photosynthesis, metabolic disorders, damage in cellular structure and disruption in growth and development (Fig 1.1) (Krasensky and Jonak 2012; Valliyodan and Nguyen 2006; Ramachandra Reddy et al. 2004; Larcher 2003). Plants have several strategies to face drought stress (/water deficit): adaptation mechanisms by which plants can survive under water deficit or avoidance mechanisms by which plants get the specific growth habit to avoid the water shortage (Levitt 1980). Plant adaptive responses under drought stress 5

Chapter 1

include the initiation and preservation of many physiological processes that are regulated by a complex network of genes (Krasensky and Jonak 2012; Shinozaki & Yamaguchi-Shinozaki 2007; Ramachandra Reddy et al. 2004). Numerous drought–regulated genes from different metabolic pathways have been identified and their function was validated by over-expressing transgenic lines with increased drought tolerance under water deficit (Umezawa et al. 2006a; Seki et al. 2007; Bartels and Sunkar 2005). Using whole-genome oligonucleotide microarrays, almost 2000 drought-responsive genes were identified in Arabidopsis under progressive drought stress (Huang et al. 2008). The specific pattern of gene regulation under water deficit is mainly determining the plant’s tolerance phenotype and down-regulation of some genes may affect positively or negatively the adaptive stress responses (Shinozaki et al.2003; Zhu 2002; Bartels and Salamini 2001).

Fig 1.1 Physiological, biochemical and molecular responses to drought stress in higher plants (Ramachandra Reddy et al. 2004).

Water deficit stress

1.4.2 Effect of water deficit (/drought stress) on plant physiology, growth characteristics and cellular structure

Water deficit has several effects on plant physiology and growth and development, it stimulates the leaf abscission, leaf wax deposition and root elongation but decreases leaf area and above ground plant growth. Water deficit stimulates the stomatal closure, decreases photosynthesis and transpiration rate and cell and tissue water potential, increases resistance to water flow and induction of cell osmotic adjustment to maintain water balance and alteration in energy dissipation from leaves (Fulda et al. 2011; Tezara et al. 1999). The stomatal closure under water deficit is modulated by drought-induced root-to-leaf signalling by xylem abscisic acid (ABA) which is promoted by soil drying through the transpiration stream (Ramachandra Reddy et al. 2004). Drought stress also causes an imbalance between light capture and its utilization in the chloroplast and negatively affects photosynthesis (Foyer and Noctor 2000). The limitation in photosynthesis and change in the cellular carbon metabolism under drought stress is a complex phenomenon that happens via metabolic impairment. The inhibition of CO2 assimilation and imbalance between the generation and utilization of electrons by down regulation of photosystem I and II activity in the chloroplast, resulting in changes in quantum yield, dissipation of excess light energy and finally generation of reactive oxygen species (ROS) (Ramachandra Reddy et al. 2004; Asada 1999). Drought stress reduces the biochemical capacity for carbon assimilation and utilization by inhibition of the Rubisco activity

(ribulose-1, 5-bisphosphate carboxylase/oxygenase), the enzyme that incorporates CO2 into plants during photosynthesis, as well as the synthesis of Rubisco substrate, RuBP (ribulose-1,5-bisphosphate) (Ramachandra Reddy et al. 2004). Seki et al. (2002) identified 79 Arabidopsis down regulated genes by drought stress that were mainly photosynthesis related genes encoding the components of photosystem I and II followed by suppression of photosynthesis under drought stress. This response can be considered as an adaptive mechanism to decrease the formation of ROS in chloroplasts. Drought inhibition effects on plant growth depend on the plant tissue, growth stage, plant species, and the condition of stress treatment (rapid or gradual stress). Shoot growth repression can be considered as a mechanism to preserve the carbohydrates for constant metabolism and solute accumulation for osmotic adjustment, but the maintenance of root growth under water deficit is an adaptive mechanism to facilitate water uptake from deeper soil layers (Sunkar and Bartels 2005). Mild water deficit inhibited leaf and stem growth while roots continued to grow under drought stress (Spollen et al. 1993). Osmotic stress might control growth rate by regulation of cyclin-dependent kinase (CDK) activity as the key regulator of cell division (Cockcroft et al. 2000, West et al. 2004). Maize ZmCdc2 (a member of the CDK family) was down regulated by water deficit resulting in a decrease in mitotic cell cycling (Setter and Flanningan 2001). Up-regulation of different cell expansion genes (EXP) in the apical region of the roots was observed under water deficit and resulted in root elongation (Wu et al. 2001). Some genes encode proteins associated with membrane transport, including ATPases and

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aquaporins that are placed in the plasma membrane (PIPs). These proteins play a critical role in plant water relation by regulation of water flow and membrane permeability (Mirzaei et al. 2012; Mahdieh et al. 2008; Tyerman et al. 2002). In addition, genes encoding proteins involved in structural composition and integrity of cell walls such as S-adenosylmethionine synthase and peroxidases that are involved in lignin biosynthesis are also regulated by drought stress (Guo et al. 2012; Pour Mohammadi et al. 2012; Fulda et al. 2011). Another large group of genes that are also regulated by osmotic stress encoding LEA proteins (late embryogenesis abundant) involved in cellular membrane protection during osmotic stress. LEA proteins accumulate in vegetative tissues during water deficit and five groups of LEA proteins are found in plants (Bray et al. 2000). Several genes encoding protective proteins such as genes encoding proteases (e.g. ERD1) are induced by drought stress, and these enzymes may degrade (remove or recycle) proteins that are denatured by osmotic stress to recycle damaged proteins or to mobilize nitrogen. Proteins like ubiquitin and heat shock proteins (HSPs) are osmotically induced and may tag, protect, renature or stabilize proteins inactivated by desiccation. The accumulation of HSPs was reported to be associated with improved drought tolerance (Mirzaei et al. 2012; Sun et al. 2001).

1.4.3 Effect of water deficit on plant metabolites

1.4.3.1 Different metabolic pathways involved in plant response to drought stress The metabolic profiles of different plant species like rice, Arabidopsis and Vitis vinifera after exposure to drought stress showed that plants dynamically react to water deficit based on the progressive adjustment of their metabolism with continuous, transient, early- and late- responsive metabolic changes (Krasensky and Jonak 2012; Rizhsky et al. 2004; Cramer et al. 2007; Urano et al. 2009). These studies suggest that plants acclimation to water deficit is based on a wide range of metabolic pathways and this adaptation is not limited to a single mechanism. Drought accumulated products are grouped in two main categories: functional proteins that presumably function in drought tolerance, and regulatory proteins (Shinozaki and Yamaguchi-Sinozaki 2007; Bartels and Sunkar 2005; Ramachandra Reddy et al. 2004) (Fig 1.2).

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Fig 1.2 Functions of drought stress-inducible genes in stress tolerance and response (Shinozaki and Yamaguchi-Sinozaki 2007).

Plant adaptations under water deficit are mostly initiated by ABA accumulation, followed by the increase of compatible solutes (osmolytes) like proline, mannitol and sorbitol, formation of oxygen radical scavenging compounds like ascorbate, glutathione, and α-tocopherol as a consequence of differential gene expression under water deficit (Fulda et al. 2011). The accumulation of sugars such as raffinose family oligosaccharides (RFO), sucrose, trehalose, sugar alcohols such as mannitol and sorbitol, amino acids such as proline and amines such as glycine betaine and polyamines under drought stress was reported in different plant species (Seki et al. 2007, Bartels and Sunkar 2005). Fulda et al. (2011) reported the accumulation of inositol, glucose, proline, fructose and sucrose in leaves of drought-stressed sunflower (Helianthus annuus L.). These accumulated metabolites under water deficit play a critical role as osmolytes to maintain cell turgor and stabilize cell proteins and structures during drought stress, and as antioxidants or ROS scavengers (Seki et al. 2007; Bartels and Sunkar 2005). A high correlation between the accumulation of some sugars (like RFO, galactinol, trehalose and fructan), sugar alcohols (like mannitol and D-ononitol) and osmotic stress tolerance was already reported through transgenic systems in several reports (Taji et al. 2002; Streeter et al. 2001; Glimour et al. 2000; Ramanjulu et al. 1994). It is hypothesized that differences between drought stress-tolerant and stress-sensitive plants might be associated with the regulation of these metabolite changes under drought condition, sensitive plants may lack the ability to synthesize the special metabolites that are naturally accumulated in stress-tolerant plants. Another hypothesis suggests that tolerance mechanism might be also present in sensitive plants but just under gradual acclimation to

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water deficit, these plants can obtain drought tolerance by proper gene expression patterns. Study of metabolite profiles in different species under water deficit elucidated a dynamic pattern in metabolite content; proline and raffinose accumulate after several days of drought while the central carbohydrate metabolism changes rapidly in a complex time-dependent manner (Krasensky and Jonak 2012).

1.4.3.2 ABA biosynthesis and catabolism under water deficit In general abscisic acid (ABA) modulates several aspects of plant development including seed development and desiccation tolerance, dormancy and functions as a crucial element in the plant’s response to abiotic stresses such as drought, salinity, cold and hypoxia as well as biotic stresses (Huang et al. 2012). Drought stress triggers the production of ABA and increased ABA levels serve as initial signal for long-term plant acclimation reactions under water deficit (Fulda et al. 2011). This ABA signalling makes stomatal closure, as one of the most important early plant responses to water deficit, induces the expression of drought stress-related genes, affects metabolic pathways and changes metabolite contents (Bartels and Sunkar 2005; Ramachandra Reddy et al. 2004). The endogenous ABA level is affected by the rate of ABA biosynthesis and catabolism in which specific key enzymes are important regulatory elements for the plant response to drought stress (Baron et al. 2012; Seki et al. 2007) (Fig. 1.3). In the ABA biosynthetic pathway, xanthoxin (a C15 precursor of ABA) is cleaved directly from 9-cis-epoxycarotenoids (C40 carotenoids) by 9-cis-epoxycarotenoid dioxygenase (NCED3) and then released from chloroplasts to the cytoplasm. AtNCED3 was shown to have a critical role in drought-stress-inducible ABA biosynthesis (Iuchi et al. 2001) and is rapidly induced by drought stress. ABA is then produced through abscisic aldehyde (ABAId) formation (Nambara and Marion-Poll 2005). There are at least two pathways for ABA catabolism, the oxidative pathway (the major pathway) and sugar conjugation (Nambara and Marion-Poll 2005). The oxidative pathway is triggered by ABA C-8’ hydroxylation to form phaseic acid, which is catalyzed by CYP707A3. The expression of CYP707A3 is greatly induced by rehydration after a dehydration condition (Umezawa et al. 2006 b). Another deactivation ABA pathway is in the formation of sugar-conjugated forms like ABA glucosylester, which is accumulated in vacuoles or apoplast pools. Lee et al. (2006) reported that under drought stress, ABA is released from the glucosylester form by AtBG1 an Arabidopsis β- glucosidase. Therefore, AtBG1 is also an important regulatory enzyme in plant response to water deficit (Seki et al. 2007). Results of other studies indicated that under drought stress both ABA synthesis and catabolism increased as an increase in both endogenous ABA levels and ABA metabolites like phaseic acid (PA), dihydrophaseic acid (DPA) and abscisic acid glucose ester (ABA- GE). This suggested that ABA degradation is still active under water deficit as the flux through the ABA metabolic pathway likely increases as biosynthesis increases. This report also indicated that the ABA treated plants showed higher drought tolerance and postponed wilting as the result of induced ABA-related genes under drought tolerance (Huang et al. 2008).

Water deficit stress

Fig 1.3 ABA biosynthetic and catabolic pathways in plants. In the final process of the ABA biosynthetic pathway, xanthoxin is cleaved from 9-cis-epoxycarotenoids by NCED and then released from the chloroplasts to the cytoplasm. ABA is then produced through abscisic aldehyde (ABAld) formation. In ABA catabolism, the major pathway seems to be an oxidative route, which is triggered by ABA 8’OH-hydroxylation catalyzed by the CYP707A family. There are other pathways for ABA inactivation, such as glucosylation. Red letters indicate drought-stress- responsive regulation. CYP707As are indicated in orange, which also indicates dehydration- and rehydration-responsive regulation. AAO, abscisic aldehyde oxygenase; ABA2, short-chain dehydrogenase/reductase; ABA3, molybdenum cofactor sulfurase; ABA-GTase, ABA glucosyltransferase; AtBG1; b-glucosidase; CYP707A, ABA 80-hydroxylase; NCED, 9-cis- epoxycarotenoid dioxygenase (Seki et al. 2007).

1.4.3.3 Osmotic adjustment under water deficit

1.4.3.3.1 Sugars and sugar alcohols In different plant species, nonstructural carbohydrates such as sucrose, hexoses and sugar alcohols accumulate during drought stress. Sugars in general can function as osmolytes to preserve the cell turgor and have the capacity to protect membranes and proteins from stress damages (Krasensky and 11

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Jonak 2012). Accumulated sugars under drought stress can also protect specific macromolecules and contribute to the stabilization of membrane structures (Black and Pritchard 2002). Drought stress commonly leads to a reduction of starch content (as the main carbohydrate store in plants) and accumulation of soluble sugars (sucrose and hexoses) in leaves (Kempa et al. 2008; Basu et al. 2007). Starch conversion to soluble sugars under drought stress is accompanied by induction of soluble acid invertase (AIV), sucrose synthase (SuSy/SUS) and sucrose phosphate synthase (SPS), key enzymes in sucrose metabolism under water deficit (Bartels and Sunkar 2005; Roitsch and Gonzalez 2004). Microarray analyses indicated that genes of the sucrose biosynthesis pathway were highly expressed during progressive drought stress, suggesting that the sucrose metabolism pathway is critical in plant response to water deficit (Huang et al. 2008). Different reports indicated that transgenic plants contain genetic engineered genes involved in sugars and sugar alcohol metabolic pathways performed different drought tolerance (Seki et al. 2007; Bartels and Sunkar 2005; Umezawa et al. 2006a; Kasuga et al. 1999). The RFO sugars like raffinose and galactinol are known as important osmolytes in plant protection under drought tolerance (Taji et al. 2002). Drought-inducible galactinol synthase (AtGolS2) was over-expressed in transgenic Arabidopsis and caused the accumulation of galactinol and raffinose which was followed by increased drought tolerance, hence, galactinol and raffinose might function as osmoprotectants during drought stress (Seki et al. 2007; Taji et al. 2002). Trehalose, a non-reducing disaccharide of glucose, functions to stabilize proteins and membrane lipids under drought stress (Gupta and Kuar 2005; Grag et al. 2002). The introduction of trehalose-6-phosphate phosphatase, an enzyme for trehalose synthesis into several plants improved their drought tolerance (Grag et al. 2002). The fructans, a family of oligo- and polyfructoses also function as important elements in protecting membranes from the effects of drought stress and have a considerable role in drought tolerance in plants. Transgenic sugar beets that contained the bacterial fructan biosynthesis showed increased drought tolerance (Pilon-Smits et al. 1999). Mannitol, a sugar alcohol, accumulated under water deficit in several plant species. Transgenic wheat with the mannitol-1-phosphate dehydrogenase of Escherichia coli (mtlD) showed improved tolerance under drought and salinity (Abebe et al. 2003). Due to the low mannitol content in these transgenic plants, the increased stress tolerance by mannitol might be conducted just by scavenging hydroxyl radicals and/or by stabilization of macromolecular structure (Seki et al. 2007; Abebe et al. 2003). The accumulation of mannitol in response to osmotic stress occurred by the down-regulation of genes associated with sucrose production and mannitol degradation (Buchanan et al. 2000). The up-regulation of myo-inositol 6-O-methyltransferase, a rate– limiting enzyme in the accumulation of the cyclic sugar alcohol pinitol under water deficit was also reported (Buchanan et al. 2000).

1.4.3.3.2 Amino acids Proline is a multifunctional metabolite and functions as an osmolyte in osmotic adjustment, a stabilizer of sub-cellular structures (protection of plasma membrane integrity,) and scavenger of free radicals

Water deficit stress

(hydroxyl radical scavenger), it acts as an energy sink, a source for carbon and nitrogen and a stress- related signal (Bartels and Sunkar 2005, Nanjo et al. 1999). Intracellular proline accumulation is a common metabolic response to osmotic stress and P5CS is the key enzyme in this pathway (Perez- Arellano et al. 2010). Proline content under stress is regulated greatly by P5CS, ProDH and P5CDH (∆1-pyrroline-5-carboxylate dehydrogenase) which catabolizes ∆1-pyrroline-5-carboxylate (P5C) under stress (Seki et al. 2007; Borsani et al. 2005; Yoshiba et al. 1997). The accumulation of proline under drought stress can occur via the glutamate-dependent pathway (Delauney and Verma 1993) (Fig. 1.4). L-proline is synthesized from L-glutamic acid via ∆1-pyrroline-5-carboxylate (P5C) that is catalyzed by two enzymes P5C synthase (P5CS) and P5C reductase (P5CR). The oxidation of L- proline is another main pathway which controls the proline level and is catalyzed by proline dehydrogenase (ProDH) that converts L-proline to P5C which is converted to L-glutamic acid by P5C dehydrogenase (P5CDH). Genes encoding enzymes involved in proline metabolism have been isolated from some plant species (Yoshiba et al. 1997). Many reports confirmed up-regulation of P5CS involved in proline biosynthesis and down-regulation of ProDH involved in proline degradation under water deficit resulting in proline accumulation (Borsani et al. 2005; Buchanan et al. 2000). Significant up-regulation of key genes of proline metabolism during progressive drought stress was also reported by microarray analysis in Arabidopsis under water deficit (Huang et al. 2008). These evidences explain the critical role of proline in plant response to water deficit. The function of proline under drought stress has also been proven by transgenic methods. The over-expression of the ∆1-pyrroline- 5-carboxylate synthase (P5CS) or antisense suppression of the proline dehydrogenase (ProDH) in various plants followed by the proline accumulation resulted in improved drought tolerance (Kavi Kishor et al. 1995; Zhu et al. 1998 ; Nanjo et al. 1999; Hong et al. 2000). The antisense suppression of the P5CS gene resulted in decreased proline and drought tolerance in Arabidopsis (Nanjo et al. 1999). Despite its protective functions under drought stress, proline excess in the ProDH knockout mutant was toxic and inhibited plant growth in Arabidopsis (Nanjo et al. 2003). An overlapping gene pair including a ∆1-pyrroline-5-carboxylate dehydrogenase (P5CDH) and SRO5, a high salinity- stress- inducible gene of unknown function was investigated by Borsani et al. (2005). It was demonstrated that the cleavage of the P5CDH transcripts by a natural antisense transcript (nat) short-interfering RNA (siRNA) created from this gene pair (P5CDH–SRO5) affects proline metabolism and salt stress tolerance (Borsani et al. 2005).

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Fig 1.4 Regulation of proline biosynthetic and catabolic pathways in plants. P5CDH: P5C dehydrogenase; P5CR: P5C reductase; P5CS: P5C synthase; ProDH: proline dehydrogenase (Seki et al. 2007).

1.4.3.3.3 Amines Other metabolites, which are abundantly involved in plant response to abiotic stress, are polyamines (PAs). The main polyamines in various organisms such as bacteria, plants and animals include putrescine, spermidine and spermine. PAs play important roles in drought stress tolerance but the function of PAs in drought stress responses remains in argument (Seki et al. 2007). Polyamines were shown to have a protective function by scavenging of ROS under drought stress (Tiburcio et al. 1994). In Arabidopsis, the stress inducible arginine decarboxylase, ADC regulates the accumulation of putrescine in response to drought and salt stress (Urano et al. 2003). Transgenic rice over-expressing Datura ADC showed accumulation of PAs with the enhancement of the plant’s drought tolerance (Capell et al. 2004). Glycine betaine (GB; common name of trimethylglycine), an amphoteric quaternary amine, plays an important role to protect plants under water deficit. GB functions by adjusting the water balance between the plant cell and the environment, and stabilizing the structure and activity of cellular macromolecules (Sakamoto and Murata 2002). GB is synthesized via a two- step oxidation of choline that is catalyzed by ferredoxin-dependent choline mono-oxygenase (CMO) and NAD+-dependent betaine aldehyde dehydrogenase (BADH). Induction of BADH under water deficit was associated with osmotic adjustment. The up-regulation of CMO and BADH gene expression leads to GB accumulation and eventually increased osmotic stress tolerance in transgenic plants (Chen and Murata 2002; Rontein et al. 2002; Hayashi et al. 1997). However, plant capacity for synthesizing GB varied between species, some plants such as spinach and barley contain a relatively 14

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high level of GB in their chloroplasts whereas Arabidopsis and tobacco do not synthesize this metabolite. A high level of GB was accumulated in transgenic Arabidopsis plants that expressed glycine sarcosine methyltransferase (ApGSMT) and dimethylglycine methyltransferase (ApDMT) from a halotolerant cyanobacterium, and this lead to an enhanced tolerance to drought, high-salinity and low-temperature stresses (Waditee et al. 2005).

1.4.3.4 Detoxification of Reactive Oxygen Species (ROS) Abiotic stresses such as water deficit cause the accumulation of reactive oxygen species (ROS) (Hossain et al. 2012; Mittler 2002). ROS accumulate in different tissues and sub-cellular compartments as the results of a disruption in electron flux homeostasis in different cellular compartments by drought stress (Miller et al. 2010). The main source for ROS production in the cells are chloroplasts, mitochondria and peroxisomes with highly oxidizing metabolic activity and intense rate of electron flow (Gill and Tuteja 2010). Uncoupling of electron metabolism pathways in cellular compartments under drought stress causes the high energy electron transfer to the molecular oxygen 1 •- • (O2) to form ROS like H2O2, O2, O2 and HO . ROS are highly reactive and toxic, cause damage to the proteins (protein oxidation), carbohydrates, DNA and lipid peroxidation (Fig 1.5; Hossain et al. 2012; Gill and Tuteja 2010). Plant capacity for scavenging of ROS plays a critical role to cope with oxidative stress like water deficit and tolerance induction (Apel and Hirt 2004; Sunkar and Bartels 2002). ROS detoxification is based on non-enzymatic and enzymatic antioxidant mechanisms (Fig 1.6). The main non-enzymatic antioxidants include ascorbate (vitamine C/AsA), glutathione (GSH), tocopherol (vitamin E), flavonoids, alkaloids and carotenoids whereas enzymatic antioxidants include superoxide dismutase (SOD), peroxidases (POX), catalase (CAT), glutathione reductase (GR), glutathione-S-transferase (GST), etc. (Mittler 2002). Aldehydes as highly reactive molecules generated via free radical-mediated lipid peroxidation are toxic for the cells. Aldehyde dehydrogenases (ALDH) are critical enzymes for detoxification of reactive aldehydes and catalyze its conversion to the less reactive carboxylic acid forms (Sunkar et al. 2003). Another osmotic stress inducible detoxification enzyme is aldose/aldehyde reductase, which reduces aldehyde to the alcohols (Obreschall et al. 2000; Sunkar 2003). Peroxiredoxins are enzymes that also act in the detoxification of cellular-toxic peroxides (Dietz et al. 2002). This enzyme reduces the peroxide to alcohol, thus protecting DNA, membranes and enzymes against the damage of ROS (Lim et al. 1993). The SOD and GST are regulated by osmotic stress (Wang et al. 2003, Shinozaki & Yamaguchi-Shinozaki 2007). The transgenic tobacco, alfalfa and potato containing overproduction of scavenging enzymes like SOD or/and APX showed improved tolerance to oxidative stresses like salt, drought and freezing stress (Lee et al. 2007; Allen et al. 1997; Allen 1995). An Ath-ALDH3 was induced in Arabidopsis under abiotic stress (Sunkar et al. 2003). Up-regulation of ALDHs under osmotic stress has been reported in other plants (Seki et al. 2002). The function of aldose/aldehyde reductases as detoxification enzymes was

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proven by transgenic plants over expressing these enzymes (Obreschall et al. 2000; Sunkar 2003). The induction of peroxiredoxins under osmotic stress in plants was also reported (Seki et al. 2001).

Fig 1.5 Abiotic stress-induced ROS/antioxidant imbalance and its cellular impact. Abbreviations: ROS, •- 1 reactive oxygen species; O2 ,superoxide radical; H2O2, Hydrogen peroxide; O2, singlet oxygen; OH•, Hydroxyl radical; SOD, superoxide dismutase; APX, ascorbate peroxidase; MDAR, monodehydroascorbate reductase; DHAR, dehydroascorbate reductase; GR, glutathione reductase; GSH, reduced glutathione; AsA, reduced ascorbate (Hossain et al. 2012).

Water deficit stress

Fig 1.6 The principal models of enzymatic ROS scavenging by superoxide dismutase (SOD), catalase (CAT), the ascorbate-glutathione cycle, and the glutathione peroxidase (GPX) cycle (Apel and Hirt 2004). SOD converts hydrogen superoxide into hydrogen peroxide. CAT converts hydrogen peroxide into water. Hydrogen peroxide is also converted into water by the ascorbate-glutathione cycle. The reducing agent in the first reaction catalyzed by ascorbate peroxidase (APX) is ascorbate, which oxidizes into monodehydroascorbate (MDA). MDA reductase (MDAR) reduces MDA into ascorbate with the help of NAD(P)H. Dehydroascorbate (DHA) is produced spontaneously by MDA and can be reduced to ascorbate by DHA reductase (DHAR) with the help of GSH that is oxidized to GSSG. The cycle closes with glutathione reductase (GR) converting GSSG back into GSH with the reducing agent NAD(P)H. The GPX cycle converts hydrogen peroxide into water using reducing equivalents from GSH. Oxidized GSSG is again converted into GSH by GR and the reducing agent NAD(P)H.

1.4.4 Signalling during drought stress

In this part, we first introduce general aspects of drought-induced signalling pathways and then will explain drought stress-related signalling molecules, transcription factors and different signalling pathways. Finally, the crosstalk between drought, other abiotic stresses and plant hormones will be discussed.

1.4.4.1 Introduction to drought-induced signalling pathways Plant acclimation under drought stress is dependent upon the activation of molecular network cascades involved in stress perception, signal transduction, and the expression of drought stress-related genes, followed by altered plant metabolism, growth and development. Differences in drought tolerance between plants can be ascribed to the variation in stress perception, signal transduction and related gene expression network, etc.. There are multiple stress perception and signalling pathways involved in drought stress that may crosstalk at different steps. Both ABA-dependent and ABA–independent 17

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signalling pathways appear to be involved in drought tolerance (Huang et al. 2012; Shao et al. 2007; Valliyodan and Nguyen 2006; Xiong et al. 2002; Shinozaki and Yamaguchi-Shinozaki 2000). In addition, ROS and sugar signalling play an important role in the plant response to drought stress (Miller et al. 2010; Jaspers and Kangasjarvi 2010; Gupta and Kaur 2005). In general, the signalling pathway under drought stress includes 1) stress perception 2) signal transduction by signalling molecules such as second messengers (e.g. ROS, Ca2+ and inositol phosphates) and a network of protein-protein reactions by kinases / phosphatases (e.g. Ca2+ sensors, MAP kinases and calcium- dependent protein kinases (CDPKs)), 3) target gene regulation by trans- and cis- acting elements (transcription factors and promoters) and finally 4) translation of proteins or metabolites. The perception of primary stress signals followed by the generation of secondary messengers like calcium, that further alter the intracellular calcium level is sensed by calcium binding proteins known as Ca2+ sensors. These sensory proteins subsequently react with their respective interacting partners often initiating a phosphorylation cascade and target the main stress responsive genes or the transcription factors controlling these genes (Huang et al. 2012; Xiong et al. 2002). The plasma membrane plays a critical role in perceiving and transmitting of the environmental signals and in plant defence responses to stresses. The signals of abiotic stresses like drought stress can be sensed through primary sensors described as the physical properties of membrane (membrane fluidity) based on lipid composition and fatty acid composition, calcium permeable channels (responsible for the Ca2+ influx into the cell cytoplasm and transient increase in cytosolic Ca2+) and some membrane proteins (Lopez-Perez et al. 2009; Scandalios 2005). Several membrane proteins have been suggested as candidates for sensing osmotic stress in plants (osmosensors) such as Sln1, Cre1 and also NtC7 encodes receptor-like membrane proteins for sensing osmotic stress (Reiser et al. 2003; Tamura et al. 2003). Several key elements involved in drought signal transduction have been identified in Arabidopsis including transcription factors like ABA-Responsive Element Binding Protein 1 (AREB1)/ ABA-Responsive Element (ABRE)-Binding Factor 2 (ABF2) and Dehydration-Responsive Element (DRE)- Binding Protein 2A (DREB2A), protein kinases such as Receptor-Like Kinase 1 (RPK1), SNF1-Related Protein Kinase 2C (SRK2C), guard cell-expressed calcium-dependent protein kinase (CDPKs), CPK3 and CPK6 (Seki et al. 2007). The complexity of the plant response to drought stress was described by Wang et al. (2003) in Fig. 1.7.

Water deficit stress

Fig 1.7 Complexity of plant response to water deficit. Drought, as the primary stress interacting with other stresses causes cellular damage and secondary stresses, such as osmotic and oxidative stress. The preliminary stress signals (e.g. osmotic and ionic effects, membrane fluidity changes, etc.) trigger the downstream signal transduction and transcription controls, which activate drought stress-responsive mechanisms to recover cellular homeostasis and protect and repair damaged proteins and membranes and eventually result in plant drought tolerance (modified from Wang et al. 2003).

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1.4.4.2 Drought stress and general signalling molecules Signal transduction pathways are the connection between the drought sensing mechanisms and the plant responses. Several classes of protein kinases and phosphatases are determined as signal transducers (Huang et al. 2012; Bartels and Sunkar 2005). The genes encoding these regulatory proteins were shown to be regulated by osmotic stress (Wang et al. 2003; Shinozaki & Yamaguchi- Shinozaki 2007; Krasensky and Jonak 2012). Similar to ROS, cellular calcium level and its dynamics through the cell act as secondary messenger molecules in the drought signalling pathway and can make a link between drought stress stimuli and intracellular responses (Huang et al. 2012; Xiong et al. 2002). The most important signalling molecules and transducers involved in drought signalling include:

1- MAPK pathways: The mitogen-activated protein kinase (MAPK) cascades in interaction with other kinases or G proteins are common signalling units in plants in response to drought stress (Huang et al. 2012; Robinson and Cobb 1997; Triesmann 1996). Many MAPK genes which are activated by osmotic stresses like AtMPK3 have been identified in the plants (Mizoguchi et al. 1996). 2- SNF-1-like kinases: SNF1-AMP-activated protein kinases are another family of protein kinases. These kinases may sense the ATP/AMP ratio and thus control fluxes between anabolism and catabolism via transcription of genes encoding enzymes related to carbohydrate metabolism. Some members of this group of kinases are expressed in response to dehydration or ABA in plants. SNF-1-related protein kinases (SnRKs) have been classified into three families,SnRK1, SnRK2 and SnRK3. Arabidopsis OST1 protein kinase belongs to this family and is activated in response to osmotic stress (Robaglia et al. 2012; Ghillebert et al. 2011). 3- Phosphatases: Phosphatases and protein kinases are counteracting in the phosphoregulatory mechanism in signal transduction. Two main phosphatase groups are the phosphoprotein(serine/threonine) phosphatases (PPases) such as PP1, PP2A, PP2B and PP2C (grouped based on their biochemical and pharmacological properties), and the phosphotyrosine (protein tyrosine phosphatases or PTPases) such as receptor-like PTPases, intacellular PTPases, and dual-specific PTPases (Cohen 1989). AtPTP1 is up regulated by salt stress in Arabidopsis (Xu et al. 1998) and the PP2Cs transcripts increased by dehydration stress in M. crystallinum, suggesting the role of these phosphatases during the dehydration stress (Huang et al. 2012; Miyazaki et al. 1999 ). 4- Phospholipid signalling: The membrane fluidity is changed by modification in phospholipids under osmotic stress (Munik 2001; Munnik and Meijer 2001). Phospholipids are cleaved by phospholipases (PLC, PLD, etc.), which result in phospholipid-derived secondary messengers as osmotic stress signals in plants. Phospholipid signalling can be regulated by G-proteins and may be associated with calcium. The main phospholipid-derived signalling molecules

Water deficit stress

involved in the signalling under osmotic and drought stress are 1, 4, 5-triphosphate (IP3), diacylglycerol and phosphatidic acid. Several reports proved that osmotic stress increased IP3 levels. Drought stress triggers IP3 induction by the activation of phospholipase C, this leads to increase in cytosolic Ca2+ in guard cells, which prompts the stomatal closure and makes a drought avoidance response (Uraji et al. 2012). 5- Salicylic acid: Salicylic acid is known as an important factor in the plant defence system during pathogen attack. It is also involved in osmotic stress and can amplify the effect of osmotic stress by increasing the ROS production during photosynthesis; this ROS acts as a signal that might improve the systemic resistance (Bartoli et al. 2012).

6- Nitric Oxide (NO): NO as a free radical is generated from L-arginine by NO synthase and has some protective role against damages by oxidative stress. NO is able to improve plant drought tolerance by maintenance of higher RWC, decreasing the transpiration rate by closing the stomata and the induction of gene expression involved in stress tolerance (Wilson et al. 2008). 7- Ca2+, a secondary messenger molecule under drought stress/ calcium signalling: Upon the perception of primary drought stress signals and transient increase of cellular calcium, Ca2+ functions as the secondary signalling molecule that regulates the signal transduction pathway (Lindberg et al. 2012; Zhang et al. 2008; Xiong et al. 2002). Three main classes of Ca2+ sensors have been identified in plants including calmodulin, calcium-dependent protein kinases (CDPKs), and calcineurin B-like proteins (CBLs) (Yang and Poovaiah 2003). All are involved in stress signal transduction (Luan et al. 2002). A change in cytosolic calcium levels was observed in Arabidopsis root cells in response to drought (Kiegle et al. 2000). The induction of CDPKs under osmotic stress was observed in several plants. Results also proved that CDPKs play a role in osmotic stress-induced gene expression (Seki et al. 2002; Kawasaki et al. 2001). A calmodulin binding protein in Arabidopsis AtCaMBP25 has been identified as the negative regulator of osmotic stress (Perruc et al. 2004). AtCBL was induced by drought stress in Arabidopsis (Kudla et al. 1999) and CBL1-overexpressing Arabidopsis plants showed higher drought tolerance (Albrecht et al. 2003). Recent studies suggest a targeted signalling component that can be explained as the specific link of signal transductions with the metabolite response under water deficit. For example, some ABA-responsive SnRK2 are specifically involved in osmotic stress-proline accumulation in Arabidopsis. In addition, recent data show a positive role of MAPK-based signalling in stress-induced proline accumulation; hence MAPK promotes proline and soluble sugar accumulation under stress conditions in some species (Kong et al. 2011; Zhang et al. 2011; Xiang et al. 2007).

1.4.4.3 Transcription factors in drought-induced signalling pathway Regulation of gene expression by regulatory proteins named transcription factors can significantly influence the plant response to drought stress, this depends on the interaction of transcription factors

Chapter 1

with cis-regulatory sequences in drought-induced gene promoter (Krasensky and Jonak 2012; Bartels and Sunkar 2005). These regulatory genes are modulated by osmotic stress via reversible phosphorylation or by de novo synthesis of transcription factors (Krasensky and Jonak 2012; Shinozaki & Yamaguchi-Shinozaki 2007; Bartels and Sunkar 2005; Wang et al. 2003). According to Yamaguchi-Shinozaki and Shionozaki (2007), the main transcription factors regulating drought stress- responsive genes include:

1- bZIP (Basic Region Leucine Zipper proteins) binding to ABREs (cis-element) such as ABFs/AREBs (e.g. ABF3) and the HD-ZIP (Homeodomain-Leucin Zipper proteins) such as ATHBs in Arabidopsis. These two groups of transcription factors are ABA responsive transcription factors. 2- Zn-finger proteins like Alfin1 (in alfalfa). 3- AP2/ERF-Type transcription factors including DREB (Dehydration Responsive Element Proteins) or CBF proteins (DREBs/CBF) binding to dehydration responsive element (DRE) or C-repeat, such as DREB2A, DREB2B and CBF1/DREB1B, CBF2/DREB1C, CBF3/DREB1A, CBF4/DREB1D that are induced by osmotic stress (e.g. OsDREB2A was induced by osmotic stress in rice) and have an important role in the transcriptional response to osmotic stress. 4- The Myb-Like and Myc-Like Proteins as ABA responsive transcription factors have cooperation/interaction in osmotic stress-activated gene expression. AtMYB2 and AtMYC2 are the positive regulator of the drought inducible gene RD22 in Arabidopsis. Recent results of metabolite profiling propose a metabolite specific regulation by transcription factors under water deficit, e.g. CBF/DREB1 over expressing plants showed superior accumulated proline and soluble sugars under normal growth condition and performed higher tolerance under freezing and /or salt stress and drought stress (Shinozaki and Yamaguchi-Shinozaki 2007; Gilmour et al. 2000, 2004; Kasuga et al. 1999; Liu et al. 1998). Arabidopsis plants over expressing MYB performed higher levels of soluble sugars, proline and glycine betaine accumulation under normal and stress condition accompanied by improved drought tolerance (Mattana et al. 2005). The up-regulation of peroxiredoxin transcripts as an antioxidant in transgenic overexpressing DREB1A, indicating that peroxiredoxin might be one of the targets of DREB1A transcription factor was reported by Kasuga et al. (1999).

1.4.4.4 ABA-dependent and -independent signalling pathways under water deficit Drought stress-accumulated ABA induces many ABA-responsive and stressed-related genes, the ABA-dependent signalling pathway has the critical role under drought stress in Arabidopsis (Huang et al. 2007; Nakashima and Yamaguchi-Shinozaki 2005). Different above described signalling molecules are involved in ABA signalling pathway under water deficit (Melcher 2010; Kempa et al. 2008). For example in cellular regulation of stomatal closure modulated by the ABA signal transduction under drought stress, different signalling components including protein kinases (SNF-related protein kinases 2 (SnRK2s), protein phosphatases (PP2Cs), Ca2+, ROS, IP3, IP6, NO and G proteins are involved 22

Water deficit stress

(Huang et al. 2012; Miller et al. 2010; Bartels and Sunkar 2005). The PYR/PYL/RCAR proteins being important ABA intracellular receptors, function upstream of these signalling molecules in ABA– mediated functions and directly link ABA perception to the signalling pathway (Bartoli et al. 2012; Melcher et al. 2010). Different genomic analyses proved that the transcriptional regulatory network under drought stress is both ABA-dependent and ABA-independent (Yamaguchi-Shinozaki and Shinozaki 2006, 2005; Shinozaki and Yamaguchi-Shinozaki 2000) (Fig 1.8). Transcript profile analysis in Arabidopsis under progressive drought stress indicated that ABA-dependent pathways are predominant in plant response (gene regulation) to drought stress but about a third of drought- regulated genes were not considerably regulated by ABA (Huang et al. 2008). In agreement with previous reports, Krasensky and Jonak (2012) also indicated that ABA acts as a fundamental regulator of osmotic stress-induced metabolic acclimation and the accumulation of many osmotic stress-induced amino acids like proline is ABA dependent, but many other osmotic-induced metabolites are induced specifically by osmotic stress and not by ABA. Two main ABA-dependent pathways that regulate the gene expression under water deficit include: 1) the pathway through ABRE as a major cis–acting element in ABA-responsive gene expression (like LEA) and bZIP transcription factors, ABRE-binding protein (AREB)/ABRE-binding factor (ABF) that bind to ABRE and activate ABA-dependent gene expression, 2) the pathway through MYC and MYB elements (Fig 1.8). The difference between the two pathways is mainly based on cis-elements in the promoters of ABA–inducible genes (Shinozaki and Yamaguchi-Shinozaki 2000). The Arabidopsis gene RD22 is an ABA-dependent inducible gene that contains MYB and MYC binding sites and is regulated by ABA under drought stress (Yamaguchi-Shinozaki and Shinozaki 2006, 2005; Abe et al. 1997). The expression of osmotic (e.g. drought) stress-responsive genes such as RD29A, RD22, COR15A, and P5CS was shown to be ABA- dependent in Arabidopsis (Huang et al. 2012; Chinnusamy et al. 2003; Xiong et al. 2002, 2001). Several other drought induced-genes are regulated under ABA-independent signal transduction pathway and do not require ABA for their expression under drought stress. These genes contain cis- acting elements named dehydration responsive element (DRE/C-repeat) which are necessary for their regulation in an ABA-independent response to dehydration (Yamaguchi-Shinozaki and Shinozaki 2006, 2005; Shinozaki and Yamaguchi-Shinozaki 2000). There are at least two signalling pathways implicated in the regulation of ABA-independent gene expression under osmotic stresses: 1) Trans- acting transcription factors called DREB1(CBF/DREB1) and DREB2 that bind to the DRE elements are apparently activated by an ABA-independent signalling cascade, 2) another ABA-independent in which osmotic stress-responsive genes appear to be directly controlled by the so-called mitogen- activate protein (MAP) kinase signalling cascade of protein kinases (Fig 1.8). CBF/DREB1 are supposed to be involved mostly in cold-responsive gene expression, while DREB2 (DREB2A and DREB2B) proteins function in drought-responsive gene expression. However, CBF4/DREB1D is up- regulated by drought stress and ABA, not by cold. Therefore, the function of the drought-inducible gene CBF4/DREB1D provides crosstalk between DREB2 and CBF/DREB1 regulatory systems (). 23

Chapter 1

CBF4 is regulated by the ABA-dependent pathway, suggesting CBF4 functions in a slow response to drought stress that depends on the accumulation of ABA (Huang et al. 2012; Yamaguchi-Shinozaki and Shinozaki 2006, 2005; Shinozaki et al. 2003; Shinozaki and Yamaguchi-Shinozaki 2000). Some identified Arabidopsis drought inducible genes contain DRE elements in their promoters that are probably targets of DREB1 and DREB2 transcription factors under water deficit (Jaspers and Kangasjarvi 2010; Huang et al. 2008). The promoters of some drought-inducible genes which are regulated by osmotic stress in an ABA-dependent manner also contain the alternative regulatory sequence element, DRE (C-repeat) which is recognized by an alternative set of proteins regulating transcription. For instance, Arabidopsis RD29A and RD29B contain both DRE and ABRE elements in their promoters and are induced by drought stress and by ABA. The DRE element acts in an initial fast reaction of RD29A to drought stress while slower ABA response of this gene is mediated by ABRE elements. Then, DRE is an essential cis-acting element in RD29A gene for the induction of this gene in the ABA-independent response to dehydration in Arabidopsis. It was also demonstrated that expression of many ‘drought only’-regulated genes may be at least partially affected by ABA signalling because ABA-responsive recognition elements (ABRE, ABF, and ABRE-like binding sites) were highly enriched in ‘drought only’ genes and many drought regulated genes have both DRE/CRT and ABRE recognition motifs (cis-acting elements) in their promoters (Huang et al. 2008). Genetic analysis suggests that there is no clear line of differentiation between ABA-dependent and – independent pathways and elements involved may often crosstalk or even join in the signalling pathways (Xiong and Zhu 2001). Therefore, it might be difficult to find the genes that are completely ABA-independent expressed under drought stress (Huang et al. 2008).

Water deficit stress

Fig 1.8 Signal transduction pathway for osmotic stress in plant cells. Osmotic stress is perceived by a receptor in the plasma membrane that activates ABA-independent and ABA-dependent signal transduction pathways. Protein synthesis participates in one of the ABA-dependent pathways involving MYC/MYB. The bZIP ABA-dependent pathways involve recognition of ABA- responsive elements in gene promoters. Two ABA–independent pathways one involving the MAP kinase signalling cascade and the other involving DREB/CBF-related transcription factors have also been demonstrated (adapted from Shinozaki and Yamaguchi-Shinozaki 2000).

1.4.4.5 ROS signalling under water deficit As a main part of plant acclimation response to drought stress, ROS signalling can significantly influence plant drought tolerance and ought to be unravelled as a helpful strategy for improvement of drought-tolerant crops. Although ROS have the potential to make oxidative damages to cells under water deficit, they also play a critical role in mediating plant response to water deficit as signal transduction molecules. Then, ROS have a dual role in the plant response to drought stress which depends on the delicate equilibrium balance between ROS production and scavenging at the proper site and time (Miller et al. 2010). At high concentration ROS is a damaging factor while at low concentration, ROS is an important second messenger that initiates the signal transduction and downstream gene regulation under drought stress and eventually stimulates protective metabolic pathways to water deficit (Miller et al. 2010, Mittler 2004) (Fig. 1.9). Studies show that cooperation between chloroplasts, mitochondria and cytosol is an important factor in modulation of cell redox homeostasis and ROS signalling under drought stress (Miller et al. 2010, Gill and Tuteja 2010). ROS signalling is

Chapter 1

dependent on other conditions like the light intensity, temperature, severity of drought stress, duration of stress and tissue ability to rapidly adapt to the energy imbalance. ROS signals and their intensity, duration and localization are created based on the balance between ROS regeneration (controlling ROS production) and scavenging by different antioxidants in each cellular organelle during drought stress (Mittler 2004). ROS signalling probably is integrated into other signalling networks like calcium, hormone and protein phosphorylation that regulate plant acclimation under water deficit (Miller et al. 2010). During drought stress, ROS signal transduction is mainly started by the oxidative burst that resulted from metabolic imbalances. Then these signals are sent through RBOH (respiratory burst oxidase homologue) proteins at the apoplast which is mediated by Rboh gene encoding membrane-associated NADPH oxidases (NOX) (Huang et al. 2012; Jaspers and Kangasjarvi 2010; Miller et al. 2010; Xiong et al. 2002). ROS signals can be sensed, transmited and translated into suitable cellular responses in the higher plants. This process needs the existence of redox-sensitive proteins that can endure reversible oxidation/reduction and may switch ‘on’ and ‘off’ depending on the cellular redox state (Huang et al. 2012). ROS can oxidize the redox-sensitive proteins directly or indirectly via the ubiquitous redox-sensitive molecules such as antioxidants (e.g. glutathione, etc.) (Huang et al. 2012). Different types of protein sensors, enzymes or receptors such as histidine kinases •- can perceive ROS (mainly O2 and H2O2) signals under water deficit (Miller et al. 2010, Mittler 2004; Apel and Hirt 2004). In some reports, at least three different mechanisms are indicated for sensing of accumulated ROS signals in the cytosol: 1) unidentified receptor proteins; 2) redox-sensitive transcription factors such as HSFs (heat shock transcription factors) and NPR1 and 3) direct inhibition of phosphatases by ROS (Huang et al. 2012; Jaspers and Kangasjarvi 2010; Foyer and Noctor 2005; Mittler 2004). ROS-associated signal transduction involves Ca2+ and Ca2+-binding proteins such as calmodulin, the activation of G-proteins and phospholipid signalling (Huang et al. 2012; Mittler 2004). ROS can influence the intracellular Ca2+ levels and initiate a protein phosphorylation cascade, finally target transcription factors and regulate the drought-responsive genes (Xiong et al. 2002). The transcription factors such as the members of the WRKY and MYB transcription families, DREB2A and JERF3 and signalling molecules like MAPK are also involved as the regulatory elements in ROS signalling transduction (Huang et al. 2012; Jaspers and Kangasjarvi 2010; Miller et al. 2010). The effect of ROS on MAPK pathways is activated via receptors such as histidine kinases and G-protein- coupled receptors, and result in the indirect activation of transcription factors (Huang et al. 2012; Xiong et al. 2002). Eventually, ROS signal transduction can alter metabolic pathways to activate plant acclimation mechanisms to oxidative stress damages under water deficit (Miller et al. 2010, Mittler 2004). ROS-responsive regulatory genes can regulate a wide range of drought-induced genes including ROS-scavenging enzymes and other antioxidants (Miller et al. 2010). Studies indicate that different metabolites and antioxidants are involved in ROS signal transduction, affect drought-induced gene expression and alter plant acclimation responses to water deficit (Suzuki et al. 2012; Miller et al. 2010). Therefore, in addition to their traditional function in cellular protection, non-enzymatic 26

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antioxidants such as ascorbic acid, glutathione, α-tocopherol (vitamin E) and amino acids like proline as well as superoxide dismutase (Mn-SOD, Fe-SOD and Cu/Zn-SOD), ascorbate peroxidase (APX), catalase (CAT), glutathione peroxidase (GPX), guaicol peroxidase (GOPX), peroxiredoxin (PrxR), Glutathione-S-transferase (GST), glutathione reductase (GR), dehydroascorbate dehydrogenase (DHAR) and monodehydroascorbate reductase (MDAR) are involved in ROS signalling under water deficit (Miller et al. 2010, Gill and Tuteja 2010). Glutathione and ascorbic acid and their redox state play an important role in maintaining cell redox equilibrium during oxidative stress as well as modulation of ROS signalling (Gill and Tuteja 2010). They function as the redox buffers and metabolic interface in ROS signalling pathway, play a role as signal transducer molecules that control ROS accumulation, determine the specificity of ROS signal and eventually regulate gene expression and plant acclimation and tolerance under water deficit (Miller et al. 2010, Foyer and Noctor 2005). The function of antioxidants in ROS signalling might be proven by transgenic lines in different species that harbor over expressing of one or more ROS scavenging enzymes such as APXs and SODs with improved drought tolerance or by means of ascorbic acid-deficient tobacco mutants that resulted in higher sensitivity to osmotic stress (Gill and Tuteja 2010, Miller et al. 2010). Besides their role in osmotic adjustment, compatible solutes such as proline function as non-enzymatic antioxidants, protect proteins, DNA, etc., by scavenging ROS and are involved in ROS signalling network under drought stress (Matysik et al. 2002). Improved drought tolerance in over expressing P5CS transgenic plants might be attributed to proline function in ROS signalling (Gill and Tuteja 2010). Soluble sugars are also involved in the regulation of ROS signalling by controlling of ROS-producing and ROS- scavenging pathways like mitochondrial respiration and photosynthesis (Seki et al. 2007, Couee et al. 2006). ABA could affect the ROS signalling and gene activation in ROS network under drought stress; e.g. ABA triggers ROS production via NOXs (NADPH oxidases) which is important in the regulation of MAPK signalling cascades in guard cells which serve as signalling intermediates to promote stomatal closure (Huang et al. 2012; Miller et al. 2010; Pitzschike et al. 2006). In some reports, ABA was shown to induce H2O2 generation, this may suggest that ROS can mediate signals for ABA function in the regulation of catalase gene expression, activation of Ca2+ channels in guard cells, stomatal closure and even ABA biosynthesis (Xiong and Zhu 2003: Xiong et al. 2002). ABA accumulation was indicated to be associated with enhanced antioxidant enzyme activities in wheat seedlings subjected to mild osmotic stress and ABA treatment increased the activity of CAT, APX and SOD together with the content of non-enzymatic antioxidants such as ascorbate and glutathione in maize leaves exposed to moderate drought stress (Bartoli et al. 2012). Some other reports also pointed to the hormone regulation in ROS signalling, e.g. H2O2 could trigger the accumulation of stress hormones like salicylic acid and ethylene. Alternatively, ROS themselves play a role as the secondary messengers in many hormone signalling pathways (Huang et al. 2012). In general, the dissection of plant ROS signalling pathway faces big challenges with regard to its complex nature and the crosstalk with the network of overall plant signalling pathways. 27

Chapter 1

Fig 1.9 Modulation of reactive oxygen species (ROS) signalling by the reactive oxygen gene network of plants. Different cellular signals (e.g. pathogen recognition or stress perception) result in the enhanced production of ROS in cells by the ROS-producing pathways of the network. ROS are perceived by different ROS sensors and activate cellular responses (e.g. stress defence). The intensity, duration and localization of the ROS signals are determined by interplay between the ROS-producing and the ROS-scavenging pathways. The ROS-scavenging pathways are also responsible for maintaining a low steady-state level of ROS on which the different signals can be registered. Modulation of ROS levels might also involve a positive feedback loop between ROS perception and ROS production (dashed line). In addition to activating or suppressing different cellular responses, ROS perception can affect growth and development (inhibition during stress or regulation during normal growth). Abbreviation: PCD, programmed cell death (Mittler et al. 2004).

1.4.4.6 Sugar signalling under water deficit (sweet sensing and signalling) Significant changes in sugar status during drought stress are mainly based on the accumulation of glucose, fructose and sucrose affecting the regulation of various genes under water deficit. Previous studies indicated that sugars not only act as osmolytes, but also play a role as signalling molecules in gene regulation as well as function in the redox signalling under abiotic stresses (Ruan et al. 2010; Rolland et al. 2006; Gupta and Kaur 2005; Sun et al. 2003; Hare et al. 1998; Ho et al. 2001). Accordingly, the key enzymes of sugar metabolism have a critical role in plant drought tolerance by regulation of the cellular activity from gene transcription and translation to protein stability. Roitsch and Gonzalez (2004) indicated sugar signalling cooperating with ABA signalling affect gene expression and plant response to water deficit. In addition, ABA synthesis and signalling are supposed

Water deficit stress

to be important elements in sugar signalling (Wingler and Roitsch 2008). Reports indicate that sugar signalling is a key regulator of carbon metabolism adaptation under water deficit by modulation of gene expression and enzyme activity in both carbohydrate exporting (source) and importing (sink) tissues (Gupta and Kaur 2005; Rolland et al. 2002). Sugar signals are created by the transport of sucrose via involved transporters in phloem loading and un-loading, and subsequently cleavage by extracellular and/or intracellular invertase isoenzymes or by sucrose synthase (SuSy) to hexose (Wingler and Roitsch 2008) (Fig 1.10). This process is followed by hexose sensing via internal receptors and subsequent signal transduction and ultimately the gene expression level is altered (Gupta and Kuar 2005; Roitsch & Gonzalez 2004). Depending on the status of soluble sugars, this signal transduction makes the change in gene expression and enzyme activity particularly those involved in photosynthesis, sucrose metabolism (SPS and SuSy) and in synthesis of osmoprotectants (Gupta and Kaur 2005). Extracellular and vacuolar invertase isoenzymes are the key metabolic enzymes in sugar signalling involved in plant response to drought stress (Roitsch and Gonzalez 2004). Hexose sensing is based on the interaction between a hexose molecule and a sensor protein that results in signal generation and transduction cascades (Gupta and Kaur 2005). Hexose sensing is manipulated via two systems, the hexokinase (HXK)-dependent and HXK-independent pathways (Gupta and Kaur 2005). Different signal transducers including MAPK, protein phosphatases, Ca2+, calmodulin and SNF 1- related protein family (AtSR2) are involved in sugar signalling (Barker et al. 2000, Chikano et al. 2001). Some cis and trans elements have been identified which participate in plant sugar signalling (Rolland et al. 2006; Sun et al. 2003). Some known types of cis- elements in sugar-regulated plant promoters as sugar (/sucrose)-responsive elements in sugar signalling include the SURE (sucrose- responsive element), SP8, TGGACGG, G-box, A-box, B-box and CMSRE-1 and 2 elements. Three putative transcription factors, SUSIBA2, SPF1, and STK involved in plant sugar signalling have been isolated. SUSIBA2 is a SURE and W-box binding transcription factor that was isolated from barley, rice and wheat and belongs to the WRKY proteins. SPF1, a WRKY-type protein binds to the SP8 sequence and STK binds to the B-box. Transcription of susiba2 was reported as sugar inducible in the sugar signalling pathway in barley (Sun et al. 2003). The independent sensors for sucrose and glucose in plant cells can sense change in sucrose/glucose ratio, consequently transfer this information into noticeably different signal transduction pathways (Gupta and Kaur 2005). Sucrose and glucose are in many plants competent mediators of gene regulation involved in plant metabolism, like genes encoding for SuSy and invertase (Koch et al. 1992, Ehness et al. 1997). Numerous stress responsive genes were reported to be induced under glucose regulation, suggesting the role of sugars in stress responses (Price et al. 2004). The accumulation of hexoses showed correlation with the increasing activity of vacuolar invertase (soluble acid invertase) in maize leaves under drought stress (Pelleschi et al. 1999). Also, as reported by Roitsch (1999), the increase of hexose contents results in up-regulation of extracellular invertase and this sugar-regulated invertase plays a critical function in plant defence system under water deficit by mediating the 29

Chapter 1

carbohydrate source-sink regulation. It was also shown that many genes encoding the enzymes of carbohydrate and sugar metabolism such as glyceraldehydes-3-phosphate dehydrogenase, SPS, SuSy along with genes encoding sugar signalling enzymes are up regulated under water deficit (Ingram and Bartels 1996) (Fig 1.11). Seki et al. (2002) reported the regulation of 31 genes encoding the enzymes of carbohydrate metabolism in Arabidopsis under cold, drought and salt stresses. These results all suggest the importance of sugar metabolism and signalling in plant response to water deficit. Extensive interaction between sugar and plant hormone signalling has been demonstrated by genetic analyses (Rolland et al. 2006).

Water deficit stress

Sieve elements

SUC SUC SUC ST

Inv-N Inv-V SUC ST eST Inv-CW

Glc + Fru Glc + Fru

Glc Metabolism + Hexoses Gene regulation Fru HT

Fig 1.10 Subcellular localization of invertase isoenzymes and phloem unloading pathways. Sucrose can be hydrolyzed by different invertase isoenzymes depending on the mechanism of the phloem- unloading pathway in the sink tissue. In symplastically isolated tissues, sucrose is unloaded from the sieve elements of the phloem into the apoplast by an assumed efflux sucrose transporter (eST); there the disaccharide can be cleaved by an extracellular invertase (Inv-CW) bound to the cell wall and the resulting hexoses will be transported into the sink cell by a hexose transporter (HT). Alternatively, sucrose can be directly transported into the sink cell by a sucrose transporter (ST). Sucrose unloaded in the sink cell, either through the apoplasm or via plasmodesmata, can be cleaved in the cytosol by neutral invertase (Inv-N) or sucrose synthase, or in the vacuole by vacuolar invertase (Inv-V). The hexoses generated by the activity of the sucrose-cleaving enzymes are not only substrates for heterotrophic growth but also function as regulators of gene expression. Coordinated regulation by metabolic stimuli has been shown for photosynthesis, sink metabolism and defence responses. Inv-CWs play a special role because any stimulus up regulating the activity will decrease the local sucrose concentration in the sink-tissue and thus increase the sink-strength, providing a feed-forward mechanism for maintaining and amplifying diverse stimuli to enhance the flow of assimilate. Abbreviations: Fru, fructose; Glc, glucose; SUC, sucrose (Roitsch and Gonzales 2004).

Chapter 1

Fig 1.11 Crosstalk among sugar signal transduction, enzyme activities and gene regulation. induction; repression; C, cotyledons; Sh, shoot; IP3, inositol triphosphate; DAG, diacyl glycerol; SuSy, sucrose synthase; SPS, sucrose phosphate synthase; Suc, sucrose; and HXK, hexokinase (Gupta and Kaur 2005).

1.4.4.7 Crosstalk of drought stress with other abiotic stresses and plant growth regulators A common regulatory system and crosstalk between drought stress with different plant growth regulators as well as other environmental stresses has been reported by different studies (Huang et al. 2012). Whole genome oligonucleotide microarray and gene annotation analyses were conducted under progressive drought stress in Arabidopsis, which revealed the strong crosstalk between signalling networks of drought related responses and different plant hormone reactions (Huang et al. 2008). Accordingly, some genes encoding proteins responsive to gibberellic acid, ethylene or auxin were characterized as drought- regulated genes and many drought-regulated genes were annotated as regulated by other plant hormones comprising auxin, cytokinin, brassinosteroids, ethylene, gibberellins, and jasmonic acid in Arabidopsis. The involvement of these hormones in drought signalling was proposed to be acting antagonistically to ABA (Huang et al. 2008). ABA receptors are involved in signal transduction under water deficit, two groups of ABA receptors such as RNA- binding protein FCA, and CHLH (the H subunit of Mg-chelatase) were already identified (Seki et al. 2007). Another recent report by Huang et al. (2012) also indicates that the signalling pathways of osmotic stresses like drought stress and ABA share common elements. This report suggests that induced changes in gene expression under abiotic stresses like drought stress may contribute in the generation of hormones like ABA, salicylic acid and ethylene. This might magnify the initial signal and initiate a second round of signalling that may follow the same pathway or use overall different 32

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components of signalling pathway. In addition, some drought-activated genes are also responsive to other environmental stresses such as cold, high salt, oxidative stress, high light intensity, heat, wounding, this might supports the crosstalk between the plant responses to different stresses (Seki, 2002; Huang et al. 2008). As described in Fig 1.7, osmotic stress and associated oxidative stress are considered as the common consequences of plant exposure to different abiotic stresses like drought, salinity and cold stress. The crosstalk between pathways might be explained by the presence of sequence elements in the promoter region of stress-induced genes that react to several environmental stresses and signalling molecules (Huang et al. 2012). For instance, genes such as RD29B, COR47 and ERD14 were verified to be responsive to both water deficit and cold stress, and RD22 was responsive to both salinity and drought stress (Huang et al. 2008). This study also suggested that ABA-related stress responses under drought stress are regulated by other environmental and developmental factors that establish a crosstalk between drought responses and other environmental factors. Calcium as the second messenger is involved in signalling pathways of different abiotic stresses, ABA, drought, cold and salinity stresses and is considered as a major candidate to mediate such crosstalk (Sanders et al. 2002). Mitogen-activated protein kinase cascade (MAPK) are also involved in different developmental, hormonal, biotic and abiotic stress signalling, and various signalling pathways of different abiotic stresses converge at MAPK cascades (Huang et al. 2012; Chinnusamy et al. 2003). The main crosstalk between different abiotic stresses is mediated by calcium, ABA signalling and MAPK cascades in various signalling pathways (Huang et al. 2012). The interface of plant response to drought stress with other stresses as well as plant hormones was also indicated by Wang et al. (2003) (Fig. 1.7).

1.5 Epigenetic control of plant response to drought stress; non-coding RNA (miRNA and siRNA) and plant drought tolerance

Epigenetic regulation affects the pattern of gene expression in response to environmental stress (Hauser et al. 2011; Khraiwesh et al. 2011). Epigenetic processes, such as DNA methylation and histone modifications were demonstrated to affect significantly the plant responses to drought stress. Induced epigenetic changes in plant genome under drought stress are considered as very important regulatory mechanisms for plant adaptation to drought stress (Chinnusamy and Zhu 2009) and have a crucial role in regulating gene expression in plant acclimation responses to drought stress. As a concise definition, the stimuli of water deficit stress can cause demethylation at coding regions of special genes and subsequently activate their expression under water deficit (Wang et al. 2011). Many reports indicated that non-coding RNAs such as small RNAs (Borsani et al. 2005; Sunkar and Zhu 2004), alternative splicing and chromatin remodeling are involved in plant responses to abiotic stress and stress tolerance (Seki et al. 2007). The microRNAs (miRNAs) and putative natural antisense transcript (nat)-siRNAs might play roles in the plant drought stress response and tolerance (Seki et al.

Chapter 1

2007). Several small RNAs are mapped on the overlapping gene regions on which transcripts are regulated by drought stress. Several drought-responsive miRNAs have also been identified (Sunkar and Zhu 2004). One of the robust tools for the identification of drought stress-regulated non-coding RNAs and for analysis of alternative splicing and chromatin remodeling is whole-genome tiling array (Seki et al. 2007). The identification of large numbers of the small RNAs that are involved in drought stress responses can also be conducted by new sequencing technologies. Comparative genomics analyses using bioinformatics will also provide a better characterizing of the biological function of non-coding RNAs in plant responses to drought stress (Seki et al. 2007).

1.6 Genotype variation in plant response to water deficit

Plant response to water deficit is genotype dependent and plant capacity for drought tolerance varies between different species, even inter-species (Krasensky and Jonak 2012; Rampino et al. 2006). Metabolic profiling under water deficit can assist to compare the metabolome of drought-tolerant and sensitive accessions for their metabolic adjustment under water deficit. Evaluation of the metabolome of abiotic (drought) stress -tolerant accessions, cultivars, or species compared to the stress-sensitive ones proved that the metabolic adjustment in natural stress tolerance varied and multifaceted. These results also verified that abiotic (drought) stress - tolerant plants have higher levels of stress-induced metabolites under normal growth condition and/or accumulate higher levels of protective metabolites, such as proline and soluble sugars under stress.Hence, the metabolism of these stress-tolerant plants is ready for unfavorable growth conditions (Krasensky and Jonak 2012). Metabolite profiling also illustrated that different plant species may accumulate different metabolites under abiotic (drought) stress conditions (such as proline, glycine betaine, trehalose) and there is no specific or universal metabolite for adaptation to stress. Concerning to the ploidy level as one of the sources for genotype variation, improved stress tolerance has been observed in polyploids comparing to related diploids under water deficit and polyploid crops can survive better under multi stress conditions (Van Laere et al. 2011; Chandra and Dubey 2010). Polyploid plants were proven to contain less ROS accumulation like H2O2 and stronger reactive oxygen scavenging capacity like higher SOD activity, higher redox value of ascorbic acid (AsA)/dehydroascorbate (DHA), and glutathione (GSH) /glutathione disulfide (GSSG) than diploids during metabolic acclimation under abiotic (drought) stress (Deng et al. 2012).

1.7 Osmotic stress (dehydration and salt stress) in strawberry (Fragaria spp.): an overview

Up to now, different physiological aspects like plant growth and development, gas exchange, photosynthesis and transpiration rate, water use efficiency (WUE), stomatal conductance as well as some metabolite adjustment have been evaluated in Fragaria under water deficit. Grant et al. (2010) evaluated physiological and morphological diversity of 10 cultivated strawberries (Fragaria ×

Water deficit stress

ananassa) in response to water deficit. Results proved a significant difference between evaluated cultivars for the transpiration rate, leaf area and water potential, water use efficiency (WUE) and photosynthetic rate. The variation between cultivars in plant growth and transpiration efficiency was genotype dependent and not affected by the environment. Genotype variation in these parameters could be utilized in breeding programs for improved drought tolerance in strawberry. The results of a recent study showed a superior drought resistance in F. chiloensis accessions compared to strawberry cultivars that resulted from a conservative vegetative growth strategy and a reduced water losing (Grant et al. 2012). A reduction in strawberry yield was the result of water deficit in the field studies (Yuan et al. 2004; Liu et al. 2007; Li et al. 2010). Klamkowski and Treder (2006) reported a decrease in the leaf water potential, biomass and leaf area, stomatal density and dimension, and gas exchange rate in stressed plant compared to well watered plants in strawberry (F. ×ananassa Duch. cv. Salut) under gradual water deficit. This study also indicated a decrease in photosynthesis and transpiration rate, stomatal conductance and intercellular CO2 concentration under water deficit. Van Der Zanden and Cameron (1996) found variable Ψ predawn and biomass production between different F. chiloensis clones after two imposed water deficit/recovery cycles as well as a significant relationship between

Ψpredawn and carbon assimilation resulted from the negative effect of water deficit on photosynthesis. Drought stressed plants of F. chiloensis performed more capacity for osmotic adjustment than F. virginiana based on maintaining a higher turgor potential and lower osmotic potential; wilting in F. chiloensis occurred later than in F. virginiana under severe drought stress (Archbold and Zhang 1993). In other report, drought stress reduced the net photosynthesis and transpiration in the strawberry plant under water deficit (Chandler and Ferree 1990). Remotely measured canopy temperature was higher in drought-stressed plants under mild water deficit in greenhouse strawberries; and leaf temperature was verified as a useful index for assessment of very mild water deficit stress in strawberry under greenhouse conditions (Penuelas et al. 1992). Under mild water deficit, the canopy architecture of strawberries was changed from monolayer to polylayer leaf distribution and leaf orientation from south to north (Savé et al. 1993). Terry et al. (2007) demonstrated that water deficit affected the fruit quality (biochemistry and physiology) of strawberry F. × ananassa ‘Elsanta’, reduced fruit size, whilst enhanced fruit ABA levels, dry matter content, monosaccharide content, sugar/acid ratios, antioxidant capacity and total phenolics. Methyl jasmonate (MJ) could improve the adaptation metabolism of strawberry under water deficit by induction of higher levels of catalase and SOD activity and ascorbic acid content, reduction of transpiration, membrane-lipid peroxidation and malondialdehyde (MDA) content under water deficit. This proved the cross talk between plant response to growth regulator and water deficit in strawberry (Wang 1999). The effect of water deficit on strawberry fruit expansion was studied by Breen and Promper (1997), and confirmed that drought-stressed green-white fruits developed an osmotic adjustment based on 2.5-fold increase in glucose and sucrose levels. Zhang and Archbold (1993) investigated the solute contribution in osmotic adjustment in F. chiloensis and F. virginiana under water deficit, and proved that the wilting for F. chiloensis was observed in lower leaf 35

Chapter 1

water potential and RWC than in F. virginiana, indicating that F. chiloensis is more capable for osmotic adjustment under water deficit. Water deficit considerably enhanced total soluble sugar (TSC) contents (mainly glucose and fructose), TSC/ starch ratios and total free amino acid concentrations, but decreased starch concentrations in drought-stressed F. chiloensis, while no consistent changes in solute or starch content were observed in F. virginiana under water deficit. This obviously indicates that F. chiloensis is more capable to acclimate and conquer the water deficit stress. NaCl salinity had negative effects on strawberry growth and reproduction and reduced plant biomass, fruit yield and quality depends on the cultivar; ‘Elsanta’ was more sensitive while ‘Korona’ performed better growth and higher fruit yield under highest salinity (Said et al. 2005). Furthermore, salt stress increased free proline, asparagine and glutamine in both ‘Elsanta’ and ‘Korona’ that might be involved in osmotic adjustment. The level of these amino acids was indicative of salt stress damage in strawberry (Pawelzik and Keutgen 2008).

1.8 Recommended genomics and genetics approaches for studying of drought tolerance in Fragaria

1.8.1 Introduction

The genetic basis of abiotic stress resistance like drought tolerance is currently considered as important target for crop improvement in Rosaceae. Drought tolerance is classified as a quantitative (complex or continuous) trait that is controlled by many genes (or by a few genes with low heritability). Understanding of the biological and physiological processes underlying drought tolerance in strawberry (Fragaria) can be accomplished by genomics such as candidate gene approach, transgenic systems, transcript analysis, genetic mappings and QTL analysis, marker- assisted breeding, metabolomics (secondary metabolites) and proteomics (including enzymes). Arabidopsis as the model system has been widely used for dissection of the molecular basis of drought stress tolerance (Shinozaki et al. 2003; Zhu 2002). Using translational genomics, the fundamental results of drought tolerance from Arabidopsis can be translated in to practical results and applied in crops like Fragaria, but there are lot of limitations in using the Arabidopsis information for crops (Schwab et al. 2009). Fragaria vesca has a small genome (about 240Mbp),which gives the opportunity to be used as the genomic model system in the Rosaceae family for better understanding of physiological traits (Shulaev et al. 2010; Schwab et al. 2009). Candidate gene approach based on the association analysis between the candidate genes and drought tolerance makes a benefit in studying drought tolerance in Fragaria (Rosaceae). Candidate gene-specific markers are also a very robust method for verification of genetic control of drought tolerance in Fragaria. Development of saturated maps with co-dominant and transferrable candidate gene-specific markers within Fragaria may support the marker-assisted selection and characterization of genes that are involved in strawberry drought tolerance. Evaluation

Water deficit stress

of Fragaria transgenic lines developed by Agrobacterium-mediated transformation that hold over expression or reduced expression (RNAi) of candidate drought-regulated genes can be used for the functional analysis of drought tolerance in Fragaria (Peace and Norelli 2009). These strategies aid functional analysis of Fragaria response to water deficit and results could be extrapolated to other species within the Rosaceae family.

1.8.2 Structural and functional genomics

Genetic linkage mapping using a combination of different marker systems like SSRs, SCARs (sequence-characterized amplified regions), RAPD, candidate gene specific markers, isozymes and morphological markers has been developed in diploid Fragaria species (Sargent et al. 2004; Hadonou et al. 2004; Davis and Yu 1997). In addition, one octoploid map has been published to date using AFLP markers (Lerceteau-Kohler et al. 2003). Candidate gene-specific markers like STS (gene- specific sequence-tagged site), SSR (simple sequence repeat/ microsatellite markers), SNP (single nucleotide polymorphism), EST (expressed sequence tag) and gene-specific intron length polymorphisms and cleaved–amplified polymorphic sequence (CAPS) might be very robust methods for verification of genetic control of drought tolerance in Fragaria (Sargent et al. 2007; Davis et al. 2008, 2007a, b; Davis and Yu 1997). These markers that represent biological function are suited for linkage map construction, trait-association mapping and marker-assisted selection (MAS), bringing markers for genes of known-function in Fragaria. They are also highly conserved and can be transferred between species and even distantly-related genera in Rosaceae (Sargent et al. 2009). Some QTLs associated with different characters of interest have been identified in strawberry,therefore, QTL analysis as a potential approach for studying the genetic control of complex traits, can be used to discover QTLs associated with drought tolerance in Fragaria (Sargent et al. 2009; Peace and Norelli 2009).

1.8.3 Functional molecular biology

Functional molecular analyses are necessary to verify the molecular foundation of metabolite adjustment and gene regulation under water deficit as well as the genetic control of drought tolerance in Fragaria. To date, many studies have mainly focused on the physiological and metabolic response of strawberry to drought stress (osmotic stress), while the molecular biology of plant adaptation under water deficit remains to be characterized in Fragaria. The plant response to some abiotic stress has already been characterized at the molecular level in Fragaria (Schwab et al. 2009). Strawberry non- specific lipid transfer (Fxaltp) was shown to be up-regulated by abscisic acid, salicylic acid and mechanical wounding while repressed by cold stress (Yubero-Serrano et al. 2003). Transgenic strawberry containing the LEA3 (late embryogenesis abundant protein) from barley showed significantly higher salt tolerance compared to the control plants (Wang et al. 2004). A novel stress- response gene, tuf (turbulent flow) was cloned from strawberry by the investigation of gene expression 37

Chapter 1

responding to hydrodynamic stress (Takeda et al. 2003). This study showed tuf and CDPK (calcium- dependent protein kinase) were up-regulated under artificial hydrodynamic stress and suggested they have a protection function in plants against natural abiotic stress. Transgenic strawberry lines containing Wcor410a, a plasma membrane associated gene correlated with the degree of freezing tolerance, expressed the protein at a level comparable with that in cold–acclimated plants, but only cold-acclimated transgenic plants showed freezing tolerance, indicating that the WCOR410 protein is activated by extra factors induced during cold acclimation (Houde et al. 2004). The peptide methionine sulphoxide reductase (PMSR) which plays a critical role in cell protection against oxidative damage under pathogen infection and salt stress was cloned from strawberry fruit but Fapmsr was only expressed in the receptacle of red mature fruits (Lopez et al. 2006). This confirms the theory that the strawberry–ripening transcriptional program is an oxidative stress-induced process (Aharoni et al. 2002). The genes encoding key enzymes in the sugar biosynthesis pathway (including soluble acid invertase, sucrose synthase and sucrose phosphate synthase) have been characterized in strawberry (Xie et al. 2007). The regulation of these functional genes in sugar metabolism as well as other genes encoding key enzymes of drought- inducible metabolic pathways and regulatory proteins like transcription factors and signalling molecules should be analyzed under water deficit in strawberry by using different functional molecular assays such as gene isolation, quantitative gene expression analysis, etc.

1.8.4 Transgenic system and genetic engineering

Developed genetic transformation protocols in Fragaria represents a valuable system for high- throughput studies of gene function known for controlling important agronomic traits like drought tolerance in both diploid and cultivated strawberry (Folta et al. 2006). Agrobacterium-tumefaciens- mediated transformation of regenerated strawberry plants from calli or leaf discs has already been developed (Mezzetti 2009). Efficient transformation of diploid strawberry has been defined for some genetic lines (Alsheikh et al. 2002; Oosumi et al. 2006). In octoploid Fragaria, a genetic line ‘Laboratory Festival 9’ (LF9), derived from cultivated F. × ananassa ‘Festival’ was identified by Folta et al. (2006) supporting efficient and rapid transformation and regeneration based on an optimized protocol. Transgenic lines which harbor drought stress-related genes from important metabolic pathways have shown increased tolerance to drought stress (Umezawa et al. 2006 a). Plant adaptation mechanisms under water deficit such as osmolyte accumulation by amino acids or sugars can be verified in Fragaria by transformation of the key genes of these metabolic pathways followed by analysis of Fragaria transgenic lines under drought stress. Therefore, gene function from different adaptive metabolic pathways under water deficit can be characterized by using a gene transformation system in Fragaria. The gene silencing (e.g. RNAi) and over expression technology can be applied in this system. Due to strong sequence similarity between members of Rosaceae for many genes, the test of gene function in the heterologous strawberry system is also informative for other important species 38

Water deficit stress

in Rosaceae with low efficient transformation systems such as peach or species which are efficient transformable but slow growing like apple (Folta et al. 2006). Transgenic approaches can also be applied for improvement of plant tolerance to abiotic stress, e.g. the incorporation of gene like CBF1 may provide the genetic variability in frost tolerance during bloom and winter cold hardiness to improve this trait in strawberry (Kasuga et al. 2004).

1.9 Conclusions

The improvement of crop water use efficiency (WUE) is a major issue in agricultural crops and is also an important aspect for future strawberry breeding and production. As a first step, the mechanisms controlling plant response to water deficit in strawberry (Fragaria) should be identified. Drought tolerance is a complex network involving multifaceted cross talk between different regulatory levels and many metabolic pathways that are highly dynamic during drought stress to adjust plant metabolism under drought condition. Different drought–inducible signalling pathways and genes as well as metabolites, physiological and eco-physiological traits have already been characterized in different plant species under drought stress. Little however is known about variation in drought tolerance within Fragaria genotypes as well as the molecular aspects and genetic control of plant response to drought stress in this species. The physiological and molecular (genetic) screening of drought tolerance in different Fragaria genotypes may prove useful in strawberry breeding programs for improved WUE and drought tolerance. The characterization of Fragaria adaptation response to water deficit should be oriented at the different levels of plant physiological, metabolic and genetic responses. Biochemical analysis of plant metabolites incorporated with the data of gene expression regulation under water deficit as well as the study of genetic control of drought tolerance is essential for characterization of drought tolerance in Fragaria. Many known drought-induced genes encoding the key enzymes of different metabolic pathways as well as the regulatory proteins like transcription factors can be utilized as candidate genes for development of gene-specific markers associated to drought tolerance in Fragaria, and their regulation under water deficit should be characterized as well. In general, transcription factors or upstream signalling molecules in drought-inducible responses are promising candidate genes for more functional analysis under drought stress. Studies should also focus on the identification of molecules connecting pathways and the key regulators in each metabolic pathway under water deficit. A combination of such master genes that act in different pathways, e.g. ROS scavenging and osmotic adaptation, can be a valuable base for development of functional molecular markers associated to drought tolerance or for their transcription analysis under drought stress in Fragaria. Transgenic systems should also be applied for more functional characterization of stress-inducible genes that are involved in the synthesis or catabolism of drought-stress-associated metabolites in Fragaria.

Chapter 2 Evaluation of chlorophyll fluorescence as a probe for drought stress in strawberry

Chapter 2

This chapter is adapted from: Razavi F., Pollet, B. Steppe, K. Van Labeke, M.C. (2008). Chlorophyll fluorescence as a tool for evaluation of drought stress in strawberry. Photosynthetica, 46 (4): 631-633

Chlorophyll fluorescence as a probe for drought stress

2.1 Introduction

Water deficiency limits plant growth and development as well as a wide range of physiological processes such as photosynthesis; it leads to a progressive suppression of photosynthetic carbon assimilation (Van Kooten and Snel 1990; Hassan 2006). A decrease in leaf water potential suppresses photosynthetic activity in plants thus lowering the rate of CO2 fixation. This affects the activity of the electron-transport chain in chloroplasts, quantum yield of O2 evolution and activity of ATP-synthesis which is ultimately reflected in growth and yield (Ogren and Oquist 1985; Nogues et al. 2002). Photosystem II (PSII) plays an important role in the plant’s response to environmental conditions. It is a sensitive component to stresses, so primary damage of water deficiency occurs in the electron transport chains between photosystem II (PSII), photosystem I (PSI), the oxygen-evolving complex, and the reaction centre of PSII. It is well established that upon different stresses such as water deficiency, impairment of photochemistry in the photosynthetic system occurs as a primary event; a strong dehydration impairs the photo-induced electron flow to the primary acceptor in PSII, QA, and drastically decreases the effectiveness of electron flow from QA to the secondary electron acceptor QB (Ogren and Oquist 1985; Nogues et al. 2002). Under these conditions, the probability of singlet oxygen production at PSII and superoxide production at PSI increases and causes direct oxidative damage to the cells (Bartoli et al. 2005). Chlorophyll fluorescence (Chl a fluorescence) emission provides a fast indicator of the primary photochemistry of photosynthesis and reflects the primary processes that take place in the chloroplast such as light absorption, excitation energy transfer and the photochemical reaction in PSII (DeEll et al. 1999). The perturbations of photosynthetic metabolism which are induced by drought stress will significantly modify the fluorescence emission kinetic characteristics of plants. Therefore, chlorophyll fluorescence provides promising and useful information about leaf photosynthetic performance under drought stress (Lavorel and Etienne 1977; Baker and Rosenqvist 2004). Moreover, it is a rapid, powerful and non-destructive technique, with the potential to identify photosynthetic tolerance to drought stresses in plants and can be used as a predictor of stress injury in plant physiology before damage to plants is visible (DeEll et al. 1999). Fluorescence methods were successfully used in identification of drought tolerant genotypes such as Lolium × Festuca hybrids (Koscielniak et al. 2006) and wheat (Araus et al. 1998). Well-known fluorescence parameters for assessing the functional integrity of PSII include: Fv/F0 & Fv/Fm (maximum quantum efficiency of PSII photochemistry after dark adaptation or variable to maximum chlorophyll fluorescence), ΦPSII (effective quantum yield of PSII electron transport), qP (photochemical quenching of chlorophyll fluorescence) and qN (non-photochemical quenching of chlorophyll fluorescence). Based on the type of physiological stress and the effect on the photosynthesis pathway, one or all of them could be critical and sensitive indicators

Chapter 2

in this research (DeEll et al. 1999). However, studies on strawberry under drought stress are still limited and focus mainly to research on irrigation scheduling (Kruger et al. 1999). In order to better understand the response of strawberry to the onset and progress of drought stress, we studied Chl a fluorescence parameters in parallel with plant growth performance.

2.2 Materials and Methods

2.2.1 Plant material, water treatments and environmental conditions

A greenhouse experiment was conducted from October 2006 till February 2007 with the short day cultivar F. × ananassa Duch. ‘Elsanta’. Temperature and relative humidity (Humidity/Temperature probe, SKH 2011, Wells, UK), PAR (Quantum Sensor QS, Delta T Devices, Cambridge, UK) as well as volumetric substrate moisture content (Theta probe, ML2X, Delta T Devices, Cambridge, UK) were recorded at hourly intervals with a data logger (DL2, Delta T Device, Cambridge, UK). Plants were grown in 3-L pots in 80:20% peat and perlite mixture at 18/14°C day/night temperature, a daily average vapour pressure deficit (VPD) of 0.5-0.9 kPa, and a 16h photoperiod. Natural day length was extended with metal halide lamps giving 30-40 μmol (PAR) m-2s-1 at canopy level and daily average value of PAR (Photosynthetic active radiation ) ranged from 20 till 150 μmol m-2s-1. Plants were weekly supplied with 100 ml of a standard nutrient solution for strawberry (Lieten 1995) and with rain water on the other days maintaining the pots - - close to field capacity. The nutrient solution contained 10mmol/L NO3 , 1.5mmol/L H2PO4 , 1mmol/L 2- + 2+ 2+ SO4 , 5mmol/L K , 3.25mmol/L Ca , 1mmol/L Mg (Lieten et al. 1995; Kruger et al. 1999). Plant protection was carried out according to good agricultural practices. After 9 weeks of preconditioning, plants were brought to field capacity and randomly assigned to a control and a drought stress (irrigation stop) treatment in a completely randomized block design with three replicates and 25 plants for each experimental plot. Volumetric substrate moisture content θv (Theta probe, ML2X, Delta T Devices, Cambridge, UK) was recorded at hourly intervals. Drought stress was applied by withholding water to the plants and continued until at least 50% of the plants were visibly wilted at noon (progressive drought stress); this stage was reached seven weeks after the start of the experiment. In the control, plants were well-watered throughout the experiment. During seven weeks, from the start of the treatment till visible wilting stage, measurements were performed at day 1, 16, 27, 40, 44 and 51 after water withholding, respectively representing stages 1 to 6. These stages were determined based on the substrate moisture content during the experiment.

Chlorophyll fluorescence as a probe for drought stress

2.2.2 Leaf water potential

The leaf water potential (w) of the upper leaflet of the youngest fully developed leaf (n=6 per experimental plot) was measured at the selected stages of the drought experiment with a thermocouple psychrometer between 08h00 and 09h00. The thermocouple psychrometer, operated in the psychrometric mode, comprised 6 standard C-52 sample chambers (Wescor Inc, Logan, UT, USA). Chambers were kept at a constant temperature. The sample chambers were calibrated using NaCl solutions over the range of 0 to -3.15 MPa. w was determined for 6 plants, randomly selected per experimental plot; the upper leaflets of the youngest fully developed triplicate leaf was excised in leaf discs 6mm and were used for measurement of Ψw.

2.2.3 Measurement of chlorophyll fluorescence

For each sampling date, Chl a fluorescence was measured on the youngest fully expanded triplet leaf (n=4 per experimental plot in both control and drought treatments) using a portable modulated fluorometer (PAM-2000, Walz, Effeltrich, Germany). Leaves were allowed to dark adapt for 20 min (Genty et al. 1998; Toivonen et al. 1988). All measurements were carried out on the upper side of leaf. After a dark adaptation period, maximum quantum efficiency of photosystem II (PSII) was estimated as Fv/Fm. The minimal fluorescence level F0 was measured under a weak red modulated measuring beam (about 0.1 -2 -1 µmol m s ) and the maximum fluorescence level Fm was attained during a 0.8 s saturation pulse (>1500 -2 -1 µmol m s ) of white light thus enabling determination of Fv/Fm. After 5 min of illumination with -2 -1 continuous red non-saturating actinic light (110 µmol m s ), the photochemical quenching (qP) and the non-photochemical quenching component (qN) were calculated according to Bilger and Schreiber (1986) and Bilger and Björkman (1990), respectively. The quantum yield of PSII electron transport (ΦPSII) was determined according to Genty et al. (1989).

2.2.4 Measurement of morphological traits

Morphological traits were determined at the start (6 plants randomly taken over the experimental plots) and at the end of the drought stress experiment (5 plants/experimental plot). Measurements included number of leaves, leaf area and fresh and dry weight of the aerial plant parts. Leaf area was determined using a Portable leaf area meter model ‘LI-3000’ (LI-COR, Lincoln, Nebraska, USA). Dry weight was determined after drying for 24 hours at 80°C (Pirlak et al. 2004; Turhan et al. 2005). Water content of aerial plant parts was calculated as (FW-DW)/FW.

Chapter 2

2.2.5 Statistical analysis

Data were subjected to one-way ANOVA using SPSS 15.0.1 (SPSS, Chicago, USA).

2.3 Results

2.3.1 Climatic data

Throughout the experiment, daily average value of PAR ranged from 20 till 150 µmol m-2 s-1 (Fig 2.1A), and daily average VPD (vapour pressure deficit) of the air ranged from 0.5 till 0.9 kPa (Fig 2.1B).

160 1,0 A B 140 0,9 ] 120

-1

s 0,8

-2 100

80 0,7

60 VPD [kPa ] 0,6 40

PAR [µmolm 0,5 20 PAR VPD 0 0,4 0 10 20 30 40 50 60 0 10 20 30 40 50 60 EXPERIMENTAL DAY EXPERIMENTAL DAY Fig 2.1 Daily average values of recorded microclimatic parameters A: Photosynthetic active radiation (PAR), B: Vapour pressure deficit (VPD), throughout the experiment. Vapour pressure deficit is calculated from temperature and relative humidity data.

2.3.2 Volumetric moisture content (θv) of the substrate and leaf water potential

The volumetric substrate moisture content (θv) of the control ranged from 55 vol% to 60 vol% throughout the experiment. Drought stress resulted in a gradual decrease of θv from 55 vol % at the start (saturation condition – stage 1) to 4 vol % (severe water deficit – stage 6) at the end of the experiment (Fig 2.2A). For the early stages of drought stress no significant difference in Ψw was observed between the control and the stress treatment (Fig 2.2B). From stage 4 on (θv<20%, 40 days after water withholding) a significant lower

Ψw was observed for the stressed plants. During the last three stages (4, 5 and 6), Ψw varied between -1.4 and -1.6 MPa, while in control plants Ψw ranged between -1 and -1.2 MPa (Fig 2.2B).

Chlorophyll fluorescence as a probe for drought stress

-0,8 70 B A a 60 -1,0 a a 50 -1,2

40 -1,4 30 b -1,6 20

-1,8 Control b 10 Control b Stress Stress -2,0 0 LEAF WATER POTENTIAL [MPa] 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7

SOIL VOLUMETRIC WATERSOIL VOLUMETRIC CONTENT [%] DROUGHT STAGE DROUGHT STAGE Fig 2.2 Substrate volumetric water content (A) and leaf water potential in Fragaria × ananassa ‘Elsanta’ (B), at the time of sampling (means ± SE, n= 3, significant differences were calculated by student F- test and indicated by a, b (p≤0.05), for leaf water potential)

2.3.3 Effect of drought stress on Chl a fluorescence

Changes of Chl a fluorescence parameters in stressed plants were observed from the 4th stage on

(θv<20%). In response to water deficit, non-photochemical quenching (qN) rose significantly in stressed plants whereas photochemical quenching (qP) declined (Fig 2.3). The quantum yield of PSII electron transport (ΦPSII) tended to decrease in stressed plants (Fig 2.3), while Fv/Fm was not affected by the applied stress and ranged between 0.83 and 0.86 (data not shown). Fv/F0 ranged from 5 to 6 and was neither affected by the applied drought stress (data not shown).

2.3.4 Effect of drought stress on morphological parameters

Drought stress significantly decreased the initiation and unfolding of new leaves (leaf number). Moreover, the leaf area of the youngest fully developed leaf was significantly smaller for stressed than for control plants. Biomass production as assessed by fresh and dry weight was also significantly reduced by the drought stress treatment (Table 2.1, Fig 2.4). Water deficit did not reduce the water content of plant aerial parts (Table 2.1).

Chapter 2

0,96 0,95 a A 0,94 a 0,93

P 0,92 q b 0,91 0,90 b

0,89 Control Stress 0,88 1 2 3 4 5 6

0,78 B 0,77

0,76

0,75

PSII



0,74

0,73 Control Stress 0,72 1 2 3 4 5 6

0,22 C 0,20 a

0,18

0,16

0,14 b

0,12 Control Stress 0,10 1 2 3 4 5 6 DROUGHT STAGE

Fig 2.3 Effect of increasing drought stress on chlorophyll fluorescence induction in F. × ananassa ‘Elsanta’, (A) photochemical quenching (qP), (B) the quantum yield of PSII electron transport (ΦPSII), (C) non photochemical quenching (qN). (means ± SE, n=3, significant differences were calculated by student F-test and indicated by a, b (p≤0.05))

Chlorophyll fluorescence as a probe for drought stress

Table 2.1 Effect of the drought stress on plant growth parameters and the water content of the aerial parts of F. × ananassa ‘Elsanta’ Trait Start of the experiment End of the experiment (Stage 6) (Stage 1) Control Drought stress Number of leaves 6.83 ± 0.33 9.55 ± 0.62 a 7.88 ± 0.77 b Youngest fully opened leaf – leaf 47.00 ± 3.80 81.40 ± 9.10 a 54.20 ± 3.60 b area (cm²) Fresh mass (g per plant) 12.47 ± 0.39 22.13 ± 2.10 a 14.71 ± 1.90 b Dry mass (g per plant) 3.41 ± 0.13 5.38 ± 0.46 a 3.66 ± 0.40 b Shoot water content (%) 72.00 ± 0.80 75.00 ± 1.40a 74.00 ± 0.60 a (Means ± SE, n= 3, significant differences were calculated by student F-test and indicated by a, b (p≤0.05))

Fig 2.4 The effect of drought stress on strawberry ‘Elsanta’ (left: well-watered plants, right: drought-stressed plants)

2.4 Discussion

2.4.1 Drought stress and leaf water potential

The continuing decrease of substrate moisture content, θv (Fig 2.2A) and of leaf water potential (Ψw) (Fig 2.2B) during the experiment demonstrated an increasing drought stress imposed on the strawberry plants.

In control plants, unexpectedly important fluctuations of leaf water potential (Ψw) were observed during the experiment that might be caused by short-term weather conditions. Pre-dawn Ψw is not dependent on these conditions and might therefore be a more sensitive measure of water status (Jones 1990). Sircelj et al. (2005), however, demonstrated that pre-dawn and midday Ψw of apple leaves under progressing

Chapter 2

drought stress showed comparable responses. The level of water stress severity might be described according to a decrease in leaf water potential. In our experiment, the differences in Ψw between control and stressed plants were not higher than 0.5 MPa which would suggest a mild drought stress according to Hsiao (1973). The visible wilting of strawberry leaves at noon after withholding water for 51 days suggests, however, that this classification cannot be applied to all plants.

2.4.2 Drought stress and sensitivity of fluorescence parameters in strawberry

The photochemical quenching (qP) provides information on the redox state of QA (the primary electron acceptor of PSII); the non-photochemical quenching (qN) reflects the loss of excess energy through heat dissipation (DeEll et al. 1999). In strawberry leaves, the distribution of absorbed photons between photochemical and non-photochemical quenching changed with increasing drought, especially from day

40 on (θv<20%) as indicated by a significant decline of qP and rise of qN (Fig 2.3) such an effect was also shown in drought-stressed tomato (Haupt-Hertig and Fock 2000), wheat (Biehler and Fock 1996) and grasses (Koscielniak et al. 2006).

Due to the low fluence rates during our experiment in winter, qN increased rather moderate under severe stress, but thermal dissipation of absorbed photon energy is well established in studies with higher fluence rates (Flexas and Medrano 2002). Haupt-Herting and Fock (2000) demonstrated in tomato that under 90 -2 -1 -2 -1 μmol m s , qN increased but to a lower extent compared to the same stress at 400 μmol m s . The effects on qN and qP for both irradiances were also moderate in tomato for Ψw of -1.2 MPa, but were very pronounced for Ψw of -2.0 MPa. Therefore, a further decrease of Ψw in the strawberry leaves might further enhance the increase in qN. A slight decrease of ΦPSII was noted when θv decreased below 30 vol% (day

27, Fig 2.3), while Fv/Fm was not significantly affected by the applied drought stress (data not shown) which corresponds with previous reports (Baker and Rosenqvist 2004), indicating that the PSII primary photochemistry was not damaged by the applied water deficit under the given light intensities. The reduction of ΦPSII might therefore be attributed to the decline of qP. Similar results were also reported for maize seedlings (DeEll and Toivonen 2003), tomato (Haupt-Herting and Fock 2000) and shade leaves of

Picea (Duan et al. 2005). Under low light conditions, the low internal CO2 concentration is probably not limiting and is able to sustain photosynthesis and thereby electron transports (DeEll and Toivonen 2003). The slow development of drought stress in this experiment might enable the activation of different acclimation mechanisms, leading to the maintenance of photosynthetic capacity (Flexas et al. 1999; Van

Kooten and Snel 1990). Membrane leakage is well correlated with decrease of Fv/Fm in other stresses such as chilling injury or heat shocking (DeEll et al. 1999; Genty et al. 1989). Fv/Fm can as such be considered as a ratio to measure membrane constituent integrity. Under our experimental conditions, drought stress in

Chlorophyll fluorescence as a probe for drought stress

F. × ananassa ‘Elsanta’ did not induce changes in Fv/Fm and therefore no changes in thylakoid membrane were accepted to occur. Based on the results, Chl a fluorescence is a good indicator of dehydration in strawberry especially qP and qN.

2.4.3 Drought effects on morphological traits

Under dry environment conditions plants develop morphological adaptations. Drought stress significantly reduced both fresh and dry biomass, leaf area, and leaf number in strawberry ‘Elsanta’ (Table 2.1). A decrease in leaf area reduced transpiration and was observed in many crops under drought stress (Hsiao 1973) as well as in strawberry cultivars (Klamkowski and Treder 2006).

2.5 Conclusion

The effect of progressive drought stress on chlorophyll fluorescence as well as growth parameters was studied in strawberry F. × ananassa ‘Elsanta’. Measurements were taken at 6 different stages of drought stress. Our results demonstrate that under low irradiances, drought stress in strawberry induces changes in leaf water potential, photosynthetic parameters as well as morphological adaptations. Among four measured parameters of chlorophyll fluorescence, qP (photochemical quenching) and ΦPSII (quantum efficiency in photosynthesis) decreased, while qN (non-photochemical quenching) increased with the onset of drought stress as indicated by an increased negative leaf water potential in stressed plants. Fv/Fm

(maximum efficiency of PSII) was not affected by drought stress. The Chl a fluorescence parameters qP and qN did reflect the photosynthetic responses to drought stress better than ΦPSII and Fv/Fm. However, it cannot be excluded that these parameters might also be a useful indicator for rapid screening of tolerance to drought stress if irradiances were high enough. These results could be used for future programs seeking to evaluate different strawberry genotypes with different sensitivity to drought stress.

Chapter 3 Osmotic solute content and antioxidant defence in strawberry under drought stress

Chapter 3

Osmotic solute content and antioxidant defence in strawberry under drought stress

3.1 Introduction

Osmotic adjustments as well as antioxidant defence systems correlate with improved plant tolerance to different environmental stresses (Chaitanya et al. 2002). One of the main mechanisms of drought tolerance is osmotic adjustment which is based on the accumulation of compatible solutes such as amino acids (like proline), sugars or sugar alcohols (Loutfy et al. 2012; Seki et al. 2007; Bartels and Sunkar 2005; Chavez et al. 2003; Hare et al. 1998). Osmotic adjustment helps the cell to maintain its hydrated state, the structural integrity of the membranes and it helps stomata to remain partially open and thus to continue CO2 assimilation during drought stress (Sunkar and Bartels 2002; Hare et al. 1998). Water deficit also generates excessive ROS resulting in oxidative damage at the cellular components and disrupting their metabolic functions by the damage to proteins, lipids, terpenoids, carbohydrates and nucleic acids (Loutfy et al. 2012; Gue et al. 2006; Asada et al. 1998). Plants possess to a variable extent antioxidant metabolites that have the capacity to detoxify ROS such as ascorbic acid (AsA) and reduced glutathione (GSH) as well as enzymatic antioxidant mechanisms including superoxide dismutase (SOD), ascorbate peroxidase (APX) and catalase (CAT) (Gue et al. 2006; Bartels and Sunkar 2005; Apel and Hirt 2004; Mittler 2002; Noctor and Foyer 1998; Asada et al. 1998; Bowler et al. 1992). Compatible solutes such as proline and glycine betaine induced by water deficit are also involved in the sequestration of ROS and protection of complex molecules and organelles (Szabados & Savouré 2009; Moller et al. 2007). Different species have variable ways to cope with oxidative damage caused by water deficit (Pirlak and Esitken 2004). The activity of SOD, CAT and APX under drought stress depends on the species capacity for scavenging oxygen radicals and thus to maintain the cellular membrane stability. In general, the levels of antioxidant enzymes are higher in tolerant than in sensitive species under drought stress (Wang et al. 2009; Guo et al 2006). This correlation between antioxidant enzyme activity and water deficit tolerance was demonstrated by comparison of tolerant and sensitive cultivars in several plant species, such as rice (Srivalli et al. 2003; Vaidyanathan et al. 2003; Dionisio-Sese et al. 1998), foxtail millet (Setaria italica) (Sreenivasulu et al. 2000) and tomato (Mittova et al. 2002). Higher CAT and APX activity conferred tolerance to the water deficit in cherry tomato, Poa pratensis and moss (Sànchez-Rodriguez et al. 2010; Wang and Huang 2004; Dhindsa and Matowe 1981). In sugar beet and tomato SOD, APX and CAT activities increased to a higher extent in tolerant wild species than sensitive cultivars under salt stress (Ramagopal 1987). Strawberry responses to different abiotic stresses include osmotic adjustment and ROS detoxification mechanisms. Zhang and Archbold (1993) reported foliar accumulation of compatible solutes such as proline and soluble carbohydrates in F. chiloensis and F. virginiana under water deficit. Keutgen and Pawelzik (2007) indicated that strawberry fruit antioxidant capacity increased under moderate salinity stress, this was more pronounced in ‘Korona’ than in ‘Elsanta’. Salinity stress also caused an oxidation of ascorbate and

Chapter 3

glutathione redox pairs and suppression of the activities of scavenging enzymes such as CAT and APX in strawberry leaves (Tanou et al. 2009). Under salinity stress, a higher activity of antioxidant enzymes including CAT, APX and GR was observed in the salt-tolerant strawberry cultivars F. × ananassa ‘Camarosa’ and ‘Tioga’ compared to the salt-sensitive ‘Chandler’ (Turhan et al. 2008). Gulen and Eris (2004) showed that peroxidase activity was higher in heat-stressed strawberry plants (grown under constant high temperature at 25°C), while total protein content was lower in stressed plants. Heat treatment of strawberry fruits changed the oxidative metabolism of the fruit; higher antioxidant capacity and APX and SOD activity were observed (Vicente et al. 2006). In this chapter, the effect of drought stress in strawberry ‘Elsanta’, the main cultivar in Belgium, is studied. The potential for osmotic adjustment and the possible role of enzymatic and non-enzymatic antioxidant mechanisms under different levels of water deficit are discussed.

3.2 Materials and methods

3.2.1 Plant material, drought treatment and environmental conditions

For establishment of a progressive drought stress in strawberry ‘Elsanta’, a greenhouse experiment was conducted (exp 1, October-February). The details of the experimental set up, treatments, plant material and environmental conditions during the experiment are described in chapter 2 (2.2.1). During seven weeks from the start of the progressive drought treatment till the onset of visible wilting at noon, measurements were performed at day 1, 16, 27, 40, 44 and 51 in both control and drought treatment, representing stages 1 to 6 (as described in chapter 2). At each drought stage, the youngest fully expanded triplet leaves from 6 random selected plants per experimental plot were pooled. Samples were taken in triplicate from both control and stressed plants between 8 and 9 AM and were immediately frozen and stored at –80°C until analysis. The experiment was repeated in summer (exp 2, 12-17 June). Strawberry ‘Elsanta’ plants were randomly assigned to a drought stress treatment (by withholding water supply) or a well-watered control treatment. Leaf samples were taken 7 days after the start of water withholding, when plants were close to the incipient midday wilting as described for the first experiment, from both control and stressed plants between 8 and 9 AM. Temperature, relative humidity and photosynthetic active radiation (PAR) were measured as described in chapter 2 (part 2.2.1).

3.2.2 Leaf water potential (Ψw)

In experiment 1, leaf water potential (Ψw) in drought-stressed and well-watered plants was measured at the sampling stage as described in chapter 2 (2.1.2). In experiment 2 two leaves per plant were excised for the

Osmotic solute content and antioxidant defence in strawberry under drought stress

determination of the midday leaf water potential using a pressure chamber (PMS Instrument Co., Corvallis, OR, USA), this in 3 replicates.

3.2.3 Carbohydrate analysis

Sugars were extracted from 300 mg fresh leaf material with 6 ml 80% ethanol at 70°C for 10 min and further at 45°C for 3 hours, followed by centrifugation at 5000 g for 5 min. Glucose, fructose and sucrose were analysed using high pH anion-exchange chromatography with pulsed amperometric detection (Waters; CarboPac MA1 column with companion guard column, eluent: 50 mM NaOH, 22°C). The remaining ethanol insoluble fraction was washed twice with 1.5 ml ethanol 80%, centrifuged at 5000g for

5min and the supernatant was discarded. For starch hydrolysis, the residual pellet was treated with 5ml HCl 1M for 2 hours at 95°C followed by centrifugation at 5000g for 5min and a second extraction with 1ml HCL 1M followed by centrifugation at 5000g for 5min and the two supernatants were pooled for quantification of starch. Starch content was determined spectrophotometrically at λ=340nm based on the measurement of the enzymatic reduction of NADP+ to NADPH by the glucose molecules presents in the extract (UV-VIS, Biotek Uvikon XL) (Chaplin and Kennedy 1994).

3.2.4 Proline content

Extraction and determination of proline was done according to Bates et al. (1973). Fresh leaf material (500 to 1500 mg) was extracted with 10ml of 3% sulfosalicylic acid. After filtration, 2ml ninhydrin and 2ml glacial acetic acid was added to the extracts (2ml) and this was kept at 100°C for 1 hour when the reaction was stopped in an ice-bath. The formed chromophore is extracted from the acid aqueous solution by means of cold toluene (4ml) and measured spectrophotometrically at λ=520nm (InfiniteM200 TECAN Group Ltd., Switzerland).

3.2.5 Protein concentration

Protein was extracted from 300mg fresh leaf material with 1600 µl of ice-cold extraction buffer (50mM potassium phosphate buffer, 1mM Na-EDTA, 1mM L-ascorbic acid, 0.02M sodium bisulphite, 20% sorbitol and 2% PVPP, (pH 7.8). Protein concentration was determined according to Bradford (1976) with bovine serum albumin (BSA) as the standard. In a 96-well plate, 300µl Bradford reagent was added to 10µl aliquot of sample protein extract or to the protein standards and the absorbance was measured at λ=595nm (InfiniteM200 TECAN Group Ltd., Switzerland).

Chapter 3

3.2.6 Total Antioxidant Capacity (TAC)

TAC was analysed according to Re et al. (1999) with L-ascorbic acid (L-AA) as the standard. This assay involves the oxidation of ABTS (2, 2’-azinobis [3-ethylbenzothiazoline-6-sulphonate]) to an intensely- coloured nitrogen-centred radical cation, ABTS•. The intensely-coloured ABTS• radical is obtained by adding 1ml 20mM ABTS to the working solution (7ml 75 mM KH2PO4, 1ml 1.75 % H2O2 and 1ml 0.08% horse radish peroxidase). Fresh leaf material (100 mg) was extracted with 200µl extraction buffer 3% metaphosphoric acid, 1mM EDTA and 2% PVPP followed by centrifugation at 5000g at 4°C, the pellet was extracted again and supernatants were pooled. 100µl of the aliquot is added to 900µl ABTS• solution and after 6 min the remaining ABTS• is quantified spectrophotometrically at λ=734 nm (InfiniteM200 TECAN Group Ltd., Switzerland).

3.2.7 Catalase (CAT) activity

CAT activity was determined in the protein extract according to Aebi (1974). This method is based on the −1 determination of the rate constant (s , k) of the H2O2 decomposition rate. The reaction mixture contained

30mM H2O2 in a 50mM phosphate buffer (pH 7.0) and 0.1 mL of enzyme in total volume of 3mL. CAT activity was estimated by the decrease in the absorbance of H2O2 at λ=240nm by spectrophotometer (UV- VIS, Biotek Uvikon XL). Enzyme specific activity is expressed in unit per mg protein.

3.2.8 Ascorbate peroxidase (APX) activity

APX activity was determined in the protein extract according to Nakano and Asada (1981). For the assay, the decomposition of H2O2 was monitored by the decrease in absorbance at 290 nm by spectrophotometer (UV-VIS, Biotek Uvikon XL) after addition of 0.05 ml of sample extract (enzyme) to a 0.95 ml reaction mixture consisting of 50 mM potassium phosphate buffer, 0.5 mM ascorbic acid, 0.1 mM hydrogen peroxide and 0.1 mM EDTA (pH 7.8) in a total volume of 1 mL. Enzyme specific activity is expressed in mM min-1mg-1 protein.

3.2.9 SOD activity

The enzyme extraction was performed from 250mg fresh leaf material by using 1500µl ice-cold extraction buffer (EB) containing 0.05 M phosphate buffer (pH 7.8), PVPP (75mg/L), 1M Tris, sucrose (68g/L), Na2–EDTA (170mg/L, pH 7.8), Tween 20 (0.0031ml), and sodium-thioglycolate (800mg/L). The SOD activity was conventionally determined according to a modified method described by Beauchamp and Fridovich (1971), based on the photo-reduction of nitroblue tetrazolium (NBT). The reaction mixture consisted of 200µL reaction buffer (pH 7.8) containing 86mg L-1 NBT, 234g L-1 Tris-chloride (pH 7.5), 11.4g L-1 EDTA, 44mg L-1 riboflavin, and 100µL of 25%, 50%, 75% and 100% concentration of plant

Osmotic solute content and antioxidant defence in strawberry under drought stress

extraction (enzyme). Riboflavin was added as a source of superoxide and as the last component. Cuvettes containing the reaction mixture were illuminated by 15-W fluorescent lamps until mixtures without SOD achieved a moderate blue colour. The absorbance of the reaction mixture containing leaf extract was measured at 560 nm by spectrophotometer (UV-VIS, Biotek Uvikon XL) and the volume of the extract corresponding to 50% inhibition of the reaction was assigned as one SOD enzyme unit. Enzyme specific activity was expressed in Umg-1protein. Protein concentration in the extract was quantified by Bradford’s method (Bradford 1976).

3.2.10 Statistical analysis

Data were subjected to analysis of variance (ANOVA) using SPSS 15.0.1 (IBM® SPSS® Statistics 15).

3.3 Results

3.3.1 Climatic data, volumetric substrate moisture content and leaf water potential

During the progressive drought stress (exp 1), the range of daily average values of PAR (photosynthetic active radiation) and VPD (vapour pressure deficit) are shown in Fig 2.1. The changes in the volumetric substrate moisture content (θv) and leaf water potential (Ψw) throughout this experiment in both control and drought stress is shown in Fig 2.2A &B. From stage 4 on (θv<20%, 40 days after water withholding), a significant lower Ψw was observed for the stressed plants. Under summer conditions (exp 2), average daily temperatures ranged from 20 till 23°C, with maxima of 38°C and average values of PAR ranged from 50 µmol m-2s-1 till 167 µmol m-2s-1 with maxima of 828 µmol m-2s-1. Under these climatic conditions

Ψw decreased significantly in stressed plants to -2.12 MPa after 7 days of water withholding, but remained at -0.6MPa in control plants (Table 3.1).

3.3.2 Carbohydrates

In the control treatment (exp 1), glucose and fructose ranged from 0.1 to 0.2 g/100 g FW and from 0.04 to 0.10 g/100 g FW, respectively (Fig 3.1A & B), while sucrose ranged between 0.6 and 1.1 g/100 g FW (Fig 3.1C). Under progressive drought stress accumulation of soluble carbohydrates was notable from stage 4 on (day 40, Ψw ~ -1.6 MPa), and significant higher levels of sucrose were found, while starch content was not affected (Fig 3.1D). Drought stress in summer conditions (exp 2), significantly decreased the starch content in stressed plants (Ψw = -2.12 MPa) compared to the control (Ψw = -0.6 MPa); sucrose was not detectable in control plants but reached significant higher levels in stressed plants (Table 3.1). Fructose and glucose levels were high compared to the winter experiment but were not influenced by the applied drought stress.

Chapter 3

0,35 0,16 Control Control Stress 0,30 0,14 Stress

gFW]

gFW] 0,12

-1 -1 0,25 0,10 0,20 0,08 0,15 0,06

GLUCOSE[g100 0,10 0,04

FRUCTOSE [g100 FRUCTOSE

0,05 0,02 1 2 3 4 5 6 1 2 3 4 5 6 DROUGHT STAGE DROUGHT STAGE A B

1,2 0,8 a a Control Control 1,1 Stress 0,7 Stress 1,0

gFW] a

gFW] 0,6

-1

0,9 -1 0,5 0,8 0,4 0,7 b b 0,3 0,6

STARCH [g100 STARCH SUCROSE [g100 SUCROSE 0,5 0,2 b 0,4 0,1 1 2 3 4 5 6 1 2 3 4 5 6 DROUGHT STAGE DROUGHT STAGE D C

Fig 3.1 Exp 1: Effect of increasing drought stress on soluble sugar content in leaves of F. × ananassa ‘Elsanta’. A: glucose, B: fructose, C: sucrose and D: starch content (Means ± SE, n=3, significant differences were calculated by F- test and indicated by a, b (p≤0.05))

Osmotic solute content and antioxidant defence in strawberry under drought stress

Table 3.1 Exp 2: Effect of drought stress on leaf water potential, soluble sugars and proline content in leaves of F. × ananassa ‘Elsanta’ (Means ± SE, n=3, significant differences were calculated by F- test and indicated by a, b (P≤0.05) Leaf water Starch Glucose Fructose Sucrose Proline -1 -1 -1 -1 -1 potential (Ψw) (g100 gFW) (g100 gFW) (g100 gFW) (g100 gFW) (µmolgFW ) (MPa) Control -0.6±0.033 a 1.09±0.13 a 1.27±0.14 1.06±0.10 0.00±0.00 a 0.16±0.014 a Stress -2.12±0.10 b 0.52±0.15 b 0.90±0.09 0.80±0.06 1.18±0.13 b 0.34±0.046 b

3.3.3 Proline content

Under progressive drought stress (exp 1), foliar proline content increased in stressed plants at stage 4 (day

40, Ψw ~ -1.6 MPa) compared to the control plants (Fig 3.2). With the progress of drought stress higher levels of proline were also observed in stressed plants at stage 5 although not statistically significant (Fig 3.2). Drought stress in summer conditions (exp 2), significantly increased proline content in stressed plants (Ψw = -2.12 MPa) compared to the control (Ψw = -0.6 MPa) after 7 days of water withholding (Table 3.1).

0,26 Control 0,24 a Stress

]

-1 0,22

0,20

0,18

0,16 b 0,14

0,12

PROLINE CONTENT [µmolgFW CONTENT PROLINE 0,10

0,08 1 2 3 4 5 6 DROUGHT STAGE Fig 3.2 Exp 1: Effect of increasing drought stress on proline contents in leaves of F. × ananassa ‘Elsanta’. (Means ± SE, n=3, significant differences were calculated by F- test and indicated by a, b (p≤0.05))

Chapter 3

3.3.4 Total antioxidant content (TAC)

TAC content was measured in exp 1. Foliar TAC content tended to increase when the leaf water potential reached -1.6 MPa in stressed plants (stage 4). Higher levels of TAC were observed in stressed plants compared to the control at stages 4, 5 and 6 (days 40, 44 and 51), though only significant at stage 5 (Fig 3.3).

]

-1 60 Control Stress 50

20 a

10 b

TOTAL ANTIOXIDANT CAPACITY (TAC) [µMgFW (TAC) CAPACITY ANTIOXIDANT TOTAL 0 1 2 3 4 5 6 7 DROUGHT STAGE Fig 3.3 Exp 1: Effect of increasing drought stress on TAC contents in leaves of F. × ananassa ‘Elsanta’. (Means ± SE, n=3, significant differences were calculated by F- test and indicated by a, b (p≤0.05))

3.3.5 Protein content and the activity of antioxidant enzymes: CAT, APX and SOD

Protein content and antioxidant enzyme activities were measured in exp 1. Protein content was only significantly higher in stressed plants 44 days after the start of the drought stress (stage 5), compared to well-watered plants (Fig 3.4A). Under prolonged drought stress, CAT activity tended to decrease and was significantly lower than for control plants at stage 5 (day 44) (Fig 3.4B). In general, no significant effect of drought stress on APX and SOD activity was observed in ‘Elsanta’. The APX activity tended to decrease in stressed plants and this was most pronounced at the stages 3 and 4 (days 27 and 40) (Fig 3.4C), while the SOD activity was only lower in stressed plants at stage 6 (day 51) compared to well- watered plants (Fig 3.4D). The enzyme activity was also calculated as total enzyme activity, based on the enzyme units presents in the fresh biomass (data not shown). However, the pattern of the enzymatic activity expressed as total activity or specific activity was similar for all three enzymes.

Osmotic solute content and antioxidant defence in strawberry under drought stress

3,0 60 Control Control

) Stress Stress a -1 50 ] 2,5

-1

2,0 40

mg protein mg

-1

1,5 b 30

1,0 20

a PROTEIN CONTENT [mggFW CONTENT PROTEIN 0,5 10

CAT ACTIVITY (unitmin ACTIVITY CAT

b 0,0 0 1 2 3 4 5 6 1 2 3 4 5 6 DROUGHT STAGE DROUGHT STAGE A B

400 control 5000 stress Control 350 Stress

4000 300

protein]

protein)

-1 -1 250

-1. 3000 200

150 2000

100

SOD ACTIVITY [unit mg [unit SODACTIVITY 1000 50

APX ACTIVITY (mM min (mM APXACTIVITY 0 0 1 2 3 4 5 6 1 2 3 4 5 6 DROUGHT STAGE DROUGHT STAGE C D

Fig 3.4 Exp 1: Effect of increasing drought stress on protein content and stress enzyme activity in leaves of F. × ananassa ‘Elsanta’. A: protein content, B: CAT activity, C: APX activity, D: SOD activity (Means ± SE, n=3, significant differences were calculated by F- test and indicated by a, b (p≤0.05))

3.4 Discussion

3.4.1 Plant water relations under drought stress

Plant water relations during drought stress play a critical role in the activation or regulation of different metabolic defence mechanisms (Sànchez-Rodriguez et al. 2010). In the first experiment, a progressive drought stress under low light intensities was imposed and a gradual decline of the leaf water potential until visible wilting of strawberry leaves at noon (stage 6, day 51) was observed. The same experimental set-up in summer conditions (exp 2) resulted already 7 days after water withholding in midday Ψw of -2.12

Chapter 3

MPa and in wilting at noon in stressed plant. The rate of stress development differed in both experiments and due to climatic conditions plants suffered from a combined drought and heat stress in exp 2.

3.4.2 Osmotic adjustment

3.4.2.1 Carbohydrates The progressive development of drought stress enabled the strawberry plants to acclimate and to develop adaptive mechanisms to cope with water deficits. In exp 1, foliar soluble carbohydrates especially sucrose increased under water deficit from stage 4 on. Also in exp 2, sucrose content increased while starch content significantly decreased. This confirms the role of sucrose in osmotic adjustment of strawberry under both progressive (exp 1) and severe (exp 2) drought stress which is consistent with previous observations in Fragaria chiloensis (Zhang and Archbold 1993) and apple (Sircelj et al. 2005). Also in other crops the partitioning of sugars is markedly changed under water deficit; more sucrose and less starch are synthesized, which combined with conversion of starch to sucrose lead to the increase of sucrose content for osmotic adjustment (Yordanov et al. 2000; Quick et al. 1989). In exp 1, the cytoplasmatic accumulation of hexose sugars might contribute to osmotic adjustment and also ameliorate the deleterious effects of free radicals produced in response to water deficit. Also in Fragaria chiloensis (Zhang and Archbold, 1993) increased hexose sugars contributed directly to osmotic adjustment.

3.4.2.2 Proline content Normally, under different stress conditions, proline accumulates in plants with various functions such as mediating of osmotic adjustment, stabilizing of sub-cellular structures, scavenging of free radicals, buffering the redox potential and also as an essential component of cell-wall proteins (Sànchez-Rodriguez et al. 2010; Mattana et al. 2005; Hong et al. 2000; Nanjo et al. 1999; Hare and Cress 1997). In our research, no high amounts of proline accumulation were detected in ‘Elsanta’. In exp 1, proline increased in stressed plants at stage 4 and 5, indicating that it did contribute to osmotic adjustment, though it might not be the main component in osmotic adjustment in strawberry. This corresponds to previous reports on Fragaria sp. (Zhang and Archold 1993). However, in exp 2, proline considerably increased in the cytosol of stressed plants. This suggests that in strawberry, proline accumulation under water deficit can be influenced by the method of plant exposure to drought stress and the severity of the stress. It is also possible that the combination of drought stress and relative high irradiance levels resulted in higher oxidative damage and thus proline accumulation. This is in agreement with other reports that evaluated the effect of salinity or drought stress on foliar accumulation of proline in strawberry, cherry tomato, tomato and rice and where proline accumulation was species/cultivar specific and proportional to the stress condition (Sànchez-Rodriguez et al. 2010; Pirlak and Esitken 2004; Alian et al. 2000; Aziz et al. 1999; 64

Osmotic solute content and antioxidant defence in strawberry under drought stress

Zhu et al. 1998). A significant rise in proline content was reported under moderate drought stress in cherry tomato, though the lowest level of proline was found in ‘Zarin’ a drought tolerant cultivar (Sànchez- Rodriguez et al. 2010). A higher proline accumulation is thus not always indicative for a greater drought tolerance (Sànchez-Rodriguez et al. 2010). Rampino et al. (2006) also reported that drought tolerant wheat plants showed a higher RWC and this was related to a lower proline concentration. Taking together, it seems that in strawberry, soluble sugars will increase before a build-up in proline under drought stress suggesting that proline is a less sensitive indicator of drought stress than the soluble sugars.

3.4.3 Protein content

Protein modifications, both quantitative and qualitative, in response to drought stress have been reported by different studies but the type of effect of drought stress on protein content is variable. Total protein content changed in drought-stressed plants of tall fescue (Festuca arundinacea L.), both decrease and increase was observed depending on the cultivar (Jiang and Huang 2002), while a decrease in protein content of sesame cultivars (Sesamum indicum L.) was reported under water deficit (Fazeli et al. 2007). In our experiment, foliar protein content only slightly increased under prolonged drought stress at stage 5 (day 44). Further proteomic analyses are required to dissect the qualitative changes of proteins in strawberry under water deficit.

3.4.4 Total antioxidant capacity (TAC) and antioxidant enzymatic activities

In order to determine the nature of the antioxidant responses of strawberry to drought stress, we measured both foliar TAC content and the enzymatic activity of CAT, APX and SOD in strawberry ‘Elsanta’ under progressive drought stress (exp 1).

3.4.4.1 TAC content TAC increased in stressed plants from stage 4 on (40 days after water withholding). This higher TAC level will help the strawberry plants to cope with the overproduction of ROS. This corresponds to a previous report in which moderate drought stress enhanced total antioxidant capacity in tomato (Sànchez- Rodriguez et al. 2010). Foliar TAC levels in strawberry may reflect indirectly ascorbic acid contents and high levels of ascorbic acid are favourable to detoxify H2O2 and scavenge hydroxyl radicals. In literature limited data about TAC/ascorbic acid levels in strawberry leaves under abiotic stress were found. The ascorbic acid content in strawberry leaves was reduced under drought stress (Wang 1999) and an oxidation in ascorbate/glutathione redox pairs (decrease of ascorbic acid content) occurred in strawberry leaves under salt stress (Tanou et al. 2009). However, strawberry fruit antioxidant capacity increased

Chapter 3

under moderate salinity stress (Keutgen and Pawelzik 2007). Our results suggest that foliar TAC and thus ascorbic acid might be important in strawberry acclimation under progressive drought stress.

3.4.4.2 CAT activity CAT is localized in the peroxisomes in higher plants and is like peroxidases responsible for detoxification of H2O2 (Bowler et al. 1992; Tolbert 1971). Under prolonged drought stress, the catalase degradation by

H2O2 exceeds the enzyme recovery and subsequently causes the loss of CAT activity; though this depends on the plant tolerance capacity (Yoshimura et al. 2000; Scandalios 1993). In our experiment, CAT activity decreased in stressed plants which can be attributed to the catalase degradation by H2O2 under prolonged water deficit. Likewise, CAT activity significantly decreased in sensitive rice cultivars under drought stress; as well as in tolerant cultivars under severe water deficit (Gue et al. 2006). The decrease in CAT activity under water deficit was also reported in drought sensitive wheat cultivars (Sairam et al. 2000), tomato, wheat and sunflower (Tahi et al. 2008; Zhang and Kirkham 1994; Quartacci and Navari 1992). CAT activity remained constant in drought stressed tomato plants compared to the control plants (Zgallai et al. 2006). Likewise no significant rise in the CAT activity in different varieties of cherry tomato under moderate drought stress was noted, except in the tolerant cultivar ‘Zarin’ where an increase of CAT activity was observed (Sànchez-Rodriguez et al. 2010).

3.4.4.3 APX activity APX is a component of the ascorbate-glutathione pathway and an important enzyme in the detoxification of H2O2, which uses ascorbate as electron donor for its reduction to H2O (Asada and Takhashi 1987). Different isozymes of APX exist and these are located respectively in chloroplasts, mitochondria, microbody-membranes (including glyoxysome and peroxisome) and cytosol. Under oxidative stress, the ascorbate level in chloroplasts affects the stability of chlAPX, which is rapidly inactivated when the ascorbate level is too low for the operation of catalytic cycle of APX isozymes (Yoshimura et al. 2000).

With the accumulation of H2O2 in chloroplasts as a result of both higher photorespiration and leakage of electrons to O2 by the Mehler reaction under drought stress, ascorbate converts to dehydroascorbic acid which results in inactivation of chlAPX which in turn is followed by the escape of H2O2 to the cytosol to be captured by cAPX and finally APX activity decreases (Yoshimura et al. 2000). In our study, the APX activity was not strongly affected by drought stress but generally tended to decrease. This is consistent with other observations in stress sensitive plants. In spinach APX showed no response to drought stress (Yoshimura et al. 2000) and in rice APX significantly decreased in drought sensitive cultivars (Gue et al. 2006). Lower APX activity was also observed in drought sensitive cultivars of wheat compared to tolerant cultivars (Sairam et al. 2000). In contrast, an increase in APX activity was reported in the drought tolerant

Osmotic solute content and antioxidant defence in strawberry under drought stress

cherry tomato ‘Zarin’ under moderate drought stress, while no changes in less drought tolerant cultivars were reported (Sànchez-Rodriguez et al. 2010). In our experiment, the progress of drought stress probably leads to accumulation of H2O2 up to the start of inactivation of APX.

3.4.4.4 SOD activity SOD activity was hardly affected by the progressive drought stress, though decreased at the last stage (51 days after water withholding). A similar decrease in SOD activity was reported in drought sensitive cultivars of rice, maize, cucumber and wheat compared to tolerant cultivars when exposed to drought stress (Gue et al. 2006; Huang et al. 2005; Innelli et al. 1999; Shen et al. 1999; Sairam et al. 2000). In contrast, SOD activity increased under drought stress in tomato (Zgallai et al. 2006), tolerant rice cultivars (Guo et al. 2006) and mosses (Burke et al. 1985; Dhindsa and Matowe 1981). In strawberry cultivars SOD activity in relation to biotic stress was studied; a higher SOD activity was linked with higher cultivar tolerance to Mycosphaerella fragariae (Ehsani-Moghadam et al. 2006). The severity of drought stress is also a criterion that affects the behaviour of SOD enzyme, the change of SOD activity in tomato plants under progressive moderate drought stress was negligible compared to a higher SOD activity under severe drought stresses (Zgallai et al. 2006). Therefore, a slow developing drought stress under low light conditions as in our experiment might be the cause for the absence of noticeable changes in SOD activity in strawberry ‘Elsanta’.

3.5 Conclusion

Plants can cope with water deficit via different adaptation and defence mechanisms. The main focus in this experiment was to investigate the plant osmotic adjustment via sugar and proline content as well as antioxidant defence mechanism by scavenging enzymes and TAC content in strawberry ‘Elsanta’ under drought stress. Greenhouse experiments in winter and summer conditions were performed to induce drought stress. Our results emphasis the consistent role of sucrose as the main component in strawberry/Fragaria osmotic adjustment to maintain its hydrated state under drought stress, this was independent of the severity of drought stress or the method of water deficit settlement. However, the contribution of proline in osmotic adjustment is not as consistent as sucrose, its function as an osmolyte as well as a ROS scavenger might be dependent on the severity or the method of drought stress settlement, and is stronger under severe water deficit similar to our summer experiment with combined drought and heat stresses under high fluence rates. TAC content considerably increased in stressed plants of ‘Elsanta’ under prolonged drought stress, while the CAT activity decreased and APX and SOD activity tended to decline under drought stress. Overall, our results confirm that both osmotic adjustment and ROS scavenging mechanisms are involved in Fragaria response to water deficit. 67

Chapter 4 Isolation of candidate genes

Chapter 4

Isolation of candidate genes

4.1 Introduction

Drought stress induces a sequence of molecular, biochemical and morphological changes, which adversely affect plant growth and productivity. For survival, plants should adapt to drought conditions and this depends on the initiation and maintenance of numerous physiological processes that are controlled by a complex network of genes (Krasensky and Jonak 2012; Shinozaki & Yamaguchi- Shinozaki 2007). In general, many dehydration-inducible genes with various functions which can be ABA-dependent (long-term acclimation) and/or ABA-independent (short-term responses), have been identified and transgenic plants over-expressing these genes have shown increased tolerance to drought stress (Huang et al. 2008; Seki et al. 2007; Bartels and Sunkar 2005). The accumulation of ABA is one of the crucial elements for the response to water deficit and serves as an initial signal for long-term acclimation reactions, such as the accumulation of compatible compounds like proline, mannitol and sorbitol or the formation of oxygen radical scavenging compounds like ascorbate, glutathione and α-tocopherol. These long-term acclimation processes involve the differential expression of genes leading to changes in transcript and protein patterns (Yordanov et al. 2003). Functionally, the gene products can be illustrated into osmolyte synthases (Chen & Murata 2002), protection factors for macromolecules (e.g. chaperones, late embryogenesis abundant proteins (LEA)/dehydrin-type proteins), proteases, membrane proteins (e.g. aquaporins, transporters), detoxification enzymes (e.g. glutathione-S-transferase-GST, superoxide dismutase-SOD), genes for regulatory proteins such as protein kinases, phosphatases and genes for transcriptional factors which play important roles via transcriptional regulation of downstream genes responsible for plant tolerance to dehydration (Shinozaki & Yamaguchi-Shinozaki 2007; Wang et al. 2003). To date, the molecular study of abiotic stress tolerance like drought tolerance in Fragaria is still limited. A commonly used technique for detecting underlying genes of complex adaptive traits is the candidate gene approach. Candidate gene analysis is based on the hypothesis that known-function genes (the candidate genes) could correspond to loci controlling traits of interest like drought tolerance (Pflieger et al. 2001). In this chapter, the selection of candidate genes involved in plant response to drought stress and their isolation in Fragaria sp. is described. Isolated sequences will be applied later in our study for development of EST markers associated to drought tolerance and as the genes of interest (GOI) for quantitative expression analysis by RT-qPCR under water deficit in Fragaria. The selection and isolation of reference genes (R) that will be used later as the internal controls for RT-qPCR are also described in this chapter.

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4.2 Materials and methods

4.2.1 Selection of candidate genes

Candidate genes from different known pathways involved in plant response to drought stress (abiotic stress) were initially selected to be isolated (Table 4.1). The selection of candidate genes of interest was partly based on previous results on biochemical adaptation under drought stress in F. × ananassa (Razavi et al. 2008). Moreover, coding sequences of twenty two candidate genes were selected for the use as reference gene for normalization of gene expression in RT-qPCR assays in Fragaria under drought stress (Table 4.1).

4.2.2 In silico identification of candidate genes in Fragaria

Some of the selected candidate genes were already reported to be functional in Fragaria (Table 4.1). For the others, the coding sequence of Arabidopsis (for both reference genes and genes of interest), Vitis vinifera and other species from Rosaceae like Prunus persica and Malus × domestica (for genes of interest) as well as ESTs of Rhododendron (for reference genes) were blasted (TBLASTX; Altschul et al. 1997) against the Fragaria EST database of the Genome Database of Rosaceae (GDR; Jung et al. 2004) or NCBI database (non-human ESTs; organism: Fragaria sp.) and Fragaria EST with the highest homology was selected as the putative Fragaria sequence matched to the selected candidate genes.

4.2.3 Primer development and PCR amplification

For development of EST derived markers, primers were designed on identified Fragaria’s EST sequences according to De Keyser et al. (2009) (melting temperature 58-60 °C, primer length 22-25 bp and amplicon length 350-400 bp) using Vector NTI 10 (Invitrogen). Primers were first evaluated on genomic DNA of 6 Fragaria genotypes (F. vesca, F. chiloensis, F. × ananassa ‘Figaro’, ‘Elsanta’, ‘Sonata’ and ‘Selva’) for being intron-spanning. In the case an intron was amplified, the EST sequence could be considered as as a potential resource for the presence of an EST marker and primers were retained for EST marker amplification in later studies. Otherwise new primers were developed. For RT-qPCR specific primers were developed on Fragaria’s ESTs corresponding to the candidate genes or on functionally characterized Fragaria’s sequences using Vector NTI 10 (melting temperature 58-60 °C, primer length 20-24 bp and amplicon length 75-95 bp) and primers were tested on Fragaria genomic DNA or cDNA (Table 4.2). For CAT, P5CS, GalLDH, P5CDH, DHAR, CuZnSOD (CSD 1&2), APX42 and NCED3 primers were designed for the amplicon length around 350-400 bp for easier cloning and finally specific RT-qPCR primers were designed on the long cloned fragment with the amplicon length 75-95 bp. All primers were

Isolation of candidate genes

ordered from Invitrogen Belgium. All amplifications were performed in 20µl reaction mixture containing 1µl template (DNA/cDNA) (15ng/20µl), 0.5 pmol/µl primer, 0.2mM of dNTP’s (Invitrogen), 1×PCR buffer and 0.04U/µl of AmpliTaq DNA polymerase (Applied Biosystems) on a GeneAmp 9700 thermocycler (Applied Biosystems). The cycling conditions were: 1min at 94°C, 94° C for 30s, 60°C 30s, and 72°C 1 min for 35 cycles and final extension at 72°C for 10 min. Gel electrophoresis was used to confirm the expected amplification specificity (single bands). Electrophoresis was carried out in 1x TAE buffer (1.5% Ultar Pure Agarose, Invitrogen) and bands were visualized by UV illumination after staining with ethidium bromide. On each gel a size marker (GeneRuler 100bp Plus DNA ladder, Fermentas, USA) was loaded for sizing of the fragments.

4.2.4 Cloning of PCR products and verification of selected gene amplicons for RT-qPCR

PCR products were cloned with TOPO TA Cloning Kit (Invitrogen) according to the manufacturer’s instructions. Amplified fragments were cloned in the pCR2.1-TOPO cloning vector and transferred to TOP10F’ chemically competent cells by heat shock. After plating of transformed E. coli, 8 white colonies of each cloned fragment were picked and used for direct colony PCR. Direct colony PCR was performed on 50µl reaction mixture containing 10µl of boiled colonies, 0.5µM of M13 primer (Invitrogen), 0.5µM of dNTP’s (Invitrogen), 1×PCR buffer and 1.25 U of AmpliTaq DNA polymerase (both Applied Biosystem) on a GeneAmp 9700 thermocycler (Applied Biosystem). By gel electrophoresis clones were selected for sequencing. If the amplification of the eight colonies of each cloned fragment showed identical bands, only one PCR product was sequenced. If one or more different bands were visualized all PCR products were sequenced. PCR fragments were cleaned according to Werle et al. (1994). Sequencing was performed with the M13 forward and reverse primers (3.2µM; Invitrogen), following the protocol of the Big DYE terminator V1.1 Cycle Sequencing Kit (Applied Biosystems) on an ABI Prism® 3130xI Genetic analyzer (Applied Biosystem) and with the use of Sequencing Analysis Software V5.2 (Applied Biosystem). The sequenced amplicons were aligned (NBCI/CLUSTALW2: Larkin et al. 2007) with the initial sequences on which the specific primers were developed (Fig 4.1). Eventually, the primer specificity was confirmed for RT-qPCR primers. For longer cloned fragments, specific primers for RT-qPCR were designed on these validated sequences (Fig 4.1).

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A B

Query 1 AGGGGGTATGACTGCTAAAGTAAAAGCTGCTGTTAATGCTGCTTATGCTGGCATCCCTGT 60 C |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 74 AGGGGGTATGACTGCTAAAGTAAAAGCTGCTGTTAATGCTGCTTATGCTGGCATCCCTGT 133

Query 61 TATCATCACCAGKGGATATGCTCCTGAAAACCTCACCAAAGTCCTTCAAGGGCAACGCAT 120 |||||||||||| ||||||||||||||||||||||| ||||||||||||||| ||||||| Sbjct 134 TATCATCACCAGTGGATATGCTCCTGAAAACCTCACTAAAGTCCTTCAAGGGGAACGCAT 193

Query 121 TGGTACCCTCTTCCATCAAGATGCACATTTATGGTGTTCTGTTAAAGATGTTGATGCAAG 180 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 194 TGGTACCCTCTTCCATCAAGATGCACATTTATGGTGTTCTGTTAAAGATGTTGATGCAAG 253

Query 181 AGGGATGGCAATTGCAGCAAGGGAAAGTTCCAGACGTCTACAGGCCATGACTTCAGAGCA 240 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 254 AGGGATGGCAATTGCAGCAAGGGAAAGTTCCAGACGTCTACAGGCCATGACTTCAGAGCA 313

Query 241 AAGGaaaaaaaTTCTTCTGGATATAGCCGATGCAATTGAAGCAAATGCAAAAAAGATCAA 300 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 314 AAGGAAAAAAATTCTTCTGGATATAGCCGATGCAATTGAAGCAAATGCAAAAAAGATCAA 373

Query 301 TGTTGAAAATGAAGCCGATGTTTCTGCTGCACAACAAGCAGGATATGAAAAATCCTTGGT 360 ||||||||||||||| |||||||||||||||||||||||||||||||||||||||||||| Sbjct 374 TGTTGAAAATGAAGCTGATGTTTCTGCTGCACAACAAGCAGGATATGAAAAATCCTTGGT 433

Query 361 AGCTCGTCTGGC 372 |||||||||||| Sbjct 434 AGCTCGTCTGGC 445

Fig 4.1 The method for verification of cloned amplicon (e.g. for P5CS): A) PCR amplification for TOPO TA cloning (amplicon length 350-400 bp) B) direct colony PCR screening, C) alignment of sequenced amplicon (query) with the initial F. vesca sequence ‘DY668033’ (subject), specific RT- qPCR primers were designed on the red part with the highest homology between cloned fragment and initial F. vesca sequence (NBCI/CLUSTALW2: Expect=0.0, Identities= 368/372 (98%), Gaps= 0/372 (0%), Strand= Plus/Plus)

Isolation of candidate genes

4.3 Results and discussion

4.3.1 Characteristics of isolated Fragaria sequences

The preliminary set of selected candidate genes of interest (GOI) included 24 genes comprising the genes encoding ROS scavenging enzymes, osmotic-stress protective proteins like dehydrin (DHN), Late-Embryogenesis-Abundant (LEA) and osmotin-like proteins (OLP), ripening-induced protein (that is also induced under water deficit), the key enzymes of proline and sucrose metabolism, ABA biosynthesis, ascorbic acid metabolism and ascorbate-glutathione redox cycle (GSH-ASC). Also some transcription factors and the downstream dehydration responsive genes that are regulated under water deficit were selected as candidate genes of interest (Table 4.1). In total, 6 of these selected candidate genes were already functionally characterized in Fragaria from which 5 genes were used for EST marker development (Table 4.1 & 4.2). Nine APX alleles are characterized in Fragaria (Kim et al. 2001); 3 of them were used for EST marker development in different regions. Likewise, for OLP and SPS, 2 different sequences were selected for marker development. In total, 9 functional EST markers were developed based on functionally characterized genes in Fragaria. For other selected candidate genes, the Fragaria sequence with the highest homology was selected; otherwise, sequences derived from stressed plants were preferred for EST marker development. In total 24 Fragaria ESTs (out of 20 selected candidate genes) were isolated for EST marker development (Table 4.2). For use as the genes of interest (GOI) in RT-qPCR assay, 16 Fragaria ESTs (out of 15 selected candidate genes) were isolated from which 6 were already functionally characterized in Fragaria and 10 others corresponded to the selected candidate genes of interest (Table 4.1 & 4.2). The CAT and APX42 encoding ROS scavenging enzymes, AKR encoding the key enzyme of ascorbic acid metabolism, FaSPS and FaAIV encoding the key enzymes of sucrose metabolism and P5CS encoding the key enzyme of proline biosynthesis involved in osmotic adjustment pathways under water deficit, OLP encoding osmotin-like protein involved in cellular protective activity and DREB2A as transcriptional regulatory factor induced by dehydration (osmotic-stress) were selected and the Fragaria’s ESTs/sequences were isolated for both EST marker development and for RT-qPCR assays as genes of interest (GOI) (Table 4.2). For RAB18 encoding the protective protein (dehydrin) involved in cellular protective activity against osmotic-related stresses, DREB3 as the transcriptional regulatory factor induced by dehydration (osmotic-stress), RD22 as the dehydration responsive gene, APX18, APX27 and MnSOD encoding the ROS scavenging enzymes, the Fragaria’s ESTs/sequences were only isolated for EST marker development (Table 4.2). For DHAR encoding the key enzyme involved in ascorbate-glutathione redox cycle (GSH-ASC), GalLDH encoding the key enzyme in ascorbic acid biosynthesis, NCED3 encoding the key enzyme of ABA biosynthesis, P5CDH encoding the key enzyme in proline

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metabolism and CSD (Cu-Zn SOD) encoding the ROS scavenging enzyme as well as the CBF4 and MYB2 as drought-inducible transcription factors, the Fragaria’s ESTs/sequences were only isolated for RT-qPCR assay. For CSD (Cu-Zn SOD), 2 ESTs were identified and isolated for RT-qPCR assay (Table 4.2). The isolation of Fragaria sequences corresponding to DREB2B, CBF1 and RD29A as the preliminary selected candidate genes was not successful. Furthermore, 22 Fragaria’s ESTs/sequences were isolated for use as reference genes (R) in RT-qPCR from which 14 Fragaria’s ESTs were homologues for the selected candidate reference genes and 8 sequences were already characterized in Fragaria (Table 4.1 & 4.2). Sequence analysis of all isolated amplicons revealed that isolated fragments were identical to the original sequences were used for primer designing from Fragaria EST database or from Fragaria functionally characterized sequences.

Isolation of candidate genes

Table 4.1 Database of selected candidate genes; gene description and references (the genes already functionally characterized in Fragaria are indicated by an ‘*’) Candidate gene Gene definition Reference

APX * Cytosolic ascorbate peroxidase Lin et al. (2010); Rubio et al. (2009); Lee et al. (2007); Lee SH. et al. (2007); Bartels and Cell protective and detoxification enzyme in oxidative Sunkar (2005); Shao et al. (2005); Zhang et al. (2004); Mittler et al. (2004); Park et al. (2004); stresses like water deficit Aharoni et al. (2002); Bray (2002); Seki et al. (2002); Ingram and Bartels (1996); Allen (1995) CAT Catalase Miller et al. (2010); Lin et al. (2010); Bian and Jiang (2009); Rubio et al. (2009); Du et al Detoxification enzyme in oxidative stresses like water (2008); Yang et al. (2006); Bartels and Sunkar (2005); Shao et al. (2005); Luna et al. (2004); deficit Mittler et al. (2004); Chavez et al. (2003); Aharoni et al. (2002); Bray (2002) ; Zhang and Kirkham (1994) Cu/Zn SOD Copper / zinc superoxide dismutase Miller et al. (2010); Bian and Jiang (2009); Rubio et al. (2009); Li et al. (2008); Seki et al. (CSD) Detoxification enzyme in oxidative stresses like water (2007); Lee et al. (2007); Lee SH. et al. (2007); Bartels and Sunkar (2005); Mittler et al. (2004); deficit Wang et al. (2003); Bagnoli et al. (2002); Borsani et al. (2001); Wu et al. (1999); Allen (1995); MnSOD Mitochondrial manganese superoxide dismutase 1 Zhang and Kirkham (1994); Bowler et al. (1992) Detoxification enzyme in oxidative stresses like water deficit Ripening – induced Some ripening-related proteins were identified being Bray (2002); Aharoni et al. (2002) protein induced under conditions of waterdeficit stress RAB 18 Late-Embryogenesis-Abundant (LEA) / dehydrin (DHN) Shinozaki and Yamaguchi-Shinozaki (2007); Rampino et al. (2006); Wisniewski et al. (2006); (Responsive to ABA 18) proteins (Class II) Bray (2004); Puhakainen et al. (2004); Bartels and Souer (2003) ; Chavez et al. (2003); Aharoni Protective proteins during cellular dehydration (ABA et al. (2002); Bray (2002); Sunkar and Bartels (2002); Caruso et al. (2002); Seki et al. (2002); dependent) Ingram and Bartels (1996) DREB2A DREB regulon (DRE-binding protein (AP2/ERF type)) Saiful Islam and Wang (2009); Shinozaki and Yamaguchi-Shinozaki (2007); Yamaguchi- ABA independent transcriptional regulatory factors Shinozaki and Shinozaki (2006); Nakashima and Yamaguchi-Shinozaki (2006); Xiao et al. DREB2B induced by dehydration (osmotic-stress) (2006); Bartels and Sunkar (2005); Nakashima and Yamaguchi-Shinozaki.(2005); Saleh et al.

(2005); Zhang et al. (2004); Bartels and Souer (2003); Chavez et al. (2003); Bray (2002); Haake DREB3 et al. (2002); Seki et al. (2002); Desikan et al. (2001); Liu et al. (1998); CBF4 DREB regulon (AP2/ERF type) Nakashima and Yamaguchi-Shinozaki (2006); Yamaguchi-Shinozaki and Shinozaki (2006); (CBF4/DREB1D) ABA dependent transcriptional regulatory factors induced Xiao et al. (2006); Bartels and Sunkar (2005); Nakashima and Yamaguchi-Shinozaki (2005); by dehydration (osmotic-stress) Zhang et al. (2004); Bartels and Souer (2003); Wang et al. (2003); Haake et al. (2002); Liu et al. (1998)

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CBF1 (CBF/DREB1) DREB regulon (AP2/ERF type) Nakashima and Yamaguchi-Shinozaki (2005), Owens et al. (2003), Owens et al. (2002), Zhang C-repeat-binding factor/dehydration responsive element- et al. (2004), Wang et al. (2003) binding factor 1 (ABA independent transcriptional regulatory factors), a cold-induced transcription factor important in the cold acclimation response (abiotic stress) MYB2 MYB regulon, Myb-like proteins (myeloblastosis Shinozaki and Yamaguchi-Shinozaki (2007); Nakashima and Yamaguchi-Shinozaki (2006); oncogene) Yamaguchi-Shinozaki and Shinozaki (2006); Bartels and Sunkar (2005); Nakashima and ABA dependent transcriptional regulatory factors induced Yamaguchi-Shinozaki (2005); Zhang et al. (2004); Bartels and Souer (2003); Wang et al. by dehydration (osmotic-stress) (2003); Aharoni et al. (2002); Haake et al. (2002); Seki et al. (2002); Desikan et al. (2001); Bray (2002); Ingram and Bartels (1996) RD22 Responsive to dehydration 22 Shinozaki and Yamaguchi-Shinozaki (2007); Yamaguchi-Shinozaki and Shinozaki (2006); Induced gene by MYB (ABA dependent) Nakashima and Yamaguchi-Shinozaki (2006); Nakashima and Yamaguchi-Shinozaki (2005); RD29A (COR78) Responsive to dehydration 29A Bartels and Sunkar (2005); Zhang et al. (2004); Chavez et al. (2003); Wang et al. (2003); Seki Induced gene by DREB2 and CBF4 (ABA independent) et al. (2002); Xiong et al. (2002); Bray (2002); Hare et al. (1999); Ingram and Bartels (1996)

AKR* NADPH-dependent aldo-keto reductase, D-galacturonic Hemavath et al. (2009); Ioannidi et al. (2009); Horvath et al. (2006); Jing-Hui et al. (2005); (Aldo-keto reductase) acid reductase, Key enzyme in L-Ascorbate biosynthesis, Bartels and Sunkar (2005); Bartels and Souer (2003); Agius et al. (2003); Wang et al. (2003); AsA as a non-enzymatic antioxidant involved in Bartels (2001); Oberschall et al. (2000) detoxification system in oxidative stress Detoxification enzyme by reducing the level of reactive aldehydes (ROS scavenging) NCED3 9-cis-epoxycarotenoid dioxygenase (NCED) Seki et al. (2007), Shinozaki and Yamaguchi-Shinozaki (2007); Yamaguchi-Shinozaki and Key enzyme in ABA biosynthesis Shinozaki (2006); Bartels and Sunkar (2005); Chavez et al. (2003); Xiong et al. (2002); Seki et al. (2002); Iuchi et al. (2001) FaOLP * Osmotin - like proteins Zhang and Shih (2007); Pearce (1999) Involved in plant defence system with protective activity against osmotic- related stresses

Isolation of candidate genes

P5CS Delta-1-pyrroline 5-carboxylate synthetase (Osmotic stress- Seki et al. (2007); Ramanjulu et al. (2007); Kavi Kishor et al. (2005) ; Bartels and Sunkar responsive gene) (2005); Fabro et al. (2004); Bartels and Souer (2003); Chavez et al. (2003); Wang et al. (2003); Key enzyme in proline biosynthesis Chen and Murata (2002); Seki et al. (2002); Bray (2002); Sunkar and Bartels (2002); Hare et al. ProDH Proline dehydrogenase (Osmotic stress-responsive gene) (1999); Ingram and Bartels (1996); Yoshiba et al. (1997) ; Yushiba et al. (1995) Key enzyme in proline degradation P5CDH Proline dehydrogenase (Osmotic stress-responsive gene) Key enzyme in proline degradation FaAIV* Soluble acid invertase Hubbard et al. (2006); Bartels and Souer (2003); Chavez et al. (2003); Etienne et al. (2002); Key enzymes in sucrose degradation in plant cell Sasaki et al. (2001); Winter and Huber (2000); Sturm (1999); Ingram and Bartels (1996); Yamaki (1995) FaSPS * Sucrose phosphate synthase Fresneau et al. (2007); Hubbard et al. (2006); Bartels and Souer (2003); Sunkar and Bartels Key enzymes in sucrose biosynthesis in plant cell (2002); Sasaki et al. (2001); Winter and Huber (2000); Ingram et al. (1997); Ingram and Bartels (1996); Yamaki (1995); Quick et al. (1989) GalLDH * L-Galactono-1,4-lactone dehydrogenase Do Nascimento et al. (2005) Key enzyme in L-Ascorbate biosynthesis, ascorbic acid (AsA) as a non-enzymatic antioxidant involved in detoxification system in oxidative stress DHAR Dehydroascorbate reductase Li et al. (2011) Key enzyme involved in ascorbic acid recycling (ascorbate–glutathione redox cycle) ACT Actin, Cytoskeletal structural protein (traditional reference Hu et al. (2009); Czechowski et al. (2005); Huggett et al. (2005) gene in RT-qPCR) EF-1α Elongation factor-1 alpha, Translational elongation Hu et al. (2009); Czechowski et al. (2005); Huggett et al. (2005) (traditional reference gene in RT-qPCR) TUA Alpha-tubulin, Structural constituent of cytoskeleton Hu et al. (2009); Czechowski et al. (2005); Huggett et al. (2005) (traditional reference gene in RT-qPCR) UBQ10 Ubiquitin 10, Protein binding, protein modification Hu et al. (2009); Czechowski et al. (2005); Huggett et al. (2005) (traditional reference gene in RT-qPCR) SAND SAND family protein, Endocytic transport, vacuolar fusion Czechowski et al. (2005) protein Mon1, trafficking protein Mon1 (novel reference gene in RT-qPCR) UBC9 Ubiquitin conjugating enzyme 9, Protein degradation Czechowski et al. (2005) (novel reference gene in RT-qPCR) PTB Polypyrimidine tract-binding protein (novel reference gene Czechowski et al. (2005) in RT-qPCR)

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PDF2 Protein phosphatase 2A regulatory subunit (novel reference Czechowski et al. (2005) gene in RT-qPCR) YLS8 Yellow-leaf-specific gene 8 (Splicing factor hioredoxin- Czechowski et al. (2005) like U5) (novel reference gene in RT-qPCR)

CACS Clathrin adaptor complex subunit, Binding to vesicle Czechowski et al. (2005) (endocytosis) (novel reference gene in RT-qPCR) FaAP * Aspartic proteinase (reference gene in RT-qPCR) Personal communication with Prof. Kevin Folta, University of Florida FaAATP * Plastid ATP/ADP transport protein (reference gene in RT- Personal communication with Prof. Kevin Folta, University of Florida qPCR) FaCHP * Conserved hypothetical protein (reference gene in RT- Personal communication with Prof. Kevin Folta, University of Florida qPCR) FaGAPDH * Glyceraldehyde 3-phosphate dehydrogenase, Glycolytic Personal communication with Prof. Kevin Folta, University of Florida protein (reference gene in RT-qPCR) FaENP * Endoplasmin-like protein (reference gene in RT-qPCR) Personal communication with Prof. Kevin Folta, University of Florida FaEF1-α * Elongation factor 1 alpha, Translational elongation Personal communication with Prof. Kevin Folta, University of Florida (reference gene in RT-qPCR) HK47 Nucleosome assembly protein (reference gene in RT- De Keyser et al. (2007) qPCR) HK96 Expansion factor (reference gene in RT-qPCR) De Keyser et al. (2007) HK134 Chlorophyll a/b binding protein (reference gene in RT- De Keyser et al. (2007) qPCR) HK173 Pyruvate dehydrogenase (reference gene in RT-qPCR) De Keyser et al. (2007)

Isolation of candidate genes

Table 4.2 Sequence and primer information of isolated ESTs corresponding to the selected candidate genes. ESTs/contigs functionally characterized in Fragaria are marked with an ‘*’. In case of a TBLASTX search, homologues are indicated. Acc No Putative function Clone Primer pairs (forward and reverse 5’-3’) Source TBLASTX Cloning name AF159627 APX18 * EST-1 TCTACCAGTTGGCTGGAGTTGTTGC F. × ananassa GCGGAAGACAGGGTCTGACAGAA AF159629 APX27 * EST-2 GGAGGCCCATTCGGAACCAT F. × ananassa CGAATCCAGACCGTTCCTTGTGT AF159630 APX42 * EST-3 AGGAGTACAAGAAGGCCATCGACAA F. × ananassa CAGAACCCTTTCCAGCATCAGGA GOI-1 GAAGGCCTTCTACAGCTTCCAACTG DNA TTCATCCGCAGCGTATTTCTCAA F. × ananassa ‘Elsanta’ DY674761 CAT EST-4 GGAGCGAATCCCAGAACGTGTT F. vesca A.thaliana GGATGGTGGGAAAAGAAGTCAAGGA (cold-stressed) AY113854 1 GOI-2 TCTTACATGTGCTGACTTCCTTCGA (0.0) cDNA CCACGCTCATGGATAACAGTTG F. × ananassa ‘Figaro’ CO816707 Unknown EST-5 AGCTAATGGCGATCCTGGTGGT F. × ananassa ACACAATTCCAAAGCAGGGTGCT (salicylic acid treated) CO381280 MnSOD EST-6 CCATTGGTTACCAAAGGAGCAAGTT F. × ananassa Prunus persica CCATAACCAACTCCTTCCAAATCGT AJ238316 (2e-39) 1 CO817459 Unknown EST-7 AGGAGGAGAAGCCTGTCATCATGAA F.× ananassa (salicylic acid GGACGTTCGTGTTCCTCGTGGTA treated) CX661438 RAB18 EST-8 AATACACCCCCGATTTGCAGTCTT F. vesca A. thaliana ATCCTGCTGCTGTCCACCGTAA (heat-stressed) NM_001037085 (3e-21) 1 CO817183 DREB2A EST-9 ACAAAGCAAACTCTACCCTCCACCA F. × ananassa (salicylic acid A.thaliana GCCCGTCTCAACTTTCCTAAGGTTA treated) NM_001036760 (7e-31) 1

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EX660807 GOI-14 TGCCAGGGCCATGTACGGTT F. vesca A.thaliana DNA ACAGGAAGGAGCCAGTACAGTTGCT AY063972 F. × ananassa (7e-41)1 ‘Elsanta’

DY674391 Unknown EST-10 GCTCTCTCTCTCTCCCGAGTTCTGA F. vesca GCCGGTTTGGGATGTGGGTAAG (cold-stressed) DV438427 DREB3 EST-11 CGATGTGGCTGCTCGGGCTAT F. vesca A.thaliana GCTGCACAGAGATGCCATGATGA AY940160 (1e-39) 1 DY670596 Unknown EST-12 CGGCGGATCGGATAAAGGGT F. vesca GGCTGATGCTCCGGCGACAT (cold-stressed) CO817580 RD22 EST-13 TGACATGTCCACTATGGGATGCTTT F. × ananassa (salicylic acid Prunus persica GGCAGTTGAAAATTCCATTGTGTGT treated) AF319165 (5e-39) 1 CO817057 Unknown EST-14 TTGAACTCATGGCTCTCTCATTCCA F. × ananassa (salicylic acid TCTCTTCCTTCCCTGCACCACAA treated) CO816798 Unknown EST-15 GTGAAGGCGAAAGCGAAGAAGTTGA F. × ananassa (salicylic acid TCTTAGCCCCACCAGAGTCGTGT treated)

AY663110 AKR * EST-16 TTCCTTCAGTAACCCTCAGCTCCTG F.× ananassa CCCAATTTGAATGGCCAGTGTATGA GOI-10 CTGAAACCGCCAAGGCTGCT DNA GGCGGTGTCGAAATGTCGGTA F. × ananassa ‘Elsanta’ CO816877 Unknown EST-17 CCCTCCTTTCTTTTTCGCTTCTTCT F. × ananassa (salicylic acid TGGATCTTCATGGGTTTGGAACAT treated) AF199508 FaOLP1 * EST-18 TACCCTAAAGATGATGCAACCAGCA F.× ananassa TTAACCAAAGAGATAACGGCATGCA GOI-9 AACACCCTCGCCGAATATGCA DNA CATCGGGACATTGAAGCCATCA F. × ananassa ‘Elsanta’

Isolation of candidate genes

DQ325524 FaOLP2 * EST-19 TTCTTCAACGTGAACCGGACGA F. × ananassa AGCAAAGGGCGGTGATCCAT DY668033 P5CS EST-20 AGTGGGAAGAGGGGGTATGACTGCT F. vesca A.thaliana NM_129539 GCCAGACGAGCTACCAAGGATTTT (cold-stressed) (3e-132) 1 GOI-7 AACGCATTGGTACCCTCTTCCATC cDNA TGCTGCAATTGCCATCCCTCT F. × ananassa ‘Figaro’ DY668934 P5CDH GOI-8 TCCAAAGAGCTACCAGCAGGCC F. vesca A.thaliana cDNA GGAAACGGACCTGATCACCAGAG AT5G62530 F. × ananassa (3e-143)1 ‘Figaro’ DY670745 Unknown EST-21 TCCGATCAAATTTACCAAACCTGCC F. vesca (cold-stressed) GAGCACCAACCGGATCGAGTTAT

AB275667 FaAIV * EST-22 GACCGGTTCCGCCACGCTACT F. × ananassa CCCACATACCCGTACCCGGA GOI-5 TTGGATTGGGTCAAGTACTCGGGT cDNA TGGTGGGGTCACGGAAATCC F. vesca AB267868 FaSPS1 * EST-23 ATGTAGTGGAACTTGCAAGGGCTTT F.× ananassa AGAGTCACCTGCATCTGCATAGTGC GOI-6 GCCGAGGAGTTATCCCTTGATGC cDNA CAGCGCCATTGCTCGTCAAT F. vesca AB267869 FaSPS2 * EST-24 AACAAGTGTGGCCAGTGGCAAT F.× ananassa AGGTATTACAACCGTCCGAGGCAT EX679208 Cu-Zn SOD (CSD1) GOI-3 AATTGCTAATGCTGATGGAGAGGCA F. vesca A.thaliana cDNA GGCTCTTCCAATTACAGCGTTAGGA AY064050 F. × ananassa (2e-128)1 ‘Figaro’ EX682785 Cu-Zn SOD (CSD2) GOI-4 GCTCCTGAAGATGAGAATCGTCATG F. vesca A.thaliana cDNA TGTCAACAATTGTGAAGCAAGCCTT EF408820 F. × ananassa (8e-110)1 ‘Figaro’ CO818085 NCED3 GOI-11 GTCCCCTGTTTTGGTGACCCTTAC F. × ananassa A.thaliana cDNA AACGCCGGCCTCGTCTACTTC AY056255 F. × ananassa (5e-83)1 ‘Figaro’

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AY102631 GalLDH* GOI-12 CCCTGGACAATCCAATCCCATT F. × ananassa cDNA AGTGGTCAAGGAGGCCAATGCTA F. × ananassa ‘Figaro’ CO817488 DHAR GOI-13 CTCGGGGATGGTCTTGGCAA F.× ananassa Malus x domestica cDNA GTGGACTGTCCCTAAAGCTTTGACC DQ322706 F. × ananassa ( 0.0)1 ‘Figaro’ EX679095 CBF4 GOI-15 CTAAGGACATACAGACGGCGGCT F. vesca Vitis vinifera DNA CCGCCGCTTTCGACACAACT DQ497624 F. × ananassa (2e-82)1 ‘Elsanta’ CX661233 MYB2 GOI-16 CAGGACGGTGCGGCAAGTCT F. vesca Malus × domestica cDNA GCAGAAAAGGGCTTGCGCTT DQ074459 F. vesca (7e-153)1 EX679278 PTB R-1 TAATGTGCTTCTGGCTTCCA F. vesca A. thaliana cDNA GCTGAAAATACCGTGTGAAT AT3G01150 F.×ananassa (8e-68) 1 ‘Ventana’ DY668958 PDF2 R-2 CGGACGCGGAAGGAGTTGAT F. vesca A. thaliana cDNA GACCCCCAACTCCTCTGCCA AT1G133320 F.×ananassa (0.0)1 ‘Ventana’ EX681254 YLS8 R-3 TGCTTCCGCATCTTCACTCCG F. vesca A. thaliana cDNA ATCGTGGCCGAATCGGATGA AT5G08290 F. vesca (2e-150) 1 DY673865 Clathrin adaptor complex subunit R-4 GAAGGTGTGCGCTCCCCATT F. vesca A. thaliana cDNA (CACS) CGCCAACCAACAGCACCTGT AT5G46630 F. vesca (3e-180) 1 DY675727 HK96 R-5 ACCTCTCTCAGCCTGTATTCCAGCA F. vesca Rhododendron simsii cDNA GATGCCTCCCCTTCTTCTGCAG (6e-79) 1 F. ×ananassa ‘Figaro’ DY669421 HK47 R-6 CAGAGCGTGATGAAGGTGCTCTAAA F. vesca Rhododendron simsii DNA CCCTTGGGGTTTTCTATCCTAGACC (4e-55) 1 F.×ananassa ‘Elsanta’

Isolation of candidate genes

DY669581 HK173 R-7 AGCAGACGGGGGTAAGCTTGAG F. vesca Rhododendron simsii DNA GGCCGAAATCAGCAGATTCCTT (3e-73) 1 F.×ananassa ‘Elsanta’ CO817324 HK134 R-8 GCTCGCTTCCAGGTGACTATGGT F. × ananassa Rhododendron simsii DNA CTGTACCACTTGAGGAAGGCTGGAT (4e-79) 1 F.×ananassa ‘Elsanta’

DY666716 SAND R-9 AGGCAAGCTGCTGGTGCAATATT F. vesca A. thaliana cDNA GGGCACCAACAAGACTGATAACCTT AT2G28390 F. ×ananassa (e-111) 1 ‘Figaro’ CO381815 UBC9 R-10 CCAAGGTGTTGCTTTCAATCTG F. × ananassa A. thaliana DNA TGAGCAATCTCTGGCACCAAT AT4G27960 F.×ananassa (2e-127) 1 ‘Elsanta’ FV03409 2 FaAP (contig 25571)* R-11 GGCCCAACGACAATTATTACTGA F. vesca cDNA AACCACAGTCTTGCATTCTTGACT F. chiloensis FV19005 2 FaCHP(contig 19988)* R-12 TGTGGTCCAATGCCCATACTATT F.vesca cDNA AACGGCTCCTCAGGAAGAGAA F. vesca FV28639 2 FaEF1-1α * R-13 GCCCATGGTTGTTGAAACTTT F.vesca cDNA GGCGCATGTCCCTCACA F.×ananassa ‘Figaro’ FV19237 2 FaCHP(contig 21335)* R-14 TGCATATATCAAGCAACTTTACACTGA F.vesca cDNA ATAGCTGAGATGGATCTTCCTGTGA F. chiloensis FV07104 2 FaGAPDH (contig 16067)* R-15 CATTGAGAGCAGGCAGAACCT F.vesca cDNA CCTCATTCAACATCATTCCTAGCA F.×ananassa ‘Figaro’ FV08540 2 FaENP(contig 22543)* R-16 GCCACGTCTCTTTGACATTGACT F.vesca cDNA TTCCGAATGGGCTTTCCA F. vesca FV07494 2 FaCHP (contig 18191)* R-17 TCAAGAAAGGGAAGAGCAGTTTG F.vesca cDNA ACCCTCTTCTTCCTCTGCAATTC F. vesca FV06024 2 FaAATP(contig 18391) * R- 18 TGGTACAATCTTCTTGAGGGTTGA F. vesca cDNA GCTGATGGTTTGGTGAAGTTGA F.× ananassa ‘Figaro’

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GO578562 ACT R-19 GCTTCGTATTGCTCCTGAAGA F. × ananassa A.thaliana DNA TGTTGGCCTTGGGCTTAAGA (1e-145) 1 F.×ananassa ‘Elsanta’ GW402872 EF-1α R-20 ACTGGAGGTTTTGAGGCTGGTAT F. × ananassa A.thaliana DNA TCTGCCTGACACCAAGAGTGA (1e-149) 1 F.×ananassa ‘Elsanta’ CO817539 TUA R-21 AAGACCAAGAGGACCATTCAGTTT F.×ananassa A.thaliana DNA AGTAGGAGGCTGGTAGTTGATACCA (5e-140) 1 F.×ananassa ‘Elsanta’ CO817396 UBQ10 R-22 AATTCTCCTTCAATCTCTCTCAA F. × ananassa A.thaliana cDNA TCAGAAGACTCCACCTCAAGGGTGAT (8e-144) 1 F. vesca 1 E-value of homologues assigned with TBLASTX, 2 All numbers are as assigned by GeneMark Plus Hybrid Gene Model Predictions against Strawberry Genome v8 (these reference genes were selected based on personal communication with Prof. Kevin Folta, UF), R: candidate reference genes in RT-qPCR, GOI: candidate genes of interest in RT-qPCR

Isolation of candidate genes

4.4 Conclusion

Initially, 24 candidate drought-inducible genes as well as 22 candidate reference genes known as stably expressed in different RT-qPCR studies were selected for isolation from Fragaria. Some of these selected candidate genes were already functionally characterized in Fragaria and for the others, the Fragaria putative ESTs corresponding to the selected candidate genes were identified by in silico searching in Fragaria EST database. Specific primers were designed on identified ESTs for sequence amplification. Finally, 24 Fragaria EST sequences corresponding to selected candidate drought-inducible genes were successfully isolated. The primers developed for amplification will be used for the analysis of EST derived markers. In Chapter 5 these EST markers are tested for association to drought tolerance in Fragaria. Also another set of 16 Fragaria sequences corresponding to the selected candidate genes of interest as well as 22 Fragaria sequences corresponding to the selected reference genes were cloned from Fragaria genomic DNA/cDNA, sequenced and the specificity of the cloned fragment was confirmed for later RT-qPCR analysis.

Chapter 5 Genotype variation in drought tolerance in Fragaria & associated AFLP and EST candidate gene markers

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This chapter is adapted from: Razavi F., De Keyser E., De Riek J., Van Labeke M.C. (2011). A method for testing drought tolerance in Fragaria based on fast screening for water deficit response and use of associated AFLP and EST candidate gene markers. Euphytica, 180: 385-409

Fragaria & associated AFLP and EST candidate gene markers

5.1 Introduction

Responses to drought in plants are extremely different according to their genetic background; inter- and intra-species variation in drought tolerance is described (Rampino et al. 2006). Adaptive mechanisms of drought-tolerant plants are mainly determined by genetic and metabolism characteristics (Krasensky and Jonak 2012). Wild germplasm and landraces that are better adapted to local ecological conditions can be valuable genetic resources for breeding towards drought tolerance. The potential of such material and the available variability in the gene pools for tolerance to drought must be properly characterized at the physiological, morphological and genetic level as was already reported for cowpea and wheat (Hegde and Mishra 2009; Rampino et al. 2006; Peleg et al. 2005). Drought stress responses in strawberry include a decrease in net photosynthesis and in the leaf water potential at cellular level and a reduction of leaf area and yield at crop level (Klamkowski and Treder 2008; Razavi et al. 2008). In addition, osmotic adaptation resulted in higher sucrose levels under drought stress in strawberry ‘Elsanta’ (Razavi et al. 2008). Variation in drought tolerance was already assessed within the genus Fragaria, indicating Fragaria chiloensis as a more drought tolerant species compared to F. virginiana (Zhang and Archbold 1993). In this study interspecific variation was detected: higher solute accumulation and osmotic adjustment was observed under water deficit in F. chiloensis compared to F. virginiana. Also, plant water relation parameters like leaf water potential and relative water content (RWC) were variable between Fragaria species under water deficit (Archbold and Zhang 1993). Other reports confirm that response to water deficit is species specific; each particular Fragaria species shows a unique adaptive response to drought condition (VanDerZanden and Cameron 1996). Intraspecific variation in drought tolerance within Fragaria chiloensis clones was also described in this study. Up to now, only a small set of genes known to be of interest for abiotic stress tolerance have been characterized in Fragaria (Schwab et al. 2009), and specially the information about the genetic control of drought tolerance in Fragaria is missing. An improved understanding of the genome structure in Fragaria enables to dissect the structural and functional basis of adaptive traits like drought tolerance (Folta and Davis 2006). In this view, a study of the genetic background of different Fragaria genotypes can be informative and this information ultimately might result in the detection of some DNA markers correlated to drought tolerance in Fragaria. In tea plants (Camellia sinensis), a DNA marker association study for drought stress could provide a useful alternative for QTL mapping in a limited set of genotypes (Mishra and Sen-Mandi 2004). To date, DNA marker techniques such as AFLPs, RAPDs, SSRs and iSSRs have been employed to fingerprint Fragaria genotypes (Govan et al. 2008; Bassil et al. 2006; Milella et al. 2006; Kashyap et al. 2005; Kraus et al. 2004; Ashley et al. 2003; Sargent et al. 2003; Arnau et al. 2003; Tyrka et al. 2002; Korbin et al. 2002; Degani et al. 2001; Degani et al. 1998; Graham et al. 1996). Also gene specific markers like STS (gene-specific sequence-tagged site), EST (expressed sequence

91 Chapter 5 tag) (Sargent et al. 2007; Davis and Yu 1997), gene-specific intron- length polymorphisms and cleaved–amplified polymorphic sequence (CAPS) derived from functional genes coding sequences (Davis et al. 2007a, 2008) were already developed and applied for analysis different traits of interest in Fragaria. Moreover, a few studies in Fragaria used EST-derived SSRs to determine linkage with candidate genes (Bassil et al. 2006; Gil-Ariza et al. 2006; Sargent et al. 2004). EST markers developed in the coding region of functional genes have already proven to be transferable through genotypes and are powerful markers for fingerprinting in some crops (Scariot et al. 2007). However, these type of markers linked with drought tolerance are lacking in Fragaria. Therefore, the main objectives of the experiments described in this chapter were to come to a fast screening method for drought tolerance of Fragaria genotypes and to correlate the genetic structure of different Fragaria genotypes, assessing by Expressed Sequence Tag (EST) and Amplified Fragment Length Polymorphism (AFLP) markers, and plant responses to drought stress. A standard method for measurement of dehydration tolerance is not yet available; several parameters are commonly used to define the dehydration tolerance of a plant (Verslues et al. 2006). To evaluate the degree of drought tolerance in Fragaria genotypes, we performed a short-term water deficit experiment already tested for other species (Rampino et al. 2006; Suprunova et al. 2004; Liu and Baird 2003; Teulat et al. 2003; Malatrasi et al. 2002; Ristic and Jenks 2002; Clarke and McCaig 1982) and determined leaf relative water content (RWC) and leaf water losing rate (WLR). RWC can be considered as an integrated measure of the plant water status and is an indicator for the metabolic activity in leaf tissues (Flower and Ludlow 1986) and WLR estimates the rate of leaf water loss during the exposure to drought. Both parameters are used here as a quick screening method for monitoring of genotypes in response to dehydration (Suprunova et al. 2004). The main experimental approach was:  To establish DNA fingerprints for selected Fragaria genotypes using AFLP and EST markers  To characterize Fragaria genotypes in their response to drought stress by measurement of two eco-physiological parameters associated to the leaf water status (leaf WLR and RWC)  To investigate the correlation between specific DNA markers and leaf WLR and RWC  Finally, to test the possibility of using association mapping in a small set of Fagaria accessions to create a set of correlated markers to the physiological traits which can be involved in drought tolerance in Fragaria.

5.2 Materials and Methods

5.2.1 Plant material

In total, 20 strawberry cultivars (Fragaria × ananassa Duch.) from different breeding programmes were used in this study: 6 European genotypes, 13 genotypes from USA and one genotype obtained

Fragaria & associated AFLP and EST candidate gene markers from Turkey; two ecotypes of the European diploid species F. vesca and one American octaploid species F. chiloensis were also included (Table 5.1). F. chiloensis is drought tolerant (Zhang and Archbold 1993) while F. vesca is probably less tolerant to drought as can be assumed from its natural habitat in marshlands in Europe. Cultivars were selected not only from different breeding programmes but also from different production regions, with an annual rainfall ranging from 497 mm (Iran, semi-arid) till 1500 mm (Western Europe, temperate). Several abiotic stresses cause similar responses at cellular level, therefore relevant information with respect to abiotic stress tolerance of the studied genotypes is given (Table 5.1). The parentage of the genotypes is also given as extra information. All plants were grown for 3 months in a greenhouse of Ghent University (51.3 N, 3.4 E) according to good horticultural practices for runner production. Daughter plants were cut from the stolons, and after a production cycle of 18 months plants were transferred to a growth chamber two weeks prior to the start of the experiment for preconditioning. They were well-watered and grown under constant temperature (22°C) and relative humidity (60%). An 16 hour photoperiod (from 6h00 till 22h00) was given and 100 µmol m-2 s-1 PPFD was supplied during the light period (Philips, MASTER HPI-T Plus, 400 Watt).

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Table 5.1 Overview of the studied Fragaria genotypes, grouped according to their parentage. Data on abiotic stress tolerance are given if available

C Genotype Abiotic stress tolerance Abbreviation Parentage Place cultivar developed DNA code

Darselect b Tolerant to winter or frost, tolerant to Dar. Elsanta x Parker France, EU 7 water deficit (Qian et al. 2005) No. Plant 10, 402 Elsantab Sensitive to salinity, tolerant to cold Els. Gorella x Holiday 1 The Netherlands, EU 11 (Hietaranta and Linna 1997) Figaro b Tolerant to high temperature (heat Fig. Elsanta x Pajaro The Netherlands, EU 9 tolerant), low chilling requirement No. pp18, 079P2 Honeoye b Winter hardy, tolerant to cold Hon. Vibrant x Holiday 1 New York, US 5 Lambada b Lam. (Sivetta x Holiday) x (Karina x Primella) 2 The Netherlands, EU 3 Selva b Fairly cold hardiness, tolerant to cold Sel. CA 70.3-177 (Sib of Brighton) x CA 71.98-605 [Tufts x California, US 1 63.7-10 (parent of Pajaro)] or Brighton x (Turfts x Pajaro) 1 Sonata b Tolerant to high temperature (heat Son. Elsanta x Polka The Netherlands, EU 6 tolerant) No. US PP18, 000 P2

Aliso a Ali. CA 52.16-15x self ((CA 39.177-4 x 39.96-18) x self) 1 California, US 19 Sequoia a Low chilling requirement, average Seq. CAL 52.16-15 (a sib of Wiltguard and parent of Aliso) x California, US 21 water need CAL 51s1-1 (selected from a first generation selfed population of Lassen) 1 Yalouva a Tolerant to salinity Yal. Arnavutkoy x Aliso 3 Turkey, Middle East 17

Frezno a Fairly tolerant to salinity, low chilling Frez. Lassen x CAL 42.8-16 (Sib of Tioga) 1 California, US 14 requirement (Turhan et al. 2008) Tioga a Fairly tolerant to salinity (Turhan et Tio. Lassen x CAL 42.8-16 (Sib of Frezno) 1 California, US 13 al. 2008), tolerant to high temperature

Missionary a Mis. Hoffman x Lady Thompson or Michel or by a native Virginia, US 20 wild F. Virginiana1 Tennessee Susceptible to drought Ten. Missionary x Premier 1 Tennessee, US 18 Beauty a

Fragaria & associated AFLP and EST candidate gene markers

Camarosa b Tolerant to salinity (Turhan et al. Cam. Douglas x CAL 85.218-605 California, US 12 2008) No. Plant 8, 708 Catskill a Average water need Kat. MarshallxHoward 17 1 New York, US 15 Diamante b Dia. CAL 87.112-6xCAL 87.270-1 California US 8 No. Plant 10, 435 Korona b Sensitive to salinity less than Elsanta Kor. Tamella x Induka 4 EU 2 (Keutgen and Pawelzik 2007), tolerant to cold (Hietaranta and Linna 1997) Kurdistan a Kur. Unknown (dominant cultivar in Iran) France, EU 16 Ventana b Ven. CAL 93.170-606 x CAL 92.35-601 California US 10 No. US pp13, 469 P3 Fragaria vesca Ves.1 Fragaria sp. 22 L. (Ecotype 1) Fragaria vesca Ves.2 4 L. (Ecotype 2) Fragaria Drought tolerant (Zhang and Chl. Fragaria sp. 23 chiloensis Archbold, 1993) No.: US plant patent number a Cultivars were obtained from the Agricultural Research Center, Kurdistan, Iran, b Cultivars were obtained from Fragaria Holland, The Netherlands F. vesca ecotype 1 = accession nr 19911660, Botanical Garden of Ghent University, source: Ravels, Belgium F. vesca ecotype 2 = accession nr NPB 19790383, National Botanical Garden, Meise, Belgium source: Meise, Belgium F. chiloensis = accession nr NPB 20040573-82, National Botanical Garden, Meise, Belgium population original from the Channel Islands and was obtained through Chelsea Physic Garden,UK C DNA sample number in all expermental and data analyses 1 Parentage based on NCGR-Corvallis - Fragaria Germplasm, US 2 Parentage based on Plant Research International, Wageningen, The Netherlands 3 Parentage based on Kepenek and Koyuncu (2002) 4 Parentage based on Davik and Honne (2005)

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5.2.2 RWC and WLR measurements and dehydration condition

As a fast screening of the Fragaria genotypes, the leaf water losing rate (WLR) and relative water content (RWC) of detached leaves was assessed as described by Suprunova et al. (2004) and Verslues et al. (2006). Leaves were harvested between 8 and 9 a.m. RWC was measured at two time intervals: immediately after detachment of the leaves (RWC at harvest) and after a dehydration time of 4 hours (RWC after 4h). For RWC at harvest young fully expanded leaves were excised. Fresh weight (FW) was immediately recorded; then leaves were soaked for 4h in distilled water at room temperature under a constant light (40 µmol m-2 s-1) to determine the turgid weight (TW). Total dry weight (DW) was recorded after drying for

24 h at 80°C. Leaf RWC was measured also after leaf dehydration for 4h (RWC after 4h) on a second set of leaves by applying the same protocol but allowing the leaves to dry for 4 h on filter paper (23°C, 40 µmol m-2s-1 and 30% relative humidity). Leaf RWC at both time points was calculated according to Barr and

Weatherley (1962): RWC (%) = [(FW-DW)/(TW-DW)] ×100. The difference between leaf RWC after 4h and leaf RWC at harvest (ΔRWC) was calculated as: Leaf RWC at harvest - Leaf RWC after 4h. Leaf water losing rate (WLR) was determined on a third set of young fully expanded leaves. Fresh weight was noted after 0 -2 -1 (FW) and 4h drying on filter paper (W4) (23°C, 40 µmol m s and 30% relative humidity). Total dry weight (DW) was recorded after drying for 24 h at 80 °C. Leaf WLR was calculated according to -1 -1 Suprunova et al. (2004): WLR (gh g DW) = [(FW-W4)] / [DW× 4]. Each measurement was repeated three times and the experiment was repeated twice.

5.2.3 DNA isolation and AFLP amplification

For DNA extraction of the leaf material, the Qiagen DNeasy Plant Mini Kit was used. A modified AFLP protocol (Vos et al. 1995) was followed according to De Riek et al. (1999). Selective amplification was carried out using four fluorescent 6-FAM or HEX labeled EcoRI/MseI primers combinations with six selective bases: EcoRI-ACT/MseI-CGA (PC1), EcoRI-AGG/MseI-CAA (PC2), EcoRI-ACC/MseI-CAT TM (PC3), EcoRI-ACC/MseI-CTA (PC4). Of the final PCR product, 1 µl was mixed with 13.5 µl Hi-Di Formamide (Applied Biosystems) and 0.5 µl of the GeneScanTM-500 Rox® Size Standard (Applied Biosystems). Products were denatured by heating for 3 minutes at 90°C. Capillary electrophoresis and fragment detection were performed on an ABI Prism 3130xl Genetic Analyzer (Applied Biosystems). Polymorphic bands were scored as present or absent (1/0) using the GeneMapper® 4.0 software (Applied Biosystems). Thresholds for marker frequency (0.15<ƒ<0.85) and average marker peak height (h>100) were set according to De Riek et al. (1999).

Fragaria & associated AFLP and EST candidate gene markers

5.2.4 EST marker development and amplification

Candidate genes for the development of EST markers were initially selected based on their putative function in plant drought tolerance as described in chapter 4 (Table 4.1). The EST marker development was described in chapter 4. EST marker amplification and screening for polymorphic bands was conducted on the full set of selected Fragaria accessions (Table 5.1). EST marker analysis was performed as described in De Keyser et al. (2006). Polymorphic alleles of EST markers were scored as present or absent (1/0). Quantity One software (Version 4.5.1) from BioRad (Hercules, CA, USA) was used for scoring and sizing of all bands.

5.2.5 Statistical analysis

In the case of ESTs, we calculated the number of polymorphic alleles per locus (np) and also the average number of alleles per locus nav= number of polymorphic alleles (np)/ number of loci (L) (Scariot et al. 2007; Belaj et al. 2003). RWC and WLR were analyzed with one way ANOVA and cultivars were grouped using Tukey’s test (P=0.01). Calculation of genetic similarities (Jaccard similarity coefficient), canonical discriminant analysis, principle coordinate analysis (PCO), Kruskal-Wallis analysis (P≤0.05), correlation analysis (Pearson coefficient), hierarchical cluster analysis (UPGMA), chi-square (X2) test (P≤0.05) and biplot (scatter dot graph) were performed with SPSS 11.01 for Windows (SPSS Inc., Chicago IL). To evaluate the reproducibility, bootstrapping (1000 permutations) was done using the Tree Con package for Windows (version 1.3b). Mantel analysis (Mantel 1967; Mantel Nonparametric Test Calculator for Windows, Version 2.00, 1999, by Adam Liedloff) was employed for testing of correspondence between matrices. The significance of the statistics was evaluated by permutations (1000×) and expressed as a probability (Smouse et al. 1986).

5.3 Results

5.3.1 RWC and WLR measurements

Genotype variations were observed for both the RWC at harvest and the RWC after 4h. RWC at harvest ranged from 90.2 % in ‘Sequoia’ to 96.3 % in ‘Darselect’ (data not shown); RWC after 4h ranged from 44.2 % in F. vesca to 67.1 % in ‘Figaro’ (Fig. 5.1). The rate of decline of RWC (ΔRWC) showed also significant differences between the genotypes and ranged from 26.3 % in ‘Figaro’ to 47.7 % in F. vesca (P=0.01) (data not shown). The rate of water loss as assessed by WLR varied significantly between the genotypes (P=0.01); WLR ranged from 0.15 gh-1g-1DW for ‘Figaro’ to 0.44 gh-1g-1DW for F. vesca (Fig. 5.2). The

Pearson correlation study showed strong correlations between RWC after 4h, WLR and ΔRWC, significant

97 Chapter 5

although rather low correlations between RWC at harvest and RWC after 4h as well as WLR and no correlation between RWC at harvest and ΔRWC (Table 5.2). A canonical discriminant analysis was employed to maximize separation of the genotypes. Three canonical discriminant functions were generated: canonical function 1 (Can1) was responsible for 63% of the separation, canonical function 2 (Can2) accounted for 24% of the separation while the third canonical function (Can3) explained a significant although rather low amount of the variation (only 13%). Can1 was tightly correlated with RWC at harvest (r= 0.69) and RWC after 4h (r= 0.67), Can2 was associated with ΔRWC (r= -0.70) and Can3 was dominated by WLR (r= 0.82). Sorting along Can 1 suggested two classes: class a was defined as all genotypes which were placed below -1 -1 0.00 (<0.00, group mean= - 0.8) with lower RWC after 4h (<57.20 %) and higher WLR (≥ 0.32 gh g DW) and class b defined all genotypes which placed above 0.7 (>0.7, group mean=1.85) with higher RWC after 4h (>57.20 %) and lower WLR (<0.32 gh-1g-1DW) (Fig. 5.3). Class a was defined as drought sensitive; and class b defined as the drought tolerant (Fig. 5.3). Hierarchical clustering (Euclidean distance coefficient) of studied genotypes based on the canonical discriminant functions also suggested these two phenotypic classes (data not shown).

Fig 5.1 Relative Water Content after a dehydration time of 4h (RWC after 4h) of the strawberry genotypes (Mean ± STD). (a, b significant different according to Tukey (P = 0.01)). Class a: Genotypes defined as drought sensitive showing a high WLR and low RWC after 4h . Class b: Genotypes defined as drought tolerant showing a low WLR and high RWC after 4h

Fragaria & associated AFLP and EST candidate gene markers

Fig 5. 2 Water losing rate (WLR) after a dehydration time of 4h of the strawberry genotypes (Mean ± STD). (a, b significant different according to Tukey (P = 0.01)). Class a: Genotypes defined as drought sensitive showing a high WLR and low RWC after 4hClass b: Genotypes defined as drought tolerant showing a low WLR and high RWC after 4h

Fig 5.3 Grouping of Fragaria genotypes based on discriminant factors of measured ecophysiological traits (Class a <0.00 and class b >0.7 on the reference line of canonical axis 1) (arrow A shows RWC at harvest, arrow B shows RWC after 4h and arrow C shows WLR, based on standardized canonical discriminiant function coefficients), Class a: Genotypes defined as drought sensitive showing a high WLR and low RWC after 4h, Class b: Genotypes defined as drought tolerant showing a low WLR and high RWC after 4h, (two exceptions in class b:‘Kurdistan’ with low RWC after 4h and high WLR; ‘Sonata’ with high WLR)

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Table 5.2 Correlation coefficient between eco-physiological traits in Fragaria genotypes (Pearson coefficient)

WLR RWC after 4h Δ RWC ** ** RWC at harvest -0.279 0.262 -0.014 WLR - -0.846 ** 0.797** ** RWC after 4h - - -0.969 **Correlation is significant at the 0.01 level (2-tailed)

5.3.2 Molecular marker characteristics

Using the selection settings for frequency and average marker peak height, total number of polymorphic AFLP markers included in the analysis were 369. The polymorphism rate was similar between primer combinations (PC1: 98; PC3: 103; PC4: 104), except for PC2 with only 64 polymorphic markers. Twenty candidate genes were selected for EST marker development (Table 4.2). Five of these genes were already discovered in strawberry (Table 4.2). Nine functional polymorphic EST markers were developed on characterized genes in Fragaria and seven polymorphic EST markers could successfully be developed in seven Fragaria sequence corresponding to the selected candidate genes. In total we developed 16 EST markers in candidate genes (Table 4.2). We also picked a set of random EST sequences from libraries of stress-induced Fragaria (Table 4.2) for marker development; this resulted in another eight markers. In the end, 24 EST-based markers were available for genetic characterization (Table 5.3). In 23 Fragaria genotypes, 121 alleles could be disclosed, 99 of them were polymorphic (Table 5.3). The total number of alleles per locus ranged from 2 (EST-12 and EST-20) to 12 (EST-8); on average 78% of these markers were polymorphic in the dataset (Table 5.3). The number of polymorphic bands per genotype was variable between different ESTs and genotypes, for instance in EST-8 minimum one and maximum nine polymorphic bands per genotype were scored in different accessions while for EST-20 or EST-12 one polymorphic band was only found in a limited number of genotypes. The average number of alleles per locus (nav) for an EST was four.

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Table 5.3 The polymorphism rate of all markers in the studied Fragaria genotypes is calculated

Clone name Acc No Putative function Polymorphism rate EST-1 AF159627 APX18 * 2/3 EST-2 AF159629 APX27 * 2/3 EST-3 AF159630 APX42 * 4/4 EST-4 DY674761 CAT 2/3 EST-5 CO816707 Unknown 7/8 EST-6 CO381280 MnSOD 7/7 EST-7 CO817459 Unknown 6/7 EST-8 CX661438 RAB18 10/12 EST-9 CO817183 DREB2a 3/4 EST-10 DY674391 Unknown 3/4 EST-11 DV438427 DREB3 4/5 EST-12 DY670596 Unknown 1/2 EST-13 CO817580 RD22 5/6 EST-14 CO817057 Unknown 3/4 EST-15 CO816798 Unknown 6/7 EST-16 AY663110 AKR * 4/4 EST-17 CO816877 Unknown 5/6 EST-18 AF199508 FaOLP1 * 4/5 EST-19 DQ325524 FaOLP2 * 3/4 EST-20 DY668033 P5CS 1/2 EST-21 DY670745 Unknown 9/10 EST-22 AB275667 FaSAI * 3/4 EST-23 AB267868 FaSPS1 * 3/4 EST-24 AB267869 FaSPS2 * 2/3

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5.3.3 Genetic relationship as revealed by AFLP and EST markers

For both marker types, the pairwise genetic similarities between individual plants and hierarchical cluster analysis revealed the general relationship of the genotypes. The diploid species Fragaria vesca was clearly separated from the other genotypes both with AFLP (Fig 5.4a) and EST (Fig 5.4b). The octaploid species Fragaria chiloensis clustered with the other octaploids but was clearly distant from the strawberry cultivars. AFLP confirmed the genetic similarity between some related genotypes like ‘Tennessee Beauty’ and its parent ‘Missionary’, the full-sibs ‘Tioga’, ‘Frezno’ and ‘Elsanta’ and its descendants ‘Figaro’ and ‘Darselect’. However, the other descendant of ‘Elsanta’, ‘Sonata’ appeared to be very distant from this cluster (Fig 5.4a). By hierarchical cluster analysis, EST markers confirmed the genetic similarity within studied strawberry cultivars like ‘Tioga’ and ‘Frezno’ and ‘Tennessee Beauty’ and ‘Missionary’. ‘Darselect’, ‘Sonata’ and ‘Elsanta’ cluster together with ‘Honeoye’ this time, ‘Figaro’ however is not in this group (Fig 5.4b). ESTs appears to be better to group cultivars adapted to semi-arid conditions including ‘Sequoia’, ‘Aliso’, ‘Tennessee Beauty’, ‘Missionary’, ‘Tioga’ and ‘Frezno’. Overall, clustering as generated from the AFLP and EST data were generally in good agreement with the taxonomic classification of Fragaria genotypes. The Mantel test showed significant (P=0.001) correlation among Jaccard matrices calculated from both marker techniques (R=0.81). Genetic relationships were also revealed and quantified by using PCO. Jaccard similarity matrix of 23 genotypes was calculated based on combined AFLP and EST data and used as input for PCO analysis. Four component axes generated by this analysis indicated 76% of variance in 23 Fragaria genotypes (Eigen values ≥1), in which component axes 1 and 2 with the highest effect were responsible for 66% of this variance. Fragaria species were strongly separated based on the axes 1 and 2 (Fig 5.4c).

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Fig 5.4 a: Dendrogram of 23 Fragaria genotypes obtained using 369 AFLP markers, b: Dendrogram of 23 Fragaria genotypes obtained using 24 EST markers (121 alleles)

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Fig 5.4 c: Biplot of principal coordinate analysis (PCO) using combined AFLP and EST data (a Cultivars are adapted to Iranian climate condition; in dendrograms: Jaccard similarity coefficient, UPGMA clustering, bootstrap values from 1000 re-sampling cycles)

5.3.4 Association testing of DNA markers with physiological traits

Kruskal-Wallis analysis (P≤0.05) was applied to determine AFLP and EST markers linked to the individual measured physiological traits as well as to the canonical discriminant factors (Table 5.4). In AFLP, most of the markers correlated to the individual physiological traits are also linked to the discriminant factors of these traits, although there remain still some differences (data not shown). In ESTs, 10 markers linked to the measured physiological traits are also correlated to their derived discriminant factors, while 9 ESTs are only linked to the traits and not to their discriminant factors (Table 5.4 & 5.5).

Hierarchical clustering of Fragaria genotypes according to AFLP/EST markers linked to the WLR, RWC after 4h at one side and discriminant factors of all measured physiological traits at the other side was made; the correspondence between the obtained ordinations with two main physiological classes of genotypes (tolerant/sensitive) was studied (Fig 5.5 a- c & Fig 5.6 a- c ). The effect of the common correlated

AFLP/EST markers on RWC after 4h was the opposite on WLR; markers having a negative correlation with

WLR showed a positive correlation with RWC after 4h and vice-versa. This behavior was the same for all

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common correlated AFLP and EST alleles. Mantel testing indicated a significant correlation (P=0.001, permutation 1000×) between similarity matrices of Fragaria genotypes which made separately based on

EST’s linked markers and AFLP’s linked markers to the WLR (R= 0.78), RWC after 4h (R= 0.43) and discriminant functions (R= 0.79). Also a significant correlation was proven between the Euclidean distance matrix of Fragaria genotypes which was made based on canonical discriminant functions and the matrices which was made based on the linked AFLP/EST markers to the traits (P≤0.01) (Table 5.6). There was a low but still significant correlation (P=0.05) between the Euclidean distance matrix generated by canonical discriminant functions and the Jaccard similarity matrices generated by total AFLP/EST markers (Table 5.6). In both marker types, the reproducibility and effectiveness of markers correlated to the traits in separation of physiological groups, was investigated by comparison between the matrices generated by linked AFLP/EST markers and the ones generated by random markers (with the same allele’s number) due to their correlation with the matrices of total AFLP/EST markers. Results showed a lower correlation for linked markers to different traits than random (Table 5.6). The effect of population structure on these correlated markers was tested by nonparametric Kruskal-Wallis analysis. The individual scores of the 23 genotypes on PCO axes 1 and 2 with the highest effect on the variation of genotypes, were taken as input and the correlation between these axes with all markers was investigated (P=0.05). Results showed for both AFLP/EST that some correlated markers significantly differentiate for phylogenetic structure while some are neutral with no effect on genetic structure (Table 5.4 & 5.5). The marker distribution or allele frequency of correlated markers among 23 genotypes was determined using chi-square test (X2) test (P≤0.05). Allele frequencies indicating a lower presence or absence than 7/23 were considered as deviation from a 50/50 distribution (Table 5.4). The allele’s effect on traits is indicated compared to the average of the total data set (%) ([0] band’s absence and [1] band’s presence). For the canonical discriminant functions only the significant loci are indicated as ‘*’, N.: The neutral effect (no significant effect) on genetic structure determined by Kruskal- Wallis analysis (P≤0.05), N.R.: Neutral rare marker, N.F.: Neutral frequent marker, determined by X2 test (P≤0.05)

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Table 5.4 The number of markers correlated to the physiological traits and canonical discriminant functions determined by Kruskal-Wallis analysis (P≤0.05) [the number of frequent/rare neutral markers determined by X2 test (P≤0.05)]

RWC at RWC after 4h WLR Δ RWC Additive Common Canonical harvest correlated correlated discriminant markers 1 markers 2 functions3 AFLP 75 47 85 31 130 19 114 (Total) AFLP 24 15 18 11 37 6 41 (Neutral in [13/11] [2/13] [4/14] [2/9] [14/23] [2/4] [16/25] G.S.) EST 9 5 13 6 19 1 10 (Total) (60 (21 alleles) (58 alleles) (26 alleles) (99 alleles) (4 alleles) (60 alleles) alleles) EST 6 1 2 2 8 0 7 (Neutral in [5/1] [0/1] [1/1] [0/2] [5/3] [4/3] G.S.) 1All correlated markers which are at least linked to one of the measured traits 2Common correlated markers which are linked to all measured traits 3Correlated markers to the canonical discriminant functions Neutral in G.S.: No effect on genetic structure, determined by Kruskal-Wallis analysis (P≤0.05)

Table 5.5 EST markers correlated to eco-physiological traits determined by Kruskal-Wallis analysis (P≤0.05), the putative function is between the brackets

EST marker RWCat harvest WLR RWCafter 4h Δ RWC Canonical discriminant functions EST-1 (APX 18) [0]= 36.64 [0]= -2.95 [0]= 4.60 [1]= -3.5 [1]= 8.36 [1]= -13 EST-3 (APX 42) [0= -2.52 [0]= 3.67 * [1]= 9.07 [1]=-13.23 EST- 4(CAT) [0]= 36.64 [0]= -2.81 [0]= 3.8 *N.R. [1]= -3.5 [1]= 8 [1]= -10.8 N.R. N.R. EST- 11 (DREB3) [0]= 0.55 [0]= 36.64 [1]= -0.72 [1]= -3.5 N.F. EST- 7 [0]= -0.65 *N.F. [1]= 0.85 N.F. EST- 9 (DREB2A) [0]= 36.64 [1]= -3.5 EST- 22 (FaSAI ) [0]= -0.84 [0]= 11.37 *N.R. [1]= 0.92 [1]= -12. 4

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EST- 23 (FaSPS1) [0]= -0.25 *N.R. [1]= 2.63 N.R. EST- 16 (AKR ) [0]= 0.81 [0]= -7.45 [0]= 3.27 [0]= -3.31 * [1]= -1.25 [1]= 11.59 [1]= -5.1 [1]= 5.15 EST- 12 [0]= 36.64 [1]= -3.5 EST- 17 [0]= 28.31 [1]= -4.25 EST- 18 (FaOLP1) [0]= 17.2 [0]=11.23 *N.F. [1]= -6 [1]=-4 N.R. N.R. EST- 20 (P5CS) [0]= 36.64 [1]= -3.5 EST- 21 [0]= -1.1 [0]=6.25 *N.F. [1]= 1 [1]= -14.28 N.F. N.F. EST- 8 (Rab18) [0]= -1.1 *N.F. [1]= 0.48 N.F. EST- 13 (RD22) [0]= 36.64 [1]= -3.5 EST- 14 [0]= 36.64 [1]= -3.5 EST- 15 [0]= -0.58 [1]= 0.76 EST- 6 (MnSOD) [0]= -0.62 [0]= -1.4 [0]= 1.11 * [1]= 0.81 [1]= 14.41 [1]= -24.5 N.F. The allele’s effect on traits is indicated compared to the average of the total data set (%) ([0] band’s absence and [1] band’s presence) For the canonical discriminant functions only the significant loci are indicated as ‘*’ N.: The neutral effect (no significant effect) on genetic structure determined by Kruskal-Wallis analysis (P≤0.05), N.R.: Neutral rare marker, N.F.: Neutral frequent marker, determined by X2 test (P≤0.05)

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Fig 5.5 Dendrograms of 23 Fragaria genotypes obtained using: a: 85 AFLP markers correlated to the leaf WLR, b: 47 AFLP markers correlated to the leaf RWC after 4h, c: 114 AFLP markers correlated to the canonical discriminant functions derived of measured physiological traits by canonical discriminant analysis (Jaccard similarity coefficient, UPGMA clustering, bootstrap values from 1000 re-sampling cycles; class b is marked as underlined bold names)

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Fig 5.6 Dendrograms of 23 Fragaria genotypes obtained using: a: 13 EST markers correlated to the leaf WLR, b: 5 EST markers correlated to the leaf RWC after 4h, c: 10 EST markers correlated to the canonical discriminant functions derived of measured physiological traits by canonical discriminant analysis (Jaccard similarity coefficient, UPGMA clustering, bootstrap values from 1000 re-sampling cycles; class b is marked as underlined bold names)

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Table 5.6 The correlation (R) between similarity matrices of Fragaria genotypes based on linked EST/AFLP markers to the trait (/canonical discriminant functions) and: R1: Euclidean distance matrix of Fragaria genotypes generated based on canonical discriminant functions, R2: Jaccard similarity matrices of genotypes generated based on total EST/AFLP markers (P=0.001)

Marker type Trait of linked markers R1 R2 Specific linked Random markers (with the markers same allelle’s number)

AFLP WLR 0.32 ** 0.77 0.99 ** RWC at harvest 0.46 0.80 0.99 ** RWC after 4h 0.51 0.72 0.99 Δ RWC 0.53 ** 0.62 0.99 Additive correlated markers1 0.39 ** 0.87 0.99 Canonical discriminant functions2 0.48 ** 0.86 0.99 Total alleles / markers 0.25 * EST WLR 0.28 ** 0. 84 0.99 ** RWC at harvest 0.30 0.90 0.99 ** RWC after 4h 0.24 0.54 0.99 Δ RWC 0.24 ** 0.57 0.99 Additive correlated markers1 0.26** 0.98 0.99 Canonical discriminant functions2 0.30 ** 0.71 0.99 Total alleles / markers 0.25 * *Significant at P= 0.05, **Significant at P= 0.01 1All correlated markers which are at least linked to one of the measured traits 2Canonical discriminant functions derived of all measured physiological traits by canonical discriminant analysis

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5.4 Discussion

5.4.1 Phylogenetic relationship

AFLP was already described as an effective marker technique for genetic analyses and fingerprinting of strawberry cultivars (Degani et al. 2001). As a multilocus but dominant marker technique, it was very effective for revealing genetic relationships. Clustering of Fragaria genotypes based on AFLP or EST markers was generally in good agreement with their pedigree background (Table 5.1; Fig 5.4a, b). Both marker techniques proved to be highly effective in discriminating the 23 different genotypes. Some ESTs like EST8 and EST21 with the higher polymorphic detection capacity performed much better than others and can be considered as more appropriate EST markers in phylogenetic studies of Fragaria genotypes (Table 5.3). Nevertheless, these EST markers reveal differences in intron length and by their nature they might encompass point mutations (SNP’s) which are not differentiated. Only null alleles (no amplification because of mutations in the primer binding sites) could be revealed but it is very unlikely that they appear homozygously in a polyploid species. Overall, both ESTs and AFLPs are comparable and appropriate markers to find paternity relationships and discriminating of ancestry groups of Fragaria genotypes as already mentioned in other genera (Scariot et al. 2007, Buhariwalla et al. 2005, Gupta et al. 2003). The Mantel testing showed a high correlation between Jaccard similarity matrices of genotypes generated by all AFLP markers and the complete EST dataset. This confirms a general fit to the overall genetic relationship that is revealed by each marker technique. Some differences in details can be interpreted due to the dominant and co-dominant nature of these two markers and the different types of polymorphism detection (Belaj et al. 2003). EST markers target expressed coding regions that are more likely to be conserved across genotypes and species than non-coding regions (Scariot et al. 2007). In our study, EST markers were more capable to differentiate drought adapted genotypes compared to AFLPs (Fig. 5.4b). Combination of both marker data sets, PCO analysis clearly separated species from cultivars. The groups resulting from this analysis were in good agreement with the classifications which were already induced by each marker type (Fig. 5.4c).

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5.4.2 Plant water relations in response to drought stress at the physiological and molecular level

5.4.2.1 Plant response to water deficit: the quick screening of Fragaria genotypes by measuring leaf dehydration parameters To enable a fast screening of Fragaria genotypes for the level of drought tolerance, two parameters WLR and RWC as indicators of leaf water status were measured. Genotype differences were established for

RWC at harvest although this time point is considered to be less indicative for drought tolerance. Therefore

ΔRWC was calculated to integrate the initial differences in RWC. The high correlations between RWC after

4h, WLR and ΔRWC indicate that these three parameters refer to a similar reaction mechanism of the plant to water deficit (Table 5.2). Canonical discriminant analysis, aiming at maximizing the genotype differences for these three physiological measurements, revealed a drought tolerant and sensitive class (Fig 5.3). Drought tolerance of a plant is related to its ability to maintain under water deficit higher relative water content in the leaves and to reduce water losses (Rampino et al. 2006; Suprunova et al. 2004; Bohnert et al. 1995). Rates of leaf water loss are associated with an altered accumulation of ABA and changes in stomatal conductance (Verslues et al. 2006). Abiotic stress signal transduction by ABA results in the activation of various genes involved in cell homeostasis (Zhu 2002). WLR measurements using detached leaves were already applied to characterize drought tolerance in crops like wheat, maize, wild barley and sunflower (Rampino et al. 2006; Suprunova et al. 2004; Teulat et al. 2003; Ristic and Jenks 2002; Clarke and McCaig 1982). RWC was a relevant screening tool for drought tolerance in cereals (Muthurajan et al. 2010; Teulat et al. 2003) and castor bean (Ricinus communis L.) (Wei et al.

2010). Characterization of our genotypes by leaf WLR and RWCafter 4h confirms that F. chiloensis is drought tolerant. This is in accordance with earlier reports (Zhang and Archbold 1993). An obvious difference was observed between the two F. vesca genotypes in their RWC after 4h. One clone was classified as drought susceptible while the other showed an intermediate response. Within-species variation in response to dehydration is most likely. Indeed, VanDerZanden and Cameron (1996) already indicated a variation among eleven Fragaria chiloensis clones with respect to water deficit stress.

5.4.2.2 Testing association between random markers (AFLP) / candidate gene EST markers and physiological parameters The breeding of drought tolerant crops is important for crop production under deficit irrigation. Functional analyses, correlating genetic variation in DNA markers or candidate genes with the physiological traits related to drought tolerance is necessary. One of the main goals for present and future research is to find

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the association between variation in drought-related quantitative traits like RWC and WLR and ultimately the effects of these traits on drought tolerance (Cattivelli et al. 2008). Association mapping can be a valuable additional tool in this search; recently being documented for pine (Gonzalez-Martinez et al. 2006), barley (Comadran et al. 2008; Ivandic et al. 2003), maize (Belo et al. 2008; Yu and Buckler 2006), wheat (Rhone et al. 2007; Tommasini et al. 2007; Ravel et al. 2006), barley (Kraakman et al. 2006; Malysheva-Otto and Röder 2006; Rostoks et al. 2006; Kraakman et al. 2004), sorghum (Hamblin et al. 2004), ryegrass (Xing et al. 2007; Skot et al. 2005), soybean (Hyten et al. 2007) and rice (Garris et al. 2003) for drought tolerance or other different types of traits. This approach is particularly useful for dissection of drought tolerance when linkage mapping in a segregating population cannot be easily generated (Tuberosa and Salvi 2006). Genetic association mapping or linkage disequilibrium mapping (LD) is a method that relies on linkage disequilibrium to study the relationship between phenotypic variation and genetic polymorphisms (Breseghello and Sorrells 2006; Gupta et al. 2005). Actually, LD- based association analysis locates quantitative trait loci (QTL) by the strength of the correlation between a trait and a marker (Sorkheh et al. 2008). Instead of biparental crosses between contrasting genotypes, a collection of cultivars, lines, or landraces are genotyped with densely spaced markers (Sorkheh et al. 2008). Association mapping based on LD seeks to establish a statistical association between allelic (or haplotype) variation at a locus and the phenotypic value of a trait across a large enough sample of unrelated accessions (Thornsberry et al. 2001). Significant regression between the data of trait and the individual marker genotypes will identify the markers associated with the phenotype (Remington et al. 2001). In tea (Camellia sinensis) as an alternative to linkage mapping, fingerprinting of close clones by AFLP and RFLP techniques generated DNA markers associated to APX and SOD enzyme activities which are involved in drought tolerance (Mishra and Sen-Mandi 2004). In our case, first we needed to come to a reliable approach to analyze the association between AFLP and EST markers and measured physiological traits in a small set of Fragaria genotypes. Actually, we developed a technique based on Kruskal-Wallis analysis for the association testing between different markers and individual measured physiological traits as well as to their canonical discriminant factors. This yielded a set of correlated AFLP and functional EST markers (Table 5.4). Clusters of genotypes based on these correlated markers did correspond better to the classes of tolerant and sensitive genotypes, defined by the physiological characterization (Fig 5.5a- c, 5.6a-c; Table 5.6). For each marker technique, the validity of clusters generated by the linked markers was proven not to be an effect of reduction of the data set as it was compared to random re-sampled alleles (Table 5.6). The higher correlation between matrix based on canonical discriminant functions and similarity matrices based on the linked ESTs and on AFLPs, compared to the non-selected marker data sets, indicates that these linked markers better reveal

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the underlying functional genetic structure related to drought tolerance in the studied Fragaria genotypes

(Table 5.6). Also, the occurrence of common EST/AFLP markers linked to both WLR and RWC after 4h indicates the presence of some common genetic loci. Moreover, a low but still significant correlation (R=0.25; P=0.05) between the non-selected EST or AFLP data sets to the physiological measurements (Table 5.6) was observed. Drought tolerance, although not directly selected for, has probably always been a critical element in the selection of the most productive genotypes. Therefore, a general fit of the genetic structure as revealed by markers to the functional diversity considering drought tolerance of Fragaria genotypes can be accepted. The general role in many association genetic studies is to use unlinked and putatively neutral markers to characterize genetic variation in the accessions used in the mapping study and to account for population structure (Hall et al. 2010). Therefore, the analysis of genetic structure is a prerequisite for successful implementation of the association mapping approach in an admixed population. To control this problem in our study we used the PCO analysis to estimate population structure or genetic relationship using a mixture of AFLP and EST data, and then the resulted axes were examined for association with correlated markers using Kruskal-Wallis analysis (P≤0.05). The applied approach to check the effect of population structure is based on commonly used methods in association mapping researches (Hall et al. 2010). With the ignorance of rare alleles (X2; P≤0.05), results indicated that only some of the frequent correlated markers to physiological traits are neutral with no effect on phylogenetic structure while many correlated markers are significantly effective on genetic structure as well (Table 5.4). This clearly shows the effect of genetic structure of our population on discovered associated markers to the traits. In spite of this and due to the low number of our accessions, these introduced markers including those with significant effects on genetic structure or the neutral ones with low frequency still might be considered as valuable markers that have to be further evaluated with a larger number of accessions. Therefore, the DNA fragments associated with physiological parameters in this study appear to be good candidates for further differentiating of Fragaria genotypes as tolerant or sensitive to drought especially in the case of EST markers. Overall, the approach applied in our study resulted in a set of valuable functional and non-functional markers that need to be further evaluated in the next study for association mapping analyses. These markers eventually can be analyzed in a larger number of accessions for marker trait association analysis and MAS for drought tolerance. Hence, our research was an optimization study to examine a marker-trait association method for preliminary dissection of QTLs involved in drought tolerance in Fragaria collection with no crossing and segregation history.

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5.4.3 Candidate genes from known metabolic pathways behind drought tolerance, EST markers and association with the eco-physiological parameters

Although drought tolerance is a quantitative trait, single genes such as those controlling physiological traits related to drought tolerance like osmotic adjustment (OA) may have important roles in adaptation to drought-prone environments (Tuberosa and Salvi 2006; Forster et al. 2004). The functional markers which are developed based on these genes are perfect sources for marker-trait association analysis and investigation of QTLs involved in drought tolerance (Gupta et al. 2005; Gupta and Rustgi 2004; Simko et al. 2004; Andersen and Lubberstedt 2003). In this experiment, 16 out of the 24 developed EST markers are candidate genes for drought stress response (Table 4.1, 2). The important role of antioxidant defense systems, sucrose metabolism and osmotic adjustment in the regulation of plant responses to drought stress is well-known (Table 4.1). Also our own results elucidated the role of sucrose as the major factor of osmotic adjustment under water deficit conditions in strawberry (Razavi et al. 2008). Therefore, a direct functional linkage for these ESTs to genes involved in the regulation of Fragaria response to drought stress from sucrose metabolism, osmotic adjustment and antioxidative mechanisms can be accepted. For the EST markers that were developed from non-characterized homologues in Fragaria to functional genes in other plants we can infer a supposed similar function. To this group belong homologue sequences to CAT and MnSOD1 as antioxidant and detoxification enzymes, DREB2A and DREB3 as the transcriptional regulatory factors in drought condition and RD22 as the responsive gene to the dehydration, P5CS as the key enzyme in proline biosynthesis and Rab18 as the dehydrin protective protein (Table 4.1, 2 & 5.5). The last group of ESTs were unknown EST sequences of Fragaria which were generated by cold, salicylic acid or heat stress. Obviously, these Fragaria sequences need to be better characterized for their function in plant responses to the drought stress. Nevertheless, the fact that they were generated from other types of abiotic stress in Fragaria and appear to be correlated to drought stress in this experiment suggests their general role in stress response. The generation of molecular–linkage maps based on linkage disequilibrium and candidate genes (molecular–function maps), is one strong way to identify the genetic determinants of QTLs in plants without a mapping population in spite of the time-consuming and fine mapping that represent an interesting shortcut to QTL cloning (Causse et al. 2004; Chen et al. 2001). For specific traits of interest like drought tolerance, candidate-gene association mapping is one of the most important categories for association mapping studies (Zhu et al. 2008). Many association studies which were based on candidate genes have been successfully completed using ten to hundreds of markers in mapping populations consisting of a few hundred individuals (Hall et al. 2010). Due to the small size of the population in this

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study, the correlated ESTs generated based on Fragaria homologues of involved candidate genes in drought tolerance can be considered as the valuable source for the next mapping analysis in a larger population (with higher number of individuals) or even in other genera of Rosaceae for drought tolerance (Table 5.5). In this view the ESTs with neutral effects on the genetic structure of Fragaria genotypes but frequently correlated with the phenotype, like EST6, EST7, EST8, EST21 and EST18, can be more valuable as functional markers, although they still need to be further investigated (Table 5.5). Therefore, the EST markers which were developed based on the functional genes in our study are useful for next marker-trait association studies to identify QTLs involved in drought tolerance in Fragaria.

5.5 Conclusion

In a limited set of Fragaria genotypes, it was possible to come to an integrated method, combining fast screening tools for plant leaf dehydration and associated markers from random AFLP or candidate gene ESTs. Phenotypic classes of plants grouped to their drought response better corresponded to groupings made on correlated markers. Through this study, also some potential candidate genes involved in Fragaria drought tolerance were identified but need further characterization. As plant responses to drought stress are a complex quantitative phenomenon, higher drought tolerance could be attributed to the combination of different factors that cannot be genetically analyzed as a monogenetic character. Therefore, mapping of them as QTLs is needed to better identify the regions involved in the regulation of this trait (Suprunova et al. 2004; Teulat et al. 2003). The resulted correlated markers in our study still need to be further evaluated for their association to the plant function in drought condition and LD-mapping in a larger set of unrelated genotypes. A more powerful approach would be to analyze these markers and check their trait-association and neutrality on genetic structure. This is probably not so easy for the highly bred and narrow gene pool of Fragaria × ananassa, but the linked markers identified in this study still are a good starting point for using this approach. Eventually these markers might be applied in germplasm screening for drought tolerance in Fragaria sp. Here, the use of wild F. chiloensis and F. vesca ecotypes could be a good alternative. F. vesca is being characterized intensively as one of the model plant species for Rosaceae (Shulaev et al. 2008) and is also to be considered as an important genetic resource for introduction of many important traits in cultivated strawberries. Further investigation is necessary to characterize the sequences which were used for development of correlated EST markers to the plant responses to drought stress in this work. The expression analysis of the genes that were evaluated in the present study will give more information about the genetic control of drought response in Fragaria sp.

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Chapter 6 Expression profiling and characterization of candidate genes under water deficit by RT-qPCR

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6.1 Introduction

The characterization of genes functionally involved in plants response to water deficit is an important step to reveal the adaptive pathways in Fragaria sp. Up to now, only a small set of genes involved in abiotic stress tolerance has been isolated and characterized in this species (Schwab et al. 2009). Metabolic and transcriptional profiling in cultivated strawberry (F. ×ananassa) and F. vesca showed the up-regulation of dehydrins as well as some carbohydrates, amino acids and secondary metabolites like ascorbic acid in response to cold stress (Rohloff et al. 2009). Some cold-induced genes were isolated and characterized in Fragaria. CBF1 as an ortholog of a cold-induced transcription factor important in the cold acclimation response in Arabidopsis thaliana was cloned from strawberry. FaCBF1 transcript levels were up-regulated in leaves following exposure to cold (4°C) (Owens et al. 2002). Isolated Fragaria cold-regulated gene ‘Fcor’ showed differential regulation under low temperature; Fcor1 was identified as a useful molecular marker for freezing/cold tolerance in Fragaria (NDong et al. 1997). An isolated full-length cDNA from a strawberry fruit library encoding a calcium-dependent protein kinase (FaCDPK1) was significantly up- regulated in fruit after 10 h of cold treatment (4°C), suggesting its involvement in fruit response to low temperature (Llop-Tous et al. 2002). Heat-shock proteins (HSPs) are synthesized in response to high temperature in different organisms. A strawberry HSP was isolated from a subtractive cDNA library with a significant sequence identity to cytoplasmic class-I low molecular weight HSPs (Medina-Escobar et al. 1998). Under gradual increasing heat stress, peroxidase activity increased in strawberry plants, suggesting that expression levels of some peroxidases might be affected by high temperature (Gulen and Eris 2004). An osmotin-like protein (FaOLP2) has been cloned from strawberry and was up-regulated under three abiotic stimuli (abscisic acid, salicylic acid and mechanical wounding) which suggest its protective role under environmental stresses like drought (osmotic) and pathogens. Many candidate genes have been reported as differentially expressed genes under water deficit in different species, but to date, almost no information about gene transcription profiling under drought stress has been reported in strawberry (Fragaria). The study of metabolite levels and gene expression in drought tolerant and sensitive genotypes under drought stress could provide important information about the mechanisms that underlie plant adaptation to water deficit. In this chapter, the response of both drought sensitive and tolerant Fragaria genotypes to water deficit at metabolite and expression level of selected candidate genes involved in plant adaptation to drought stress is described. This was achieved by: 1) measurement of different metabolites involved in drought adaptation, 2) validation of appropriate reference genes for normalization of real-time PCR data in Fragaria sp. for the experimental conditions, 3) transcription profiling of candidate genes of interest (GOI) by RT-qPCR, and 4) comparison of differences in gene expression under water deficit in

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both drought tolerant and sensitive Fragaria genotypes. For transcription analysis, first quantitative reverse transcription polymerase chain reaction (RT-qPCR) as a rapid, sensitive and reliable method (Bustin et al. 2005) was developed for expression analysis of candidate genes in Fragaria under water deficit. To identify gene expression changes associated with drought adaptation in Fragaria we performed 2 controlled soil water deficit experiments and analysed different time points of drought stress.

6.2 Materials and methods

6.2.1 Plant material

Four genotypes contrasting in their response to drought stress were used in the experiments, namely two drought sensitive (F. vesca and F. x ananassa ‘Ventana’) and two drought tolerant genotypes (F. chiloensis, F. x ananassa ‘Figaro’) (Razavi et al. 2011). Plants were preconditioned by growing them for 8 weeks under controlled conditions: well-watered, 23°C day/night temperature and relative humidity averaging 24%; a 16 h photoperiod was given supplying 115 µmol m-2 s-1 PPFD at plant level (Philips, MASTER 227 HPI-T Plus, and 400 W).

6.2.2 Drought stress treatments

Control plants (6 plants per genotype, randomly assigned) remained well-watered while plants of the drought stress treatment (6) were subjected to stress by progressively withholding water supply. The volumetric moisture content of the substrate was monitored daily with a Wet-2-sensor (Delta-T Devices, Cambridge UK) in both control and stressed plants. After 10 days of water withholding, the substrate humidity fell to 20-25 vol% which was nonlethal and above the wilting point (the start of drought stress settlement); well-watered control plants remained above 40 vol%. From that moment on, pots were weighed daily and watered to a constant pot weight (19-20 vol% in the drought treatment). Youngest fully expanded triplet leaf samples were taken at 13, 23 and 28 days after the drought stress settlement (DAS) in two replicates per treatment and genotype (exp 1, Fig 6.1). Leaves were immediately frozen in liquid nitrogen and kept at -80°C for further analysis. The experiment was repeated with a new set of plants (exp 2). After 7 days of water withholding, the soil volumetric water content fell to 19-20 % in stressed plants (start of drought stress settlement); well- watered control plants remained at ± 57 vol%. Again, substrate moisture level was maintained at 19-20 vol% by daily weight control and controlled watering. Youngest fully expanded triplet leaf samples were collected 9 days after drought stress settlement (DAS) in three replicates per treatment in each genotype for further analysis (Fig 6.1).

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Fig 6.1 Change of soil volumetric water content (%) during the drought treatments (above slow development of stress, below fast development of stress)

6.2.3 Relative water content (RWC) and leaf water potential

Fully expanded leaves were excised and fresh weight was immediately recorded; then leaves were soaked in distilled water (4h; room temperature, constant light) to determine the turgid weight (TW). After drying (24h, 80°C) total dry weight was recorded and RWC was calculated according to Suprunova et al. (2004) and Verslues et al. (2006). RWC was measured only in the first experiment. The leaf water potential (Ψw) was measured for three fully developed triplicate leaves randomly sampled from six plants using a pressure chamber (PMS Instrument Co., Corvallis, OR, USA). In the first experiment, leaf water potential was measured shortly after midday, while in the second experiment leaf water potential was measured early in the photoperiod.

6.2.4 Carbohydrates

Sugars were extracted from 300 mg fresh leaf material with 6 ml 80% EtOH at 70°C for 10 min and further at 45°C for 3 hours, followed by centrifugation at 5000g for 5 min. Glucose, fructose and sucrose were analysed in the supernatant using high pH anion-exchange chromatography with pulsed amperometric detection (Waters; CarboPac MA1 column with companion guard column, eluent: 50 mM NaOH, 22°C). The remaining ethanol insoluble pellet was washed twice with 1.5 ml 80% EtOH,

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centrifuged at 5000g for 5min and the supernatant was discarded. For starch hydrolysis, the residual pellet was treated with 5ml HCl 1M for 2 hours at 95°C followed by centrifugation at 5000g for 5min and a second extraction with 1ml HCL 1M followed by centrifugation at 5000g for 5min and the two supernatants were pooled for quantification of starch. Starch (as glucose equivalents) was determined spectrophotometrically at λ=340nm based on the measurement of enzymatic reduction of NADP+ to NADPH (UV-VIS, Biotek Uvikon XL) (Chaplin and Kennedy 1994).

6.2.5 Proline

Extraction and determination of proline was done according to Bates et al. (1973). Fresh leaf material (500 to 1500 mg) was ground and homogenized in liquid nitrogen with a mortar and pestle and extracted with 10ml of 3% sulfosalicylic acid. After filtration, 2ml ninhydrin and 2ml glacial acetic acid was added to the extracts (2ml) and this was kept at 100°C for 1 hour when the reaction was stopped in an ice-bath. The formed chromophore was extracted from the acid aqueous solution by means of cold toluene (4ml) and measured spectrophotometrically at λ=520nm (InfiniteM200 TECAN Group Ltd., Switzerland).

6.2.6 Total Antioxidant Capacity (TAC)

TAC was analysed according to Re et al. (1999) with L-ascorbic acid (L-AA) as the standard. This assay involves the oxidation of ABTS (2, 2’-azinobis[3-ethylbenzothiazoline-6-sulphonate]) to an intensely- coloured nitrogen-centred radical cation, ABTS•. The intensely-coloured ABTS• radical is obtained by adding 1ml 20mM ABTS to the working solution (7ml 75 mM KH2PO4, 1ml 1.75 % H2O2 and 1ml 0.08% horse radish peroxidase). Fresh leaf material (100 mg) was ground and homogenized in liquid nitrogen with a mortar and pestle and extracted with 200µl extraction buffer 3% metaphosphoric acid, 1mM EDTA and 2% PVPP followed by centrifugation for 15 min at 14000 rpm at 4°C, the pellet was extracted again and supernatants were pooled. 100µl of the aliquot was added to 900µl ABTS• solution and after 6 min the remaining ABTS• was quantified spectrophotometrically at λ=734 nm (InfiniteM200 TECAN Group Ltd., Switzerland).

6.2.7 Protein concentration and catalase (CAT) activity

All steps of protein extraction were performed at 0-4°C. Protein was extracted from 300mg fresh leaf material ground in liquid nitrogen with a mortar and pestle and homogenized with 1600 µl of ice-cold extraction buffer (50mM potassium phosphate buffer, 1mM Na-EDTA, 1mM L-ascorbic acid, 0.02M sodium bisulphite, 20% sorbitol and 2% PVPP, pH 7.8). The homogenate was kept for 30 min on ice and subsequently centrifuged for 17 minutes at 13000g, 4°C; the supernatant was separated in another pre-

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cooled Eppendorf tube and re-centrifuged for 10 minutes at 13000g, 4°C. This final supernatant was used for determination of protein concentration and CAT enzyme activity. Protein concentration was immediately determined according to Bradford (1976) with bovine serum albumin (BSA) as the standard. In a 96-well plate, 300µl Bradford reagent was added to a 10µl aliquot of sample protein extract or to the protein standards and the absorbance was measured at λ=595nm (InfiniteM200 TECAN Group Ltd., Switzerland). The CAT activity was determined in the protein extract according to Aebi (1974). This −1 method is based on the determination of the rate constant (s , k) of the H2O2 decomposition rate. The reaction mixture contained 30 mM H2O2 in a 50 mM phosphate buffer (pH 7.0) and 0.1 ml of enzyme in total volume of 3ml. CAT activity was estimated by the decrease in the absorbance of H2O2 at λ=240nm by spectrophotometer (UV-VIS, Biotek Uvikon XL).

6.2.8 Superoxide dismutase (SOD) activity

All procedures for SOD enzyme extraction were performed at 0-4°C. The SOD enzyme was extracted from 250mg fresh leaf material ground in liquid nitrogen with a mortar and pestle and homogenized with 1500µl ice-cold extraction buffer containing 0.05 M phosphate buffer (pH 7.8), PVPP (75mg/L), 1M TRIS base, sucrose (68g/L), Na2–EDTA (170mg/L, pH 7.8), Tween 80 (0.0031ml), and sodiumthioglycolate (800mg/L). The homogenate was kept for 30 min on ice and subsequently centrifuged for 20 min at 12000g, 4°C, supernatant was separated in pre-cooled Eppendorf tube for determination of SOD activity. The activity of SOD is based on utilizing tetrazolium salt, WST-1 [2-(4- Iodophenyl)-3-(4-nitrophenyl)-5-(2, 4-disulfophenyl)-2H-tetrazolium, monosodium salt] that produces a - water-soluble formazan dye upon reduction with a superoxide anion (O2 ). The rate of the reduction with

O2 is linearly related to the xanthine oxidase (XO) activity, and is inhibited by SOD (SOD determination kit, Sigma-Aldrich Chemie GmbH, Switzerland). The volume of the leaf extract corresponding to 50% inhibition of the reaction was assigned as one SOD enzyme unit. The absorbance was measured at λ=440 nm by spectrophotometer (InfiniteM200 TECAN Group Ltd., Switzerland).

6.2.9 Malondialdehyde (MDA) content

The extraction and determination of MDA as a final product of lipid peroxidation was performed using the method described by Hodges et al. (1999). Fresh leaf material (1g) was ground in liquid nitrogen with a mortar and pestle and homogenized in 25ml 80% EtOH, followed by centrifugation at 3000g for 10min. 1 ml of sample extract was added to 1ml of thiobarbituric acid (TBA, 0.65% w/v) as well as to 1ml of trichloroacetic acid (TCA, 20% w/v) and homogenates were incubated at 95°C for 25min, cooled and centrifuged at 3000g for 10min. The MDA content was eventually measured based on the reaction with

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thiobarbituric acid (TBA) and the absorbance was measured at λ=532nm by spectrophotometer (InfiniteM200 TECAN Group Ltd., Switzerland). Malondialdehyde (MDA) equivalents were calculated as described by Hodges et al. 1999.

6.2.10 RNA isolation and cDNA synthesis

RNA was isolated from each biological replicate as one sample pool including 4-5 youngest fully opened triplet leaves collected from different sides of a single pot plant, two (exp1)/three (exp2) biological replications per genotype per treatment were included. In each sample pool, fresh leaf material was ground and homogenized in liquid nitrogen with a mortar and pestle and approximately 80-100 mg of leaf frozen powder was used for RNA isolation. RNA was isolated according to the protocol of the RNAqueous kit (Ambion, USA) in combination with the Plant RNA Isolation Aid (Ambion). Elution was done in three steps using warm elution buffer (72°C) supplied in the kit (40, 30, 20µl) and eluents were pooled. DNA- free kit (Ambion, USA) was used for DNase treatment on 80µl of RNA. DNaseI buffer (10µl) and rDNaseI (1.5µl) were added and samples were incubated for 30 min at 37°C. Then 10µl of DNase Inactivation Reagent was added and samples were incubated for 2 min at room temperature followed by a centrifugation for 90s at 10000g. The supernatant was transferred to a new Eppendorf tube. DNase-treated samples were further purified as described by Moore and Dowhan (2002). Purification was performed using 0.3 M sodium acetate pH 5.5 (Ambion). 100% EtOH was added to the samples (2.5 volumes) and samples were incubated overnight at -20°C. After 25min centrifugation at 14000g (4°C) the supernatant was removed and 1ml 70% EtOH was added. Centrifugation was done for 20 min at the same conditions and supernatant was discarded. Using a vacuum-desiccator the RNA pellet was dried and dissolved in 25µl of RNase-free water. Total RNA concentration as well as RNA quality was measured using a NanoDrop spectrophotometer (Isogen, The Netherlands). SuperScriptTM III First-Strand Synthesis SuperMix (Invitrogen, USA) was used to create the first strand cDNA based on the protocol of Invitrogen. 100ng RNA or 6 µl of the samples with a low RNA concentration (<17ng/µl) was used for cDNA synthesis. For the priming, oligo (dT)20 supplied in the kit was used. All incubations were performed in a thermal cycler (Perkin Elmer 2720, Applied Biosystems, USA). As a control for DNA contamination, no Reverse Transcriptase samples (noRTs) were produced in the same way as cDNA samples but in the absence of the SuperScriptTMIII/RNaseOUTTM Enzyme Mix. Both cDNA and noRT samples were diluted 1/3 in nuclease free water and stored at -20°C.

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6.2.11 Construction of standard curves for the RT-qPCR assay

The isolation of reference genes (R) and genes of interest (GOI) is described in chapter 4 (Table 4.2). For construction of the standard curves, amplified fragments of both reference and target genes were cloned using the TOPO TA cloning kit (Invitrogen), as was described in chapter 4. Plasmid DNA of the cloned sequences was purified using the pure link TM HQ Mini Plasmid Purification Kit (Invitrogen). The plasmids were linearized as described by De Keyser et al. (2007). Depending on the restriction map of the cloned fragment, 10U of HindIII/ EcoRI/ ApaI (Invitrogen) or SacI (Fermentas) was used and samples were incubated for 2 h at 37°C, followed by an enzyme inactivation step for 10 min at 70°C. The stock concentration of plasmids was diluted to a working solution of about 1ng/µl in 50 ng/µl yeast tRNA (Invitrogen). Starting from this working solution, six serial dilutions (1/10) were prepared in yeast tRNA (50ng/µl) as standard curves for RT-qPCR. To determine if the Cq (quantification cycle) value of samples are within the range of the standard curve, the Cq value of the plasmid dilution were tested in the preliminary RT-qPCR assays. Depending on the Cq value of the standard curves, the stock solution was further diluted to cover the whole range of samples. To set up the range of dilution, the cDNA samples of F. chiloensis, F. vesca, F. × ananassa ‘Ventana’ and ‘Figaro’ from both control and stressed plants were used.

6.2.12 RT-qPCR assay

RT-qPCR analysis for all Fragaria cDNA samples, noRTs and NTCs (No Template Control samples) and the standard curves was carried out in a LightCycler 480® (Roche, Switzerland). Samples and noRTs were measured in one technical replication and NTCs and standards were measured in two technical replications. RT-qPCR primers are described in chapter 4 (Table 4.2); 375nM of each primer and 5 µl of lightCycler®480 SYBR Green I Master (Roche) was used with 2µl of sample in a total volume of 10 µl. Samples were loaded in a white 384-well plate (Roche). Cycling conditions were as follows: activation for one cycle 5 min at 95°C, followed by amplification and quantification for 45 cycles of 95°C 10s, 60°C 12s and 72°C 10s; data achievement was done at the end of every cycle. Melting curve analysis was performed as follows: 95°C 5s, 1min 65°C and heating to 97°C with a ramp rate of 0.06°C/s; data achievement occurred 10 times every °C. Data were analysed using the LighCycler480 software version 1.5 (Roche). Quantification cycle (Cq; Bustin et al. 2009) values were determined by means of the second derivative maximization method (Luu-The et al. 2005). For both reference genes and genes of interest, Cq-values were exported to qBasePLUS (Biogazelle, Belgium) for quantification. The GeNorm application in qBasePLUS was used for reference gene selection. Eventually, the normalized relative quantity (NRQ) of

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all samples was calculated using gene specific amplification efficiencies and a sample specific normalization factor (Vandesompele et al. 2002; Hellemans et al. 2007) based on a set of validated reference genes, as determined by GeNorm.

6.2.13 Statistical analysis

Physiological and biochemical measurements were analysed by ANOVA; T-tests were used to detect significant difference for each genotype and sampling date. In gene expression analysis, NRQ values were transformed using a Log10 function (and mean centring; Willems et al. 2008) prior to statistical analysis (Derveaux et al. 2010). Non-parametric analysis, Related-Samples Wilcoxon Signed Rank Test was performed to compare NRQ values of gene expression in Fragaria. All analyses were performed using SPSS 19.0.1 (IBM® SPSS® Statistics)

6.3 Results

First, the effect of water deficit on physiological parameters including plant water relations, metabolites and enzymes is described and compared between well-watered and drought stressed plants in four Fragaria genotypes. Then, the results of RT-qPCR assay development for gene expression analysis in Fragaria under water deficit will be detailed. Finally, the changes in gene expression level of selected target genes in four Fragaria genotypes under drought stress will be stated.

6.3.1 Plant water balance

Differences in midday leaf water potential between control and stressed plants were obvious in the first experiment (Fig 6.2). The Ψw (midday) of control plants varied from -0.1MPa to -0.3MPa for all cultivars and sampling dates whereas in stressed plants more negative leaf water potentials were found. The strongest response to drought stress was observed in the sensitive genotype, F. vesca, at 23 and 28 DAS, Ψw (midday) reaching values of -1.23 MPa and -2.37 MPa. In contrast, in F. chiloensis, a tolerant genotype, Ψw (midday) dropped to -0.18 MPa and -1.21 MPa respectively at 23 and 28 DAS (Fig 6.2). The relative water content (RWC) remained nearly constant throughout the experiment in leaves of control plants (with RWC values around 93%). At 13 DAS, RWC decreased in stressed plant of F. × ananassa ‘Ventana’ (P= 0.034) and F. chiloensis (P=0.038). At 23 DAS RWC was significantly lower in stressed plants of F. × ananassa ‘Ventana’ (85%, P= 0.006) and F. vesca (78%, P= 0.022). Drought stress induced no statistically significant differences in RWC for F. × ananassa ‘Ventana’ at the end of experiment, 28 DAS (82%), whereas for other genotypes significant lower values were noted (P≤ 0.05). For F. × ananassa ‘Figaro’, RWC in stressed plants decreased to 87% at 28 DAS (P≤ 0.05). The most important changes in RWC at

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the end of the experiment (28 DAS) were found in F. vesca and F. chiloensis for which the RWC decreased to 76% and 81%, respectively (P= 0.01).

In the second experiment, Ψw of control plants varied from -0.05 MPa to -0.1 MPa for all genotypes and sampling dates whereas more negative leaf water potentials were observed in stressed plants. At 9 DAS,

Ψw significantly decreased (P≤ 0.05) in stressed plants of all genotypes (Fig 6. 2). The strongest response to drought stress was again observed in F. vesca.

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First experiment Second experiment

Drought Tolerant Drought

Drought Senstive Drought

Fig 6.2 Leaf water potential in control and drought stressed plants. Mean ±SE, *significant different, T-test (P ≤ 0.05)

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6.3.2 Metabolites and Enzymes

6.3.2.1 Carbohydrate metabolism The effect of increasing drought stress on carbohydrate content was monitored in the first experiment. Glucose concentration was not significantly affected by the treatment at 13 DAS, and was only significantly higher in stressed plant of F. vesca (P=0.01) at 23 DAS (Fig 6.3). At 28 DAS, the glucose content in well-watered plants was lower than in stressed plants (P= 0.006). However, significant differences between well-watered and control plants were only observed for F. × ananassa ‘Figaro’ (Δ=0.67g/100g FW) and F. chiloensis (Δ=0.31g/100g FW). Glucose content tended to increase with prolonging drought stress in F. × ananassa ‘Figaro’, ranging from 0.43g/100gFW at 13 DAS to 0.92g/100gFW at 28 DAS (Fig 6.3). Fructose concentration was not significantly affected by the treatment at 13 and 23 DAS. At 28 DAS, fructose increased in stressed plants (P=0.003), though varietal differences were observed; only in stressed plants of F. × ananassa ‘Figaro’ and F. chiloensis fructose was significantly higher compared to the control plants (Δ= 0.53g/gFW and Δ= 0.16g/g FW, respectively) (Fig 6.3). Under prolonged drought stress, fructose content in stressed plants tended to increase in F. × ananassa ‘Figaro’, ranging from 0.37g/100gFW at 13 DAS to 0.78g/100gFW at 28 DAS (Fig 6.3). Sucrose content increased throughout the experiment in both control and stressed plants (Fig 6.4). These changes were small in well-watered plants and differences were in the range from 0.21g/100gFW to 0.71g/100gFW. The general increase in sucrose concentration was higher in plants subjected to water deficit (P=0.00) and highest at 28 DAS. At 28 DAS an overall significant increase of sucrose under drought stress was found (P=0.002) but pairwise comparison showed genotype effects. Significant differences between control and stressed plants were only noticed for F. vesca at 23 DAS (P=0.030), and at 28 DAS (P=0.008) (Fig 6.4). Starch content appeared to be species/cultivar dependent; F. chiloensis in general had the lowest content while F. × ananassa ‘Figaro’ had the highest starch concentration, in stressed plants ranging respectively from 0.54g/100gFW to 0.70g/100gFW and from 3.11 g/100gFW to 2.60g/100gFW, between 13 and 28 DAS (Fig 6.4). In control plants the starch content generally tended to increase throughout the experiment, this was very pronounced for ‘Figaro’ (P=0.04) and F. vesca (P=0.02) (Fig 6.4). No differences in starch content of stressed plants compared to control were observed for F. chiloensis and F. × ananassa ‘Ventana’ at 13, 23 and 28 DAS; starch content increased in stressed plants of F. vesca at 13 DAS (P=0.002) and 23 DAS (P=0.043) and decreased in F. × ananassa ‘Figaro’ at 28 DAS (P=0.025) (Fig 6.4).

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The hexose/sucrose ratio generally tended to decrease throughout the experiment in both control (P=0.004) and stress (P=0.000), the highest values were at 13 DAS (no data for control of F. vesca) (Fig 6.5). This ratio was genotype dependent for control plants (P= 0.024) and the highest ratio was observed for F. × ananassa ‘Ventana’. A significant decrease of the hexose/sucrose ratio was found in stressed plants of F. × ananassa ‘Ventana’ at 23 and 28 DAS (P=0.03), while the hexose/sucrose ratio significantly increased in F. × ananassa ‘Figaro’ at 28 DAS (P=0.015). The ratio of total soluble sugars per starch content was significantly different between genotypes for both control and stress (P=0.000), generally the highest value was observed in F. chiloensis while the lowest was observed in F. × ananassa ‘Figaro’ (Fig 6.5). This ratio increased in stressed plants of F. × ananassa ‘Figaro’ (P= 0.033) and F. vesca (P=0.008) with the highest value at 28 DAS, but changes in stressed plants of F. × ananassa ‘Ventana’ were not constant throughout the experiment. In general, the soluble sugars/starch content increased in drought stressed plants at 28 DAS (P=0.038), but varietal differences were present with only a significant increase in F. × ananassa ‘Figaro’ (P=0.005).

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Glucose Fructose

1,2 1,0 F. × ananassa 'Figaro' control F. × ananassa 'Figaro' control 1,0 * drought * 0,8 drought

gFW] gFW] 0,8

-1 -1 0,6 0,6 0,4 0,4

0,2

Glucose [g100 Glucose 0,2 [g100 Fructose

0,0 0,0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS)

1,2 1,0 F. chiloensis control F. chiloensis control 1,0 drought 0,8 drought

gFW] gFW] 0,8

-1

-1 * 0,6 0,6 ** 0,4 0,4

0,2

Fructose [g100 Fructose

Glucose [g100 Glucose 0,2

0,0 0,0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS)

1,2 1,0 F. × ananassa 'Ventana' control F. × ananassa 'Ventana' control 1,0 drought 0,8 drought

gFW] gFW] 0,8

-1 -1 0,6 0,6 0,4 0,4

0,2

Fructose [g100 Fructose Glucose [g100 Glucose 0,2

0,0 0,0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS)

1,2 1,0 F. vesca control F. vesca control 1,0 drought 0,8 drought

gFW] gFW] 0,8

-1 -1 * 0,6 0,6 0,4 0,4

0,2

Glucose [g100 Glucose 0,2 [g100 Fructose

0,0 0,0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS) Fig 6.3 Glucose and fructose (hexose) content in control and drought stressed plants. Mean of two biological replications ±SE, */** Significant different (P≤0.05/0.01), T-test (1st experiment)

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Sucrose Starch

4 6 F. × ananassa 'Figaro' control F. × ananassa 'Figaro' control drought 5 drought 3

gFW]

gFW] 4

-1

-1 2 3 *

2 1

Starch [g100 Starch

Sucrose [g100 Sucrose 1

0 0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS)

4 F. chiloensis 6 control F. chiloensis control drought 5 drought 3

gFW]

gFW] 4

-1

2 3

2 1

Starch [g100 Starch

Sucrose [g100 Sucrose 1

0 0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS)

4 6 F. × ananassa 'Ventana' control F. × ananassa 'Ventana' control drought 5 drought 3

gFW]

gFW] 4

-1

2 3

2 1

Starch [g100 Starch

Sucrose [g100 Sucrose 1

0 0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS)

4 F. vesca 6 control F. vesca control drought 5 drought 3

gFW]

gFW] 4

-1

-1 2 3 * * ** ** 2 1 Starch [g100 Starch 1

Sucrose [g100 Sucrose

0 0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS) Fig 6.4 Sucrose and starch content in control and drought stressed plants. Mean of two biological replications ±SE, */** Significant different (P≤0.05/0.01), T-test (1st experiment)

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Hexose/Sucrose Soluble sugars/Starch content

7 F. × ananassa 'Figaro' 8 control F. × ananassa 'Figaro' control 6 7 drought drought 5 6 5 4 4 3 3 2 Hexose/Sucrose 2 **

* sugars/Starch Soluble 1 1

0 0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS)

7 8 F. chiloensis control F. chiloensis control 6 7 drought drought 5 6 5 4 4 3 3 2 2

Hexose/Sucrose

1 sugars/Starch Soluble 1

0 0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS)

7 F. × ananassa 'Ventana' 8 control F. × ananassa 'Ventana' control 6 7 drought drought 5 6 5 4 4 3 3 2

Hexose/Sucrose * 2

* sugars/Starch Soluble * 1 1

0 0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS)

7 8 F. vesca control F. vesca control 6 7 drought drought 6 5 5 4 4 3 3 2 2

Hexose/Sucrose

Soluble sugars/Starch Soluble * 1 1

0 0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS) Fig 6.5 The ratio of hexose to sucrose and soluble sugars to starch content in control and drought stressed plants. Mean of two biological replications ±SE, */** Significant different (P≤0.05/0.01), T-test (1st experiment).

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6.3.2.2 Proline The effect of increasing drought stress on proline content was monitored in the first experiment. A high variation was observed in proline content for sampling dates and for the replications as well, which is illustrated by high standard error bars (Fig 6.6). The proline variation from 13 to 28 DAS was lowest for F. × ananassa ‘Figaro’, ranging from 0.23µM/gFW to 0.10µM/gFW in control plants and from 0.15µM/gFW to 0.10µM/gFW in stressed plants. For control plants of F. × ananassa ‘Ventana’ proline tended to decrease throughout the experiment (from 0.48 to 0.17µM/gFW) while for stressed plants a decrease in proline concentration was observed between 13 and 23 DAS (0.54 µM/gFW to 0.28µM/gFW) but was followed by an increase at 28 DAS (0.68µM/gFW). This decreasing tendency was also observed for F. vesca for both control and stressed plants. Proline content significantly decreased in well-watered F. vesca plants (0.90 µM/gFW to 0.29µM/gFW) (P= 0.046) from 13 to 28 DAS, whereas in stressed plants the decrease was less pronounced (0.41 µM/gFW to 0.21µM/gFW). No significant differences between control and stressed plants of F. vesca were present although stressed plants always had lower proline contents. At 13 DAS, no effect of drought stress on proline content was observed in F. × ananassa ‘Ventana’ and F. chiloensis, while in F. × ananassa ‘Figaro’ proline tended to decrease in stressed plants. At 23 DAS, proline content in stressed plants was significantly higher than control in F. × ananassa ‘Figaro’ (P= 0.021) and tended to increase in stressed plants of F. × ananassa ‘Ventana’ and F. chiloensis. At 28 DAS, proline content tended to be higher in drought stressed plants in F. × ananassa ‘Ventana’ and F. chiloensis compared to well–watered (though not statistically significant due to high variations between replications) and no change was observed for F. × ananassa ‘Figaro’ (Fig 6.6). Control plants of F. chiloensis had almost no proline at 28 DAS, whereas in stressed plants a considerable though not significant increase of the proline content was noticed (0.31 µM/gFW to 10.74µM/gFW) from 13 to 28 DAS.

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1,0 18 F. × ananassa 'Figaro' F. chiloensis control 16 control drought drought

] 0,8 ] 14

-1

-1 12 0,6 10 8 0,4 6

Proline [µMgFW Proline 0,2 * [µMgFW Proline 4 2 0,0 0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS)

1,0 1,0 F. × ananassa 'Ventana' control F. vesca control drought drought

] 0,8 ] 0,8

-1

0,6 0,6

0,4 0,4

Proline [µMgFW Proline Proline [µMgFW Proline 0,2 0,2

0,0 0,0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS) Fig 6.6 Proline content in control and drought stressed plants. Mean of two biological replications ±SE, */** Significant different (P≤0.05/0.01), T-test (1st experiment)

6.3.2.3 Antioxidant defence against oxidative stress The effect of increasing drought stress on TAC content (total antioxidant capacity) and CAT activity was evaluated in the first experiment. No significant effects of drought stress on TAC content were found irrespective the genotype or the sampling date (Fig 6.7). In general, CAT activity was different between Fragaria genotypes (P≤0.001), the highest activity was observed in F. vesca (Fig 6.8). CAT activity was low in control plants with exception of F. vesca. In F. × ananassa ‘Figaro’ no effect of drought stress on CAT activity was observed. For stressed plants of F. chiloensis and F. × ananassa ‘Ventana’, CAT activity tended to increase from 13 to 23 DAS and to decrease at 28 DAS, though no significant difference between control and stress was observed, irrespective to the drought stage. In F. vesca, CAT activity tended to be lower in stressed plants compared to control plants at 13 DAS, significantly decreased in stressed plants at 23 DAS, and no CAT activity could be detected in stressed plants at 28 DAS.

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SOD activity was evaluated in the second experiment at 9 DAS. In well-watered plants SOD ranged from 494.3 Umg-1proteins in F. × ananassa ‘Figaro’ to 1124.5 Umg-1proteins in F. vesca and in stressed plants ranged from 423.4 Umg-1proteins in F. × ananassa ‘Figaro’ to 1410 Umg-1proteins in F. × ananassa ‘Ventana’. The lowest SOD activity was observed in F. × ananassa ‘Figaro’ in both control and stressed plants (Fig 6.9). Generally, there was high variation between replications and no significant effects of drought stress on SOD activity was found at 9 DAS, this irrespective the genotype. Under drought stress the SOD activity tended to increase in F. × ananassa ‘Ventana’ and to decrease in F. vesca but no changes were noticeable in F. chiloensis and F. × ananassa ‘Figaro’ (Fig 6.9).

6.3.2.4 Malondialdehyde (MDA) content MDA was measured in the second experiment at 9 DAS. MDA contents differed between genotypes (P=0.007), the highest content for both control and stressed plants was detected in F. vesca and F. chiloensis, respectively (Fig 6.9). Under drought stress MDA tended to increase in F. × ananassa ‘Figaro’ and F. vesca and increased significantly in F. chiloensis, but no change was detected in F. × ananassa ‘Ventana’ (Fig 6.9).

F. × ananassa 'Figaro' control F. chiloensis control 0,3 drought 0,3 drought

]

-1

0,2 0,2

0,1 0,1

TAC [mmolgFW TAC

0,0 0,0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS)

F. × ananassa 'Ventana' control F. vesca control 0,3 drought 0,3 drought

]

-1

0,2 0,2

0,1 0,1

TAC [mmolgFW TAC

0,0 0,0 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS) Fig 6.7 TAC content (Total Antioxidant Capacity) in control and drought stressed plants. Mean of two biological replications ±SE, */** Significant different (P≤0.05/0.01), T-test (1st experiment)

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Expression profiling and characterization of candidate genes under water deficit by RT-qPCR

]

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30 30 *

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CAT activity [Umg protein [Umg activity CAT 13 23 28 13 23 28 Day after stress settlement (DAS) Day after stress settlement (DAS) Fig 6.8 CAT activity in control and drought stressed plants. Mean of two biological replications ±SE, */** Significant different (P≤0.05/0.01), T-test (1st experiment), no detection of enzyme activity in stressed F. vesca

SOD activity MDA content

3000 50 control control drought drought 2500 40 *

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0 0 F. chiloensis Figaro Ventana F. vesca F. chiloensis Figaro Ventana F. vesca

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Fig 6.9 MDA content and SOD activity in control and stressed plants, 9 DAS. Mean of three biological replications ±SE, */** Significant different (P≤0.05/0.01), T-test (2nd experiment)

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6.3.3 Gene expression analysis

6.3.3.1 RT-qPCR method development

6.3.3.1.1 Checking DNA contamination and amplification specificity RNA was isolated from leaf samples harvested 9 (exp 2), 23 and 28 (exp 1) DAS. The average RNA concentration was about 100ng/µl in exp 1 and 500ng/µl in exp 2. RNA quality was determined by A260/280 (mostly 1.8 - 2.2) and A260/230 ratios (mostly higher than 1.9); accordingly, RNA was considered to be free of proteins and polysaccharides (Wilfinger et al. 1997). To remove possible DNA contamination, DNase treatment was applied on all samples. Amplification specificity was verified in the noRT samples. If the Cq-value of the noRT was at least 5 cycles higher compared to the sample, DNA contamination could be ignored (Hellemans et al. 2007). Contaminated samples were excluded from quantification. The melting curves of all genes were verified for the formation of primer dimers (De Keyser 2010). No primer dimers were detected in the cDNA samples. In a preliminary test, the melting peak of the standards appeared to be different from those of the samples for Actin, TUA, Elongation factor-1 alpha (EF-1α), UBQ10 and FaAATP (contig 18391); therefore these genes were excluded from further experiments. RT-qPCR primers were chosen to amplify a gene specific fragment; they were not intended to be allele specific. In such way, the primer sets will amplify all alleles that come to expression unless they carry a mutation in the primer binding site. Moreover, when in RT-qPCR amplification melting curve analysis at the end of the run indicates the presence of (allelic) fragments, a new set of primers was developed. Nevertheless, in the final analysis, no such heterozygous amplifation products were detected. This does not exclude that between different genotypes different alleles have been amplified.

6.3.3.1.2 Gene-specific qPCR efficiency According to Hellemans et al. (2007), the PCR efficiency (E) can be calculated from the equation of the standard curves using the slope value (E= (10(-1/slope)-1)*100). An acceptable range of efficiency was defined from 1.8 to 2.2 (Derveaux et al. 2010). In our study, all genes had acceptable PCR efficiencies (Table 6.1).

6.3.3.1.3 Evaluated genes of interest (GOI) in RT-qPCR In total, 16 genes of interest (GOI-1 to GOI-16) were used in the final RT-qPCR analysis (Table 4.2). All 16 genes of interest (GOI) were analysed in the first sample set (exp 1), but GOI-4 (CSD2), GOI-10 (AKR) and GOI-13 (DHAR) were not analysed in the second sample set, because of the lack of cDNA samples.

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6.3.3.1.4 Validation of reference genes For the normalization of gene expression data, the use of multiple reference genes is essential (Vandesompele et al. 2002). In total, 17 reference genes (R-1 to R-17) were selected (Table 4.2). Only 11 reference genes (R-1 to R-10 and R-14) were evaluated in the first sample set (exp 1), because not all reference genes were isolated yet for this sample set, while all 17 reference genes were analysed in the second sample set (exp 2). The geNorm platform in the qBasePLUS software (Biogazelle) was implemented to identify both the most stable and the optimal number of reference genes for normalization of gene expression during drought stress in Fragaria sp. (Hellemans et al. 2007).

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Table 6.1 Run specific PCR efficiencies (E) and the standard errors of PCR efficiencies (SE) of all genes in both experiments, generated by the LightCycler480 and qBasePLUS (Biogazelle) software Exp 1 Exp 2 Gene E SE E SE AKR 2.027 0.005 APX42 1.930 0.004 1.896 0.002 CAT 1.883 0.002 1.865 0.002

CBF4 1.930 0.003 1.906 0.005

) CSD1 1.909 0.005 1.965 0.004

CSD2 1.982 0.004 GOI DHAR 1.978 0.003 DREB2A 1.946 0.007 1.990 0.003 FaAIV 1.947 0.005 1.858 0.006 FaSPS 1.995 0.005 1.985 0.007 GAlLDH 1.965 0.008 1.970 0.003

Gene of interest ( MYB2A 1.890 0.004 1.901 0.003 NCED3 1.963 0.008 1.934 0.005 OLP1 1.918 0.006 1.916 0.005 P5CDH 1.994 0.003 1.945 0.003 P5CS 1.963 0.003 1.963 0.002 Fa EF-1α 1.940 0.006 FaAP 1.804 0.002 FaCHP (contig 18191) 1.905 0.004 FaCHP (contig 19988) 1.829 0.053

FaENP 1.901 0.003

) FaGAPDH 1.844 0.013 R FaCHP (contig 21335) 1.661 0.004 1.682 0.006 Clathrin 1.966 0.004 1.968 0.007 HK134 1.744 0.003 1.705 0.003 HK173 1.977 0.004 1.993 0.006 HK47 1.806 0.003 1.842 0.003

Reference genes ( HK96 1.909 0.005 1.954 0.003 PDF 1.992 0.002 1.995 0.007 PTB 1.814 0.005 1.807 0.005 SAND 1.874 0.004 1.944 0.003 UBC9 1.925 0.004 1.923 0.004 YLS8 1.851 0.002 1.799 0.004

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Expression profiling and characterization of candidate genes under water deficit by RT-qPCR

Stability of the reference genes was expressed by the M-value (ideally M<0.5 in homogenous tissues), the pairwise variation V indicated the number of genes that had to be included in the normalisation factor (V<0.15). In the first sample set (exp 1), the use of two reference genes (PTB and YLS8; M=0.452;

V2/3=0.15) was sufficient (Fig 6.10). The PDF and FaCHP (contig 21335) contained missing data for geNorm analysis and were excluded from the analysis in the first sample set. In the second sample set (exp 2), two other and even more stable reference genes could be used (FaCHP (contig 18191) and PDF; M=

0.296; V2/3=0.12) (Fig 6.11). The amplification efficiency of FaCHP, contigs 19988 and 21335 was not confirmed for geNorm analysis; they were excluded from the analysis in the second sample set.

Fig 6.10 A) Average expression stability (M) of the reference genes examined in four Fragaria genotypes in control and drought stressed plants at 23 and 28 DAS (exp 1). B) Evaluation of optimal number of reference genes for normalization (exp 1), the cut-off value (geNorm V) is 0.15 (Hellemans et al. 2007). Both graphs were generated by the geNorm module of the qBasePLUS software (Biogazelle)

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Fig 6.11 A) Average expression stability (M) of the reference genes examined in four Fragaria genotypes in control and drought stressed plants at 9 DAS (exp 2). B) Evaluation of the optimal number of reference genes for normalization (exp 2), the cut-off value (geNorm V) is 0.15 (Hellemans et al. 2007). Both graphs were generated by the geNorm module of the qBasePLUS software (Biogazelle)

6.3.3.2 Drought stress-induced alteration in gene expression level at different drought stages and genotypes The expression profiles of genes of interest (GOIs) under drought stress at 9, 23 and 28 DAS for different Fragaria genotypes are presented through the figures 6.12 - 6.19. Skilful measurement of expression levels requires a relative quantification of transcript levels of GOIs compared to a relevant set of reference (R) genes, normalization between samples based on Cq-values of the set of R genes, relative calibration

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Expression profiling and characterization of candidate genes under water deficit by RT-qPCR

versus a (chosen) reference sample and log transformation of NRQ data before statistical analysis. In addition, qPCR data are nonlinear and typically suffer from heterogeneity of variance across biological replicates within treatments. Moreover, experiments that include samples with a very low transcript level (high Cq) inherently generate a higher error. Due to these typical features of NRQ values conventional parametric statistics don’t apply but one requires non-parametric analysis. Here, the Related-Samples Wilcoxon Signed Rank Test, was used for each evaluated GOI, to identify significant effects of drought stress in the sample pool of each drought stage (9, 23 and 28 DAS), as well as in the sample pool of each tolerant (F. chiloensis and F. × ananassa ‘Figaro’) and sensitive (F. vesca and F. × ananassa ‘Ventana’) group; this was by comparing the control versus stressed treatments as the related pairs. Accordingly, in the Figures 6.12 - 6.19 we assigned the significant effect of drought stress on GOI, in each drought stage and tolerant/ sensitive group. An overview of these results is summarized in the tables 6.2, 6.3 and 6.4. Regarding to the functional category, the main drought stress-induced changes in the expression level of GOIs in the four genotypes at 9, 23 and 28 DAS included:

6.3.3.2.1 Transcription factors: DREB2A: It tended to be down-regulated in F. × ananassa ‘Ventana’ at 9, 23 and 28 DAS and in F. vesca at 28 DAS, while tended to be up-regulated in F. chiloensis at 23 DAS and in F. ×ananassa ‘Figaro’ at 28 DAS. No change was observed in F. chiloensis and F. ×ananassa ‘Figaro’ at 9 DAS (Fig 6.12). CBF4: At 9 DAS, no change was observed in F. chiloensis, a tendency of up-regulation was observed in F. × ananassa ‘Figaro’ and F. vesca and of down-regulation in F. × ananassa ‘Ventana’ (Fig 6.12). At 23 and 28 DAS, CBF4 was significantly down-regulated in F. chiloensis and F. × ananassa ‘Figaro’ and tended to be down-regulated in F. vesca while at 23 DAS a tendency for up-regulation in F. × ananassa ‘Ventana’ was present. At 28 DAS, the expression level of CBF4 was generally low, this irrespective the genotype. MYB2: It was significantly up-regulated under drought stress at 9 and 28 DAS, this irrespective of the genotype. MYB2 was significantly up-regulated in F. chiloensis and F. × ananassa ‘Figaro’ at all stages (no data for stressed plants of F. × ananassa ‘Figaro’ at 23 DAS). In F. vesca and F. × ananassa ‘Ventana’ MYB2 was significantly up-regulated at 9 DAS. A tendency of MYB2 up-regulation was also observed at 23 and 28 DAS in F. vesca, but in F. × ananassa ‘Ventana’ a tendency of down-regulation at 23 DAS and up-regulation at 28 DAS was observed (Fig 6.13). At 9 DAS, the expression level of MYB2 in both stressed and control plants was significantly different between genotypes (P=0.029), the highest was observed in F. chiloensis and F. vesca, respectively. Also at 23 and 28 DAS, the expression level of

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MYB2 in stressed plants was significantly different between genotypes (P=0.019), the highest was observed in F. × ananassa ‘Figaro’ at 28 DAS and in F. chiloensis at 23 DAS (no data in stressed plants of ‘Figaro’ at 23 DAS).

6.3.3.2.2 ABA biosynthesis NCED3: At 9, 23 and 28 DAS, NCED3 was significantly up-regulated in all genotypes (Fig 6.13). The expression level of NCED3 especially in control plants was low at 23 and 28 DAS, this irrespective the genotype (Fig 6.13).

6.3.3.2.3 ROS scavenging enzymes: CAT: It was significantly up-regulated in stressed plants at all stages, this irrespective of the genotype. At 9 DAS, CAT tended to be up-regulated in F. chiloensis but no change was observed in F. × ananassa ‘Figaro’. At 23 and 28 DAS, CAT was significantly up-regulated in both. CAT tended to be up-regulated in F. × ananassa ‘Ventana’ and F. vesca at 9 DAS and was also significantly up- regulated in both at 23 and 28 DAS (Fig 6.14). At 9 DAS, the expression level of CAT in both control and stressed plants was significantly different between genotypes (P=0.017), the highest was observed in F. chiloensis. APX: No change was observed in APX regulation in F. chiloensis irrespective the stage, but in F. ×ananassa ‘Figaro’ a tendency of up-regulation was observed at 9 DAS. At 23 and 28 DAS, no significant effect of water deficit was observed on APX regulation in stressed plants, this irrespective the genotype. In F. vesca, APX was significantly down-regulated at 9 DAS but tended to be up-regulated at 23 and 28 DAS. In F. × ananassa ‘Ventana’, a tendency of down-regulation and up-regulation was observed at 23 and 28 DAS, respectively (Fig 6.14). Cu-Zn SOD1 (CSD1): It was significantly down-regulated in stressed plants at 9 DAS, this irrespective to the genotype. In F. chiloensis and F. × ananassa ‘Figaro’, CSD1 was significantly down-regulated at 9 DAS, but a tendency of up-regulation was observed at 28 DAS in F. × ananassa ‘Figaro’. CSD1 tended to be down-regulated at all stages in F. vesca. In F. × ananassa ‘Ventana’, a tendency of down-regulation and up-regulation was observed respectively at 23 and 28 DAS (Fig 6.15). The expression level of CSD1 in control plants was significantly different between genotypes at 23 and 28 DAS (P= 0.009), the highest was observed in F. vesca and F. × ananassa ‘Ventana’ (Fig 6.15). Cu-Zn SOD2 (CSD2): The expression level of CSD2 was not analysed at 9 DAS. No considerable change was observed at 23 DAS, this for all genotypes (Fig 6.15). At 28 DAS, CSD2 tended to be up-regulated in F. chiloensis, F. × ananassa ‘Figaro’ and F. × ananassa ‘Ventana’ but down-regulated in F. vesca. The expression level of CSD2 in control plants was significantly different between genotypes at 23 and 28 DAS (P= 0.019), the highest was observed in F. vesca and F. × ananassa ‘Ventana’ (Fig 6.15).

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6.3.3.2.4 Ascorbic acid metabolism: GalLDH: It was significantly down-regulated in stressed plants at 9 DAS, this irrespective the genotype. GalLDH was significantly down-regulated in F. chiloensis, F. vesca and F. × ananassa ‘Ventana’ and tended to be down-regulated in F. × ananassa ‘Figaro’ at 9 DAS. At 23 and 28 DAS, GalLDH tended to be down-regulated in F. vesca (as well as in ‘Ventana’ at 23 DAS), while up-regulated in F. chiloensis at both stages. At 28 DAS, GalLDH tended to be up-regulated in F × ananassa ‘Figaro’ and ‘Ventana’ (Fig 6.16). The expression level of GalLDH in control plants was significantly different between genotypes at 23 and 28 DAS (P= 0.020), the highest was observed in F. vesca (Fig 6.16). AKR: The expression level of AKR was not analysed at 9 DAS. At 23 DAS, AKR (GalUR) was significantly up-regulated in F. chiloensis and tended to be up-regulated in F. × ananassa ‘Ventana’. At 28 DAS, AKR (GalUR) was significantly up-regulated in F. chiloensis and F. × ananassa ‘Figaro’ and tended to be up-regulated in F. × ananassa ‘Ventana’. The RT-qPCR amplification and expression level of AKR was not detectable for F. vesca (data not shown) (Fig 6.16). DHAR: The expression level of DHAR was not analysed at 9 DAS. At 23 DAS, DHAR tended to be down-regulated in F. vesca and F. × ananassa ‘Ventana. At 28 DAS, however, it tended to be down- regulated in Fragaria species, F. chiloensis and F. vesca and up-regulated in F. × ananassa ‘Figaro’ and F. × ananassa ‘Ventana’ (Fig 6.17).

6.3.3.2.5 Protective osmotin like protein OLP: At 9 DAS, OLP was significantly down-regulated in all genotypes. OLP was significantly down- regulated in F. × ananassa ‘Ventana’ and tended to be down-regulated in ‘Figaro’ at 23 and 28 DAS. In F. vesca, OLP was significantly down-regulated at 28 DAS, while in F. chiloensis a tendency of up- regulation was observed at this stage. The lowest expression level of OLP in both control and stressed plants was observed in F. chiloensis at all stages (Fig 6.17). At 9 DAS, the expression level of OLP in control plants was significantly different between genotypes (P= 0.041), the highest level was observed in F. vesca and lowest observed in F. chiloensis. Also, at 23 and 28 DAS, the expression level of OLP in control plants was significantly different between genotypes (P=0.007), the highest level was observed in F. vesca and F. × ananassa ‘Ventana’ (Fig 6.17).

6.3.3.2.6 Osmotic adjustment by sucrose metabolism FaSPS: At 9, 23 and 28 DAS, FaSPS was significantly up-regulated in stressed plants, this irrespective the genotype, though at 23 and 28 DAS, the expression level was generally low (Fig 6.18). In F. chiloensis and F. × ananassa ‘Figaro’, FaSPS was significantly up-regulated at 9 DAS and tended to be up-regulated at 23 and 28 DAS (no data for stressed plants of ‘Figaro’ at 23 DAS). In F. vesca, FaSPS tended to be up-

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regulated at 9 and 28 DAS, and was significantly up-regulated at 23 DAS (Fig 6.18). In F. × ananassa ‘Ventana’, FaSPS tended to be up-regulated at 9 DAS, and was significantly up-regulated at 23 and 28 DAS. FaAIV: At 9 and 28 DAS, it was significantly down-regulated in stressed plants, this irrespective the genotypes. In F. chiloensis no change was observed in the regulation of FaAIV at 9 and 23 DAS, while it tended to be down-regulated at 28 DAS and in F. × ananassa ‘Figaro’ tended to be down-regulated at all stages. In F. × ananassa ‘Ventana’ and F. vesca, it was significantly down-regulated at all stages (Fig 6.18).

6.3.3.2.7 Osmotic adjustment by proline metabolism P5CS: At 9 and 28 DAS, P5CS was significantly up-regulated in stressed plants, this irrespective the genotype. At 9 DAS, P5CS was significantly up-regulated in F. chiloensis and F. × ananassa ‘Figaro’ and tended to be up-regulated in F. vesca and F. × ananassa ‘Ventana’. At 23 DAS, P5CS was significantly up-regulated in F. chiloensis and F. vesca but no change was observed in others. At 28 DAS, it was significantly up-regulated in all genotypes (Fig 6.19). In general, the expression level of P5CS in control plants was more constant between different genotypes, whereas in stressed plants more genotype- dependent variation was observed. At 9 DAS, the expression level of P5CS in stressed plants of Fragaria species, F. chiloensis and F. vesca tended to be higher than other genotypes; this was the same for stressed plants of F. chiloensis at 28 DAS. At 23 DAS, the expression level of P5CS in both control and stressed plants was significantly higher in F. chiloensis and F. vesca than other genotypes (P=0.008) (Fig 6.19). P5CDH: At 9 DAS tended to be up-regulated in F. × ananassa ‘Figaro’ and ‘Ventana’ but no change was observed in F. chiloensis (no data for F. vesca). At 23 and 28 DAS, P5CDH was significantly up- regulated in stressed plants, irrespective the genotype. At 23 and 28 DAS, P5CDH was significantly up- regulated in all genotypes, except in F. × ananassa ‘Ventana’ at 23 DAS (Fig 6.19).

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CBF4 DREB2A

1,2 1,2 CBF4 (9 DAS) DREB2A (9 DAS) control control drought drought 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

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0,2 0,2

0,0 0,0 F. chiloensis Figaro Ventana F. vesca F. chiloensis Figaro Ventana F. vesca Genotype Genotype

T S T S

1,2 1,2 CBF4 (23 DAS) control DREB2A (23 DAS) control drought drought 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 0,14 1,2 F.chiloensis Figaro Ventana F. vesca F.chiloensis Figaro Ventana F. vesca CBF4 (28 DAS) DREB2A (28 DAS) Genotype Genotype 0,12 1,0

0,10 0,8

0,08

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0,02 0,2

0,00 0,0 F.chiloensis Figaro Ventana F. vesca F.chiloensis Figaro Ventana F. vesca Genotype Genotype

T * S T S Fig 6.12 Mean normalized relative quantities (NRQ) of RT-qPCR analysed CBF4 and DREB2A genes. Error bar indicate the standard error, */** indicate significant differences between control and stress (related-sample Wilcoxon test, P≤0.05/0.01), Gene *: significant difference between control and stress in each drought stage, irrespective the genotype, T / S *: significant difference between control and stress in each group of drought tolerant /sensitive genotype

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MYB2 NCED3

1,2 1,2 MYB2 (9 DAS) ** control NCED3 (9 DAS) ** control drought drought 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

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0,0 0,0 F. chiloensis Figaro Ventana F. vesca F. chiloensis Figaro Ventana F. vesca Genotype Genotype T * S * T * S *

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0,8 0,8

0,6 0,6

CNRQ CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 1,2 1,2 F.chiloensis Figaro Ventana F. vesca F.chiloensis Figaro Ventana F. vesca MYB2 (28 DAS) * NCED3 (28 DAS) * Genotype Genotype 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 F.chiloensis Figaro Ventana F. vesca F.chiloensis Figaro Ventana F. vesca Genotype Genotype T * S T * S *

Fig 6.13 Mean normalized relative quantities (NRQ) of RT-qPCR analysed MYB2A and NCED3 genes. Error bar indicate the standard error, */** indicate significant differences between control and stress (related-sample Wilcoxon test, P≤0.05/0.01), Gene *: significant difference between control and stress in each drought stage, irrespective the genotype, T / S *: significant difference between control and stress in each group of drought tolerant /sensitive genotype

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Expression profiling and characterization of candidate genes under water deficit by RT-qPCR

CAT APX

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CNRQ

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0,2 0,2

0,0 0,0 F. chiloensis Figaro Ventana F. vesca F. chiloensis Figaro Ventana F. vesca Genotype Genotype T S T S *

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0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 1,2 F.chiloensis Figaro Ventana F. vesca 1,2 F.chiloensis Figaro Ventana F. vesca CAT ( 28 DAS) * APX (28 DAS) Genotype Genotype 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 F.chiloensis Figaro Ventana F. vesca F.chiloensis Figaro Ventana F. vesca Genotype Genotype T * S * T S

Fig 6.14 Mean normalized relative quantities (NRQ) of RT-qPCR analysed CAT and APX genes. Error bar indicate the standard error, */** indicate significant differences between control and stress (related- sample Wilcoxon test, P≤0.05/0.01), Gene *: significant difference between control and stress in each drought stage, irrespective the genotype, T / S *: significant difference between control and stress in each group of drought tolerant /sensitive genotype

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CSD1 CSD2

1,2 CSD1 (9 DAS) ** control drought 1,0

0,8

0,6 Not analysed

CNRQ

0,4

0,2

0,0 F. chiloensis Figaro Ventana F. vesca Genotype T * S

1,2 1,2 CSD1 (23 DAS) control CSD2 (23 DAS) control drought drought 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 1,2 1,2 CSD1 (28F.chiloensis DAS) Figaro Ventana F. vesca CSD2 (28F.chiloensis DAS) Figaro Ventana F. vesca Genotype Genotype 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 F.chiloensis Figaro Ventana F. vesca F.chiloensis Figaro Ventana F. vesca Genotype Genotype T S T S

Fig 6.15 Mean normalized relative quantities (NRQ) of RT-qPCR analysed CSD1 and CSD2 genes. Error bar indicate the standard error, */** indicate significant differences between control and stress (related- sample Wilcoxon test, P≤0.05/0.01), Gene *: significant difference between control and stress in each drought stage, irrespective the genotype, T / S *: significant difference between control and stress in each group of drought tolerant /sensitive genotype

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GalLDH AKR

1,2 GalLDH (9 DAS) ** control drought 1,0

0,8 Not analysed 0,6

CNRQ

0,4

0,2

0,0 F. chiloensis Figaro Ventana F. vesca Genotype

T * S *

1,2 1,2 GalLDH (23 DAS) control AKR (23 DAS) control drought drought 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 1,2 1,2 F.chiloensis Figaro Ventana F. vesca F. chiloensis Figaro Ventana GalLDH (28 DAS) AKR (28 DAS) * Genotype genotype 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 F. chiloensis Figaro Ventana F.chiloensis Figaro Ventana F. vesca Genotype genotype T S T * S Fig 6.16 Mean normalized relative quantities (NRQ) of RT-qPCR analysed GalLDH and AKR genes. Error bar indicate the standard error, */** indicate significant differences between control and stress (related-sample Wilcoxon test, P≤0.05/0.01), Gene *: significant difference between control and stress in each drought stage, irrespective the genotype, T / S *: significant difference between control and stress in each group of drought tolerant /sensitive genotype

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DHAR OLP

1,2 OLP1 (9 DAS) ** control drought 1,0

0,8 Not analysed 0,6

CNRQ

0,4

0,2

0,0 F. chiloensis Figaro Ventana F.vesca Genotype T * S *

1,2 1,2 DHAR (23 DAS) control OLP1 (23 DAS) control drought drought 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 1,2 F.chiloensis Figaro Ventana F. vesca 1,2 F.chiloensis Figaro Ventana F. vesca DHAR (28 DAS) OLP1 (28 DAS) Genotype Genotype 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 F.chiloensis Figaro Ventana F. vesca F.chiloensis Figaro Ventana F. vesca Genotype Genotype T S T S *

Fig 6.17 Mean normalized relative quantities (NRQ) of RT-qPCR analysed DHAR and OLP1 genes. Error bar indicate the standard error, */** indicate significant differences between control and stress (related- sample Wilcoxon test, P≤0.05/0.01), Gene *: significant difference between control and stress in each drought stage, irrespective the genotype, T / S *: significant difference between control and stress in each group of drought tolerant /sensitive genotype

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FaSPS FaAIV

1,2 1,2 FaSPS (9 DAS) ** control FaAIV (9 DAS) ** control drought drought 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 F. chiloensis Figaro Ventana F. vesca F. chiloensis Figaro Ventana F. vesca Genotype Genotype T * S T S *

0,20 1,2 FaSPS (23 DAS) * control FaAIV (23 DAS) control drought drought 1,0

0,15

0,8

0,10 0,6

CNRQ

0,4

0,05

0,2

0,00 0,0 0,20 1,2 F.chiloensis Figaro Ventana F. vesca F.chiloensis Figaro Ventana F. vesca FaSPS (28 DAS) * FaAIV (28 DAS) * Genotype Genotype 1,0

0,15

0,8

0,10 0,6

CNRQ

0,4

0,05

0,2

0,00 0,0 F.chiloensis Figaro Ventana F. vesca F.chiloensis Figaro Ventana F. vesca Genotype Genotype T S * T S * Fig 6.18 Mean normalized relative quantities (NRQ) of RT-qPCR analysed FaSPS and FaAIV genes. Error bar indicate the standard error, */** indicate significant differences between control and stress (related-sample Wilcoxon test, P≤0.05/0.01), Gene *: significant difference between control and stress in each drought stage, irrespective the genotype, T / S *: significant difference between control and stress in each group of drought tolerant /sensitive genotype

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P5CS P5CDH

1,2 1,2 P5CS (9 DAS) ** control P5CDH (9 DAS) control drought drought 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 F. chiloensis Figaro Ventana F. vesca F. chiloensis Figaro Ventana Genotype Genotype T S T * S

1,2 1,2 P5CS (23 DAS) control P5CDH (23 DAS) * control drought drought 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 1,2 1,2 F.chiloensis Figaro Ventana F. vesca F.chiloensis Figaro Ventana F. vesca P5CS (28 DAS) * P5CDH (28 DAS) * Genotype Genotype 1,0 1,0

0,8 0,8

0,6 0,6

CNRQ

0,4 0,4

0,2 0,2

0,0 0,0 F.chiloensis Figaro Ventana F. vesca F.chiloensis Figaro Ventana F. vesca Genotype Genotype T * S * T * S *

Fig 6.19 Mean normalized relative quantities (NRQ) of RT-qPCR analysed P5CS and P5CDH genes. Error bar indicate the standard error, */** indicate significant differences between control and stress (related-sample Wilcoxon test, P≤0.05/0.01), Gene *: significant difference between control and stress in each drought stage, irrespective the genotype, T / S *: significant difference between control and stress in each group of drought tolerant /sensitive genotype

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Table 6.2 The effect of water deficit on NRQ of isolated target genes in Fragaria genotypes at 9 DAS (exp 2) Tolerant Susceptible Description Gene (EST) F. chiloensis F. × ananassa F. × ananassa F. vesca ‘Figaro’ ‘Ventana’ Transcription factor CBF4 0 (++) (--) (++) DREB2A 0 0 (--) 0 MYB2 ** (+++) (+++) (+++) (+++) Sucrose metabolism FaSPS ** (+++) (+++) (++) (++) FaAIV ** 0 (--) (---) (---) Osmotic adjustment P5CS ** (+++) (+++) (++) (++) P5CDH (0) (++) (++) No data Protective osmotin-like OLP ** (---) (---) (---) (---) protein Scavenging enzymes CSD1 ** (---) (---) (0) (--) CAT * (++) (0) (++) (++) APX (0) (++) (0) (---) Ascorbic acid GalLDH ** (---) (--) (---) (---) metabolism ABA biosynthesis NCED3 ** (+++) (+++) (+++) (+++) Under drought stress: (0) no change, (++) tendency to up-regulate, (+++) significant up-regulation, (--) tendency to down regulate, (---) significant down-regulation, */** significant difference between control and stress for the gene (P≤0.05/ 0.01)

Table 6.3 The effect of water deficit on NRQ of isolated target genes in Fragaria genotypes at 23 DAS (exp 1) Tolerant Susceptible Description Gene F. F. × ananassa F. × ananassa F. vesca (EST) chiloensis ‘Figaro’ ‘Ventana’ Transcription factor CBF4 (---) (---) (++) (--) DREB2A (++) (0) (--) (0) MYB2 (+++) No data for (--) (++) stressed plants Sucrose metabolism FaSPS * (++) No data for (+++) (+++) stressed plants FaAIV (0) (--) (---) (---) Osmotic adjustment P5CS (+++) (0) (0) (+++) P5CDH * (+++) (+++) (0) (+++) Protective osmotin- OLP (0) (--) (---) (0) like proteins Scavenging enzymes CSD1 (0) (0) (--) (--) CSD2 (0) (0) (0) (0) CAT * (+++) (+++) (+++) (+++) APX (0) (0) (--) (++) Ascorbic acid GalLDH (++) (0) (--) (--) metabolism DHAR (0) (0) (--) (--) AKR (+++) (0) (++) No detectable amplification ABA biosynthesis NCED3 * (+++) (+++) (+++) (+++) Under drought stress: (0) no change, (++) tendency to up-regulate, (+++) significant up-regulation, (--) tendency to down regulate, (---) significant down-regulation, */** significant difference between control and stress for the gene (P≤0.05/ 0.01)

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Table 6.4 The effect of water deficit on NRQ of isolated target genes in Fragaria genotypes at 28 DAS (exp 1) Tolerant Susceptible Description Gene F. chiloensis F. × ananassa F. × ananassa F. vesca (EST) ‘Figaro’ ‘Ventana’ Transcription factor CBF4 (---) (---) (0) (--) DREB2A (0) (++) (--) (--) MYB2 * (+++) (+++) (++) (++) Sucrose metabolism FaSPS * (++) (++) (+++) (++) FaAIV * (--) (--) (---) (---) Osmotic adjustment P5CS * (+++) (+++) (+++) (+++) P5CDH * (+++) (+++) (+++) (+++) Protective osmotin- OLP (++) (--) (---) (---) like proteins Scavenging enzymes CSD1 (0) (++) (++) (--) CSD2 (++) (++) (++) (--) CAT * (+++) (+++) (+++) (+++) APX (0) (++) (++) (++) Ascorbic acid GalLDH (++) (++) (++) (--) metabolism DHAR (--) (++) (++) (--) AKR * (+++) (+++) (++) No detectable amplification/Rather low exp. ABA biosynthesis NCED3 * (+++) (+++) (+++) (+++) Under drought stress: (0) no change, (++) tendency to up-regulate, (+++) significant up-regulation, (--) tendency to down regulate, (---) significant down-regulation, */** significant difference between control and stress for the gene (P≤0.05/ 0.01)

6.4 Discussion

6.4.1 Plant water balance under water deficit in Fragaria

We applied a controlled drought stress and maintained low soil moisture contents (18- 20 vol %). To evaluate the general stress response of the strawberry genotypes, we relied on measurements of RWC, WLR and leaf water potential. In chapter 5 we classified F. chiloensis and F. × ananassa ‘Figaro’ as drought tolerant and F. vesca and F. × ananassa ‘Ventana’ as susceptible to water stress, based on RWC and leaf water loosing rate (WLR) in detached leaf tests (Razavi et al. 2011). RWC is an appropriate estimate of plant water status in terms of cellular hydration which is affected by both leaf water potential and osmotic adjustment. The RWC results of the in vivo experiment (exp 1) only partly coincided with the detached leaf test. RWC decreased strongly from day 13 to day 28 in the drought treatment, for the species a Δ=17% for F. vesca and Δ=12% for F. chiloensis was found while for the cultivars RWC changed by Δ=11% for F. × ananassa ‘Ventana’ and Δ=6% for F. × ananassa ‘Figaro’. The level of drought stress severity for the genotypes was also assessed by the change of leaf water potential. In exp 1, F. chiloensis could best maintain its leaf water potential (Ψw (midday)) while the most negative leaf water potential was

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found in F. vesca. Klamkowski and Treder (2006) classified drought stress response in strawberry based on the difference between midday leaf water potentials of control and stressed plants. At 28 DAS this Ψw

(midday) was in the range of 1.12- 1.5 MPa with exception for F. vesca for which the difference reached 2.16 MPa. According to Klamkowski and Treder (2006) three genotypes were exhibiting a mild stress response and were thus able to maintain their water balance under the applied drought stress while F. vesca was showing a severe stress response ( Ψw (midday) > 2 MPa) confirming its classification as drought susceptible. In exp 2, the leaf water potential was measured early in the photoperiod. Diurnal changes of leaf water potential have been documented for F. chiloensis and F. virginiana (Zhang and Archbold 1993). For the non-stressed condition under field capacity pre-dawn leaf water potential is around -0.2 MPa and decreases to -0.6 MPa around midday for F. chiloensis and to -0.9 MPa for F. virginiana. In exp 2 control plants had always a Ψw above -0.2 MPa which is in the range of the reported pre-dawn Ψw values under non-stressed conditions and will therefore be described as Ψw (pre-dawn). Under drought stress Ψw (pre-dawn) was significantly lower for all genotypes at 9 DAS. This difference was in the range of 0.2-0.3 MPa for 3 genotypes but for F. vesca a difference of 0.6 MPa was observed. Therefore, we can conclude that the stress response in exp 2 is comparable to exp 1 (Fig 6.2). Combined in vivo RWC and leaf water potential measurements confirmed that F. vesca is very susceptible to drought stress and F. × ananassa ‘Figaro’ is a tolerant cultivar. Based on the results of leaf water potential, F. chiloensis has to be considered as drought tolerant as already stated by Zhang and Archbold (1993). For F. × ananassa ‘Ventana’ RWC indicates susceptibility to drought as already stated in chapter 5.

6.4.2 Metabolite and gene transcript regulation under water deficit in Fragaria

In general, metabolic adjustment under drought stress is dynamic and multifaceted and depends on the type and severity of the drought stress as well as the plant genotype (Krasensky and Jonak 2012). In this study, the expression pattern of GOIs at different drought stages in both drought tolerant and sensitive Fragaria genotypes was evaluated and sometimes combined with metabolite contents. Observed drought– induced changes in the regulation of GOIs suggest them as functional genes in Fragaria response to water deficit. However, different studies of metabolic pathways under abiotic stresses demonstrated that the alterations in gene expression level under the stress are not necessarily translated to changes in metabolite levels, because of post-transcriptional and post-translational modifications, compartmentalization, metabolite stability, substrate availability, etc. (Krasensky and Jonak 2012; Kempa et al. 2008).

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6.4.2.1 Osmotic adjustment by carbohydrate and proline metabolism Osmotic adjustment allows maintaining leaf turgor when leaf water potential decreases. Key components of osmotic adjustment in this study are the carbohydrates and proline.

6.4.2.1.1 Carbohydrate metabolism During drought stress soluble sugars accumulate and starch decreases in leaves of many species (Ingram and Bartels, 1996), despite the reduction in carbon fixation during drought stress due to both stomatal closure and down-regulation of the Calvin cycle (Xue et al. 2008; Sunkar and Bartels 2002). During the period of decreased photosynthetic rates in drought-stressed leaves photosynthetic production is insufficient to meet its demand and the breakdown of temporary starch pools may sustain metabolism. In our study the increase of total soluble sugars and sucrose content under drought stress was observed for all genotypes. Under prolonged water deficit at 28 DAS, the starch content decreased in stressed plants of F. × ananassa ‘Figaro’, tended to decrease in F. chiloensis and F. vesca, while the ratio of soluble sugar per starch content generally increased in stressed plants under drought stress. This illustrates that starch breakdown to soluble sugars plays a role in osmotic adjustment under water deficit in strawberry. Earlier reports indicated that metabolic changes in carbohydrate synthesis result in enhanced starch degradation, diminished starch formation and increased formation of sucrose and soluble sugars in response to water deficit (Arndt et al. 2001, Sanchez et al. 1998). However, genotypes did differ into the partitioning between hexoses and sucrose. For the drought sensitive genotypes F. × ananassa ‘Ventana’ and F. vesca this increase depended on the raise of sucrose content (relative hexose concentration decreased) whereas in F. × ananassa ‘Figaro’ the increase of total soluble sugars was due to an increase of hexose content (the relative sucrose concentration decreased). Under drought stress, the hexose/ sucrose ratio increased in F. × ananassa ‘Figaro’ and decreased in F. × ananassa ‘Ventana’ and F. vesca. In F. chiloensis no changes were observed in partitioning and both contents of hexoses and sucrose increased. In general in F. × ananassa ‘Figaro’ and F. chiloensis hexoses significantly increased at 28 DAS and this to a higher extent than for sensitive genotypes. This indicates that the tolerant genotypes maintained better their osmotic balance by release of hexoses by sucrose hydrolysis. Also, tolerant genotypes tend to keep a higher total carbohydrate content at the prolonged drought stage (28 DAS) although there was a clear difference in partitioning between F. chiloensis, investing merely in sucrose and hexoses, and F. × ananassa ‘Figaro’ who still is able to store quite some starch. How plants define the balance between hexoses versus sucrose seems to be species or even genotype dependent. Water deficit caused an increase in the concentration of soluble sugars in alfalfa, Ziziphus mauritiana, etc. (Shao et al. 2009). Constant sucrose levels during drought stress have been reported in the leaves of barley (Villadsen et al. 2005).

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Glucose and fructose content in drought-stressed wheat leaves increased markedly (Xue et al. 2008). An increase in sucrose content in drought-stressed leaves has been observed in many other plant species (Marques da Silvba and Arrabaca 2004). Under drought stress soluble sugars can function in two ways: directly, as osmotic agents and indirectly, as osmoprotectors. As osmoprotectors sugars stabilize membranes and proteins, most likely by water substitution in the formation of hydrogen bonds with polypeptide polar residues. Sucrose hydrolysis, resulting in the formation of two hexose molecules has obviously a very direct impact on water potentials; nevertheless, sucrose as a non-reducing sugar might be preferred as an osmoprotector. Moreover, to identify factors involved in increased soluble carbohydrate accumulation in drought stressed Fragaria genotypes, the regulation of certain key-enzymes in sucrose metabolism was characterized. Sucrose phosphate synthesis (SPS) is the main key enzyme of sucrose biosynthesis; sucrose synthase (SUS/SuSy) and acid invertase (AIV) (exist in vacuoles and plant cell wall) are the key enzymes which catalyse sucrose to glucose and fructose (Kruger 1992) and SUS catalyses the sucrose biosynthesis as well. Gene expression was analysed for FaSPS and FaAIV. The relative expression level of FaSPS was significantly affected by water deficit at all drought stages: 9, 23 and 28 DAS. The up-regulation of FaSPS in stressed plants irrespective the genotype and the drought stage confirms the induction of sucrose biosynthesis and accumulation under water deficit in Fragaria. It seems that this pattern is identical between drought tolerant and sensitive groups. This observation confirms that intermediary foliar starch build-up under drought stress is retarded and synthesis of soluble sugars prevails. The up-regulation of foliar SPS has been previously reported under water deficit (Ingram and Bartels 1996). A significant up- regulation of several SPS was also observed in drought stressed leaves of wheat (Xue et al. 2008). The role of SPS activity in sucrose accumulation during water deficit was also proved in potato by antisense expression of SPS (Ramanjulu and Bartels 2002; Geigen-Berger et al. 1999). In contrast, foliar SPS activity decreased in maize plants subjected to drought/mild water deficit (Pelleschi et al. 1997). FaAIV mostly tended to be down-regulated under water deficit in the tested genotypes; it was significantly down-regulated in the drought sensitive genotypes at all drought stages. In tolerant F. chiloensis at early stages of drought stress (9 DAS) and at 23 DAS FaAIV expressions were not altered compared to the control; its down-regulation under prolonged drought stress (28 DAS) was less pronounced. Down- regulation of FaAIV was more pronounced in sensitive Fragaria genotypes and might reduce the activity of AIV and decrease the sucrose degradation under water deficit. This suggests that drought sensitive genotypes, in their reaction to handle the effects of drought, are not sufficiently capable to stimulate sucrose hydrolysis by inducing FaAIV to keep up water potentials. This is in line with our observation that drought tolerant genotypes are able to establish a higher hexose/sucrose ratio. Although, the function of

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carbohydrates in plants is not only involved in the osmotic regulation; as they also fuel the general plant metabolism as well. Moreover, and probably more important when evaluating the role of carbohydrate partitioning in osmotic adjustment, we also have to take the total amount of available carbon into account. Tolerant genotypes under prolonged drought stress are able to maintain a higher total carbohydrate level and also better preserve the optimal hexose/sucrose balances for osmotic adjustment (the situation as observed in the non-stressed control plants). Therefore, we can speculate that sensitive genotypes, when coping with an increased demand for carbon by their increased stress-induced respiration have to shut down earlier or to a higher extent the drain of sugars towards easily metabolized hexoses, as observed by a down-regulation of invertase gene expression. This could explain their lower capacity to maintain optimal hexose/sucrose levels and, as a result, osmotic adjustment to preserve leaf water potential. This evidence proposes the critical role of AIV in plant adaptation under water deficit in Fragaria which can be partly correlated to the drought tolerance. Also in literature conflicting data are found. Down-regulation of AIV and decrease in the AIV activity and associated hexose to sucrose ratio was also reported in maize under drought stress (McLaughlin and Boyer 2004; Anderson et al. 2002; Zinselmeier et al. 1999). Conversely, the effect of drought stress on induction of AIV for mobilization of sucrose stored in vacuoles and conversion to glucose and fructose a the results of up- regulation of some specific vacuolar invertases (AIV) was reported in crops such as wheat and maize (Xue et al. 2008; Rotisch et al. 2003; Kim et al. 2000; Roitsch 1999; Pelleschi et al. 1997). In the sensitive genotypes of our study, a low rate of sucrose degradation resulted from considerable down-regulation of FaAIV along with the normal rate of sucrose biosynthesis by SPS might explain the higher relative sucrose content under water deficit. In contrast, in F. chiloensis, down-regulation of FaAIV was not as high as in sensitive genotypes under prolonged water deficit. Therefore, the sucrose synthesis by SPS and degradation by AIV, results in the increase in both hexose and sucrose in F. chiloensis. However, this hypothesis can’t completely explain the reason for the higher hexose/sucrose ratio in ‘Figaro’ compared to the sensitive genotypes, because FaAIV was also down-regulated in this genotype. Another important element is that one cannot ignore the role of SUS which is a reversible enzyme that can act both in sucrose hydrolysis and synthesis. Possibly, the regulation of SUS might be crucial in eventual hexose content and correlated to the observed difference in hexose/sucrose ratio between tolerant and sensitive Fragaria genotypes under water deficit. The ratio of AIV to SUS has also important implication in sugar metabolism (Roitsch and Gonzales 2004) and might effect on the eventual ratio of hexose/sucrose under water deficit. The up-regulation of SUS under water deficit has been reported in wheat and other species (Xue et al. 2008; Ingram and Bartels 1996). The expression profile of SUS still needs to be evaluated in different Fragaria genotypes under water deficit.

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6.4.2.1.2 Proline metabolism A second but important player in osmotic adjustment is proline. Proline, an amino acid, occurs widely in plants and normally accumulates in large quantities in response to abiotic stresses (Ingram and Bartels 1996). In addition to its role in osmotic adjustment, proline contributes to stabilizing sub-cellular structures (membranes, proteins etc.), scavenging free radicals and buffering cellular redox potential under stress conditions (Ashraf and Foolad 2007). In many species, the accumulation of proline under water stress has been correlated with stress tolerance and its concentration is shown to be generally higher in stress resistant than stress sensitive plants (Shao et al. 2009). Yet, this relationship is not always universal; a higher proline accumulation does not always result in a greater drought tolerance (Sànchez-Rodriguez et al. 2010; Ashraf and Foolad 2007; Hanson et al. 1980; Rampino et al. 2006). Higher proline levels under prolonged drought stress (28 DAS) were found in F. chiloensis and absolute values in stressed plants were 10x higher compared to the other genotypes (Fig 6.8). Furthermore, in F. vesca, drought stressed plants had lower proline content than control plants. For the cultivars, however, this correlation was less clear: under prolonged drought stress, proline tended to increase in stressed plants of ‘Ventana’ though no change was found in ‘Figaro’. Also Zhang & Archbold (1993) found no significant changes in proline content of Fragaria under water deficit. In addition, as already indicated (chapter 3), it seems that in strawberry, the proline accumulation under drought stress depends on the severity of the applied stress (or drought stage). This was also reported in strawberry under salinity stress (Pirlak and Esitken 2004) and tomato and rice under both salinity stress or water deficit (Alian et al. 2000; Aziz et al. 1999; Zhu et al. 1998). Proline accumulation is controlled by transcriptional regulation of key enzymes like ∆1 pyrroline- carboxylate synthase (P5CS) and ∆1 pyrroline-carboxylate reductase (P5CR) in proline biosynthesis, and proline dehydrogenase (ProDH) and ∆1 pyrroline-carboxylate dehydrogenase (P5CDH) in proline degradation respectively (Sharma and Verslues 2010). At the transcription level, P5CS was up-regulated in both drought tolerant and sensitive Fragaria genotypes under water deficit this for both early and prolonged stages of drought stress (9 and 28 DAS). At 9 and 23 DAS, the extent of up-regulation of P5CS appeared to be higher in Fragaria species than cultivars. Under prolonged water deficit, the highest extent of up-regulation was observed in F. chiloensis, which can explain the high level of proline accumulation in this species. Also in ‘Ventana’ P5CS up-regulation coincided with increased proline content under prolonged water deficit. The up-regulation of P5CS under water deficit along with the accumulation of proline in plants was already reported by Yoshiba et al. (1997). However, in our study, the up-regulation of P5CS did not always coincide with proline accumulation, especially in F. vesca where proline content was lower in drought-stressed plants despite the significant up-regulation of P5CS, or in ‘Figaro’ where

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proline content was unaffected but P5CS was up-regulated at 28 DAS. It is clear that the induction of P5CS did not necessarily result in higher proline contents in Fragaria. The expression profile of P5CDH, the key gene regulating proline degradation was also analysed in our experiment. A general tendency of up-regulation of P5CDH in stressed plants was observed, this irrespective the Fragaria genotype and drought stage. This up-regulation was significant under long term water deficit (at 23 and 28 DAS) and increased towards 28 DAS in all genotypes. Yet, the extent of up- regulation of P5CDH was variable between genotypes and drought stages, the lowest was observed in stressed plants of F. vesca under prolonged water deficit (28 DAS), and the highest extent of up-regulation in stressed plants of F. chiloensis, ‘Figaro’ and ‘Ventana’ was at 28 DAS. The consistent up-regulation of P5CDH in Arabidopsis under water deficit was already reported (Sharma and Verslues 2010). Proline catabolism is an important regulator of cellular ROS balance. In mitochondria the oxidation of proline by PDH and P5CDH provides reducing equivalents under the form of NADH for the respiration and contributes to the energy supply (Szabados and Savouré, 2009). Therefore, the general up-regulation of P5CDH and subsequent increase in proline degradation explains the absence of proline accumulation in Fragaria but also explains its function in protecting mitochondrial respiration under stress conditions. As a general view to our results, no considerable proline accumulation was observed under water deficit; but the expression profiles of P5CS and P5CDH under water deficit especially at 28 DAS indicates the participation of proline metabolism in Fragaria adaptation mechanism under water deficit. Proline is probably not the main compound in osmotic adjustment in Fragaria genotypes such as in F. × ananassa ‘Figaro’ and ‘Ventana’ but the up-regulation of both the biosynthetic and the catabolic pathway helps to scavenge ROS and to maintain the cellular redox balance. Reviewing the complexity of the regulation of proline metabolism and its multiple functions Szabados & Savouré (2009) hypothesized that not the actual proline content, but also the enhanced rate of proline metabolism is an important factor for stress adaptation. Furthermore, post-transcriptional/ translational control affects proline content and this can be dependent on the genotype or the severity of water deficit. The evaluation of enzymatic activity of P5CS and P5CDH in Fragaria genotypes under water deficit is a necessary step to further elucidate this relation. The regulation of ProDH another gene involved in proline degradation could further increase our understanding. Down-regulation of ProDH under water deficit along with the accumulation of proline in the plants was already reported (Yoshiba et al. 1997). In Medicago sativa under salt stress down- regulation of ProDH resulted in proline accumulation while the expression profile of P5CS (biosynthesis) and P5CDH (catabolism) was not correlated (Miller et al. 2005). Therefore it would also be interesting to

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study the expression level of this gene as well as the enzymatic activity under water deficit in Fragaria in future.

6.4.2.2 Antioxidant defence system for ROS scavenging under water deficit in Fragaria Under various environmental stresses, antioxidant enzymes and metabolites increase and a comparatively higher activity of this defence system has been reported in tolerant cultivars than in susceptible ones (Ahmad et al. 2010). Some studies reported enhanced stress-induced CAT and/or SOD activity, whereas in others there was a down-regulation of their activity under drought stress. These differences in response of antioxidant enzyme activity may be related to the severity of stress intensity and duration (Bian and Jiang 2009, Lee et al. 2009) and the drought tolerance of the plant species and cultivars (Nikolaeva 2010). The maintenance of a high level of antioxidant enzyme activities may contribute to drought tolerance by increasing the capacity of the protection mechanisms against oxidative damage (Bian and Jiang 2009). Lipid peroxidation indicates the prevalence of free radical reactions in tissues which affect the membrane stability. The MDA production is inversely related to the plant’s capability to effectively scavenge free radicals using antioxidant enzymes and it functions as well as an indicator for the severity of the stress (Fu and Huang 2001; Smirnoff 1993). In our study, MDA content increased in stressed plants of F. chiloensis and tended to increase in F. vesca and F. × ananassa ‘Figaro’ at 9 DAS (exp 2). In general, irrespective the effect of drought stress, the MDA content was higher in F. vesca and F. chiloensis, compared to the strawberry cultivars indicating a great diversity in lipid peroxidation metabolism between different strawberry genotypes. The observed increase in MDA content under water deficit was in agreement with previous studies in other plant species (Fu and Huang 2001). Similarly, a mild and severe water deficit resulted in an increase in MDA content in strawberry cultivars (Qian et al. 2005). The increase of MDA content in our study indicates the increasing imbalance between generation and elimination of ROS and the occurrence of oxidative stress under water deficit in Fragaria.

6.4.2.2.1 Ascorbic acid metabolism and TAC content High levels of antioxidants are present in strawberry fruits and leaves (Cruz-Rus et al. 2011; Tulipani et al. 2008; Wang and Lin 2000; Kähkönen et al. 1999). Strawberry leaves accumulate ascorbic acid in high amounts but under drought stress ascorbic acid (AsA) decreased while the membrane-lipid peroxidation as expressed by MDA content increased in leaves (Wang et al. 1999). Ascorbic acid is an important non- - • enzymatic antioxidant which reacts with H2O2, O2 , OH and lipid hydroperoxidases (Munne Bosch and Algere 2003). In plants, the main AsA biosynthesis pathway involves L-galactose but an alternative pathway proceeds by the GalUR enzyme (D-galacturonic acid reductase) from the AKR superfamily of aldo-keto reductases. The last step of AsA biosynthesis in both pathways is catalysed by L-galactono-1, 4-

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lacton dehydrogenase (GalLDH) (Linster and Van Schaftingen 2007; Agius et al. 2003). The contribution of each biosynthetic pathway to the synthesis of AsA varies between species (Cruz-Rus et al. 2011). The expression of AKR (GalUR) was shown to be correlated with ascorbic acid content in strawberry fruit (Cruz-Rus et al. 2011; Agius et al. 2003). Over-expression of GalUR increased the AsA content in A. thaliana (Agius et al. 2003). Here, we examined the expression pattern of AKR (GalUR) and GalLDH in Fragaria genotypes under water deficit. AKR (GalUR) was considerably up-regulated in all Fragaria genotypes under prolonged water deficit (28 DAS), this irrespective the genotype. Therefore, AsA biosynthesis via D-galacturonic acid reductase (GalUR) pathway might be involved in plant adaptation and ROS detoxification in Fragaria under water deficit. This is in agreement with the results where over-expression of strawberry D- galacturonic acid reductase (GalUR) in potato led to the accumulation of AsA with enhanced abiotic stress tolerance (Hemavathi et al. 2009). The expression profile of GalLDH under water deficit in Fragaria was dependent on the genotype and the drought stage, a dynamic expression profile related to the time scale of the water deficit was observed. All genotypes showed a down-regulation of GalLDH at 9 DAS which might result in the inhibition of ascorbic acid (AsA) biosynthesis at early stage of water deficit. With the progress of water deficit at 23 DAS, GalLDH tended to up-regulate in the drought tolerant F. chiloensis and showed no change in the drought tolerant ‘Figaro’, but still tended to down-regulate in drought sensitive genotypes. However, under prolonged water deficit (28 DAS) general tendencies of up- regulation were observed in F. chiloensis and F. ×ananassa ‘Figaro’ as well as in ‘Ventana’, while a tendency of down-regulation was still observed in the drought susceptible F. vesca. Accordingly, the expression profile of GalLDH under water deficit depends on the drought stage and is partly correlated to the genotype drought tolerance. In this study, we did not determine AsA contents; however, foliar TAC which is correlated with ascorbic acid levels remained unaffected under long-term water deficit (23 and 28 DAS, exp 1) in the studied Fragaria genotypes. Therefore, the expression profile of AKR and GalLDH did not result in a higher TAC content when drought proceeds. Extra regulatory elements might affect the ascorbic acid and TAC content under water deficit in Fragaria. In wheat, GalLDH activity was not a good indicator for changes in AsA biosynthesis under drought stress and AsA biosynthesis was constrained by other factors possibly by AsA redox state and regeneration in maintaining AsA, also by the redox regulation of GalLDH under stress (Bartoli et al. 2005b). Nevertheless, an increase in TAC content under water deficit has been reported tomato (Sànchez- Rodriguez et al. 2010). Also in strawberry ‘Elsanta’ TAC content increased under prolonged water deficit (chapter 3). This different TAC response in the two experiments can be a genotype effect or be linked to the method of drought stress settlement.

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The expression level of DHAR (dehydroascorbate reductase) involved in the ascorbate-glutathione cycle was also studied; DHAR is used in GSH-ASC redox cycle to regenerate AsA from the oxidized state which is crucial for tolerance to various abiotic stresses (Noctor and Foyer 1998). In our analysis, some changes occurred in DHAR expression level in stressed plants of Fragaria under long term water deficit (at 23 and 28 DAS). Yet, the expression profile of DHAR was so variable and not consistent with the drought stage or drought tolerance of the studied genotypes. DHAR was down-regulated in Poa pratensis L. (Bian and Jiang 2009), while up-regulated in Agropyron cristatum leaves under water deficit (Shan and Liang 2010). Drought stress enhanced the expression of DHAR in apple leaves during drought stress suggesting that the ascorbate-glutathione cycle is up-regulated in response to drought stress but could not be up-regulated at severe drought stress condition (Ma et al. 2011). Over-expression of DHAR increased drought tolerance in tobacco (Eltayeb et al. 2006). Therefore, further analysis is necessary to elucidate the expression pattern of DHAR in Fragaria under water deficit.

6.4.2.2.2 Antioxidant enzymes (ROS scavenging enzymes) CAT: The involvement of CAT activity as antioxidant enzyme in plant tolerance to water deficit was already shown in different species. In this study (exp 1), a variable response of CAT activity was observed. Its activity decreased in F. vesca and was not affected in ‘Figaro’ under drought stress. Temporal effects were observed in ‘Ventana’ and F. chiloensis with a higher activity at one time point (23 DAS). This temporal enhanced CAT activity was also reported in grasses by Fu and Huang (2001). In strawberry cultivars an increase in foliar CAT activity was reported under a mild drought stress while under severe water deficit the CAT activity dropped rapidly and was followed by enhanced membrane lipid peroxidation and MDA content (Qian et al. 2005). In general, under prolonged drought stress, the catalase inactivation by H2O2, exceeds the enzyme recovery and subsequently causes the loss of CAT activity (Yoshimura et al. 2000; Scandalios 1993). Likewise, CAT activity under cold stress gradually increased to a certain degree and then decreased in strawberry (Yong et al. 2008). Our results suggest that the effect of drought stress on CAT activity in Fragaria is genotype dependent, but partly correlate to drought tolerance. Similar to our results CAT activity decreased in F. vesca under water deficit (Wang et al. 1999). The effect of salt stress on CAT activity in strawberry was also dependent on the genotype salt tolerance; CAT activity remained unchanged or increased under salt stress in tolerant strawberry cultivars (Turhan et al. 2008). Irrespective the effect of water deficit, the level of CAT activity is genotype dependent in strawberry, and was highest in F. vesca. The gene expression profiles indicate a general up-regulation of CAT under water deficit, both in drought tolerant and sensitive groups indicating the involvement of CAT regulation in the antioxidant defence

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system under water deficit in Fragaria. No correlation was observed between the regulation of CAT and the drought tolerance of the genotype. Despite this up-regulation of CAT in all genotypes under water deficit, no coincidence between CAT expression and the enzyme activity was found (see above). As already indicated the changes in the expression levels are not necessarily translated to changes in metabolite levels or enzyme activity (Krasensky and Jonak 2012). Furthermore the inactivation of the CAT enzyme by ROS might occur under water deficit. Similarly, the up-regulated CAT transcript was found in horse gram seedlings under drought stress (Reddy et al 2008), but no difference in transcript level of CAT was observed under drought stress in Poa pratensis L. (Bian and Jiang 2009). SOD: SOD plays an important role in the plant drought tolerance by scavenging of superoxide anions generated under water deficit in both chloroplasts and cytosol (Shao et al. 2009; Bowler et al. 1992). In this study, SOD activity was only analysed at one stage of water deficit (9 DAS, exp 2). Despite a slight increase in F. × ananassa ‘Ventana’ and decrease in F. vesca, no considerable change of SOD activity at the early stage of water deficit was observed. It was already reported that drought stress decreased SOD activity in F. vesca and strawberry cultivars (Qian et al. 2005; Wang et al. 1999). In Sorghum bicolor and wheat a mild to moderate water deficit did not influence SOD activity (Zhang and Kirkham 1996; Zhang et al. 1995). Also in tomato, the change of SOD activity in plants under progressive moderate drought stress was negligible compared to severe drought stresses (Zgallai et al. 2006). Other reports also indicated that the response of SOD activity to water deficit varied according to the drought severity and duration and depended on the plant species (Fu and Huang 2001). No correlation was found between drought tolerance of the Fragaria genotypes and SOD activity under early stage of water deficit. At this time point the cytosolic Cu-ZnSOD(CSD1) under water deficit was generally repressed (except for ‘Ventana’ with no change). In general, no correlation between CSD1 expression level and enzyme activity was found at this early stage of water deficit, except for F. vesca where both tended to decrease under water deficit. Down- regulation of cytosolic Cu-ZnSOD under drought stress was also indicated in Poa pratensis L. (Bian and Jiang 2009). At 23 DAS, CSD1 still tended to be down-regulated in sensitive genotypes, while no noticeable change was observed in tolerant genotypes. Under prolonged water deficit (28 DAS), CSD tended to be induced in F. chiloensis (only for CSD2), ‘Figaro’ and ‘Ventana’ (both CSD1&2), while repressed in F. vesca but we have no enzyme activities of this time point. Mittler and Zilinskas (1994) also found that transcript level of cytosolic Cu-ZnSOD increased in pea leaves under drought stress. In the drought-sensitive F. vesca however, CSD tended to be down-regulated in all drought stages suggesting that its regulation might be partly correlated to drought tolerance. In general, these results suggest the participation of Cu-ZnSOD expression in Fragaria antioxidant defence system under prolonged water deficit but it is not as strong as reported for many other crops. This might be genotype dependent though

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other environmental factors such as light intensity might also interfere. In Picea seedlings under drought stress SOD activity increased if high light levels were given and remained unaffected if the same stress was given under low light levels (70% reduction of outside light conditions) (Yang et al, 2008). It is therefore worthwhile to analyse the SOD response at both enzyme activity and transcript levels in drought-stressed Fragaria under higher levels than the ± 100 µmol.m-².s-1 of the growth chamber.

APX: APX is active in the GSH-ASC redox cycle in plants, plays an important role in H2O2 detoxification in higher plants (Shan and Liang 2010, Shigeoka et al. 2002). APX activity was not analysed in this study; only APX transcript levels were evaluated. The expression pattern of APX was not affected in the drought tolerant F. chiloensis and tended to be up-regulated at 9 DAS in ’Figaro’. The induction of APX at early stages of drought stress might improve drought tolerance and detoxify H2O2. However in the drought sensitive F. vesca a significant down-regulation was observed at 9 DAS. Prolonged drought stress (28 DAS) tended to up-regulate APX in F. vesca and F. × ananassa ‘Ventana’. The APX transcript level and enzyme activity increased under water deficit in Agropyron cristatum (Shan and Liang 2010) and in apple leaves during drought stress (Ma et al. 2011). Also, APX activity increased under drought stress in Poa pratensis L., Euphorbia escula, Zea mays, wheat and soybean (Bian and Jiang 2009; Shao et al. 2009). In contrast, in trifoliate orange there was no significant change in APX activity under mild water deficit (Nayyar and Gupta 2006). In this study, the expression profile of APX in Fragaria under water deficit was not very informative. As in higher plants, APX belongs to a multigenic family (nine genes in Arabidopsis, eight in rice, and seven in tomato) and nine cytosolic APX homologous (95 to 99 %) gene groups were already characterized in Fragaria on the basis of their nucleotide sequences, which classified into four groups of similar (>98%) polypeptides on the basis of their deduced amino acid sequences (Kim et al. 2001). These results imply that the cytosolic APX genes show co-dominant expression resulting from multiple alleles. In this study we actually monitored the expression of the cytosolic APX42, therefore, further studies are necessary to investigate the expression pattern of other cytosolic APXs under water deficit in Fragaria.

6.4.2.3 Protective osmotin-like proteins Osmotin-like proteins are stress-responsive, multifunctional pathogenesis-related (PR) proteins but play also an important role in salt and drought tolerance (Husaini and Rafiqi 2011). In strawberry, FaOLP regulates an osmotin-like protein that may help to protect against osmotic-related environmental stresses (Zhang and Shih 2007). The expression of OLP under water deficit in Fragaria was examined in this study. Irrespective the effect of water deficit, the expression of OLP was genotype dependent the lowest level was observed in F. chiloensis while it was quite high in F. vesca. OLP was down-regulated at early

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stages of water deficit (at 9 DAS) in all Fragaria genotypes. Under prolonged water deficit (at 28 DAS), OLP was markedly down-regulated in the drought sensitive F. vesca and ‘Ventana’, tended to be down- regulated in ‘Figaro’, but tended to be up-regulated in F. chiloensis. These results indicate that OLP was repressed and not functionally protective at early stage of water deficit. Under prolonged water deficit, OLP induction was partly correlated with the genotype drought tolerance. It was already reported that salt tolerance improved in transgenic strawberry plants containing an osmotin gene from tobacco (Husaini and Abdin 2008). The OLP induction in F. chiloensis under prolonged water deficit might stimulate the accumulation of protective osmotin-like protein.

6.4.2.4 ABA biosynthesis and signalling pathway Increased ABA levels observed under drought stress are achieved by the induction of genes that catalyse hormone synthesis. NCED3 (9-cis-epoxycarotenoid dioxygenase), a key enzyme of the ABA biosynthesis pathway showed the strongest induction after water stress among all nine members of the gene family (Tan et al. 2003). A strong induction of NCED3 under osmotic stress (NaCl) was also reported (Barrero et al. 2006). The over-expression of NCED3 improved drought tolerance in transgenic Arabidopsis plants (Iuchi et al 2001). Drought induction of AtNCED3 in vascular parenchyma of A. thaliana provided a novel insight in plant systemic response to drought stress (Endo et al. 2008). In our study up-regulation of NCED3 at all drought stages and for all Fragaria genotypes confirms that the ABA synthesis and accumulation in Fragaria under water deficit is a fundamental response to drought stress, this irrespective of the sampling time points or genotype.

6.4.2.5 Transcription factors MYB2: MYB2 is classified as an AREB/ABF (ABA-responsive element) regulon/transcription factor involved in ABA-dependent gene expression under water deficit (Nakashima and Yamaguchi-Shinozaki 2005). In this study, MYB2 mostly tended to be up-regulated under water deficit at the early stress stage as well as under long-term water deficit. At the early stage of water deficit (9 DAS) MYB2 was up-regulated in both the sensitive and tolerant group, while under long term drought stress (at 23 and 28 DAS), the tendency of up-regulation was more consistent in drought tolerant genotypes. The expression of MYB2 under water deficit in Fragaria was thus dependent on the genotype as well as drought stage. These results firstly report the involvement of MYB2 in the signalling pathways under water deficit in Fragaria. Different MYB genes encoding MYB-type transcription factors were also reported to be induced under different abiotic stresses in soybean (Glycine max) (Liao et al. 2008). Secondly, these results show the involvement of the ABA-dependent signalling pathways in Fragaria response to water deficit, in which

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the up-regulation of MYB2 is more noticeable in drought tolerant genotypes. This is in agreement with other studies where MYB2-overexpressed Arabidopsis plants had a higher osmotic-stress tolerance (Abe et al. 2003). Also in apple, the overexpression of OsMYB4 enhanced the drought tolerance (Pasquali et al. 2008). The global metabolite profiling proved that Arabidopsis overexpressing OsMYB4 showed higher soluble sugars, proline accumulation under normal condition and improved drought tolerance (Krasensky and Jonak 2012). DREB2A: Also DREB2A is a regulon/transcription factor involved in ABA-independent gene expression pathway under drought stress and mainly induced by dehydration leading to the expression of various genes with dehydration-responsive cis-acting element (Nakashima and Yamaguchi-Shinozaki 2005). The expression of DREB2A varied between genotypes and drought stages, in the tolerant genotypes it remained unchanged or tended to be up-regulated under water deficit while in the sensitive group it generally tended to be down-regulated. Although no statistically significant change was observed in the expression level of DREB2A, our results indicate that some DREB2A up-regulation might be involved in plant adaptation to the water deficit in Fragaria which is in agreement with other results showing a higher drought tolerance in over-expressing DREB2-transgenic Arabidopsis lines (Krasensky and Jonak 2012; Sakuma et al. 2006). CBF4: The drought-inducible expression of CBF4 is controlled by the ABA dependent pathway suggesting that CBF4 functions in the response to drought stress that relies on accumulation of ABA (Nakashima and Yamaguchi-Shinozaki 2005). It was shown that CBF4 is a critical regulator of gene expression in the signal transduction during drought adaptation in Arabidopsis and CBF4 was up- regulated by drought stress. CBF4 over-expressing transgenic Arabidopsis was more drought tolerant (Chinnusamy et al. 2003; Haake et al. 2002). In our study, however, under long term water deficit (at 23 and 28 DAS), CBF4 was down-regulated in tolerant genotypes as well as in F. vesca, but its expression was highly variable between genotypes at early stage of water deficit. Further studies are necessary to elucidate the action of CBF4 in drought-stressed Fragaria genotypes.

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6.5 Conclusion

Plant adaptation to controlled water deficit was studied in both drought tolerant (F. chiloensis and F. × ananassa ‘Figaro’) and sensitive (F. vesca and F. × ananassa ‘Ventana’) genotypes at the gene transcript level as well as at metabolic level. The expression level under drought stress of 16 selected genes of interest from different metabolic pathways was analysed by RT-qPCR. A differential response to water deficit at both gene regulation and metabolite adjustment was observed in the studied genotypes. These responses can be generally described as:

1) Drought stress increases the total foliar soluble sugars concentration in Fragaria. 2) Under drought stress, carbohydrate partitioning in favour of hexoses was found for a tolerant Fragaria genotype (F. × ananassa ‘Figaro’), while for susceptible Fragaria genotypes partitioning was more directed towards the accumulation of sucrose. 3) In general, drought-induced accumulation of proline was not a general response in Fragaria, but under prolonged drought stress an increase in proline might support the drought tolerance of Fragaria chiloensis. 4) Drought stress results in lipid peroxidation (MDA production) in Fragaria though the applied drought stress did hardly influence the total antioxidant capacity in strawberry plants. 5) The effect of water deficit on antioxidant enzymes like CAT and SOD is highly related to the drought stage and severity in Fragaria, CAT activity is induced in the initial stage of drought stress, but repressed under prolonged stress while no effect on SOD activity was noted. CAT activity might be partly correlated to the drought tolerance. 6) Overall, irrespective the Fragaria genotype, NCED3, P5CS, P5CDH, CAT, FaSPS and AKR (GalUR) tended to become up-regulated under water deficit. MYB2, an ABA-dependant transcription factor mostly tended to be up-regulated and FaAIV involved in the sucrose degradation pathway mostly tended to be down-regulated in drought-stressed Fragaria plants. The expression profile of OLP, Cu-ZnSOD (CSD1&2), DREB2A and GalLDH under water deficit was more variable and dependent to the genotype as well as the stage of drought stress. The extent of the changes in the relative expression especially of FaAIV, CAT, P5CS and P5CDH might discriminate drought tolerant and sensitive genotypes in Fragaria.

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Chapter 7 Genetic transformation of Fragaria with candidate genes involved in plant response to drought stress

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This chapter is adapted from:

Razavi F., Folta K., Zamariola L., Geelen D., De Riek J., Van Labeke M.C. (2012). Genetic transformation of Fragaria with candidate genes involved in drought stress. Acta Horticulture, 961, 397-404

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7.1 Introduction

Fragaria is a rapidly growing herbaceous perennial with a small genome which makes it a valuable species for functional and translational genomic models in the Rosaceae. The functional role of genes linked to drought tolerance may be studied in Fragaria due to its efficient gene transformation and fast regeneration potential (Folta and Dhingra 2006). In chapter 6 the gene expression of CAT (catalase), P5CS (Δ1-pyrroline-5-carboxylate synthetase) and FaAIV (sucrose acid invertase) was up/down regulated under drought stress in the studied genotypes. A transformation and regeneration system is needed for further functional analysis of these three genes under water deficit by gene silencing and over-expression approaches. The main aims of this study are to: 1) construct RNAi suppression and CaMV 35S over- expression constructs for CAT, P5CS and FaAIV, 2) transfer RNAi and over-expression constructs into 1 diploid (F. vesca) and 3 octoploid genotypes F. chiloensis and F. × ananassa ‘Ventana’ and ‘EL02.2011’ and 3) compare the transformation and shoot regeneration efficiency, that are described in this chapter. The plants generated will be utilized in long-term experiments that examine how these genes contribute to plant response to water deficit stress in strawberry.

7.2 Materials and methods

7.2.1 Construction of RNAi and over- expression vector

In total four different constructs were created. Three constructs were made encoding a hairpin RNA containing inverted repeat of F. vesca homologue sequences for CAT, P5CS and F. × ananassa cDNA sequences of FaAIV. Another construct was created for over-expression of F. × ananassa whole coding sequence of FaAIV (Table 7.1). All selected sequences were separately inserted into the pDONRTM222 vector by BP reaction to create a Gateway® entry clone based on Invitrogen protocol. After verification by colony PCR screening, the fragment was transferred to the binary vector (destination vector) to create a Gateway® expression clone using the LR reaction protocol (Invitrogen Inc.). Identical methods were used for RNAi and over- expression constructs. The binary gateway vector PK7GWIWG2D (II) was used for RNAi and binary gateway vector PK7WG2D (I) (Karimi et al. 2005) was used for over-expression (Fig 7.1& 2). The constructs contained kanamycin as selection marker and enhanced green-fluorescent protein (Egfp) gene fused to the endoplasmic reticulum-targeting signal (EgfpER) as a visible marker under the control of a 35S promoter for visual identification of transgenic callus, adventitious shoots and plants.

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7.2.2 Bacterial Strain

The E. coli strain Omni Max (Invitrogen) was used for all cloning and plasmid amplification. Agrobacterium tumefaciens strain GV3103 was used for plant transformation.

7.2.3 Plant materials, transformation, tissue culture and regeneration

Four genotypes were selected for transformation based on transformability (F. vesca) or sensitivity to drought stress (Razavi et al. 2011), namely 1 diploid (F. vesca) and 3 octoploid genotypes F. chiloensis and F. × ananassa ‘Ventana’ and ‘EL02.2011’. The constructs of both RNAi and over-expression were transferred into the A. tumefaciens by electroporation. Leaves were co-cultivated with A. tumefaciens GV3103 essentially as described by Oosumi et al. (2006).

Table 7.1 Cloned sequence, constructs, and their functions Clone name Accession No. Source Putative function Construct function LR1 DY674761 F. vesca CAT RNAi LR2 DY668033 F. vesca P5CS RNAi LR3 AB275667 F.× ananassa FaAIV RNAi LR14 AB275667 F.× ananassa FaAIV Over- expression

Fig 7.1 Vector map of construct PK7WG2D(I)

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Fig 7.2 Vector map of construct PK7GWIWG2D (II)

For transformation and regeneration, young leaf segments and petioles from in vitro grown F. vesca ‘Yellow Wonder’ (5AF7) known as good transformable with Agrobacterium (Slovin et al. 2009), F. chiloensis and F. × ananassa ‘EL02.2011’ and in vivo grown F. × ananassa ‘Ventana’ were co-cultivated with A. tumefaciens containing RNAi or over-expression constructs (at an optical density of 0.3-0.5 at 600nm) on MS medium supplemented with 2% sucrose for at least 1 hour. Inoculated tissues were transferred to solid co-cultivation medium without any antibiotics for 2-4 days at 22°C and a light intensity of 40µmol.m-1s-1 and consequently transferred to the medium with selective antibiotics. Regeneration of transformed tissues of F. vesca (5AF7) was performed on the “Hawaii 4” medium as described for diploid Fragaria species (Oosumi et al. 2006), while the regeneration medium for F. chiloensis, F. × ananassa ‘EL02.2011’ and ‘Ventana’ was on a slightly modified LF9 medium for octoploid Fragaria genotypes (Folta et al. 2006). GFP monitoring was performed using a Leica fluorescence microsystem with excitation filter cube GFP2 (BP480/40). Agrobacterium- infiltrated leaves of all genotypes showing GFP expression were sub-cultured every 15-20 days. Finally, higher kanamycin

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selection was used (25 mg/L) to ensure regeneration of transformants. Regenerating shoots were placed on rooting medium (1×MS, 1% glucose and 0.01mg/L IBA) and roots generated within one month.

7.2.4 Molecular characterization of transformants

Total genomic DNA was isolated from leaf tissue of putative CaMV 35S::nptII transformants in F. vesca and F. × ananassa ‘EL02.2011’ for all 4 constructs. DNA concentration and purity was measured with a NanoDrop® ND-1000 Spectrophotometer (NanoDropTechnologies, USA). The integration of T-DNA binary plasmid was analyzed by PCR using specific primers for CaMV35S gene: 35SF:5’-TGTTAGATCCTCGATCTGAATTTTTG3’ and 35SR:5’ CCACAGATGGTTAGAGAGGCCTAC 3’. Also PCR amplification of nptII gene (the kanamycin resistance gene) was performed by specific primers of nptII coding region nptIIF: 5’- AGCGGCGATACCGTAAAGCACGA-3’ and nptIIR: 5’- AAGGGACTGGCTGCTATTGGGC-3’. PCR was performed for 35 cycles of 94°C for 30s, 60°C for 30s, 72°C for 1 min. PCR products were analysed under UV light after electrophoresis in 1.5% agarose gel and staining with ethidium bromide.

7.3 Results

GFP expression was visible in infiltrated tissues within 7-12 days after transformation (Fig 7.3). Strong GFP expression was observed for both RNAi and one over-expression constructs in period of 2-3 weeks after co-cultivation initiation, this in all genotypes (short term GFP expression) (Fig 7.3-7). There was a variation in GFP intensity dependent on the construct, e.g. silenced FaAIV showed higher GFP expression while silenced P5CS showed lower GFP expression in F. vesca (Table 7.3). The stability of GFP expression varied between genotypes in our experiment; the GFP expression did not stably occur in F. vesca, as fluorescence could not be detected clearly in explants for the long duration during the callus induction and shoot proliferation. There was more stable GFP expression in callus and shoots in F. × ananassa ‘EL02.2011’ (Fig 7.6). For F. vesca callus induction on Hawaii 4 with kanamycin (5mg/L) was performed within 20-40 days followed by shoot proliferation within 15-30 days after callus induction (Fig 7.4, Table 7.2). In spite of successful and high GFP expression shortly after co-cultivation in F. chiloensis, no callusing and regeneration on LF9 medium was observed (Fig 7.5, Table 7.2). In F. × ananassa ‘EL 02.2011’ callus induction occurred on modified LF9 medium with kanamycin (5mg/L) within 10-15 days after co-cultivation and shoot proliferation within 12-15 days after callus induction (Fig 7.6, Table 7.2). For F. × ananassa ‘Ventana’ callus induction was observed on selective modified LF9 medium with kanamycin (5mg/L) about 20 days after co-cultivation, but shoot proliferation was not robust and happened only after 2-3 months after co-cultivation (Fig 7.7, Table 7.2). The integration of T-DNA in

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genomic DNA was further investigated in 20 shoot clumps of F. × ananassa ‘EL02.2011’ and 11 shoot clumps of F. vesca using PCR. The presence of nptII (kanamycin resistance gene) or CaMV35S sequences in the genome of the shoot clumps regenerated on the plates containing kanamycin was examined using specific primers. PCR amplification of nptII / CaMV35S confirmed the insertion of CaMV35S: nptII gene in 12 out of 20 shoot clumps for F. × ananassa ‘EL02.2011’ and 8 out of 11 shoot clumps for F. vesca (Fig 7.8).

Table 7.2 Timetable of the regeneration in different Fragaria genotypes Genotype Induction of callus (days after co- Shoot proliferation (days after cultivation) callus induction) F. vesca ~ 20-40 ~ 15-30 F. chiloensis No callus No shoot F.× ananassa ~ 10-15 ~ 12-15 ‘EL02.2011’ F. × ananassa ~ 20 No shoot/delayed and weak shoot ‘Ventana’ proliferation

Table 7.3 Regeneration and transformation efficiency in different Fragaria genotypes Genotype Construct Explant with callus Explants with Induction of transformed formation (%) shoot proliferation explants (GFP production in (%) short term) (%) F. vesca (5AF7) LR1 34.5 26 47 LR2 66.0 46 34 LR3 57.0 36 54 LR14 52.5 30 27 F. chiloensis LR1 0 0 47 LR2 0 0 54 LR3 6.0 0 60 LR14 23.0 0 54 F.× ananassa LR1 80 60 60 ‘EL 02.2011’ LR2 84 63 67 LR3 78 61 67 LR14 72 50 47 F.×ananassa LR1 0* 0 0 ‘Ventana’ LR2 74 0 60 LR3 68 0 67 LR14 76 0 0 * Fungus contamination

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Fig 7.3 GFP expression in callusing of Fragaria genotypes at the connection end of petiole to the leaflets (A) and the vascular tissue of leaf segment (B)

Fig 7.4 F. vesca (5AF7) A) Explant and callus on selective medium with Kanamycin (5 mg/L) B) GFP expression 7-12 days after co-cultivation, C) shoots on selective medium with kanamycin (5mg/L)

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Fig 7.5 F. chiloensis A) Agrobacterium transformed explants with no regeneration, B) GFP expression 7-12 days after co-cultivation

Fig 7.6 F. × ananassa ‘EL02.2011’ A) explants and callus, B & C) callus and shoot with GFP expression on selective medium with kanamycin (5mg/L) D) shoot proliferation on selective medium with kanamycin (5mg/L)

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Fig 7.7 F. × ananassa ‘Ventana’ A) explants and callus, B) callus with GFP production on selective medium with kanamycin (5mg/L)

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Fig 7.8 PCR analysis on a subset of transformants and wild plants, A, B & C: detection of CaMV35S gene, D& E: detection of nptII gene, M: molecular ladder, VES: F. vesca, EL: F. × ananassa ‘EL02.2011’, LR1, LR2 & LR3: RNAi and LR14 (OX): Over- expression

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Fig 7.9 Transgenic F. × ananassa ‘EL02.2011’ with shoot proliferation on selective medium with kanamycin: low concentration (5mg/ L) and high concentration (25mg/ L)

7.4 Discussion

Regeneration of transgenic strawberry plants is dependent on efficient gene transformation and the regeneration success in the presence of selection agents (Folta and Dhingra 2006). Validation of the transformation can be done by visual GFP expression followed by PCR analysis (Bosselut et al. 2011). Different constructs exhibited variation in GFP intensity, silenced FaAIV showed higher GFP expression while silenced P5CS showed lower GFP expression in F. vesca. This can be attributed to the putative function of cloned sequence, as proline is a vital amino acid in plant metabolism the silencing of P5CS might have considerable effects on cell metabolism (Kavi Kisho et al. 2005). It seems that further optimization of the regeneration medium is required for an efficient transformation in F. chiloensis and F. × ananassa ‘Ventana’. Based on our experience, early use of kanamycin (5mg/L) (before callus initiation) can prevent callus induction even in the transformed explants. Our results showed that transformation and regeneration efficiency in Fragaria is affected by the type of construct and the genotype (Table 7.3). The transformation and regeneration efficiency might be also affected by the interaction between genotype and bacterium or the type of construct. Visual observation showed that the concentration of A. tumefaciens suspension culture and the type of the explants (leaf/petiole segments) also influence the transformation and regeneration efficiency. Qin et al. (2011) showed that the transformation efficiency in Fragaria was higher with higher OD600 value of A. tumefaciens suspension culture. Further studies might be performed to investigate all these in the genetic transformation of different Fragaria genotypes. These observations are entirely consistent with observations from other laboratories that showed Fragaria transformation is highly genotype dependent (Passey et al. 2003). The results indicate the presence of T-DNA sequences

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within the genomic DNA of transformants for all four constructs in F. × ananassa ‘EL02.2011’ and F. vesca (Fig 7.8). These results demonstrate that T-DNA sequences may be identified in the plant genome when no stable GFP expression is detected. High densities of GFP production were observed during the first weeks after co-cultivation and subsequently decreased in F. vesca. This finding may be explained by the weak regeneration capacity of infiltrated cells due to the type of construct (gene) or the occurrence of transient GFP expression instead of stable transformation (Ponnapa et al. 1999). High sensitivity of diploid and octoploid strawberry tissues for increasing concentration of kanamycin up to 10mg/L was already shown in previous reports (Folta and Dhingra 2006). In our experiment the selection of transgenic plants was done on low concentrations of kanamycin (5mg/L) immediately after the callus induction, maintained in subsequent subcultures and followed by an increase of kanamycin up to 25mg/L for a period of 12 days for well-proliferating shoots. Non-transgenic plantlets were eliminated by this higher concentration of kanamycin (25 mg/L) and this was confirmed by PCR analysis (Fig 7.9). This method proved to be a good strategy to obtain transformants for all four constructs in F. vesca and F. × ananassa ‘EL 02.2011’. The increasing iterative use of selection agents has also been described (Mathews et al. 1998).

7.5 Conclusion

Agrobacterium–mediated transformation of Fragaria was performed in F. vesca, F. chiloensis and F. × ananassa ‘Ventana’ and ‘EL02.2011’. CAT (catalase), P5CS (Δ1-pyrroline-5-carboxylate synthetase) and FaAIV (sucrose acid invertase) genes were targeted independently for the silencing by RNAi and over- expression in Fragaria. Transformation for all constructs was confirmed by visible GFP expression from the same T-DNA. There was no callusing and regeneration of F. chiloensis. Callus formation was obtained in F. × ananassa ‘Ventana’ but shoot proliferation was weak. F. vesca presented reasonable callusing, shoot regeneration and transformation using kanamycin (25 mg/L) selection. F. × ananassa ‘EL02.2011’ showed high regeneration and transformation efficiency with stable GFP expression in shoots. The occurrence of transgenic plants was confirmed by PCR analysis of genomic DNA in F. vesca and F. × ananassa ‘EL02.2011’. In future the expression level of P5CS, CAT and FaAIV will be monitored by RT- qPCR to test gene silencing/over-expression in the leaves of transgenic Fragaria plants. Successful transformants will be used in subsequent experiments to test the effects of water deficit stress.

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Chapter 8 General discussion and conclusions

191 Chapter 8

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Water deficit can be harmful at several points during strawberry production. A critical phase is the establishment period of the transplants in summer; young strawberry plants have hardly developed a rooting system and are already prone to mild drought stress. But also during the production phase, yield losses and fruit size reduction by water deficits have been reported (Klamkowski and Treder 2006). Therefore, the characterization of the mechanisms by which Fragaria sp. adapt to water deficit supports the development of drought-tolerant lines in breeding programs, and eventually improves the water-use efficiency in strawberry cultivation. The primary objectives of this study were to identify and characterize the plant defence mechanisms to drought stress in Fragaria and to classify available Fragaria genotypes based on their drought tolerance capacity. Different methodologies to study drought stress are possible (Verslues et al. 2006) and comprise in vitro as well as in vivo approaches. For the screening of a large range of Fragaria genotypes a detached leaf test and the measurement of the leaf dehydration status by RWC and WLR proved to be a useful technique (chapter 5). This method allowed us to classify 23 Fragaria accessions as drought susceptible or as drought tolerant. Hence, F. chiloensis and F. × ananassa ‘Figaro’ were classified as drought tolerant while F. vesca and F. × ananassa ‘Ventana’ were grouped as drought sensitive genotypes. These four genotypes were used for a more in-depth study of adaptation mechanisms under drought stress (chapter 6). The plant response to drought stress is however mainly studied under water deprivation. In order to characterize the physiological adaptations to drought stress Fragaria x ananassa ‘Elsanta’ (chapter 2 and 3) was submitted to water withholding till visible wilting around noon. This dehydration study indicated that strawberry plants are drought stressed when the soil moisture content (θv) drops below 20 vol % water (stage 4). Indeed, both chlorophyll fluorescence parameters and metabolic adjustment indicated the presence of stress while for wilting symptoms the soil moisture content needed to decrease further. With increasing drought stress, a gradual reduction of photochemical quenching (qP) was observed while non- photochemical quenching (qN) increased. These parameters were useful indicators for the detection of drought stress. This was also shown in drought-stressed tomato (Haupt-Hertig & Fock 2000), wheat

(Biehler & Fock 1996) and grasses (Koscielniak et al. 2006). Maximum efficiency of PSII (Fv/Fm) was not affected by drought stress and only a slight decrease of ΦPSII under water deficit was found. It seems that the PSII primary photochemistry was not damaged by the applied water deficit and no changes in thylakoid membrane occurred in ‘Elsanta’ leaves. The slow development of drought stress and the relatively low levels of light intensity (winter conditions) in this experiment probably enabled the activation of different acclimation mechanisms such as osmotic adjustment, leading to the maintenance of

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photosynthetic capacity (Flexas et al. 1999; Van Kooten and Snel 1990). Indeed, from a θv below 20 vol % on we found a significant increase of sucrose content which is a well-known osmolyte. Yet, under field conditions plants will generally not suffer this almost fatal stress but will encounter periods with a reduced soil water availability ranging from some days up to 2-3 weeks. Furthermore, uncontrolled soil water moisture cannot be used to compare the performance of genotypes with differences (even small) in growth characteristics (Harb et al, 2010). Therefore, a controlled drought stress maintaining soil moisture just below 20 vol % was applied for studying physiological adaptations and changes in expression patterns of genes in genotypes with different drought susceptibility (chapter 6). In these experiments the plant water status was monitored by the leaf water potential and/or the RWC. The extent of the decrease of both parameters depends on the genotype capacity for osmotic adjustment and maintenance of its water balance. Up to our study, limited information about the molecular base of drought response was available for Fragaria. The identification of candidate genes involved in defence mechanisms against drought served a double goal. In the first place, by their gene expression analysis, we were able to monitor in drought stressed plants the regulation between the different protection mechanisms; this helped to understand the effects observed in metabolites and enzyme functions. Moreover, studying selected genotypes, tolerant or sensitive to drought but also displaying a different drought response programme, enabled to understand the genotype dependent defence strategies applied and their effectiveness. Ultimately, key factors for drought tolerance in strawberry could lead to their useful application in strawberry improvement as well as in cultivar selection for crop production under limited irrigation. For above purposes, we developed both a genetic approach, studying allelic variation in candidate genes by EST-markers and also gene expression analysis by RT-qPCR. For genes which are known to be differentially expressed under water deficit like genes encoding osmolyte synthases, protective proteins, ROS scavenging enzymes, regulatory proteins such as protein kinases, phosphatases and transcriptional factors (Shinozaki & Yamaguchi-Shinozaki 2007; Wang et al. 2003; Yordanov et al. 2003; Chen & Murata 2002) Fragaria homologues were isolated. Also, Fragaria homologues for reference genes as internal control of RT-qPCR analysis were selected (chapter 4). Finally, 24 Fragaria EST markers were tested for the development of functional markers associated to drought tolerance. The potential role of these candidate genes in drought tolerance was established at the genetic level by an association mapping approach coupling the allelic variation at candidate genes to genotype differences in drought tolerance. The developed EST markers were compared to neutral AFLP markers to reveal both the phylogenetic as the functional (in terms of drought tolerance) relationships between 23 Fragaria accessions (chapter 5). Both marker systems were highly effective in clustering of

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Fragaria genotypes, EST markers were more appropriate to separate genotypes based on their drought adaptation. We applied the variation for two physiological traits RWC and WLR and developed a technique based on Kruskal-Wallis analysis for the association testing between different markers and these physiological traits. Such approach yielded a set of associated functional AFLP and EST markers that are valuable markers for further discriminating of Fragaria genotypes as drought tolerant or sensitive. These markers can be applied in future germplasm screening for drought tolerance in Fragaria sp. Water deficit modulates plant genes encoding biosynthetic and catabolic pathways along with metabolite adjustments (Sunkar and Bartels 2002). By studying the expression profiles of candidate gene homologues, the eventual role of factors effecting regulation of structural genes can be identified. For that purpose, we evaluated metabolite adjustment and gene regulation under drought stress in 2 tolerant and 2 susceptible genotypes. In total, 16 Fragaria sequences homologous to selected genes of interest as well as 22 Fragaria sequences corresponding to reference genes were cloned from Fragaria genomic DNA/cDNA; they all were sequenced and the specificity of cloned fragments was confirmed for later RT- qPCR analysis. The accumulation of both sucrose and hexose was observed in Fragaria under water deficit (chapters 3 and 6). These accumulated soluble sugars function in both osmotic adjustment and sugar signalling (Roitsch & Gonzalez 2004). The hexose/sucrose ratio was correlated to drought tolerance, carbohydrate partitioning in favour of hexoses was found for a tolerant genotype (e.g. F. × ananassa ‘Figaro’), while for susceptible species (e.g. F. vesca) partitioning was towards the accumulation of sucrose (chapter 6). Therefore, both sucrose synthesis catalysed by SPS and sucrose hydrolysis to hexoses by AIV and SUS are supposed to be important in Fragaria adaptation to water deficit. Both the up- regulation of FaSPS and down- regulation of FaAIV along with the changes in soluble sugar composition support the osmotic adjustment by a changed sugar metabolism. The higher rate of FaAIV down- regulation in drought sensitive genotypes might be the reason for lower rates of hexose/sucrose, resulting in less efficient osmotic adjustment in sensitive genotypes. Consequently, the regulation of FaAIV might be correlated to Fragaria drought tolerance. Proline accumulation under water deficit did depend on the way drought was imposed as well as on genotype differences but no general consistent correlation to drought tolerance was found in our drought stress treatments. Also in other crops proline accumulation is not always related to a higher drought tolerance (Sànchez-Rodriguez et al. 2010). Yet, we found an up-regulation of P5CS (proline biosynthesis) and P5CDH (proline degradation) under water deficit in all drought stages and genotypes; this strongly suggest the participation of proline metabolism as an adaptive mechanism in Fragaria under water deficit (chapter 6). Drought-induced up-regulation of P5CDH might be the reason for the lack of proline accumulation although other regulatory elements might also affect the ultimate proline level. In addition,

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the induction of P5CDH under drought stress and consequently proline degradation might play an important role in cellular ROS balance influencing many regulatory pathways under stress as already indicated by Szabados and Savouré (2009). The antioxidant responses under drought stress are genotype dependent but are also known to be related to stress intensity and duration, developmental stage as well as climatic conditions such as light intensity (Nikolaeva 2010; Bian and Jiang 2009, Lee et al. 2009; Shao et al. 2009; Yang et al. 2008). Under low light conditions and a progressive increasing water deficit up to wilting at noon (chapter 3), TAC content increased in ‘Elsanta’, but under controlled water deficit (18- 20 vol %) TAC was unaffected in other Fragaria genotypes (chapter 6). This illustrates the effect of the drought imposing method or drought severity on Fragaria antioxidant response. The main part of TAC in strawberry is probably ascorbic acid, though this metabolite was not specifically assayed in our experiments. However, the expression of genes for main key enzymes in AsA biosynthesis was analysed. AKR (GalUR) was up-regulated in all Fragaria genotypes under water deficit. The regulation of GalLDH was dependent on the stage of water deficit but was always down–regulated in the drought-susceptible F. vesca. The expression of DHAR involved in AsA-GSH redox state still requires to be investigated under drought stress in Fragaria. The effect of drought stress on antioxidant enzymes was also studied (chapter 3 and 6). CAT was up- regulated under drought stress in both drought tolerant and sensitive Fragaria genotypes. However, CAT activity correlated only partially to genotype-dependant tolerance: CAT decreased in F. vesca, whereas in other genotypes a temporal pattern was present showing a tendency to increase at the initial stages of water deficit and to be inactivated with the progress of water deficit and increasing accumulation of H2O2. APX and SOD activity were not significantly affected by water deficit, only slight variations (decrease/ increase) were observed dependent on the drought stage and Fragaria genotype. The expression of Cu- ZnSOD was always repressed in the drought-sensitive F. vesca, but tended to be up-regulated in tolerant genotypes under prolonged stress. This suggests the contribution of Cu-ZnSOD in the plant defence system although the enzyme itself can be inactivated by the accumulation of superoxide as well as the accumulation of hydrogen peroxide as a result of the downstream inactivation of CAT or APX under water deficit. The APX expression profile did not change under water deficit in our study. It is possible that APX and SOD activity are important in the first hours/days in Fragaria sensing drought stress. Therefore, the effect of drought duration and severity as well as other climate parameters during drought stress should be investigated. The relatively low light intensity (± 100 µmol.m-².s-1) during the drought stress experiments might also explain the faint response of SOD and APX activity and TAC content to drought stress. Higher light intensities during drought stress can amplify these reactions as described for Picea asperata (Yang et al. 2008).

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Protective osmotin-like protein in Fragaria is encoded by FaOLP and suppressed at early stage of water deficit in all Fragaria genotypes. However, under prolonged drought stress OLP was induced in F. chiloensis and this might cause the accumulation of protective osmotin-like protein under water deficit contributing to drought tolerance in this specific genotype. One of the main challenging discussions in different studies on drought stress concerns drought-induced signalling pathways, the role of ABA and transcription regulators such as transcription factors and signalling molecules. Both ABA-dependent and -independent regulatory systems are involved in Fragaria response to water deficit. NCED3 encoding the main key enzyme in ABA biosynthesis is up-regulated under water deficit in Fragaria, irrespective the genotype and drought stage. This confirms the fundamental role of ABA in response to water deficit. MYB2, an ABA-dependent transcription factor, is generally up-regulated under water deficit in Fragaria while CBF4 was inactivated under long-term drought stress. DREB2A, belonging to the ABA-independent pathway tended to be up-regulated in the tolerant genotypes. Further analysis of down-stream drought-inducible genes under the regulation of these transcription factors will be helpful in the characterization of the main signalling pathways in Fragaria under water deficit. A summary of the main adaptive responses to water deficit in the studied Fragaria genotypes is given in Table 8.1.

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Table 8.1 Overview of the major metabolite adjustment and changes in expression profiles under water deficit of the studied Fragaria genotypes with different drought tolerance GENERALLY

 Increase of total soluble sugars  Increase in total antioxidant capacity (TAC) under prolonged water deficit in strawberry ‘Elsanta’, but no considerable changes in other Fragaria genotypes  No considerable change in proline content  No considerable change in SOD and APX activity  Induction of NCED3, CAT, P5CS, P5CDH, FaSPS, AKR and MYB2 under water deficit  Repression of FaAIV

Fragaria chiloensis  A tendency of increase of proline content under prolonged drought stress

 A tendency of increase of CAT activity followed by a decrease under prolonged stress  Increase of both hexoses and sucrose  Induction of OLP and DREB2A under long term water deficit

F. × ananassa ‘ Figaro’ RESISTANT  Increase of total soluble sugars with considerable increase of hexoses  No considerable change in CAT activity and proline content  Induction of DREB2A under prolonged water deficit F. × ananassa ‘ Ventana’

 Increase of total soluble sugars due to an increase of sucrose  Increase of CAT activity followed by a decrease under prolonged stress  A tendency of increase in proline content under prolonged drought stress

 Considerable repression of FaAIV under water deficit  Repression of OLP in all drought stages F. vesca

 The increase of total soluble sugars due to an increase of sucrose SUSCEPTIBLE  Decrease of CAT activity in all drought stages  A tendency of decrease in proline content in all drought stages  Considerable repression of FaAIV under water deficit  Repression of GalLDH and Cu-ZnSOD in all drought stages  Repression of OLP in both early stage and prolonged drought stress

F. × ananassa ‘Elsanta’

 The increase of total soluble sugars due to an increase of sucrose, slight increase in hexoses  The increase in TAC under prolonged drought stress

 An increase in proline content, stronger under severe dehydration MIDDLE

RESPONSE  Decrease in CAT activity under prolonged drought stress  Slight decrease in APX and SOD activity

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PERSPECTIVES AND DIRECTIONS FOR FUTURE RESEARCH We characterized Fragaria genotypes as drought tolerant and sensitive but our results indicate that drought tolerance can be achieved by different adaptive responses. The total carbohydrate pool as well as the carbohydrate partitioning plays certainly a central role in this adaptive process, therefore quantifying photosynthesis and respiration adjustments at the leaf level under stress conditions would further enhance our understanding of drought tolerance at the physiological level. It would also be interesting to to investigate the plant’s adaptive strategy if submitted to short but severe water deficits followed by rehydration and recovery in both tolerant and sensitive Fragaria genotypes. Under drought stress the intracellular balance between ROS generation and scavenging or ‘redox homeostasis’ is not maintained. Measurements of the antioxidant enzyme activity and ascorbic acid (and its redox state), glutathione and phenols concomitantly with the expression analysis of their marker genes should be continued. Moreover, in the next studies in Fragaria under water deficit a further search for candidate genes is necessary; such search must be oriented to the further characterization of the master keys which regulate the signalling pathways under drought stress such as ROS metabolism and ROS -dependent and – independent metabolite adjustment, sugar and proline metabolism in drought sensitive versus tolerant genotypes. ABA has a critical regulatory role in plant response to water deficit and is a master key in the connection of different regulatory pathways between drought stress and other abiotic stresses. The importance of ABA through the up-regulation of NCED3 was also confirmed in Fragaria response to water deficit in our study. It would certainly be interesting to analyse earlier time points of less severe drought stress to see if at that moment differences between drought tolerant and susceptible genotypes exist. Although it is not within the scope of this research higher ABA levels can make plants more prone for certain biotic stresses. This is certainly an interesting field of research. At the moment we have some transgenic lines for further functional analysis of CAT, P5CS and FaAIV. To validate the function of these genes in drought response in Fragaria, the level of drought resistance should be evaluated in these transgenic lines by the analysis of the expression pattern of transformed genes as well as linked metabolite adjustment under water deficit. Crossing of these transgenic lines carrying silenced or over expressed genes with the wild types should also be performed to study the genetic control, heritage and stability of manipulated drought tolerance in the progeny. Transgenic lines with other gene constructs can be employed for further functional analysis of Fragaria response to drought stress. For instance, some master genes with the regulatory functions in plant drought adaptation in other crops like Arabidopsis, rice, maize, etc., can functionaly be analyzed in Fragaria by both RNAi and over

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expression approaches. Resulted transgenic lines are valuable resources to assembly the landmarks of different plant signalling pathways, gene regulatory elements and functional metabolic pathways under drought stress. Especially emerging techniques such as comparative RNA sequencing between drought tolerant and sensitive genotypes after drought stress could also be utilized to discover new candidate genes involved in drought response and to further unravel t the principal metabolic pathways responsible for drought tolerance in Fragaria. Most of our experiments were performed in winter conditions or growth chambers with relative low light intensities (± 100 µmol/m².s). However, plants in the field are rarely confronted with only one type of stress. Also in our summer experiment it was clear that high light intensities modulated the level of osmotic adjustment by proline. Therefore, the combined effect of other environmental constrains such as heat and high light intensity with drought stress should be investigated. The developed toolbox of chlorophyll fluorescence, metabolites and expression analysis combined with photosynthesis and respiration would further provide useful information on abiotic tolerance in strawberry. And last but not least, we should also evaluate the crop performance in these combined stress experiments. In our study, plant water relations were mainly investigated by studying metabolic pathways, while the root hydraulic conductance is another main prominent element that has an affect on plant water homeostasis. Root hydraulics and especially the role of aquaporines and molecular gears in water transport through the roots is imperative. Therefore, the effect of water deficit on the expression of PIP-type aquaporin genes that regulate the aquaporin activity and a comparison between tolerant and sensitive genotypes might be informative and interesting in Fragaria. Morever, the water transport in vascular tissues (xylem) and its effect on the metabolism of nonstructural carbohydrates, carbon metabolism and ultimately on Fragaria water homostasis are other important parameters that needs to be evaluated in Fragaria genotypes with different drought tolerance capacity under water deficit. The detailed characterization of plant responses under drought stress in different genotypes will definitely help to better define appropriate testing conditions, tools and criteria for selection of better adapted strawberry cultivars to water deficit. Nevertheless, when in the long term the development of more adapted varieties is aimed for, a better insight must be generated in the genetics and heritability of traits under selection. So far, we have tested a marker-trait association analysis for drought tolerance in Fragaria. Markers linked to drought tolerance in our study could be utilized in a larger number of Fragaria accessions, for marker-trait association analysis and marker-assisted selection (MAS). Besides, AFLP and EST markers correlated to drought tolerance in our study can be employed later to identify QTLs for drought tolerance by using the linkage maps of a cross population between drought tolerant and

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sensitive Fragaria genotypes. Many other functional markers can be developed by using different drought –inducible master genes mediating the cross points of signalling pathways, the genes that encoding different transcription factors and signalling molecules or the genes that are involved in antioxidant defence system due to its high diversity between Fragaria genotypes in our study. Developing new gene- based SNPs markers based on candidate drought-inducible genes can be applied in marker-trait association studies in Fragaria. Although limited in the number of genotypes used, our study did provide a proof-of-concept for the further development of molecular markers linked to interesting traits by association analysis. Testing of new candidate genes, chosen from the diverse defence pathways that are functional within strawberry, together with detailed allele mining for proven candidate genes in larger sets of genotypes will allow to identify useful genetic variation. However, breeding for drought tolerance necessitates the mastering of key regulators for drought tolerance which can be introduced or by conventional cross-breeding or by gene transformation. Fundamental knowledge about the integration of the different defence pathways in model species will in the end reveal the identity of these master genes. Nevertheless, multi-scale phenotyping (transcriptome, proteome, metabolome, phenome) using the appropriate measures for drought evaluation under field or greenhouse culture conditions, and appropriate genetic (mapping) tools will be necessary to validate this information. Both mapping approaches, conventionally using selected parents (resistant versus susceptible) and derived mapping populations or species-wide well-established association mapping populations will be needed. Ultimately, development of lines that are better adapted to drought will stand or fall by an acceptable heritability of the finally identified selection keys. This will continue to be an along-the-road concern for improvement when initiating breeding programmes in strawberry for improved abiotic stress tolerance. The research approach developed here for this study, combining physiological and metabolic measures and enzyme activities involved in defence against drought induced stress physiology together with genetic analysis on functional candidate genes and their expression is a very effective way to monitor in a meticulous way the progress of any such breeding attempt.

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202 SUMMARY

Climate change will result in more erratic weather conditions, hotter summers with a higher evaporative demand and increasing periodic water shortages. Drought stress and its effects on crop productivity in horticultural systems are receiving increasing interest. Long-term adaptive solutions will heavily rely on breeding of more tolerant crops. Yet, a short-term alternative includes the selection of drought tolerant cultivars next to the development of more sustainable irrigation techniques. Strawberry cultivation relies on irrigation and is sensitive to even a short period of water deficit during growth and fruit development. Unravelling the response of Fragaria to water deficit is necessary for developing drought-tolerant lines. The main objectives of this study were to identify and characterize the plant defence mechanisms to drought stress in Fragaria and to classify available genotypes based on their drought tolerance capacity. To do so, first the physiological and metabolic changes under water deficit were investigated. Chlorophyll fluorescence proved to be a useful non-destructive technique to detect drought stress in strawberry. With increasing drought stress, a significant decline of photochemical quenching (qP) and rise of non-photochemical quenching (qN) was observed in strawberry ‘Elsanta’. The osmotic adjustment as well as anti-oxidative response in strawberry ‘Elsanta’ was investigated under progressive drought stress, both soluble sugars and proline accumulated, CAT activity decreased and a slight decline in SOD and APX activity was observed under water deficit. An important goal of this research was to get an insight into the molecular mechanisms that control drought tolerance in Fragaria. Therefore, the changes observed at the metabolic level under water deficit in ‘Elsanta’ plus a literature search were used to select the candidate genes that might be incorporated in plant response to water deficit, both as functional genes in different metabolic pathways in plant drought adaptation such as ABA biosynthesis, sugar and proline metabolism, antioxidant defence system, etc., and as regulators such as transcription factors. Fragaria gene sequences, homologous to documented functional genes, were isolated first. EST markers were developed from them and applied together with AFLP markers for phylogenetic clustering of Fragaria accessions as well as for a marker-trait association analysis for drought tolerance in Fragaria. The allelic variation of the EST markers could be utilized to predict drought tolerance in a set of 23 Fragaria genotypes compared to AFLP markers. Finally, EST markers associated to drought tolerance in Fragaria were identified which can be applied to select drought tolerant Fragaria lines. The next step was to study differentially expressed candidate genes along with the analysis of concomitant plant biochemical responses in 2 drought tolerant (F. chiloensis and F. × ananassa ‘Figaro’) and 2 drought susceptible species/cultivars (F. vesca and F. × ananassa ‘Ventana’). For that purpose, the differential

203 SUMMARY

expression level of Fragaria homologous sequences (candidate genes) was analysed in the selected set of 4 Fragaria genotypes under different stages of water deficit. The accumulation of soluble sugars in Fragaria under drought stress, along with the up-regulation of FaSPS and down-regulation of FaAIV which are the key enzymes involved in sucrose metabolism, stress the importance of carbohydrate metabolism in osmotic adjustment in Fragaria. Tolerant genotypes could better maintain their osmotic balance. Proline metabolism/accumulation in Fragaria was dependent on the type of drought stress and severity as well as the genotype. An up-regulation of P5CS (proline biosynthesis) and P5CDH (proline degradation) under water deficit in all drought stages and genotypes strongly suggest the contribution of proline metabolism as an adaptive mechanism in Fragaria under water deficit. The regulation of both AKR (GalUR) and GalLDH, genes encoding the key enzymes of AsA biosynthesis, was involved in Fragaria ROS detoxification under water deficit but the role of ascorbic acid and TAC content in Fragaria under water deficit was not clarified in this study. CAT expression was up-regulated under drought stress in both drought tolerant and sensitive Fragaria genotypes. However, CAT activity correlated partially to the genotype tolerance: it decreased in F. vesca, whereas in other genotypes it showed a tendency to increase at the initial stages of water deficit and to be inactivated with the progress of water deficit and increasing accumulation of H2O2. APX and SOD activity were not significantly affected by water deficit in Fragaria, but the expression pattern of Cu-ZnSOD suggests the contribution of Cu-ZnSOD in the plant antioxidant system under water deficit. The relatively low light intensity during the drought stress in our experiment might be the reason for the weak reaction of APX and SOD activity under drought stress in this study. Protective osmotin-like proteins in Fragaria encoded by FaOLP, were suppressed at early stages of water deficit in all Fragaria genotypes, but under prolonged drought stress OLP was induced in F. chiloensis, thus accumulation of OLP will contribute to the genotype drought tolerance. The up-regulation of NCED3, under water deficit in all Fragaria genotypes, encoding the main key enzyme in ABA biosynthesis, confirms the fundamental function of ABA in drought adaptation. MYB2, an ABA dependent transcription factor, was up-regulated under water deficit in Fragaria, CBF4 was inactivated under long-term drought stress and the DREB2A expression profile, an ABA-independent regulon, was partially correlated to Fragaria drought tolerance. Finally, a gene silencing (RNAi)/over expression approach was started; developed transgenic lines in our study will be applied for further functional analysis.

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206 SAMENVATTING

De gevolgen van klimaatsverandering kunnen leiden tot meer wisselvallige weersomstandigheden en warmere zomers en het frequenter voorkomen van tijdelijke watertekorten. Droogtestress en zijn effecten op de tuinbouwproductie komen hierdoor steeds meer in de belangstelling. Adaptatie en mitigatie op lange termijn zullen voornamelijk gericht zijn op het veredelen van meer tolerante gewassen, maar op korte termijn bieden de selectie van al beschikbare droogtetolerante cultivars en de ontwikkeling van duurzamere irrigatietechnieken een alternatief. De aardbeienteelt steunt sterk op irrigatie, voornamelijk bij de groei en vruchtontwikkeling is de productie gevoelig voor het minste watertekort. Een beter begrip van de respons van Fragaria op waterdeficit is dan ook een noodzakelijke voorwaarde voor de ontwikkeling van droogtetolerante lijnen. De belangrijkste objectieven van dit onderzoek waren enerzijds de identificatie en karakterisering van de verdedigingsmechanismen van aardbeiplanten tegen droogtestress, en anderzijds de rangschikking van beschikbare Fragaria-genotypen op basis van hun droogtetolerantie. Hiertoe werden eerst de fysiologische en metabolische veranderingen bij watertekort bestudeerd. Chlorofylfluorescentie bleek een bruikbare niet-destructieve methode om droogtestress in aardbei op te sporen. Bij toenemende droogtestress zagen we een significante afname van de fotochemische quenching

(qP) en een toename van de niet-fotochemische quenching (qN) in de cultivar ‘Elsanta’. Daarnaast werden zowel de osmotische aanpassingen als de antioxidatieve respons in ‘Elsanta’ bestudeerd onder progressieve droogtestress. Hierbij werden oplosbare suikers en proline geaccumuleerd, de CAT- enzymactiviteit daalde, en we merkten een lichte daling in SOD- en APX-activiteit. Een belangrijke doelstelling van het onderzoek was het verwerven van inzicht in de moleculaire mechanismen die de droogtetolerantie in Fragaria bepalen. Vertrekkend vanuit de waargenomen metabolische veranderingen bij waterdeficit in ‘Elsanta’, aangevuld met een literatuurstudie, werden kandidaat-genen geselecteerd die mogelijks een rol spelen in de respons van de plant op droogte. Hierbij werden zowel functionele genen uit verschillende metabolische pathways betrokken bij de adaptatie aan droogte (zoals ABA-biosynthese, suiker- en prolinemetabolisme en antioxidantia), als regulatoren zoals transcriptiefactoren gekozen. Eerst werden in Fragaria gensequenties geïsoleerd op basis van hun homologie met beschreven functionele genen. Vervolgens werden hiervoor EST-merkers ontwikkeld en samen met AFLP-merkers toegepast voor een verwantschapsanalyse van Fragaria genotypen en voor het opsporen van gekoppelde merkers aan droogtetolerantie in Fragaria behulp van een associatiestudie. De variatie aan allelen verkregen bij de EST-merkers kon, beter dan AFLP-merkers, gebruikt worden om droogtetolerantie te voorspellen in een set van 23 Fragaria genotypen. Ten slotte werden EST-merkers geassocieerd met droogtetolerantie in Fragaria geïdentificeerd; deze kunnen toegepast worden in de selectie van droogtetolerante Fragaria lijnen. De volgende stap was de studie van de differentiële

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expressie van kandidaat-genen, samen met de analyse van de bijhorende biochemische responsen in twee droogtetolerante (F. chiloensis en F. × ananassa ‘Figaro’) en twee droogtegevoelige species of cultivars (F. vesca en F. × ananassa ‘Ventana’). Hiertoe werd het expressieniveau van homologe Fragaria- sequenties (kandidaat-genen) bepaald in de 4 gekozen Fragaria-genotypen bij verschillende stadia van waterdeficit. De accumulatie van oplosbare suikers in Fragaria onder droogtestress, samen met de opregulatie van FaSPS en de neerregulatie van FaAIV (de belangrijkste enzymen betrokken in het sucrosemetabolisme) wijzen op het belang van het koolhydratenmetabolisme in de osmotische aanpassing in Fragaria. Tolerante genotypen konden hun osmotisch evenwicht beter behouden. Prolinemetabolisme of -accumulatie in Fragaria was afhankelijk van zowel het type en de ernst van de droogtestress, als van het genotype. De opregulatie bij waterdeficit van P5CS (prolinebiosynthese) en P5CDH (prolinedegradatie) in alle genotypen en alle stadia van droogte wijst sterk op de bijdrage van het prolinemetabolisme als aanpassingsmechanisme bij watertekort in Fragaria. De regulatie van de genen AKR (GalUR) en GalLDH, coderend voor sleutelenzymen in de AsA-biosynthese, was betrokken bij de ROS-detoxificatie in Fragaria bij waterdeficit, maar de rol van ascorbinezuur en TAC-inhoud hierbij werd in deze studie niet opgehelderd. CAT-expressie werd onder droogtestress opgereguleerd in zowel droogtetolerante als gevoelige Fragaria genotypen. CAT-activiteit was eerder slechts gedeeltelijk gecorreleerd met de graad van tolerantie in de genotypen: ze daalde in F. vesca maar vertoonde in andere genotypen aanvankelijk een stijging bij de initiële stadia van waterdeficit, om geïnactiveerd te worden bij progressief watertekort en een toenemende accumulatie van H2O2. APX- en SOD-activiteit werden niet significant beïnvloed door waterdeficit in Fragaria, hoewel de regulatie van Cu-ZnSOD wel wijst op een bijdrage daarvan in het antioxidantensysteem van de plant bij watertekort. Een potentiële verklaring voor de zwakke reactie van de APX- en SOD-activiteit bij droogtestress in dit onderzoek kan gezocht worden in de relatief lage lichtintensiteit gedurende het droogtestress-experiment. Beschermende osmotin-like eiwitten gecodeerd door FaOLP werden onderdrukt in de eerste stadia van waterdeficit in alle Fragaria- genotypen, maar onder gevorderde droogtestress werd OLP geïnduceerd in F. chiloensis, waaruit blijkt dat accumulatie van OLP bijdraagt tot de droogtetolerantie van de plant. De opregulatie van NCED3-expressie bij waterdeficit in alle Fragaria-genotypen, coderend voor het cruciale enzym in de ABA-biosynthese, bevestigt de essentiële functie van ABA in droogte-adaptatie. MYB2, een ABA-afhankelijke transciptiefactor, werd opgereguleerd bij waterdeficit in Fragaria, CBF4 werd geïnactiveerd onder aanhoudende droogtestress, en de expressie van DREB2A, een ABA- onafhankelijk regulon, was deels gecorreleerd met droogtetolerantie in Fragaria. Tenslotte werd een gene-silencing(RNAi)/-overexpressie benadering opgestart: de resulterende transgene lijnen uit dit onderzoek zullen worden toegepast in verdere functionele analyse.

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CURRICULUM VITAE

Personal information

Farzaneh RAZAVI

Nationality: Iranian, Belgian

 Faculty of Bioscience Engineering, Department of Plant Production, Copure Links 653, 9000 Gent, BelgiumTel: +32 (0) 9 264 60 77

E-mail: [email protected]

Education

 Oct. 2006- 2012: Doctoral programme

In: Bioengineering: Applied Biological Science

Faculty of Bioscience Engineering, UGent, Belgium

Thesis title: Study of molecular (genetics and functional genomics), biochemical and physiological aspects of plant response to drought stress (abiotic stress) in strawberry (Fragaria sp.)

 2004-2006: Pre-doctoral programme (equal to the 2nd Master of Science) In: Bioengineering: Applied Biological Science

Faculty of Bioscience Engineering, KULeuven, Belgium

Thesis title: Molecular, genetic and biochemical study of L-ascorbate metabolism in Malus sp. : “QTL mapping of genes involved in vitamin C mtabolism in Malus sp.”

 1997- 1999: Master of Science (M.Sc) (with G.P.A of 17.74 out of 20.00)

In: Applied Biological Science: Agricultural Engineering, Horticulture and Fruit Breeding

Department of Applied Biological Science, Faculty of Bioscience and Agricultural Engineering, Tarbiat Modarres University (TMU), Tehran, IRAN

Thesis title: Identification of local Cydonia oblonga Mill. genotypes in Iran, Isfahan province (with mark 19.5 out of 20.00)

 1990-1995: Bachelor of Science (B.Sc): (with G.P.A 16.73 out of 20.00)

In: Applied Biological Science: Agricultural Engineering, Horticulture

Department of Applied Biological Science, Faculty of Bioscience and Agricultural Engineering, Isfahan University of Technology, Isfahan, IRAN

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Dissertation: The effect of hormonal treatments (GA) on the removing of seed dormancy in wild species of pistachio (Pistachia vera L.), as salt and drought tolerant rootstocks

Career History

 Current position: PhD student in Department of Plant Production, Faculty of Bioscience Engineering, University of Gent, Belgium (acquired the rank of research seniority with 13 years experience in research sector).  From Oct. 2006: Personel and doctoral student in Gent University (Belgium) (awarded Iranian scholarship for 2 years and BOF grant from Ugent for extra 2 years)  2004-2006: Pre-doctoral student in Department of Plant Biosystem, Faculty of Bioscience and Engineering, Katholic University of Leuven, Belgium (awarded Iranian scholarship for 2 years)  1999 to 2004: Member of scientific board, academic staff and researcher in Department of Plant Biotechnology and Physiology, Institute of Seed and Plant Improvement (SPII), Agricultural Research and Educational Organization (AREO), Tehran, IRAN

Internships, scientific workshops & meetings

 September 2012: Flemish Training Network Life Sciences (FTNLS), KULeuven, workshop ‘Next generation sequencing’  August 2012: University of Gent, Doctoral School: Summer course: Plant breeding and plant sexual reproduction, Ugent  February-April 2011: University of Florida (UF), Gateway cloning, construction of RNAi and over-expression vectors for the genes up/down regulated under drought stress in Fragaria, genetic transformation of strawberry - FWO grant (Fonds Wetenschappelijk Onderzoek), Belgium.  November 2010: Belgian Plant Biotechnology Association (BPBA), KULeuven, Belgium: Plant Stress Biotechnology (with oral presentation)  November 2008: Belgian Plant Biotechnology Association (BPBA), Gent, Belgium: Secondary Metabolite and Molecular Farming (with poster presentation)  October 2008: Benelux Symposium, International Convention Center in Ghent, Belgium: 1st Benelux qPCR Symposium (with poster presentation)  November 2007: Belgian Plant Biotechnology Association (BPBA), Gembloux, Belgium: Epigenetics and Somaclonal Variation  November 2006: Belgian Plant Tissue Culture (BPTCG), Gembloux, Belgium: In Vitro Culture Facing the Future

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 April 2005: ILVO, Eenheid Plant, Modern Crop Breeding, Gent, Belgium: Toegepaste Genetica en Veredeling  June-July 2004: Agricultural Research Centre, Tehran, IRAN: Internship course: "IT (Information Technology)", completed successfully  July 2003: Agricultural Research Centre, Tehran, IRAN: Internship course: "The Complementary Statistic Experimental Designs in Agriculture" completed successfully  April 2001: Department of Biology, Tarbiat Modarres University (TMU), Tehran, IRAN: Internship course: "GLP (good laboratory practice) and HPLC analysis of herbal metabolites", completed successfully

Publications

 A1/ ISI publications (published):

Razavi F., Folta K., Zamariola L., Geelen D., De Riek J., Van Labeke M.C. (2012). Genetic transformation of Fragaria with candidate genes involved in drought stress. Acta Horticulture, 961, 397-404

Razavi F., De Keyser E., De Riek J., Van Labeke M.C. (2011). A method for testing drought tolerance in Fragaria based on fast screening for water deficit response and use of associated AFLP and EST candidate gene markers. Euphytica, 180: 385-409

Razavi F., Van Labeke, M.C., De Riek J (2009). Functional diversity for response to water deficit among different strawberry genotypes compared to their genetic structure as assessed by AFLP markers. Acta Horticulturae, 839, 491-498

Razavi F., Pollet, B. Steppe, K. Van Labeke, M.C. (2008). Chlorophyll fluorescence as a tool for evaluation of drought stress in strawberry. Photosynthetica, 46 (4): 631-633

 Papers in A3 journals (published):

Razavi F., Van Labeke, M.C. (2007). Plant growth, water and proline content and total antioxidant capacity (TAC) in strawberry cv. ‘Elsanta’ under water deficit. Comm. Agr. Appl. Biol. Sci., 72/1: 265-268 Razavi F., Davey M., Keulemans W. (2005). A study of the L-ascorbate biosynthetic capacity of apple fruit. Comm. Agr. Appl. Biol. Sci., 70/2: 213-216

Razavi F., Moosavi A. (2004). Identification and collection of different grape local genotypes (Vitis vinifera L.) in Iran "Chaharmahal va Bakhtiari" province. Final Technical Report of Seed and Plant Improvement Institute (SPII). 84: 95-115.

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Razavi F. (2003). Cultivation of Pleurotus "Sajor-Cajo" on agricultural wastes in "Chaharmahal va Bakhtiari" condition. Iranian Journal of Agricultural Extension, 10/2: 10-15.

Razavi F., Arzani K., Vezvaei A. (2000). Identification of local quince (Cydonia oblonga L.) genotypes in Isfahan province. Iranian Seed and Plant Journal (SPIJ), 15/4: 354-374

 Papers in preparation:

Razavi F., De Keyser E., De Riek J., Folta K., Van Labeke M.C. (2012). Identification and characterization of transcripts associated with osmotic adjustment under water deficit in Fragaria. (In prep: for submisson in Molecular Plant) Razavi F., De Keyser E., De Riek J., Van Labeke M.C. (2012-2013). Identification and characterization of transcripts associated with antioxidant and ROS scavenging system under water deficit in Fragaria. (In prep)

Razavi F., Folta K., De Riek J., Van Labeke M.C. (2012-2013). Drought stress and drought- induced responses in Rosaceae: A review (In prep).

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International Meetings

Razavi F., De Keyser E., Fota K., De Riek J., Van Labeke M.C. (2012). Functional analysis of candidate genes for response to water deficit in Fragaria during drought stress. International conference “Plant Abiotic Stress Tolerance II”, 22-25 February, Vienna, Austria, p. 50 (poster presentation)

Razavi F., Folta K., Zamariola L., Geelen D., De Riek J., Van Labeke M.C. (2011). Genetic transformation of Fragaria with Candidate genes involved in drought stress. Acta Horticulture, 7th International Symposium on “In Vitro Culture and Horticultural Breeding (Biotechnological Advances in Vitro Horticultural Breeding)”, 21-24 September, Belgium (oral presentation)

Razavi F., De Keyser E., De Riek J., Van Labeke M.C. (2010). Cloning and functional analysis of candidate genes for response to water deficit in Fragaria during drought stress. RGC5, 5th International Rosaceae genomics Conference, 14-17 November 2010, Capetown, South Africa (oral presentation)

Katrijn Van Laere1, Ilya Kirov, Ellen De Keyser, Farzaneh Razavi, Johan Van Huylenbroeck (2011). Cytogenetic mapping of candidate genes involved in drought resistance in a diploid Rosa wichurana x Rosa ‘Yesterday’ population using tyramide-FISH. Manchester, UK (presented by the 1st author)

Razavi F., De Riek J., Van labeke M.C. (2009). Candidate gene approach to identify genes underlying drought stress tolerance and development of markers in strawberry. International conference “Plant Abiotic Stress Tolerance”, Vienna, Austria, p. 60 (poster presentation)

Razavi F., De Riek J., Van Labeke M.C. (2008). Functional diversity for response to water deficit among different strawberry genotypes compared to their genetic structure as assessed by AFLP markers. The First International Symposium on Biotechnology of Fruit Species (ISHS Biotechfruit, 416, 2008), Dresden, Pillnitz, Germany, p. 93 (poster presentation)

Razavi F., Van Labeke M.C. (2008). Characterization of physiological responses in strawberry to drought stress. Proceeding of the SHE, First Symposium Horticulture in Europe, 17-20 February, 2008, Vienna, Austria, p. 160 (poster presentation)

Razavi F., Arzani K., Vezvaei A., Van Labeke M.C. (2008). Characterization of local quince (Cydonia oblonga Mill.) genotypes of Iran. Proceeding of the SHE, First Symposium Horticulture in Europe, 17-20 February, 2008, Vienna, Austria, p. 268 (poster presentation)

Razavi F., Van Labeke M.C. (2008). Response to drought stress in Fragaria sp.: Development of relevant molecular markers. First Biology Conference for Iranian Scholars in Europe, 6-7 December, 2008, Hamburg, Germany, p. 84 (poster presentation)

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Razavi F., Van Labeke M.C. (2008). Genetic structure of functional diversity for response to drought stress among different genotypes of Fragaria sp. The first European Conference of Iranian Scientists in Agriculture & Natural Sciences (ECISAN), p. 78 (Oral presentation)

Davey M.W., Razavi F. and Keulemans J. (2006). Breeding functional apples: Identification of QTL's for mean vitamin C contents of fruit skin and flesh. Proceeding of the 3rd International Rosaceae Genomic Conference, Napier, New Zealand, p. 104 (oral presentation by 1st author)

Razavi F., Van Labeke M.C. (2007). Characterization of the biochemical and physiological responses of strawberry to drought stress. Proceeding of the 5th Iranian Horticultural Science Congress, Shiraz, Iran, No.5, p.102 (Oral presentation)

Razavi F., Davey M.W., and Keulemans J. (2007). Preliminary study of ASA (Ascorbic acid, vitamin C) metabolism in apple: Biosynthetic capacity and localization. Proceeding of the 5th Iranian Horticultural Science Congress, Shiraz, Iran, No.5, p. 89-90 (Oral presentation)

Razavi F., Keulemans J., Davey M. (2005). Histochemical localisation of L-ascorbate in apple fruit. 13th Multi-Disciplinary Iranian Researchers Conference in Europe (IRCE), Leeds University, U.K. Extended abstract, pp. 4 (Oral presentation)

Razavi F. (2005). Collection and identification of local grape varieties in Iran. (Chaharmahal va Bakhtiari province). Proceedings of the 13th Multi - Disciplinary Iranian Researchers Conference in Europe (IRCE), Leeds University, UK (Oral presentation)

Razavi F., Arzani K., Vezvaei A. (2003). The first identification descriptor for quince (Cydonia oblonga Mill.) genotypes. Proceeding of the 3rd Iranian Horticultural Science Congress, No.3, p. 50-52 (Oral presentation)

Razavi F., Arzani K., Vezvaei A. (2003). A study of general aspects in phenology of vegetative and reproductive buds in quince (Cydonia oblonga Mill.). Proceeding of the 3rd Iranian Horticultural Science Congress, Karaj, Iran, No.3, p.73-76 (Oral presentation)

Razavi F., Arzani K., Vezvaei A. (2000). Fruit properties comparison of quince local genotypes (Cydonia oblonga Mill.) in Isfahan province. Proceeding of the 2nd Iranian Horticultural Science Congress, Karaj, Iran, No.2, p. 55-56 (Oral presentation)

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Completed projects

 Collection and identification of local grape (Vitis vinifera L.) genotypes in "Chaharmahal va Bakhtiari" province  Evaluation of qualitative and quantitative yield of different grape (Vitis vinifera L.) genotypes in "Chaharmahal va Bakhtiari" province  Evaluation of quantitative and qualitative traits of new late flowering almond genotypes and self compatible almonds with commercial (standard) late flowering almonds in "Chaharmahal va Bakhtiari" province  Setting up the cultivation of the Pleurotus sp. as the added nutritious value on agricultural wastes in ‘Chaharmahal va Bakhtiari’ province

Teaching experience

 September–December 2003: Temperate Fruit Production, Department of Horticulture, Faculty of Agriculture, Shahrekord University, Iran  August 2003: Temperate Fruit Physiology and Production, Agricultural Research and Educational Organization (AREO), Iran  April-May 2002: Cultivation of Pleurotus sp. on agricultural wastes. Department of Horticulture, Faculty of Agriculture, Shahrekord University, Iran

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